Observation List
Get a list of Observation objects.
GET /api/v3/observations/?format=api&offset=8400
{ "count": 10256, "next": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=8500", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=8300", "results": [ { "ob_id": 37776, "uuid": "caf598e54c674d219f2e245df32dbc1a", "title": "Chapter 8 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 8.18 (v20220718)", "abstract": "Data for Figure 8.18 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 8.18 shows projected long-term relative changes in seasonal mean runoff.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Douville, H., K. Raghavan, J. Renwick, R.P. Allan, P.A. Arias, M. Barlow, R. Cerezo-Mota, A. Cherchi, T.Y. Gan, J. Gergis, D. Jiang, A. Khan, W. Pokam Mba, D. Rosenfeld, J. Tierney, and O. Zolina, 2021: Water Cycle Changes. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1055–1210, doi:10.1017/9781009157896.010.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has multiple panels. Data is provided in panel-specific sub-directories.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains global maps of projected relative change (%) in runoff seasonal mean for:\r\n \r\n - December–January–February (DJF; left panels)\r\n - June–July–August (JJA; right panels)\r\n\r\n The data are averaged across CMIP6 models for the SSP1.2-6, SSP2-4.5 and SSP5-8.5 scenarios. All changes are estimated in 2081–2100 relative to 1995–2014.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n There are two NetCDF files per panel :\r\n - one for the main field, which is represented with colors and has 'rchange' or 'rmeans' in the filename\r\n - the other for the confidence information, based on fraction of models which agree about signal change sign, which is represented in figures by diagonal lines as specified by the so called AR6 simple hatching scheme; it has 'agreemeent' or 'slashes' in the filename\r\n\r\n Each datafile has NetCDF attributes which clearly describe the data.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n SSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\n SSP245 is the Shared Socioeconomic Pathway which represents the median of radiative forcing and development scenarios, consistent with RCP4.5.\r\n SSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 8)\r\n - Link to the Supplementary Material for Chapter 8, which contains details on the input data used in Table 8.SM.1\r\n - Link to the code for all figures in Chapter 8, archived on Zenodo.\r\n - Link to the documentation for CAMMAC, the tool used for AR6 analysis.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:37", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, multi-model, seasonal, mean, runoff, change, long-term", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T15:23:13", "doiPublishedTime": "2023-05-15T12:27:48.368935", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 164, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37777, "dataPath": "/badc/ar6_wg1/data/ch_08/ch8_fig18/v20220718", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3487819, "numberOfFiles": 15, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10436, "startTime": "2081-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4029, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-07-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37778, "uuid": "2ffd8a8f55fb45bfbf48572bbeba23ab", "short_code": "comp", "title": "Caption for Figure 8.18 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected long-term relative changes in seasonal mean runoff. Global maps of projected relative change (%) in runoff seasonal mean for December–January–February (DJF; left panels) and June–July–August (JJA; right panels) averaged across CMIP6 models SSP1.2-6 (a, b), SSP2-4.5 (c, d) and SSP5-8.5 (e, f) scenario respectively. All changes are estimated in 2081–2100 relative to 1995–2014. Uncertainty is represented using the simple approach. No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change, diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 62730, 62731 ], "vocabularyKeywords": [], "identifier_set": [ 12483 ], "observationcollection_set": [ { "ob_id": 32723, "uuid": "ab437cee56cb405285aac2bb59cc36d6", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 8.13\r\n- data for Figure 8.14\r\n- data for Figure 8.15\r\n- input data for Figure 8.16\r\n- data for Figure 8.17\r\n- data for Figure 8.18\r\n- data for Figure 8.21\r\n- data for Figure 8.25\r\n- data for Figure 8.26\r\n- data for Box 8.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180255, 180256, 180257, 180258, 180259, 180260, 180261, 180254 ], "onlineresource_set": [ 52565, 52555, 52535, 82552, 52536, 88623 ] }, { "ob_id": 37779, "uuid": "b03a4577108545c2a05bbae2d9759f9d", "title": "Chapter 8 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 8.21 (v20220718)", "abstract": "Data for Figure 8.21 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 8.21 is a schematic depicting understanding of large-scale circulation changes and effects of the regional water cycle.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Douville, H., K. Raghavan, J. Renwick, R.P. Allan, P.A. Arias, M. Barlow, R. Cerezo-Mota, A. Cherchi, T.Y. Gan, J. Gergis, D. Jiang, A. Khan, W. Pokam Mba, D. Rosenfeld, J. Tierney, and O. Zolina, 2021: Water Cycle Changes. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1055–1210, doi:10.1017/9781009157896.010.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The three maps subpanels in the middle of the figure have data provided in panel-specific sub-directories.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Precipitation minus evaporation (P–E) changes at 3°C of global warming relative to an 1850–1900 base period (mean of 23 CMIP6 SSP5-8.5 simulations) for:\r\n . Annual mean changes\r\n . Seasonal mean changes (DJF, JJA)\r\n \r\n - Precipitation minus evaporation (P–E) climatology (annual mean, 1850-1900).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n The map for the annual case (subdir ANN) has two data files :\r\n 1. One for the colored , P-E change, field :\r\n - Fig8-21_ANN_P-E_mean-change_ssp585_ANN_1850-1900_plus3K.nc\r\n \r\n 2. One for the P-E climatology (for red isoline 0) :\r\n - Fig8-21_ANN_P-E_mean_piControl_ANN_1850-1900_.nc. The solid and dashed contours are isolines 0 of that data, masked out over continents. The decision between solid and dashed line is a manual one, based on the latitude of the line. \r\n\r\nThe seasonal panels (subdirs DJF and JJA) have a single file for the colored, P-E change, field.\r\npanel_DJF: Fig8-21_DJF_P-E_mean-change_ssp585_DJF_1850-1900_plus3K.nc\r\npanel_JJA: Fig8-21_JJA_P-E_mean-change_ssp585_JJA_1850-1900_plus3K.nc\r\n\r\nCLARIFICATION: The following file names have been changed from the original, for the purposes of conforming to the file naming standards for the CEDA catalogue. \r\n- panel_ANN: Fig8-21_ANN_P-E_mean-change_ssp585_ANN_1850-1900_+3K.nc -> Fig8-21_ANN_P-E_mean-change_ssp585_ANN_1850-1900_plus3K.nc\r\n- panel_DJF: Fig8-21_DJF_P-E_mean-change_ssp585_DJF_1850-1900_+3K.nc -> Fig8-21_DJF_P-E_mean-change_ssp585_DJF_1850-1900_plus3K.nc\r\n- panel_JJA: Fig8-21_JJA_P-E_mean-change_ssp585_JJA_1850-1900_+3K.nc -> Fig8-21_JJA_P-E_mean-change_ssp585_JJA_1850-1900_plus3K.nc\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 8)\r\n - Link to the Supplementary Material for Chapter 8, which contains details on the input data used in Table 8.SM.1\r\n - Link to the code for all figures in Chapter 8, archived on Zenodo.\r\n - Link to the documentation for CAMMAC, the tool used for AR6 analysis.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:43", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, precipitation, evaporation, change, scenario, P-E", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T15:28:05", "doiPublishedTime": "2023-05-15T12:30:42.127715", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 165, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37780, "dataPath": "/badc/ar6_wg1/data/ch_08/ch8_fig21/v20220718", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1092598, "numberOfFiles": 7, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10437, "startTime": "2015-01-01T12:00:00", "endTime": "2050-12-31T12:00:00" }, "resultQuality": { "ob_id": 4030, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-07-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37781, "uuid": "01545bad7c4e48cf9958ec6654eeb143", "short_code": "comp", "title": "Caption for Figure 8.21 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Schematic depicting large-scale circulation changes and impacts on the regional water cycle. The central figures show precipitation minus evaporation (P–E) changes at 3°C or global warming relative to an 1850–1900 base period (mean of 23 CMIP6 SSP5-8.5 simulations). Annual mean changes (large map) include contours depicting control climate P–E = 0 lines with the solid contour enclosing the tropical rain belt region and dashed lines representing the edges of subtropical regions. Confidence levels assess understanding of how large-scale circulation change affect the regional water." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 63570, 63571 ], "vocabularyKeywords": [], "identifier_set": [ 12484 ], "observationcollection_set": [ { "ob_id": 32723, "uuid": "ab437cee56cb405285aac2bb59cc36d6", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 8.13\r\n- data for Figure 8.14\r\n- data for Figure 8.15\r\n- input data for Figure 8.16\r\n- data for Figure 8.17\r\n- data for Figure 8.18\r\n- data for Figure 8.21\r\n- data for Figure 8.25\r\n- data for Figure 8.26\r\n- data for Box 8.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180264, 180265, 180266, 180267, 180268, 180269, 180270, 180271 ], "onlineresource_set": [ 52564, 52554, 82553, 52534, 52533, 88624, 94646 ] }, { "ob_id": 37782, "uuid": "47961b1927b8492990ed92f10a514b6b", "title": "Chapter 8 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 8.25 (v20220718)", "abstract": "Data for Figure 8.25 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 8.25 shows the effect of first versus second 2°C of global warming relative to 1850-1900 on seasonal mean precipitation.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Douville, H., K. Raghavan, J. Renwick, R.P. Allan, P.A. Arias, M. Barlow, R. Cerezo-Mota, A. Cherchi, T.Y. Gan, J. Gergis, D. Jiang, A. Khan, W. Pokam Mba, D. Rosenfeld, J. Tierney, and O. Zolina, 2021: Water Cycle Changes. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1055–1210, doi:10.1017/9781009157896.010.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has multiple panels. Data is provided in panel-specific sub-directories.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains CMIP6 multi-model ensemble mean December–January–February (left panels) and June–July–August (right panels) precipitation difference for:\r\n \r\n - SSP5-8.5 scenario at +2°C\r\n - SSP5-8.5 scenario at +4°C minus SSP5-8.5 at +2°C (second 2°C warming)\r\n - Second minus first 2°C fast warming.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n There are two NetCDF files per panel :\r\n - one for the main field, which is represented with colors and has 'rchange' or 'rmeans' in the filename\r\n - the other for the confidence information, based on fraction of models which agree about signal change sign, which is represented in figures by diagonal lines as specified by the so called AR6 simple hatching scheme; it has 'agreement' or 'slashes' in the filename\r\n \r\n Each datafile has NetCDF attributes which clearly describe the data.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 8)\r\n - Link to the Supplementary Material for Chapter 8, which contains details on the input data used in Table 8.SM.1\r\n - Link to the code for all figures in Chapter 8, archived on Zenodo.\r\n - Link to the documentation for CAMMAC, the tool used for AR6 analysis.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:37", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, precipitation, response to warming, non-linear, 2 degrees", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T15:34:35", "doiPublishedTime": "2023-05-15T12:33:03.266040", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 166, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37783, "dataPath": "/badc/ar6_wg1/data/ch_08/ch8_fig25/v20220718", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3477999, "numberOfFiles": 15, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10438, "startTime": "1850-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4031, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-07-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37784, "uuid": "356ce94601d54026bf0062866822a893", "short_code": "comp", "title": "Caption for Figure 8.25 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Effect of first versus second 2°C of global warming relative to the 1850-1900 base period on seasonal mean precipitation (mm day–1). CMIP6 multi-model ensemble mean December–January–February (left panels) and June–July–August (right panels) precipitation difference for (a, b) SSP5-8.5 at +2°C (c, d) SSP5-8.5 at +4°C minus SSP5-8.5 at +2°C (second 2°C warming); (e, f) second minus first 2°C fast warming (c–a and d–b). Only models reaching the +4°C warming levels in SSP5-8.5 are considered. Differences are computed based on 21-year time windows centred on the first year reaching or exceeding the selected global warming level using a 21-year running mean global surface atmospheric temperature criterion. Uncertainty is represented using the simple approach. No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 63021, 88542 ], "vocabularyKeywords": [], "identifier_set": [ 12485 ], "observationcollection_set": [ { "ob_id": 32723, "uuid": "ab437cee56cb405285aac2bb59cc36d6", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 8.13\r\n- data for Figure 8.14\r\n- data for Figure 8.15\r\n- input data for Figure 8.16\r\n- data for Figure 8.17\r\n- data for Figure 8.18\r\n- data for Figure 8.21\r\n- data for Figure 8.25\r\n- data for Figure 8.26\r\n- data for Box 8.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180274, 180275, 180276, 180277, 180278, 180279, 180280, 180281 ], "onlineresource_set": [ 52563, 52553, 82554, 52531, 52532, 88625, 94647 ] }, { "ob_id": 37785, "uuid": "ef3dd7efa4f442b2812c4ee905f794c2", "title": "Chapter 8 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 8.26 (v20220718)", "abstract": "Data for Figure 8.26 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 8.26 shows the rate of change in basin-scale annual mean runoff with increasing global warming levels.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Douville, H., K. Raghavan, J. Renwick, R.P. Allan, P.A. Arias, M. Barlow, R. Cerezo-Mota, A. Cherchi, T.Y. Gan, J. Gergis, D. Jiang, A. Khan, W. Pokam Mba, D. Rosenfeld, J. Tierney, and O. Zolina, 2021: Water Cycle Changes. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1055–1210, doi:10.1017/9781009157896.010.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n There is one single NetCDF file for data for all panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains relative changes (%) in basin-averaged annual mean runoff estimated as multi-model ensemble median from a variable subset of CMIP6 models for each SSP over nine major river basins:\r\n \r\n - Mississippi (a),\r\n - Danube (b),\r\n - Lena (c),\r\n - Amazon (d),\r\n - Euphrates (e),\r\n - Yangtze (f),\r\n - Niger (f),\r\n - Indus (g),\r\n - Murray (h).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n The NetCDF file contains a multi-dimensionnal variable 'mrro_mean' which matches the various curves showing in the figure. These are parametric curves, and the parameter is a period index. The file also contains a variable 'tas' for the value of global warming for the period, for each scenario.\r\n \r\n Dimension 'period' is a period index, which is the parameter for the parametric curves linking variables 'mrro_mean' and 'tas'. Period value 1 stands for 1901-1920, 2 for 1911-1920 ...,\r\n \r\n Dimension ssp is the SSP index, for that order : ssp585 , ssp245 , ssp126.\r\n \r\n Variable 'tas' is the change of globally averaged surface temperature w.r.t. 1850-1900 average, indexed by period.\r\n \r\n Variable mrro_mean provide statistics of relative changes (%) of runoff in basin-averaged annual mean runoff. The basin averages have been estimated after a first-order conservative remapping of the model outputs on the 0.5° by 0.5° river network of (Decharme et al., 2019). It is indexed by :\r\n - dimension 'stats' for statistics over a CMIP6 multi-model ensemble, in that order : percentile 5, mean, and percentile 95,\r\n - dimension 'basin' for this ordered list : Mississippi, Danube, Lena, Amazon, Euphrates, Yangtze, Niger, Indus, Murray\r\n \r\n Variable mrro_std is not used in the figure.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n SSP stands for Shared Socioeconomic Pathway.\r\n SSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\n SSP245 is the Shared Socioeconomic Pathway which represents the median of radiative forcing and development scenarios, consistent with RCP4.5.\r\n SSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Curves are parametric curves. The parameter is the period index.\r\n\r\n So each curve for a given statistics 'stat', scenario 'ssp' and basin 'basin' is defined by varying period_index in:\r\n \r\n x=tas(ssp,period_index),\r\n \r\n y=mrro_mean(ssp, basin, stat, period_index)\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 8)\r\n - Link to the Supplementary Material for Chapter 8, which contains details on the input data used in Table 8.SM.1\r\n - Link to the code for all figures in Chapter 8, archived on Zenodo.\r\n - Link to the documentation for CAMMAC, the tool used for AR6 analysis.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:41", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, runoff, runoff change, runoff rate of change, runoff change with warming, basin runoff, multi-model, scenarios, Mississipi , Danube, Lena, Amazon, Euphrates, Yangtze, Niger, Indus, Murray", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T15:46:36", "doiPublishedTime": "2023-05-15T12:37:10.660000", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 167, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37786, "dataPath": "/badc/ar6_wg1/data/ch_08/ch8_fig26/v20220718", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 29728, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10439, "startTime": "1850-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4032, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-07-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37787, "uuid": "9e2b84be5e154462934ff6af2b7afcf6", "short_code": "comp", "title": "Caption for Figure 8.26 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Rate of change in basin-scale annual mean runoff with increasing global warming levels. Relative changes (%) in basin-averaged annual mean runoff estimated as multi-model ensemble median from a variable subset of CMIP6 models for each SSP over nine major river basins: (a) Mississippi, (b) Danube, (c) Lena, (d) Amazon, (e) Euphrates, (f) Yangtze, (g) Niger, (h) Indus, and (i) Murray. The basin averages have been estimated after a first-order conservative remapping of the model outputs on the 0.5° by 0.5° river network of Decharme et al. (2019). The shaded area indicates the 5–95% confidence interval of the ensemble values across all SSPs. Note that the y-axis range differs across basins and is particularly large for Niger and Murray (panels g and i). The number of models considered is specified for each scenario in the legend located inside panel b. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 63567, 63568, 63569 ], "vocabularyKeywords": [], "identifier_set": [ 12486 ], "observationcollection_set": [ { "ob_id": 32723, "uuid": "ab437cee56cb405285aac2bb59cc36d6", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 8.13\r\n- data for Figure 8.14\r\n- data for Figure 8.15\r\n- input data for Figure 8.16\r\n- data for Figure 8.17\r\n- data for Figure 8.18\r\n- data for Figure 8.21\r\n- data for Figure 8.25\r\n- data for Figure 8.26\r\n- data for Box 8.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180284, 180285, 180286, 180287, 180288, 180289, 180290, 180291 ], "onlineresource_set": [ 52562, 52552, 82555, 52529, 52530, 88626 ] }, { "ob_id": 37789, "uuid": "a1247fe8044946d29b205d655f61c9dd", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from ZAMG's Vaisala CL51 instrument deployed at Kufstein, Austria", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Zentralanstalt für Meteorologie und Geodynamik (ZAMG)'s Vaisala CL51 deployed at Kufstein, Austria.\n\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\n\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-11130.\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\n \nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T03:17:29", "updateFrequency": "daily", "dataLineage": "Data were collected by instrument and transmitted to the central E-PROFILE processing hub at the UK's Met Office before preparation and delivery to the Centre for Environmental Data Analysis (CEDA). CEDA then produces daily concatenated files before ingestion into the CEDA Archive.", "removedDataReason": "", "keywords": "E-PROFILE, ceilometer measurements", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-07-08T23:09:30", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3570, "bboxName": "Kufstein", "eastBoundLongitude": 12.162779808044434, "westBoundLongitude": 12.162779808044434, "southBoundLatitude": 47.575279235839844, "northBoundLatitude": 47.575279235839844 }, "verticalExtent": null, "result_field": { "ob_id": 37788, "dataPath": "/badc/eprofile/data/daily_files/austria/kufstein/zamg-vaisala-cl51_A", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1000783409, "numberOfFiles": 401, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 10440, "startTime": "2022-07-07T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3890, "explanation": "The data are provided as-is with no quality control undertaken by the Centre for Environmental Data Analysis (CEDA). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "E-PROFILE QC statement", "date": "2022-02-28" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37790, "uuid": "074eec4a02614e30abb72d89e3a596c6", "short_code": "acq", "title": "ZAMG: Vaisala CL51 instrument deployed at Kufstein", "abstract": "Vaisala CL51 instrument instrument deployed at Kufstein operated by Zentralanstalt für Meteorologie und Geodynamik (ZAMG) providing cloud base height and aerosol profile data." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 220 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2527, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 7, "licenceURL": "https://artefacts.ceda.ac.uk/licences/cuncgl", "licenceClassifications": [ { "ob_id": 6, "classification": "personal" }, { "ob_id": 4, "classification": "academic" }, { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 32779, "uuid": "16a48b5339ab48cd97bb680388c5cddf", "short_code": "proj", "title": "EUMETNET E-PROFILE", "abstract": "E-PROFILE is part of the EUMETNET Composite Observing System, EUCOS, managing the European networks of radar wind profilers (RWP) and automatic lidars and ceilometers (ALC) for the monitoring of vertical profiles of wind and aerosols including volcanic ash.\r\n \r\n\r\nE-PROFILE coordinates the measurements of vertical profiles of wind from radar wind profilers (vertically pointing Doppler radars) and weather radars from a network of locations across Europe and provides the data to the end users. The main goal is to improve the overall usability of wind profiler data for operational meteorology and to provide support and expertise to both profiler operators and end users.\r\nDue to technical advances of the last years ceilometers (automatic low cost lidars) provide nowadays not only cloud base height but also information on the vertical distribution of aerosols derived from the backscatter profile. To make available this new observation capacity E-PROFILE is developing a framework to produce and exchange profiles of attenuated backscatter profiles. Automatic lidars and ceilometers of stations across Europe are added to the operational network." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50358, 50359, 50360, 50361, 50362, 50363, 50365, 50366, 50367, 50368, 50370, 50371, 50372, 50373, 62345, 62346, 62347 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 34905, "uuid": "345c47d378b64c75b7957aef0c09c81f", "short_code": "coll", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from a network covering most of Europe with additional sites worldwide", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from a network of instruments within EUMETNET's E-PROFILE ALC network.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nMost datasets are available to registered CEDA users. For those not available to CEDA users application for access to those datasets under restricted access can be made using the links on one of the associated records. All use is made in accordance with the Closed-Use Non-Commercial General Licence. See datasets for further licencing links and for individual dataset citations.\r\n\r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.\r\n\r\nNote - the datasets listed on this collection are daily concatenated files produced from single time-step files for each instrument. CEDA holds an older archive of single time-step files (not linked to from the datasets or this collection) which will be aggregated together over time to extend these datasets further back to the start of the E-PROFILE holdings in the CEDA archives. Access to the older single time-step files ahead of their concatenation into daily files can be made via : https://data.ceda.ac.uk/badc/eprofile/data/. As these data are processed single time-step files will be removed from the archive.\r\n\r\nIt is not possible to support any requests for data that predates the CEDA holdings nor to back-fill any data gaps." } ], "responsiblepartyinfo_set": [ 180294, 180295, 180296, 180297, 180298, 180299, 180300, 180301, 180302 ], "onlineresource_set": [ 52549, 52548 ] }, { "ob_id": 37803, "uuid": "818f746a3ea54210a232145d95e9c819", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from NCAS's vaisala-cl51 instrument deployed at the Chilbolton Atmospheric Observatory, UK", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from National Centre for Atmospheric Science (NCAS)'s vaisala-cl51 deployed at Chilbolton Alc, UK.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-826-300-3.\r\nSee online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-07-18T04:18:19", "updateFrequency": "daily", "dataLineage": "Data were collected by instrument and transmitted to the central E-PROFILE processing hub at the UK's Met Office before preparation and delivery to the Centre for Environmental Data Analysis (CEDA). CEDA then produces daily concatenated files before ingestion into the CEDA Archive.", "removedDataReason": "", "keywords": "E-PROFILE, ceilometer measurements", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-07-11T08:34:26", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3571, "bboxName": "Chilbolton-Alc", "eastBoundLongitude": -1.4390000104904175, "westBoundLongitude": -1.4390000104904175, "southBoundLatitude": 51.14500045776367, "northBoundLatitude": 51.14500045776367 }, "verticalExtent": null, "result_field": { "ob_id": 37802, "dataPath": "/badc/eprofile/data/daily_files/uk/chilbolton-atmospheric-observatory/ncas-vaisala-cl51_A", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3397591076, "numberOfFiles": 1280, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 10444, "startTime": "2022-07-07T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3890, "explanation": "The data are provided as-is with no quality control undertaken by the Centre for Environmental Data Analysis (CEDA). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "E-PROFILE QC statement", "date": "2022-02-28" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37804, "uuid": "ae30b98c576a45578e4c8307459efa3e", "short_code": "acq", "title": "NCAS: vaisala-cl51 instrument deployed at Chilbolton Alc", "abstract": "vaisala-cl51 instrument instrument deployed at Chilbolton Alc operated by National Centre for Atmospheric Science (NCAS) providing cloud base height and aerosol profile data." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 220 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2527, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 7, "licenceURL": "https://artefacts.ceda.ac.uk/licences/cuncgl", "licenceClassifications": [ { "ob_id": 6, "classification": "personal" }, { "ob_id": 4, "classification": "academic" }, { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 32779, "uuid": "16a48b5339ab48cd97bb680388c5cddf", "short_code": "proj", "title": "EUMETNET E-PROFILE", "abstract": "E-PROFILE is part of the EUMETNET Composite Observing System, EUCOS, managing the European networks of radar wind profilers (RWP) and automatic lidars and ceilometers (ALC) for the monitoring of vertical profiles of wind and aerosols including volcanic ash.\r\n \r\n\r\nE-PROFILE coordinates the measurements of vertical profiles of wind from radar wind profilers (vertically pointing Doppler radars) and weather radars from a network of locations across Europe and provides the data to the end users. The main goal is to improve the overall usability of wind profiler data for operational meteorology and to provide support and expertise to both profiler operators and end users.\r\nDue to technical advances of the last years ceilometers (automatic low cost lidars) provide nowadays not only cloud base height but also information on the vertical distribution of aerosols derived from the backscatter profile. To make available this new observation capacity E-PROFILE is developing a framework to produce and exchange profiles of attenuated backscatter profiles. Automatic lidars and ceilometers of stations across Europe are added to the operational network." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46582, 46583, 46584, 46585, 46586, 46587, 46588, 46589, 46590, 46591, 46592, 46593, 46594, 46595, 46596, 46597, 46598 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 34905, "uuid": "345c47d378b64c75b7957aef0c09c81f", "short_code": "coll", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from a network covering most of Europe with additional sites worldwide", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from a network of instruments within EUMETNET's E-PROFILE ALC network.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nMost datasets are available to registered CEDA users. For those not available to CEDA users application for access to those datasets under restricted access can be made using the links on one of the associated records. All use is made in accordance with the Closed-Use Non-Commercial General Licence. See datasets for further licencing links and for individual dataset citations.\r\n\r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.\r\n\r\nNote - the datasets listed on this collection are daily concatenated files produced from single time-step files for each instrument. CEDA holds an older archive of single time-step files (not linked to from the datasets or this collection) which will be aggregated together over time to extend these datasets further back to the start of the E-PROFILE holdings in the CEDA archives. Access to the older single time-step files ahead of their concatenation into daily files can be made via : https://data.ceda.ac.uk/badc/eprofile/data/. As these data are processed single time-step files will be removed from the archive.\r\n\r\nIt is not possible to support any requests for data that predates the CEDA holdings nor to back-fill any data gaps." } ], "responsiblepartyinfo_set": [ 180335, 180336, 180337, 180338, 180339, 180340, 180341, 180342, 180343 ], "onlineresource_set": [ 52551, 52623 ] }, { "ob_id": 37805, "uuid": "399a75d2538a471cb529d1f0fa01410e", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 7.18 (v20220721)", "abstract": "Input Data for Figure 7.18 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.18 shows a summary of the equilibrium climate sensitivity and transient climate response assessments using different lines of evidence. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with input data provided for both panels. A link to the code to plot the figure archived on Zenodo is provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- (a) Equilibrium climate sensitivity (ECM) estimates. \r\n Assessment methods:\r\n - Process understanding\r\n - Instrumental record\r\n - Paleoclimates\r\n - Emergent constraints\r\n - Combined assessment\r\n - CMIP6 ESMs\r\n\r\n- (b) Transient climate response (TCR) estimates. \r\n Assessment methods:\r\n - Process understanding\r\n - Instrumental record\r\n - Paleoclimates\r\n - Emergent constraints\r\n - Combined assessment\r\n - CMIP6 ESMs\r\n\r\nAssessed ranges are taken from Tables 7.13 and 7.14 for ECS and TCR respectively. \r\nNote that for the ECS assessment based on both the instrumental record and paleoclimates, limits (i.e., one-sided distributions) are given, which have twice the probability of being outside the maximum/minimum value at a given end, compared to ranges (i.e., two-tailed distributions) which are given for the other lines of evidence. For example, the extremely likely limit of greater than 95% probability corresponds to one side of the very likely (5–95%) range. Best estimates are given as either a single number or by a range represented by a grey box. CMIP6 model values are not directly used as a line of evidence but presented on the Figure for comparison.\r\n \r\nECS values are taken from Schlund et al. (2020) and TCR values from Meehl et al. (2020); see Supplementary Material 7.SM.4. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 7.18:\r\n \r\n - Data file: ecs_for_faq.csv\r\n - Data file: tcr_for_faq.csv\r\n\r\nData is also provided in xlsx format, which is the format used by the plotting script linked in the Related Documents section.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nESM stands for Earth System Model. \r\nACCESS1-0 is the Australian Community Climate and Earth System Simulator coupled climate model version 1.0.\r\nACCESS1-3 is the Australian Community Climate and Earth System Simulator coupled climate model version 1.3.\r\nACCESS-CM2 is the Australian Community Climate and Earth System Simulator coupled climate model.\r\nACCESS-ESM1-5 is the Australian Community Climate and Earth System Simulator Earth system model version designed to participate in CMIP6 simulations.\r\nAWI-CM-1-1-MR is the Alfred Wegener Institute Climate Model version 1.1 - Medium Resolution, with locally-increased horizontal resolution over energetically active ocean areas.\r\nBCC-CSM1-1 is the Beijing Climate Center Climate System Model version 1.1.\r\nBCC-CSM1-1-M is the Beijing Climate Center Climate System Model version 1.1, with a moderate resolution.\r\nBCC-CSM2-MR is the Beijing Climate Center Climate System Model version 2 - moderate vertical resolution.\r\nBNU-ESM is the Beijing Normal University Earth System Model.\r\nBNU-ESM1 is the Beijing Normal University Earth System Model version 1.\r\nCAMS-CSM1-0 is the Chinese Academy of Meteorological Sciences Climate System Model version 1.\r\nCanESM5 is the Canadian Earth System Model version 5.\r\nCanESM2 is the Canadian Earth System Model version 2.\r\nCCSM3 is the Community Climate System Model version 3.\r\nCAS-ESM2-0 is the Chinese Academy of Sciences Earth System Model version 2.0.\r\nCESM2 is the Community Earth System Model version 2.\r\nCESM2-FV2 is the Community Earth System Model version 2 - Finite Volume with a 2 degree resolution. \r\nCESM2-WACCM is the Community System Model version 2 - Whole Atmosphere Community Climate Model.\r\nCMCC-CM2-SR5 is the Euro-Mediterranean Centre on Climate Change Coupled Climate Model version 2 - standard configuration.\r\nCNRM-CM5 is the Centre National de Recherches Météorologiques Climate Model for CMIP5.\r\nCNRM-CM5-2 is the Centre National de Recherches Météorologiques Climate Model for CMIP5, version 2.\r\nCNRM-CM6-1 is the Centre National de Recherches Météorologiques Climate Model for CMIP6.\r\nCNRM-CM6-1-HR is the Centre National de Recherches Météorologiques Climate Model for CMIP6 - altered Horizontal Resolution.\r\nCNRM-ESM2-1 is the Centre National de Recherches Météorologiques Earth System Model, derived from CNRM-CM6-1.\r\nCSIRO-Mk3-6-0 is the Commonwealth Scientific and Industrial Research Organisation Atmosphere Ocean Global Climate Model (GCM).\r\nE3SM-1-0 is the Energy Exascale Earth System Model version 1.0.\r\nEC-Earth3-Veg is the European Community Earth-system model version 3, with the Global Circulation Model (GCM) coupled to the dynamic vegetation model.\r\nFGOALS-f3-L is the Flexible Global Ocean-Atmosphere-Land System Model, Finite-volume version 3, low horizontal resolution. \r\nFGOALS-g2 is the Flexible Global Ocean-Atmosphere-Land System Model, Grid-point Version 2.\r\nFGOALS-g3 is the Flexible Global Ocean-Atmosphere-Land System Model, Grid-point Version 3.\r\nGFDL-CM3 is the Geophysical Fluid Dynamics Laboratory - Climate Model 3.\r\nGFDL-ESM2G is the Geophysical Fluid Dynamics Laboratory - Earth System Model version 2, multi-centennial warming.\r\nGFDL-ESM2M is the Geophysical Fluid Dynamics Laboratory - Earth System Model version 2, multi-centennial cooling. \r\nGISS-E2-1-G is the Goddard Institute for Space Studies - chemistry-climate model version E2.1, using the GISS Ocean v1 (G01) model.\r\nGISS-E2-H is the Goddard Institute for Space Studies coupled general circulation model (CGCM) - ocean configuration coupled to the \r\nHYCOM is the Hybrid Coordinate Ocean Model.\r\nGISS-E2-R is the Goddard Institute for Space Studies coupled general circulation model (CGCM) - ocean configuration coupled to the Russell OGCM.\r\nHadGEM2-ES is the Met Office Hadley Centre Global Environment Model version 2 - Earth System. \r\nHadGEM3-GC31-LL is the Met Office Hadley Centre Global Environment Model - Global Coupled configuration 3.1 - using an atmosphere/ocean resolution for historical simulation N96/ORCA1.\r\nHadGEM3-GC31-MM is the Met Office Hadley Centre Global Environment Model - Global Coupled configuration 3.1 - using an atmosphere/ocean resolution for historical simulation N216/ORCA025.\r\nHYCOM is the Hybrid Coordinate Ocean Model. \r\nINMCM4 is the Institute for Numerical Mathematics Climate Model version 4.0. \r\nINM-CM4-8 is the Institute for Numerical Mathematics Climate Model version 4.8.\r\nINM-CM5-0 is the Institute for Numerical Mathematics Climate Model version 5.0. \r\nIPSL-CM5A-LR is the Institut Pierre-Simon Laplace Climate Model for CMIP5 - Low Resolution, with re-parameterised cloud configuration.\r\nIPSL-CM5A-MR is the Institut Pierre-Simon Laplace Climate Model for CMIP5 - Mixed Resolution, with a higher horizontal atmospheric resolution.\r\nIPSL-CM5B-LR is the Institut Pierre-Simon Laplace Climate Model for CMIP5 - Low Resolution, with a LMDZ5B atmospheric component.\r\nIPSL-CM6A-LR is the Institut Pierre-Simon Laplace Climate Model for CMIP6 - Low Resolution.\r\nKACE-1-0-G is the Korean Advanced Community Earth system model. \r\nMCM-UA-1-0 is the Manabe Climate Model - University of Arizona - version 1.0. \r\nMIROC-ES2L is the Model for Interdisciplinary Research on Climate - Earth System version 2 for Long-term simulations.\r\nMIROC-ESM is the Model for Interdisciplinary Research on Climate - Earth System Model.\r\nMIROC5 is the Model for Interdisciplinary Research on Climate version 5.\r\nMIROC6 is the Model for Interdisciplinary Research on Climate version 6.\r\nMPI-ESM-1-2-HAM is the Max Planck Institute Earth System Model - version 2 - Hamburg Aerosol Model.\r\nMPI-ESM1-2-HR is the Max Planck Institute Earth System Model - version 2 - altered Horizontal Resolution.\r\nMPI-ESM1-2-LR is the Max Planck Institute Earth System Model - version 2 - Low Resolution.\r\nMPI-ESM-LR is the Max Planck Institute Earth System Model - Low Resolution.\r\nMPI-ESM-MR is the Max Planck Institute Earth System Model - Mixed Resolution.\r\nMPI-ESM-P is the Max Planck Institute Earth System Model - with reconfiguration of orbit and vegetation. \r\nMRI-CGCM3 is the Meteorological Research Institute - Coupled General Circulation Model version 3. \r\nMRI-ESM2-0 is the Meteorological Research Institute Earth System Model version 2.0.\r\nNESM3 is the Nanjing University of Information Science and Technology Earth System Model version 3.\r\nNorCPM1 is the Norwegian Climate Prediction Model version 1.\r\nNorESM1-LM is the Norwegian Earth System Model version 1 - 2 degree resolution for atmosphere and land components, 1 degree resolution for ocean and sea-ice components.\r\nNorESM2-LM is the Norwegian Earth System Model version 2 - 2 degree resolution for atmosphere and land components, 1 degree resolution for ocean and sea-ice components.\r\nNorESM2-MM is the Norwegian Earth System Model version 2 - 1 degree resolution for all model components.\r\nRussell OGCM is the Russell Ocean General Circulation Model. \r\nSAM0-UNICON is the Seoul National University Atmosphere Model version 0 with a Unified Convection Scheme.\r\nTaiESM1 is the Taiwan Earth System Model version 1.\r\nUKESM1-0-LL is the UK Earth System Model - version 1 - 2 degree resolution for all model components.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory of the Chapter 7 GitHub repository. The input data provided is used in the notebook to output figure 7.18. To reproduce the figure from the input data, you will need to edit the path 'datadir' in box 6 of the notebook based on your local directory structure. The notebook runs with data in .xlsx format but the data is also provided in .csv format here.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n- Link to the code for the figure, archived on Zenodo.\r\n - Link to notebook for plotting figure from the Chapter 7 GitHub repository", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:52", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, ECS, TCR, palaeoclimate, process, emergent constraints, observations", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-15T16:48:02", "doiPublishedTime": "2023-07-10T14:43:04.464631", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38064, "dataPath": "/badc/ar6_wg1/data/ch_07/inputdata_ch7_fig18/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 38366, "numberOfFiles": 7, "fileFormat": "XLSX, CSV" }, "timePeriod": null, "resultQuality": { "ob_id": 4268, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39592, "uuid": "91c4618b962f48ed8618d77582f99f5e", "short_code": "comp", "title": "Caption for Figure 7.18 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Summary of the equilibrium climate sensitivity (ECS panel (a)) and transient climate response (TCR panel (b)) assessments using different lines of evidence. Assessed ranges are taken from Tables 7.13 and 7.14 for ECS and TCR respectively. Note that for the ECS assessment based on both the instrumental record and paleoclimates, limits (i.e., one-sided distributions) are given, which have twice the probability of being outside the maximum/minimum value at a given end, compared to ranges (i.e., two-tailed distributions) which are given for the other lines of evidence. For example, the extremely likely limit of greater than 95% probability corresponds to one side of the very likely (5–95%) range. Best estimates are given as either a single number or by a range represented by a grey box. CMIP6 model values are not directly used as a line of evidence but presented on the Figure for comparison. ECS values are taken from Schlund et al. (2020) and TCR values from Meehl et al. (2020); see Supplementary Material 7.SM.4. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12641 ], "observationcollection_set": [ { "ob_id": 32722, "uuid": "b96ed5cbb8304a82bf1a2a4cad49bf6d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 7.3\r\n- input data for Figure 7.4\r\n- input data for Figure 7.5\r\n- data for Figure 7.6\r\n- input data for Figure 7.7\r\n- data for Figure 7.8\r\n- input data for Figure 7.10\r\n- data for Figure 7.11\r\n- input data for Figure 7.13\r\n- data for Figure 7.16\r\n- data for Figure 7.17\r\n- input data for Figure 7.18\r\n- data for Figure 7.19\r\n- input data for Figure 7.21\r\n- data for FAQ 7.3, Figure 1\r\n- input data for FAQ 7.3, Figure 1\r\n- data for 7.SM.1\r\n- input data for Box 7.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180346, 180347, 180348, 180349, 180350, 180351, 180489, 180606, 180607 ], "onlineresource_set": [ 52581, 52580, 82704, 83485, 83486 ] }, { "ob_id": 37806, "uuid": "b9303c07edb24582b45088795f347ca9", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 7.17 (v20220721)", "abstract": "Data for Figure 7.17 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.17 shows time evolution of the effective radiative forcing (ERF) to the CO2 concentration increased by 1% per year until year 70 (equal to the time of doubling) and kept fixed afterwards, and surface temperature response to the CO2 forcing calculated using the emulator.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- (a) Idealized radiative forcing. Time evolution of the effective radiative forcing (ERF) to the CO2 concentration increased by 1% per year until year 70 (equal to the time of doubling) and kept fixed afterwards (white line). The likely and very likely ranges of ERF indicated by light and dark orange have been assessed in Section 7.3.2.1. \r\n\r\n- (b) Emulated temperature response. Surface temperature response to the CO2 forcing calculated using the emulator with a given value of ECS, considering uncertainty in ΔF2×CO2, α, and κ associated with the ocean heat uptake and efficacy (white line). The likely and very likely ranges are indicated by cyan and blue, respectively. For comparison, the temperature response to abrupt doubling of the CO2 concentration is displayed by a grey curve. \r\n\r\nThe mean, likely and very likely ranges of ECS and TCR are shown at the right (the values of TCR also presented in panel b). \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 7.17:\r\n \r\n - Data file: 'Figure_17_values_panel_a.csv'\r\n - Data file: 'Figure_17_values_panel_b.csv'\r\n - Data file: 'Figure_17_values_panel_b_side.csv'\r\n\r\nERF stands for Effective Radiative Forcing.\r\nTCR stands for Transient Climate Response.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures for Chapter 7 are produced by the Jupyter Notebooks that live inside the notebooks directory of the Chapter 7 GitHub repository. \r\nThe data provided for this figure is the output plotted data as provided by the author.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n- Link to the code for the figure, archived on Zenodo\r\n - Link to the Chapter 7 GitHub repository", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:49", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, ECS, TCR, forcing, temperature, ERF, CO2", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-15T16:04:42", "doiPublishedTime": "2023-07-10T14:34:37.211653", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40052, "dataPath": "/badc/ar6_wg1/data/ch_07/ch7_fig17/v20221017", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 19825, "numberOfFiles": 6, "fileFormat": "CSV" }, "timePeriod": null, "resultQuality": { "ob_id": 4267, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39591, "uuid": "172ca1ad5306422fbe26c9ae1982e3fc", "short_code": "comp", "title": "Caption for Figure 7.17 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "(a) Time evolution of the effective radiative forcing (ERF) to the CO2 concentration increased by 1% per year until year 70 (equal to the time of doubling) and kept fixed afterwards (white line). The likely and very likely ranges of ERF indicated by light and dark orange have been assessed in Section 7.3.2.1. (b) Surface temperature response to the CO2 forcing calculated using the emulator with a given value of ECS, considering uncertainty in ΔF2×CO2, α , and κ associated with the ocean heat uptake and efficacy (white line). The likely and very likely ranges are indicated by cyan and blue, respectively. For comparison, the temperature response to abrupt doubling of the CO2 concentration is displayed by a grey curve. The mean, likely and very likely ranges of ECS and TCR are shown at the right (the values of TCR also presented in the panel). Further details on data sources and processing are available in the chapter data table (Table 7.SM.14)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12640 ], "observationcollection_set": [ { "ob_id": 32722, "uuid": "b96ed5cbb8304a82bf1a2a4cad49bf6d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 7.3\r\n- input data for Figure 7.4\r\n- input data for Figure 7.5\r\n- data for Figure 7.6\r\n- input data for Figure 7.7\r\n- data for Figure 7.8\r\n- input data for Figure 7.10\r\n- data for Figure 7.11\r\n- input data for Figure 7.13\r\n- data for Figure 7.16\r\n- data for Figure 7.17\r\n- input data for Figure 7.18\r\n- data for Figure 7.19\r\n- input data for Figure 7.21\r\n- data for FAQ 7.3, Figure 1\r\n- input data for FAQ 7.3, Figure 1\r\n- data for 7.SM.1\r\n- input data for Box 7.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180352, 180353, 180354, 180355, 180356, 180357, 180490, 180605 ], "onlineresource_set": [ 52582, 52583, 82703, 83483, 83484 ] }, { "ob_id": 37807, "uuid": "6fcc9d2c792243c1bb99de9c3cfdef2f", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 7.16 (v20220721)", "abstract": "Data for Figure 7.16 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.16 shows probability distributions of ERF to CO2 doubling and the net climate feedback, derived from process-based assessments in Sections 7.3.2 and 7.4.2. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 4 subpanels, with data written into the plotting script in the master GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Probability distributions of ERF to CO2 doubling and ECS distribution quantile (ΔF2×CO2; top) \r\n- Net climate feedback (climate feedback parameter vs. effective radiative forcing from 2xCO2, bottom left) and probability density (α; bottom right)\r\n\r\nCentral panel shows the joint probability density function calculated on a two-dimensional plane of ΔF2×CO2 and α (red), on which the 90% range shown by an ellipse is imposed to the background theoretical values of ECS (colour shading). The white dot, and thick and thin curves inside the ellipse represent the mean, likely and very likely ranges of ECS. \r\n\r\nAn alternative estimation of the ECS range (pink) is calculated by assuming that ΔF2×CO2 and α have a covariance. The assumption about the co-dependence between ΔF2×CO2 and α does not alter the mean estimate of ECS but affects its uncertainty. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n No data is provided for Figure 7.16 as the data is written in to the notebook used to plot the figure. This notebook is linked in the Related Documents section of this catalogue record.\r\n\r\nERF stands for Effective Radiative Forcing.\r\nECS stands for Equilibrium Climate Sensitivity.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. Also listed on the 'master' GitHub page linked in the documentation of this catalogue record are external GitHub repositories and locations within the contributed directory where code for figures have been supplied by other authors. These are provided \"as-is\" and are not guaranteed to be reproducible within this environment. For external GitHub locations, check out the relevant repository READMEs.\r\n\r\nWithin the processing chain, every notebook is prefixed by a number. To reproduce all results in the chapter, the notebooks should be run in numerical order, because some later things depend on earlier things (historical temperature attribution requires a constrained ensemble of the two layer climate model, which relies on the generation of the radiative forcing time series). This being said, most notebooks should run standalone, as input data is provided where the datasets are small enough (see the 'master;' GitHub page for these).\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the notebook on the Chapter 7 GitHub repository used to plot figure", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-07-21T09:38:06", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, feedbacks, forcing, ECS, ERF, CO2", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-31T14:42:18", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40275, "dataPath": "https://github.com/IPCC-WG1/Chapter-7/blob/main/notebooks/020_chapter7_fig7.16.ipynb", "oldDataPath": [], "storageLocation": "external", "storageStatus": "online", "volume": 0, "numberOfFiles": 0, "fileFormat": "Jupyter notebook on the Chapter 7 GitHub repository" }, "timePeriod": null, "resultQuality": { "ob_id": 4270, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39590, "uuid": "803a22aa5e924b0da10b15eefb35701c", "short_code": "comp", "title": "Caption for Figure 7.16 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Probability distributions of ERF to CO2doubling (ΔF2×CO2; top) and the net climate feedback ( α ; right), derived from process-based assessments in Sections 7.3.2 and 7.4.2. Central panel shows the joint probability density function calculated on a two-dimensional plane ofΔF2×CO2 and α (red), on which the 90% range shown by an ellipse is imposed to the background theoretical values of ECS (colour shading). The white dot, and thick and thin curves inside the ellipse represent the mean, likely and very likely ranges of ECS. An alternative estimation of the ECS range (pink) is calculated by assuming thatΔF2×CO2 and α have a covariance. The assumption about the co-dependence betweenΔF2×CO2 and α does not alter the mean estimate of ECS but affects its uncertainty. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2627, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 78, "licenceURL": "https://github.com/IPCC-WG1/Chapter-7/blob/main/LICENSE", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 32722, "uuid": "b96ed5cbb8304a82bf1a2a4cad49bf6d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 7.3\r\n- input data for Figure 7.4\r\n- input data for Figure 7.5\r\n- data for Figure 7.6\r\n- input data for Figure 7.7\r\n- data for Figure 7.8\r\n- input data for Figure 7.10\r\n- data for Figure 7.11\r\n- input data for Figure 7.13\r\n- data for Figure 7.16\r\n- data for Figure 7.17\r\n- input data for Figure 7.18\r\n- data for Figure 7.19\r\n- input data for Figure 7.21\r\n- data for FAQ 7.3, Figure 1\r\n- input data for FAQ 7.3, Figure 1\r\n- data for 7.SM.1\r\n- input data for Box 7.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180358, 180359, 180360, 180361, 180362, 180363, 180491, 180603, 180604, 195203 ], "onlineresource_set": [ 52584, 52585, 82702, 83501, 83502 ] }, { "ob_id": 37810, "uuid": "4dbd3ccb85d747188586735133f1d3d9", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 7.13 (v20220118)", "abstract": "Input Data for Figure 7.13 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.13 shows polar amplification in paleo proxies and models of the Early Eocene Climatic Optimum (EECO), the Mid-Pliocene Warm Period (MPWP) and the Last Glacial Maximum (LGM). \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 12 subpanels, with input data provided for panels a-l.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\nTemperature anomalies compared with pre-industrial (equivalent to CMIP6 simulation ‘piControl’) for:\r\n - the high-CO2 EECO and MPWP time periods\r\n - the low-CO2 LGM (expressed as pre-industrial minus LGM)\r\n\r\n(a), (b) and (c) Modelled near-surface air temperature anomalies for ensemble-mean simulations of the (a) EECO (Lunt et al., 2021); (b) MPWP (Haywood et al., 2020; Zhang et al., 2021); and (c) LGM (Kageyama et al., 2021; Zhu et al., 2021). Also shown are proxy near-surface air temperature anomalies (coloured circles). \r\n\r\n(d), (e) and (f) Proxy near-surface air temperature anomalies (grey circles), including published uncertainties (grey vertical bars), model ensemble mean zonal mean anomaly (solid red line) for the same model ensembles as in (a–c), light-red lines show the modelled temperature anomaly for the individual models that make up each ensemble (LGM, N=9; MPWP, N=17; EECO, N=5). \r\n\r\n(g), (h) and (i) Proxy sea surface temperature (SST) anomalies, including published uncertainties (grey vertical bars), model ensemble mean zonal mean anomaly (solid red line) for the same model ensembles as in (j-l), light-red lines show the modelled temperature anomaly for the individual models that make up each ensemble (LGM, N=9; MPWP, N=17; EECO, N=5).\r\n\r\n(j), (k) and (l) Modelled sea surface temperature (SST) for ensemble-mean simulations of the (a) EECO (Lunt et al., 2021); (b) MPWP (Haywood et al., 2020; Zhang et al., 2021); and (c) LGM (Kageyama et al., 2021; Zhu et al., 2021). \r\n\r\nBlack dashed lines show the average of the proxy values in each latitude band: 90°S–30°S, 30°S–30°N, and 30°N–90°N. \r\nRed dashed lines show the same banded average in the model ensemble mean, calculated from the same locations as the proxies. \r\nBlack and red dashed lines are only shown if there are five or more proxy points in that band. \r\n\r\nMean differences between the 90°S/N to 30°S/N and 30°S to 30°N bands are quantified for the models and proxies in each plot. \r\n\r\nFor the EECO maps – (a) and (j) – the anomalies are relative to the zonal mean of the pre-industrial, due to the different continental configuration. Proxy datasets are: (a) and (d) Hollis et al. (2019); (b) and (e) Salzmann et al. (2013); Vieira et al. (2018), (c) and (f) Cleator et al. (2020) at the sites defined in Bartlein et al. (2011); (g) and (j) Hollis et al. (2019); (h) and (k) McClymont et al. (2020); (i) and (l) Tierney et al. (2020b). Where there are multiple proxy estimations at a single site, a mean is taken. \r\n\r\nModel ensembles are:\r\n(a), (d), (g) and (j) DeepMIP (only model simulations carried out with a mantle-frame paleogeography, and carried out under CO2 concentrations within the range assessed in Table 2.2, are shown);\r\n(b), (e), (h) and (k) PlioMIP;\r\n(c), (f), (i) and (l) PMIP4. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 7.13:\r\n \r\nObserved data:\r\n - Data file: Figure7_13_obs.csv \r\n\r\nModel data:\r\n- model data in net-CDF files is contained in the directory 'Figure_7_13_mod' in separate directories for Eocene '/eocene', Mid-pliocene '/pliocene' and Last Glacial Maximum '/lgm' periods \r\n\r\nlandsea mask data:\r\n - Data file: Plio_enh_topo_v1.0_regrid.nc\r\n - Data file: peltier_ice4g_orog_21_regrid.nc\r\n - Data file: herold_etal_eocene_topo_1x1.nc\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nDeepMIP is The Deep-Time Model Intercomparison Project.\r\nPlioMIP is the Pliocene Model Intercomparison Project\r\nPMIP4 is the Paleoclimate Modelling Intercomparison Project phase 4.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThe data provided is the input data of plotting scripts which can be used to reproduce the figure. Plotting scripts for reproducing this figure are linked in the Related Documents section of this catalogue record. The notebook 'ipcc_figure_7.13.ipynb' can be run with the provided data to reproduce the figure, you need to edit the directory paths to match your local directory within the notebook.\r\nThe original script for plotting this figure can be found in the Chapter 7 GitHub repository also linked but requires IDL.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n - Link to the plotting scripts to reproduce the figure \r\n - Link to the Chapter 7 GitHub repository\r\n - Link to the code for Chapter 7, archived on Zenodo", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:18:01", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, palaeoclimate, polar amplification, near-surface air temperature, sea surface temperature, SST, EECO, MPWP, LGM", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-01T08:20:58", "doiPublishedTime": "2023-07-10T13:19:45.627735", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40092, "dataPath": "/badc/ar6_wg1/data/ch_07/inputdata_ch7_fig13/v20230118", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3794647, "numberOfFiles": 81, "fileFormat": "NetCDF, CSV" }, "timePeriod": null, "resultQuality": { "ob_id": 4283, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39589, "uuid": "7ad2a4488c2f4b9ba7cc13e7a7d56850", "short_code": "comp", "title": "Caption for Figure 7.13 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Polar amplification in paleo proxies and models of the Early Eocene Climatic Optimum (EECO), the Mid-Pliocene Warm Period (MPWP) and the Last Glacial Maximum (LGM).\r\nTemperature anomalies compared with pre-industrial (equivalent to CMIP6 simulation ‘piControl’) are shown for the high-CO2 EECO and MPWP time periods, and for the low-CO2 LGM (expressed as pre-industrial minus LGM). (a), (b) and (c) Modelled near-surface air temperature anomalies for ensemble-mean simulations of the (a) EECO (Lunt et al., 2021); (b) MPWP (Haywood et al., 2020; Zhang et al., 2021); and (c) LGM (Kageyama et al., 2021; Zhu et al., 2021). Also shown are proxy near-surface air temperature anomalies (coloured circles). (d), (e) and (f) Proxy near-surface air temperature anomalies (grey circles), including published uncertainties (grey vertical bars), model ensemble mean zonal mean anomaly (solid red line) for the same model ensembles as in (a–c), light-red lines show the modelled temperature anomaly for the individual models that make up each ensemble (LGM, N=9; MPWP, N=17; EECO, N=5). Black dashed lines show the average of the proxy values in each latitude band: 90°S–30°S, 30°S–30°N, and 30°N–90°N. Red dashed lines show the same banded average in the model ensemble mean, calculated from the same locations as the proxies. Black and red dashed lines are only shown if there are five or more proxy points in that band. Mean differences between the 90°S/N to 30°S/N and 30°S to 30°N bands are quantified for the models and proxies in each plot. Panels (g), (h) and (i) are like panels (d–f) but for sea surface temperature (SST) instead of near-surface air temperature. Panels (j), (k) and (l) are like panels (a–c) but for SST instead of near-surface air temperature. For the EECO maps – (a) and (j) – the anomalies are relative to the zonal mean of the pre-industrial, due to the different continental configuration. Proxy datasets are: (a) and (d) Hollis et al. (2019); (b) and (e) Salzmann et al. (2013); Vieira et al. (2018), (c) and (f) Cleator et al. (2020) at the sites defined in Bartlein et al. (2011); (g) and (j) Hollis et al. (2019); (h) and (k) McClymont et al. (2020); (i) and (l) Tierney et al. (2020b). Where there are multiple proxy estimations at a single site, a mean is taken. Model ensembles are (a), (d), (g) and (j) DeepMIP (only model simulations carried out with a mantle-frame paleogeography, and carried out under CO2 concentrations within the range assessed in Table 2.2, are shown); (b), (e), (h) and (k) PlioMIP; and (c), (f), (i) and (l) PMIP4. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 22883, 50300, 50496, 50542, 50543, 50609, 62353, 63050, 63558, 63559, 63560, 63561, 63562, 63563, 63564, 63565, 63566 ], "vocabularyKeywords": [], "identifier_set": [ 12639 ], "observationcollection_set": [ { "ob_id": 32722, "uuid": "b96ed5cbb8304a82bf1a2a4cad49bf6d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 7.3\r\n- input data for Figure 7.4\r\n- input data for Figure 7.5\r\n- data for Figure 7.6\r\n- input data for Figure 7.7\r\n- data for Figure 7.8\r\n- input data for Figure 7.10\r\n- data for Figure 7.11\r\n- input data for Figure 7.13\r\n- data for Figure 7.16\r\n- data for Figure 7.17\r\n- input data for Figure 7.18\r\n- data for Figure 7.19\r\n- input data for Figure 7.21\r\n- data for FAQ 7.3, Figure 1\r\n- input data for FAQ 7.3, Figure 1\r\n- data for 7.SM.1\r\n- input data for Box 7.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180376, 180377, 180378, 180379, 180380, 180381, 180494, 180601, 195341, 195340 ], "onlineresource_set": [ 52590, 52591, 82701, 83549, 83550, 83548 ] }, { "ob_id": 37812, "uuid": "a95ffffa5a734724b9cf307411208569", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 7.11 (v20220721)", "abstract": "Data for Figure 7.11 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.11 shows the temperature dependence of the feedback parameter, α (W m–2 °C–1), as a function of global mean surface air temperature anomaly relative to pre-industrial, for ESM simulations and paleoclimate proxies. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 1 panel, with data provided for the panel.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Feedback parameter α as a function of mean GSAT anomaly relative to pre-industrial for ESM simulations (red circles and lines)\r\n- Feedback parameter α as a function of mean GSAT anomaly relative to pre-industrial for paleoclimate proxies (grey squares and lines)\r\n\r\nReferences for ESM simulations: Caballero and Huber, 2013; Jonko et al., 2013; Meraner et al., 2013; Good et al., 2015; Duan et al., 2019; Mauritsen et al., 2019; Stolpe et al., 2019; Zhu et al., 2019a.\r\n\r\nReferences for paleoclimate proxies: von der Heydt et al., 2014; Anagnostou et al., 2016, 2020; Friedrich et al., 2016; Royer, 2016; Shaffer et al., 2016; Köhler et al., 2017; Snyder, 2019; Stap et al., 2019). \r\n\r\nFor the ESM simulations, the value on the x-axis refers to the average of the temperature before and after the system has equilibrated to a forcing (in most cases a CO2 doubling), and is expressed as an anomaly relative to an associated pre-industrial global mean temperature from that model. The light blue shaded square extends across the assessed range of α (Table 7.10) on the y-axis, and on the x-axis extends across the approximate temperature range over which the assessment of α is based (taken as from zero to the assessed central value of ECS; see Table 7.13). \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 7.11:\r\n \r\n - Data file: Figure7_11.csv\r\n\r\nESM stands for Earth System Model.\r\nGSAT stands for Global Surface Air Temperature.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThe data provided is the output data of the figure which can be used to reproduce the figure. Plotting scripts for reproducing this figure are linked in the Related Documents section of this catalogue record.\r\nThe original script for plotting this figure can be found in the Chapter 7 GitHub repository also linked but requires IDL.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n - Link to the code for Chapter 7, archived on Zenodo\r\n - Link to scripts used to reproduce figure from data\r\n - Link to the Chapter 7 GitHub repository", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:58", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, feedbacks, palaeoclimate, Earth System models, sensitivity, non-linearity", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-31T13:59:00", "doiPublishedTime": "2023-07-10T12:47:50.080400", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40088, "dataPath": "/badc/ar6_wg1/data/ch_07/ch7_fig11/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6794, "numberOfFiles": 4, "fileFormat": "CSV" }, "timePeriod": null, "resultQuality": { "ob_id": 4281, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39588, "uuid": "b82ba89a28024df294edc22f55c6a967", "short_code": "comp", "title": "Caption for Figure 7.11 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Feedback parameter, α (W m–2°C–1), as a function of global mean surface air temperature anomaly relative to pre-industrial, for ESM simulations (red circles and lines) (Caballero and Huber, 2013; Jonko et al., 2013; Meraner et al., 2013; Good et al., 2015; Duan et al., 2019; Mauritsen et al., 2019; Stolpe et al., 2019; Zhu et al., 2019a), and derived from paleoclimate proxies (grey squares and lines) (von der Heydt et al., 2014; Anagnostou et al., 2016, 2020; Friedrich et al., 2016; Royer, 2016; Shaffer et al., 2016; Köhler et al., 2017; Snyder, 2019; Stap et al., 2019). For the ESM simulations, the value on The x-axis refers to the average of the temperature before and after the system has equilibrated to a forcing (in most cases a CO2 doubling), and is expressed as an anomaly relative to an associated pre-industrial global mean temperature from that model. The light blue shaded square extends across the assessed range of α (Table 7.10) on The y-axis, and on The x-axis extends across the approximate temperature range over which the assessment of α is based (taken as from zero to the assessed central value of ECS; see Table 7.13). Further details on data sources and processing are available in the chapter data table (Table 7.SM.14)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12638 ], "observationcollection_set": [ { "ob_id": 32722, "uuid": "b96ed5cbb8304a82bf1a2a4cad49bf6d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 7.3\r\n- input data for Figure 7.4\r\n- input data for Figure 7.5\r\n- data for Figure 7.6\r\n- input data for Figure 7.7\r\n- data for Figure 7.8\r\n- input data for Figure 7.10\r\n- data for Figure 7.11\r\n- input data for Figure 7.13\r\n- data for Figure 7.16\r\n- data for Figure 7.17\r\n- input data for Figure 7.18\r\n- data for Figure 7.19\r\n- input data for Figure 7.21\r\n- data for FAQ 7.3, Figure 1\r\n- input data for FAQ 7.3, Figure 1\r\n- data for 7.SM.1\r\n- input data for Box 7.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180388, 180389, 180390, 180391, 180392, 180393, 180496, 180600, 195307 ], "onlineresource_set": [ 52594, 52595, 82700, 83540, 83541, 83542 ] }, { "ob_id": 37813, "uuid": "80380cfc0b10478b8b5821c0facdbdda", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 7.10 (v20220721)", "abstract": "Input Data for Figure 7.10 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.10 shows global mean climate feedbacks estimated in abrupt4xCO2 simulations of 29 CMIP5 models and 49 CMIP6 models, compared with those assessed in this Report. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 1 panel, with data provided for this panel in the master GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Global mean climate feedbacks estimated in abrupt4xCO2 simulations of 29 CMIP5 models and 49 CMIP6 models, compared with those assessed in AR6. The radiative kernels represented are the following:\r\n - Water vapour & lapse rate\r\n - Surface albedo\r\n - Cloud\r\n - Biogeophysical and non-CO2 biogeochemical\r\n - Net feedback\r\n - Planck response\r\n\r\nCMIP5 - light blue\r\nCMIP6 - orange\r\nThis Assessment Report - red\r\n\r\nIndividual feedbacks for CMIP models are averaged across six radiative kernels as computed in Zelinka et al. (2020). \r\nThe white line, black box and vertical line indicate the mean, 66% and 90% ranges, respectively. The shading represents the probability distribution across the full range of GCM/ESM values and for the 2.5–97.5 percentile range of the AR6 normal distribution. The unit is W m–2 °C–1. \r\nFeedbacks associated with biogeophysical and non-CO2 biogeochemical processes are assessed in AR6, but they are not explicitly estimated from general circulation models (GCMs)/Earth system models (ESMs) in CMIP5 and CMIP6. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 7.10:\r\n \r\n - Data file: cmip56_feedbacks_AR6.json\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project. \r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory of the Chapter 7 GitHub repository. The input json file provided is used in the notebook to output figure 7.10. To reproduce the figure from the input data, you will need to run the notebook from the same directory as the input data.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the notebook for plotting the figure on GitHub\r\n - Link to Zelinka et al. (2020)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:56", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, feedbacks, sensitivity, climate sensitivity, CMIP6, CMIP5, global mean climate feedbacks, cloud, surface albedo, water vapour, water vapor, lapse rate, Planck response, Biogeophysical and non-CO2 biogeochemical, net feedback", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-15T15:46:55", "doiPublishedTime": "2023-07-10T11:49:19", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38063, "dataPath": "/badc/ar6_wg1/data/ch_07/inputdata_ch7_fig10/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 22130, "numberOfFiles": 4, "fileFormat": "json" }, "timePeriod": null, "resultQuality": { "ob_id": 4266, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39587, "uuid": "8a368690755e4d34989bdc2e76910935", "short_code": "comp", "title": "Caption for Figure 7.10 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global mean climate feedbacks estimated in abrupt 4xCO2 simulations of 29 CMIP5 models (light blue) and 49 CMIP6 models (orange), compared with those assessed in this Report (red). Individual feedbacks for CMIP models are averaged across six radiative kernels as computed in Zelinka et al. (2020). The white line, black box and vertical line indicate the mean, 66% and 90% ranges, respectively. The shading represents the probability distribution across the full range of GCM/ESM values and for the 2.5–97.5 percentile range of the AR6 normal distribution. The unit is W m–2°C–1. Feedbacks associated with biogeophysical and non-CO2 biogeochemical processes are assessed in AR6, but they are not explicitly estimated from general circulation models (GCMs)/Earth system models (ESMs) in CMIP5 and CMIP6. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14)" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12637 ], "observationcollection_set": [ { "ob_id": 32722, "uuid": "b96ed5cbb8304a82bf1a2a4cad49bf6d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 7.3\r\n- input data for Figure 7.4\r\n- input data for Figure 7.5\r\n- data for Figure 7.6\r\n- input data for Figure 7.7\r\n- data for Figure 7.8\r\n- input data for Figure 7.10\r\n- data for Figure 7.11\r\n- input data for Figure 7.13\r\n- data for Figure 7.16\r\n- data for Figure 7.17\r\n- input data for Figure 7.18\r\n- data for Figure 7.19\r\n- input data for Figure 7.21\r\n- data for FAQ 7.3, Figure 1\r\n- input data for FAQ 7.3, Figure 1\r\n- data for 7.SM.1\r\n- input data for Box 7.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180394, 180395, 180396, 180397, 180398, 180399, 180497, 180599 ], "onlineresource_set": [ 52597, 52596, 82699, 83481, 83482, 83841 ] }, { "ob_id": 37815, "uuid": "5ef11ad195844a59b83393870a5860e1", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 7.8 (v20220721)", "abstract": "Data for Figure 7.8 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.8 shows attributed global surface air temperature change (GSAT) from 1750 to 2019 produced using the two-layer emulator (Supplementary Material 7.SM.2), forced with ERF derived in this chapter (displayed in Figure 2.10) and climate response constrained to assessed ranges for key climate metrics described in Cross-Chapter Box 7.1.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 1 panel, with data provided for this panel. A link to the code to plot the figure archived on Zenodo is provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\nAttributed global surface air temperature change (GSAT) from 1750 to 2019 produced using the two-layer emulator (Supplementary Material 7.SM.2), forced with ERF derived in this chapter (displayed in Figure 2.10) and climate response constrained to assessed ranges for key climate metrics described in Cross-Chapter Box 7.1. The temperature contributions are expressed as follows:\r\n - carbon dioxide (CO2)\r\n - methane (CH4)\r\n - nitrous oxide (N2O)\r\n - other well-mixed greenhouse gases (WMGHGs)\r\n - ozone (O3)\r\n - aerosols\r\n - other anthropogenic forcings\r\n - total anthropogenic \r\n - solar\r\n - volcanic\r\n - total forcing\r\n\r\nThe results shown are the medians from a 2237-member ensemble that encompasses uncertainty in forcing and climate response (year-2019 best estimates and uncertainties are shown in Figure 7.7 for several components). Shaded uncertainty bands show very likely (5–95%) ranges. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 7.8:\r\n \r\n - Data file: fig7.8.csv\r\n\r\nERF stands for Effective Radiative Forcing. \r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory of the Chapter 7 GitHub repository, which is linked in the 'Related Documents' section. Within the processing chain, every notebook is prefixed by a number. To reproduce all results in the chapter, the notebooks should be run in numerical order.\r\n\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n- Link to the code for the figure, archived on Zenodo\r\n- Link to the Chapter 7 GitHub repository \r\n- Link to the notebook for plotting figure", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:18:02", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, temperature, anthropogenic, natural, greenhouse gases, aerosols, Global Surface Air Temperature, GSAT", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-15T15:18:27", "doiPublishedTime": "2023-07-06T15:44:02.369757", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 39755, "dataPath": "/badc/ar6_wg1/data/ch_07/ch7_fig08/v20220721/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 190785, "numberOfFiles": 4, "fileFormat": "Data are CSV formatted" }, "timePeriod": { "ob_id": 10471, "startTime": "1750-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 4218, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39586, "uuid": "2d96254dc3974cfb9ffbdf82c36e3f5a", "short_code": "comp", "title": "Caption for Figure 7.8 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Attributed global surface air temperature change (GSAT) from 1750 to 2019 produced using the two-layer emulator (Supplementary Material 7.SM.2), forced with ERF derived in this chapter (displayed in Figure 2.10) and climate response constrained to assessed ranges for key climate metrics described in Cross-Chapter Box 7.1. The results shown are the medians from a 2237-member ensemble that encompasses uncertainty in forcing and climate response (year-2019 best estimates and uncertainties are shown in Figure 7.7 for several components). Temperature contributions are expressed for carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), other well-mixed greenhouse gases (WMGHGs), ozone (O3), aerosols, and other anthropogenic forcings, as well as total anthropogenic, solar, volcanic, and total forcing. Shaded uncertainty bands showvery likely (5–95%)ranges. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12635 ], "observationcollection_set": [ { "ob_id": 32722, "uuid": "b96ed5cbb8304a82bf1a2a4cad49bf6d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 7.3\r\n- input data for Figure 7.4\r\n- input data for Figure 7.5\r\n- data for Figure 7.6\r\n- input data for Figure 7.7\r\n- data for Figure 7.8\r\n- input data for Figure 7.10\r\n- data for Figure 7.11\r\n- input data for Figure 7.13\r\n- data for Figure 7.16\r\n- data for Figure 7.17\r\n- input data for Figure 7.18\r\n- data for Figure 7.19\r\n- input data for Figure 7.21\r\n- data for FAQ 7.3, Figure 1\r\n- input data for FAQ 7.3, Figure 1\r\n- data for 7.SM.1\r\n- input data for Box 7.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180406, 180407, 180408, 180409, 180410, 180411, 180499, 180598 ], "onlineresource_set": [ 52600, 52601, 82698, 83062, 83063, 83064 ] }, { "ob_id": 37817, "uuid": "0dd364e74c254b64bb5fddb5dceed364", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 7.6 (v20220721)", "abstract": "Data for Figure 7.6 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.6 shows the change in effective radiative forcing (ERF) from 1750 to 2019 by contributing forcing agents (carbon dioxide, other well-mixed greenhouse gases (WMGHGs), ozone, stratospheric water vapour, surface albedo, contrails and aviation-induced cirrus, aerosols, anthropogenic total, and solar). \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 1 panel, with data provided for this panel. A link to the code to plot the figure archived on Zenodo is provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\nChange in effective radiative forcing (ERF) from 1750 to 2019 by the following contributing forcing agents:\r\n - Carbon dioxide\r\n - Other well-mixed greenhouse gases (WMGHGs)\r\n - Ozone\r\n - Stratospheric water vapour\r\n - Surface albedo\r\n - Contrails and aviation-induced cirrus\r\n - Aerosols\r\n - Anthropogenic total\r\n - Solar \r\n\r\nSolid bars represent best estimates, and very likely (5–95%) ranges are given by error bars. \r\nNon-CO2 WMGHGs are further broken down into contributions from methane (CH4), nitrous oxide (N2O) and halogenated compounds. \r\nSurface albedo is broken down into land-use changes and light-absorbing particles on snow and ice. \r\nAerosols are broken down into contributions from aerosol–cloud interactions (ERFaci) and aerosol–radiation interactions (ERFari). For aerosols and solar, the 2019 single-year values are given (Table 7.8), which differ from the headline assessments in both cases. Volcanic forcing is not shown due to the episodic nature of volcanic eruptions. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 7.6:\r\n \r\n - Data file: AR6_ERF_1750-2019.csv\r\n - Data file: AR6_ERF_1750-2019_pc05.csv\r\n - Data file: AR6_ERF_1750-2019_pc95.csv\r\n\r\nERFaci stands for Effective Radiative Forcing of aerosol-cloud interaction.\r\nERFari stands for Effective Radiative Forcing of aerosol-radiation interaction.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThe data for the bars in this figure correspond to the 2019 data in final line of the csv files provided.\r\n\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory of the Chapter 7 GitHub repository, which is linked in the 'Related Documents' section. Within the processing chain, every notebook is prefixed by a number. To reproduce all results in the chapter, the notebooks should be run in numerical order.\r\n\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n- Link to the code for the figure, archived on Zenodo.\r\n- Link to the Chapter 7 GitHub repository \r\n- Link to the notebook for plotting figure", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:58", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, forcing, anthropogenic, natural, greenhouse gases, aerosols, effective radiative forcing, ERF", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-15T15:12:55", "doiPublishedTime": "2023-07-06T15:35:49.930488", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38061, "dataPath": "/badc/ar6_wg1/data/ch_07/ch7_fig06/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 314172, "numberOfFiles": 6, "fileFormat": "Data are CSV formatted" }, "timePeriod": { "ob_id": 10459, "startTime": "1750-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 4217, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39584, "uuid": "bd5e0c0d1b044d57a70dcf1e7e96ac49", "short_code": "comp", "title": "Caption for Figure 7.6 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Change in effective radiative forcing (ERF) from 1750 to 2019 by contributing forcing agents (carbon dioxide, other well-mixed greenhouse gases (WMGHGs), ozone, stratospheric water vapour, surface albedo, contrails and aviation-induced cirrus, aerosols, anthropogenic total, and solar). Solid bars represent best estimates, and very likely (5–95%) ranges are given by error bars. Non-CO2 WMGHGs are further broken down into contributions from methane (CH4), nitrous oxide (N2O) and halogenated compounds. Surface albedo is broken down into land-use changes and light-absorbing particles on snow and ice. Aerosols are broken down into contributions from aerosol–cloud interactions (ERFaci) and aerosol–radiation interactions (ERFari). For aerosols and solar, the 2019 single-year values are given (Table 7.8), which differ from the headline assessments in both cases. Volcanic forcing is not shown due to the episodic nature of volcanic eruptions. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12634 ], "observationcollection_set": [ { "ob_id": 32722, "uuid": "b96ed5cbb8304a82bf1a2a4cad49bf6d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 7.3\r\n- input data for Figure 7.4\r\n- input data for Figure 7.5\r\n- data for Figure 7.6\r\n- input data for Figure 7.7\r\n- data for Figure 7.8\r\n- input data for Figure 7.10\r\n- data for Figure 7.11\r\n- input data for Figure 7.13\r\n- data for Figure 7.16\r\n- data for Figure 7.17\r\n- input data for Figure 7.18\r\n- data for Figure 7.19\r\n- input data for Figure 7.21\r\n- data for FAQ 7.3, Figure 1\r\n- input data for FAQ 7.3, Figure 1\r\n- data for 7.SM.1\r\n- input data for Box 7.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180418, 180419, 180420, 180421, 180422, 180423, 180501, 180596 ], "onlineresource_set": [ 52604, 52605, 82696, 83059, 83060, 83061 ] }, { "ob_id": 37819, "uuid": "d32090ff8fe342788191683eb4416411", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 7.4 (v20230517)", "abstract": "Input Data for Figure 7.4 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.4 shows radiative adjustments at top of atmosphere for seven different climate drivers as a proportion of forcing. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 1 panel, with input data provided. A link to the code to plot the figure archived on Zenodo is provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Radiative adjustment for tropospheric temperature (orange)\r\n- Radiative adjustment for stratospheric temperature (yellow)\r\n- Radiative adjustment for water vapour (blue)\r\n- Radiative adjustment for surface albedo (green)\r\n- Radiative adjustment for clouds (grey)\r\n- Total adjustment (black) \r\n\r\nFor the greenhouse gases (carbon dioxide, methane, nitrous oxide and CFC-12) the adjustments are expressed as a percentage of stratospheric-temperature-adjusted radiative forcing (SARF), whereas for aerosol, solar and volcanic forcing they are expressed as a percentage of instantaneous radiative forcing (IRF). Land surface temperature response (outline red bar) is shown, but included in the definition of forcing. Data from Smith et al. (2018b) for carbon dioxide and methane; Smith et al. (2018b) and Gray et al. (2009) for solar; Hodnebrog et al. (2020b) for nitrous oxide and CFC-12; Smith et al. (2020b) for aerosol, and Marshall et al. (2020) for volcanic. \r\nIRFs come from offline calculations by Chris and Gunnar (for CAM4)\r\n\r\ntas_SW, ta_trop_SW, ta_strat_SW. alb_LW are always set to zero. Variables are included in netcdf anyways for consistency.\r\nWhen LW or SW IRFs is not available, The value is set to NaN in the netcdf.\r\nWhen the IRFs are NaN, the corresponding cloud adjustments are also set to NaN.\r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\nCanESM2 is the Canadian Earth System Model version 2.\r\nECHAM-HAM is the atmospheric General Circulation Model (GCM) from the MPI (Max Planck Institute for Meteorology) - Hamburg Aerosol Model.\r\nGISS-E2-R is the Goddard Institute for Space Studies coupled general circulation model (CGCM) - ocean configuration coupled to the Russell OGCM. \r\nHadGEM2 is the Met Offfice Hadley Centre Global Environment Model version 2.\r\nHadGEM3 is the Met Offfice Hadley Centre Global Environment Model version 3.\r\nIPSL-CM5A is the Institut Pierre-Simon Laplace Climate Model for CMIP5.\r\nMIROC-SPRINTARS is the Model for Interdisciplinary Research on Climate - Spectral Radiation-Transport Model for Aerosol Species.\r\nMPI-ESM is the Max Planck Institute Earth System Model.\r\nNCAR-CESM1-CAM4 is the National Center for Atmospheric Research - Community Earth System Model version 1 - Community Atmosphere Model version 4. \r\nNCAR-CESM1-CAM5 is the National Center for Atmospheric Research - Community Earth System Model version 1 - Community Atmosphere Model version 5. \r\nHadGEM2 is the Met Offfice Hadley Centre Global Environment Model version 2.\r\nGFDL is the Geophysical Fluid Dynamics Laboratory.\r\nBMRC is the Australian Bureau of Meteorology Research Centre.\r\nCCSM4 is the Community Climate System Model version 4.\r\nCESM is the Community Earth System Model.\r\nERF stands for Effective Radiative Forcing.\r\nIRF stands for Instantaneous Radiative Forcing. \r\nTAS stands for Temperature at Surface. \r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nThe CSV file used to plot Figure 7.4 is provided:\r\n\r\n- 'fig7.4.csv'\r\n\r\nThe github repository contains all input files to the plotting script for the figure except 'rcmip-concentrations-annual-means-v5-1-0.csv'. These are processed and combined in the code to create a single file 'fig7.4.csv' which is provided. The figure can be reproduced using just this file by running the notebook from box 22 by reading in the csv as variable 'adjustments_df'.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory of the Chapter 7 GitHub repository linked in the Related Documents section. The github repository contains all input files to the notebook except 'rcmip-concentrations-annual-means-v5-1-0.csv'. These are processed and combined in the code to create a single file 'fig7.4.csv' which is provided. The figure can be reproduced using just this file by running the notebook from box 22 by reading in the csv as variable 'adjustments_df'.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n- Link to the code for the figure, archived on Zenodo.\r\n- Link to the notebook for plotting the figure from the Chapter 7 GitHub repository which also contains input data files", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-09-11T13:08:04", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, forcing, adjustments, greenhouse gases, aerosols, solar, volcanic, radiative adjustment, tropospheric temperature, stratospheric temperature, water vapour, water vapor, surface albedo, clouds", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-17T09:46:45", "doiPublishedTime": "2023-07-06T09:46:47.856874", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38059, "dataPath": "/badc/ar6_wg1/data/ch_07/inputdata_ch7_fig04/v20230517", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 21705, "numberOfFiles": 4, "fileFormat": "CSV" }, "timePeriod": null, "resultQuality": { "ob_id": 4269, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39582, "uuid": "3dcec0b720fc464a97cf59130e23a5a0", "short_code": "comp", "title": "Caption for Figure 7.4 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Radiative adjustments at top of atmosphere for seven different climate drivers as a proportion of forcing. Tropospheric temperature (orange), stratospheric temperature (yellow), water vapour (blue), surface albedo (green), clouds (grey) and the total adjustment (black) is shown. For the greenhouse gases (carbon dioxide, methane, nitrous oxide and CFC-12) the adjustments are expressed as a percentage of stratospheric-temperature-adjusted radiative forcing (SARF), whereas for aerosol, solar and volcanic forcing they are expressed as a percentage of instantaneous radiative forcing (IRF). Land surface temperature response (outline red bar) is shown, but included in the definition of forcing. Data from Smith et al. (2018b) for carbon dioxide and methane; Smith et al. (2018b) and Gray et al. (2009) for solar; Hodnebrog et al. (2020b) for nitrous oxide and CFC-12; Smith et al. (2020b) for aerosol, and Marshall et al. (2020) for volcanic. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14)" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12632 ], "observationcollection_set": [ { "ob_id": 32722, "uuid": "b96ed5cbb8304a82bf1a2a4cad49bf6d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 7.3\r\n- input data for Figure 7.4\r\n- input data for Figure 7.5\r\n- data for Figure 7.6\r\n- input data for Figure 7.7\r\n- data for Figure 7.8\r\n- input data for Figure 7.10\r\n- data for Figure 7.11\r\n- input data for Figure 7.13\r\n- data for Figure 7.16\r\n- data for Figure 7.17\r\n- input data for Figure 7.18\r\n- data for Figure 7.19\r\n- input data for Figure 7.21\r\n- data for FAQ 7.3, Figure 1\r\n- input data for FAQ 7.3, Figure 1\r\n- data for 7.SM.1\r\n- input data for Box 7.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180430, 180431, 180432, 180433, 180434, 180435, 180503, 180594, 195202 ], "onlineresource_set": [ 52608, 52609, 82693, 83499, 83500 ] }, { "ob_id": 37820, "uuid": "6842c53c516746ea860e11512dc133c2", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 7.3 (v20220721)", "abstract": "Input Data for Figure 7.3 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.3 shows anomalies in global mean all-sky top-of-atmosphere (TOA) fluxes from CERES-EBAF Ed4.0 and various CMIP6 climate models in terms of reflected solar, emitted thermal and net TOA fluxes. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 3 subpanels, with input data provided for panels a-c. A link to the code to plot the figure archived on Zenodo is provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Anomalies in global mean all-sky top-of-atmosphere (TOA) fluxes from CERES-EBAF Ed4.0 in terms of reflected solar, emitted thermal and net TOA fluxes. \r\n- Anomalies in various CMIP6 climate models in terms of reflected solar, emitted thermal and net TOA fluxes. \r\n\r\n(a) Global mean solar flux anomaly.\r\n(b) Global mean thermal flux anomaly.\r\n(c) Global mean net flux anomaly.\r\n\r\nAnomalies in global mean all-sky top-of-atmosphere (TOA) fluxes from CERES-EBAF Ed4.0 are depicted as solid black lines.\r\nAnomalies in CMIP6 climate models are depicted as coloured lines.\r\nThe multi-model means are additionally depicted as solid red lines. \r\n\r\nModel fluxes stem from simulations driven with prescribed sea surface temperatures (SSTs) and all known anthropogenic and natural forcings. Shown are anomalies of 12-month running means. All flux anomalies are defined as positive downwards, consistent with the sign convention used throughout this chapter. The correlations between the multi-model means (solid red lines) and the CERES records (solid black lines) for 12-month running means are: 0.85 for the global mean reflected solar; 0.73 for outgoing thermal radiation; and 0.81 for net TOA radiation. Figure adapted from Loeb et al. (2020). \r\n\r\nThe models from which the input data are derived are the following:\r\n- CERES\r\n- CESM2\r\n- CanESM5\r\n- EC-Earth3\r\n- ECHAM\r\n- GFDL\r\n- HadGEM3\r\n- IPSL\r\n- multimodel\r\n- EC-Earth3-Veg\r\n- ECHAM6.3\r\n- GFDL-AM4\r\n- IPSL-CM6A\r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCERES stands for Clouds and the Earth's Radiant Energy System.\r\nCERES-EBAF Ed4.0 is the Clouds and the Earth's Radiant Energy System - Energy Balanced and Filled data product version 4.\r\nCESM2 is the Community Earth System Model version 2.\r\nCanESM5 is the Canadian Earth System Model version 5.\r\nEC-Earth3 is the European Community Earth-system model version 3.\r\nECHAM is an atmospheric General Circulation Model (GCM) from the MPI (Max Planck Institute for Meteorology).\r\nGFDL is the Geophysical Fluid Dynamics Laboratory.\r\nHadGEM3 is the Met Offfice Hadley Centre Global Environment Model version 3.\r\nIPSL is the Institut Pierre-Simon Laplace. \r\nEC-Earth3-Veg is the European Community Earth-system model version 3, with the Global Circulation Model (GCM) coupled to the dynamic vegetation model.\r\nECHAM6.3 is version 6.2 of the atmospheric General Circulation Model (GCM) ECHAM from the MPI (Max Planck Institute for Meteorology).\r\nGFDL-AM4 is the Geophysical Fluid Dynamics Laboratory Atmosphere and Land Model version 4.\r\nIPSL-CM6A is the Institut Pierre-Simon Laplace Climate Model for CMIP6.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 7.3:\r\n\r\n - Data file: Global_Net_Anomaly_Timeseries_12monthMean.txt\r\n - Data file: Global_SW_Anomaly_Timeseries_12monthMean.txt\r\n - Data file: Global_LW_Anomaly_Timeseries_12monthMean.txt\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory of the Chapter 7 GitHub repository. The input data provided is used in the notebook to output figure 7.3. To reproduce the figure from the input data, you will need to edit the path 'datadir' in box 3 of the notebook based on your local directory structure.\r\n\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n- Link to the code for the figure, archived on Zenodo\r\n- Link to the notebook for plotting the figure on the Chapter 7 GitHub repository", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-09-11T13:07:56", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, energy budget, CERES, CMIP6, top-of-atmosphere fluxes, TOA, reflected solar flux, emitted thermal flux", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-15T15:34:13", "doiPublishedTime": "2023-07-05T09:02:32.809061", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38058, "dataPath": "/badc/ar6_wg1/data/ch_07/inputdata_ch7_fig03/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 77658, "numberOfFiles": 6, "fileFormat": "txt" }, "timePeriod": { "ob_id": 10473, "startTime": "2000-08-01T00:00:00", "endTime": "2017-06-30T23:59:59" }, "resultQuality": { "ob_id": 4215, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39581, "uuid": "2e38597edadc49e4a124c00bc51d88ec", "short_code": "comp", "title": "Caption for Figure 7.3 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Anomalies in global mean all-sky top-of-atmosphere (TOA) fluxes from CERES-EBAF Ed4.0 (solid black lines) and various CMIP6 climate models (coloured lines) in terms of (a) reflected solar, (b) emitted thermal and (c) net TOA fluxes. The multi-model means are additionally depicted as solid red lines. Model fluxes stem from simulations driven with prescribed sea surface temperatures (SSTs) and all known anthropogenic and natural forcings. Shown are anomalies of 12-month running means. All flux anomalies are defined as positive downwards, consistent with the sign convention used throughout this chapter. The correlations between the multi-model means (solid red lines) and the CERES records (solid black lines) for 12-month running means are: 0.85 for the global mean reflected solar; 0.73 for outgoing thermal radiation; and 0.81 for net TOA radiation. Figure adapted from Loeb et al. (2020). Further details on data sources and processing are available in the chapter data table (Table 7.SM.14)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12631 ], "observationcollection_set": [ { "ob_id": 32722, "uuid": "b96ed5cbb8304a82bf1a2a4cad49bf6d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 7.3\r\n- input data for Figure 7.4\r\n- input data for Figure 7.5\r\n- data for Figure 7.6\r\n- input data for Figure 7.7\r\n- data for Figure 7.8\r\n- input data for Figure 7.10\r\n- data for Figure 7.11\r\n- input data for Figure 7.13\r\n- data for Figure 7.16\r\n- data for Figure 7.17\r\n- input data for Figure 7.18\r\n- data for Figure 7.19\r\n- input data for Figure 7.21\r\n- data for FAQ 7.3, Figure 1\r\n- input data for FAQ 7.3, Figure 1\r\n- data for 7.SM.1\r\n- input data for Box 7.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180436, 180437, 180438, 180439, 180440, 180441, 180504, 180593, 180592 ], "onlineresource_set": [ 52611, 52610, 82692, 83048, 83049 ] }, { "ob_id": 37823, "uuid": "9ce84c3a242e4b999c24dc1647c89794", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 7.19 (v20220721)", "abstract": "Data for Figure 7.19 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.19 shows global mean temperature anomaly in models and observations from five time periods. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 5 subpanels, with data provided for panels a-e.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Global mean temperature anomaly in:\r\n(a) Historical (CMIP6 models); \r\n(b) post-1975 (CMIP6 models); \r\n(c) Last Glacial Maximum (LGM; Cross-Chapter Box 2.1; PMIP4 models; Kageyama et al., 2021; Zhu et al., 2021); \r\n(d) mid-Pliocene Warm Period (MPWP; Cross-Chapter Box 2.4; PlioMIP models; Haywood et al., 2020; Zhang et al., 2021); \r\n(e) Early Eocene Climatic Optimum (EECO; Cross-Chapter Box 2.1; DeepMIP models; Zhu et al., 2020; Lunt et al., 2021). \r\n\r\nGrey circles show models with ECS in the assessed very likely range; models in red have an ECS greater than the assessed very likely range (>5°C); models in blue have an ECS lower than the assessed very likely range (<2°C). Black ranges show the assessed temperature anomaly derived from observations (Section 2.3). The historical anomaly in models and observations is calculated as the difference between 2005–2014 and 1850–1900, and the post-1975 anomaly is calculated as the difference between 2005–2014 and 1975–1984. \r\n\r\nFor the LGM, MPWP and EECO, temperature anomalies are compared with pre-industrial (equivalent to CMIP6 simulation ‘piControl’). All model simulations of the MPWP and LGM were carried out with atmospheric CO2 concentrations of 400 and 190 ppm respectively. However, CO2 during the EECO is relatively more uncertain, and model simulations were carried out at either 1120ppm or 1680 ppm (except for the one high-ECS EECO simulation which was carried out at 840 ppm; Zhu et al., 2020). The one low-ECS EECO simulation was carried out at 1680 ppm. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\nECS stands for Equilibrium Climate Sensitivity.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 7.19:\r\n \r\n - Data file: Figure7_19_mod.csv\r\n - Data file: Figure7_19_obs.csv\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThe data provided is the output data of the figure which can be used to reproduce the figure. Link to the plotting script for reproducing this figure 'ipcc_figure_7.19.ipynb' can be found in the Related Documents section of this catalogue record.\r\nThe original script for plotting this figure can be found in the Chapter 7 GitHub repository also linked but requires IDL.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n- Link to the code for Chapter 7, archived on Zenodo\r\n- Link to scripts used to reproduce figure from data\r\n- Link to the Chapter 7 GitHub repository", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:55", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, temperature, palaeoclimate, historical, global mean temperature", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-01T08:21:22", "doiPublishedTime": "2023-07-10T14:46:22.692247", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40120, "dataPath": "/badc/ar6_wg1/data/ch_07/ch7_fig19/v20230118", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9063, "numberOfFiles": 5, "fileFormat": "CSV" }, "timePeriod": null, "resultQuality": { "ob_id": 4290, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-31" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39593, "uuid": "54f0c9b48e7245dc82351297c7e8475e", "short_code": "comp", "title": "Caption for Figure 7.19 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global mean temperature anomaly in models and observations from five time periods. (a) Historical (CMIP6 models); (b) post-1975 (CMIP6 models); (c) Last Glacial Maximum (LGM; Cross-Chapter Box 2.1; PMIP4 models; Kageyama et al., 2021; Zhu et al., 2021); (d) mid-Pliocene Warm Period (MPWP; Cross-Chapter Box 2.4; PlioMIP models; Haywood et al., 2020; Zhang et al., 2021); (e) Early Eocene Climatic Optimum (EECO; Cross-Chapter Box 2.1; DeepMIP models; Zhu et al., 2020; Lunt et al., 2021). Grey circles show models with ECS in the assessed very likely range; models in red have an ECS greater than the assessed very likely range (>5°C); models in blue have an ECS lower than the assessed very likely range (<2°C). Black ranges show the assessed temperature anomaly derived from observations Section 2.3). The historical anomaly in models and observations is calculated as the difference between 2005–2014 and 1850–1900, and the post-1975 anomaly is calculated as the difference between 2005–2014 and 1975–1984. For the LGM, MPWP and EECO, temperature anomalies are compared with pre-industrial (equivalent to CMIP6 simulation ‘piControl’). All model simulations of the MPWP and LGM were carried out with atmospheric CO2 concentrations of 400 and 190 ppm respectively. However, CO2 during the EECO is relatively more uncertain, and model simulations were carried out at either 1120ppm or 1680 ppm (except for the one high-ECS EECO simulation which was carried out at 840 ppm; Zhu et al., 2020). The one low-ECS EECO simulation was carried out at 1680 ppm. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12642 ], "observationcollection_set": [ { "ob_id": 32722, "uuid": "b96ed5cbb8304a82bf1a2a4cad49bf6d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 7.3\r\n- input data for Figure 7.4\r\n- input data for Figure 7.5\r\n- data for Figure 7.6\r\n- input data for Figure 7.7\r\n- data for Figure 7.8\r\n- input data for Figure 7.10\r\n- data for Figure 7.11\r\n- input data for Figure 7.13\r\n- data for Figure 7.16\r\n- data for Figure 7.17\r\n- input data for Figure 7.18\r\n- data for Figure 7.19\r\n- input data for Figure 7.21\r\n- data for FAQ 7.3, Figure 1\r\n- input data for FAQ 7.3, Figure 1\r\n- data for 7.SM.1\r\n- input data for Box 7.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180454, 180455, 180456, 180457, 180458, 180459, 180488, 180602, 195477, 195476 ], "onlineresource_set": [ 52578, 52579, 82705, 83589, 83590, 83588 ] }, { "ob_id": 37824, "uuid": "f94821849dfb4ee2bd1a367a81a6b6f7", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 7.21 (v20220721)", "abstract": "Input Data for Figure 7.21 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.21 shows emissions metrics for two short-lived greenhouse gases: HFC-32 and methane (CH4; lifetimes of 5.4 and 11.8 years). \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 4 subpanels, with data provided for panels a-d.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Emissions metrics for HFC-32 and methane (CH4):\r\n(a) Temperature response to a step change in short-lived greenhouse gas emissions. \r\n(b) Temperature response to a pulse CO2 emission. \r\n(c) Conventional GTP metrics (pulse vs pulse). \r\n(d) Combined GTP metric (step versus pulse). \r\n\r\nThe temperature response function comes from Supplementary Material 7.SM.5.2. Values for non-CO2 species include the carbon cycle response (Section 7.6.1.3). Results for HFC-32 have been divided by 100 to show on the same scale. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\nGTP stands for Global Temperature-change Potential.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 7.21:\r\n \r\n - Data file: cgtp.csv\r\n\r\nThe data in this files is identical to the original data in .npz format. Link to the orginal data in this format used with the code for reproducing the figure is provided in the 'Related Documents' section.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory of the Chapter 7 GitHub repository. The link to the input .npz file provided is used in the notebook to output figure 7.21. To reproduce the figure from the input data, you will need to run the notebook from the same directory as the input data and adjust the path to the data in box 3.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n - Link to the original data in .npz format used in the code\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the notebook on the Chapter 7 GitHub repository for plotting the figure", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-09-11T13:08:06", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).\r\nData converted to CSV format for archival from original .npz format by CEDA Staff.", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, metrics, step, pulse, GTP, CGTP, HFC-32, CH4, methane", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-01T11:01:06", "doiPublishedTime": "2023-07-10T14:50:20.257961", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38065, "dataPath": "/badc/ar6_wg1/data/ch_07/inputdata_ch7_fig21/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 58873, "numberOfFiles": 4, "fileFormat": "CSV" }, "timePeriod": null, "resultQuality": { "ob_id": 4271, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39595, "uuid": "57a4499101764268aba05a585e8b25e7", "short_code": "comp", "title": "Caption for Figure 7.21 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Emissions metrics for two short-lived greenhouse gases: HFC-32 and methane (CH4; lifetimes of 5.4 and 11.8 years). The temperature response function comes from Supplementary Material 7.SM.5.2. Values for non-CO2 species include the carbon cycle response Section 7.6.1.3). Results for HFC-32 have been divided by 100 to show on the same scale. (a) Temperature response to a step change in short-lived greenhouse gas emissions. (b) Temperature response to a pulse CO2 emission. (c) Conventional GTP metrics (pulse vs pulse). (d) Combined GTP metric (step versus pulse). Further details on data sources and processing are available in the chapter data table (Table 7.SM.14)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12643 ], "observationcollection_set": [ { "ob_id": 32722, "uuid": "b96ed5cbb8304a82bf1a2a4cad49bf6d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 7.3\r\n- input data for Figure 7.4\r\n- input data for Figure 7.5\r\n- data for Figure 7.6\r\n- input data for Figure 7.7\r\n- data for Figure 7.8\r\n- input data for Figure 7.10\r\n- data for Figure 7.11\r\n- input data for Figure 7.13\r\n- data for Figure 7.16\r\n- data for Figure 7.17\r\n- input data for Figure 7.18\r\n- data for Figure 7.19\r\n- input data for Figure 7.21\r\n- data for FAQ 7.3, Figure 1\r\n- input data for FAQ 7.3, Figure 1\r\n- data for 7.SM.1\r\n- input data for Box 7.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180460, 180461, 180462, 180463, 180464, 180465, 180487, 180608, 180609, 195204 ], "onlineresource_set": [ 52577, 52576, 82706, 83503, 83504, 83591 ] }, { "ob_id": 37826, "uuid": "47586c6f52a9473ea0b1f909fb231bfc", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for FAQ 7.3, Figure 1 (v20220721)", "abstract": "Input Data for FAQ 7.3 Figure 1, from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFAQ 7.3 Figure 1 shows equilibrium climate sensitivity and future warming.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with input data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- (left) Equilibrium climate sensitivities for the current generation (CMIP6) climate models, and the previous (CMIP5) generation. The assessed range in this Report (AR6) is also shown. \r\n\r\n- (right) Climate projections of CMIP5, CMIP6 and AR6 for the very high-emissions scenarios RCP8.5, and SSP5-8.5, respectively. \r\n\r\nThe thick horizontal lines represent the multi-model average and the thin horizontal lines represent the results of individual models. The boxes represent the model ranges for CMIP5 and CMIP6 and the range assessed in AR6.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to FAQ 7.3 Figure 1.\r\n \r\n - Data file: ecs_for_faq.csv\r\n - Data file: tcr_for_faq.csv\r\n - Data files: CMIP5_means/dtas_\"+model+\".nc\r\n - Data files: CMIP5_means/tas_\"+model+\"_rcp85.nc\r\n - Data files: CMIP5_means/tas_\"+model+\"_piControl.nc\r\n - Data files: CMIP6_means/dtas_\"+model+\".nc\r\n - Data files: CMIP6_means/tas_\"+model+\"_ssp585.nc\r\n - Data files: CMIP6_means/tas_\"+model+\"_piControl.nc\r\n\r\nModels to be substituted in file names where +model+ exists:\r\n- ACCESS-CM2\r\n- ACCESS-ESM1-5\r\n- AWI-CM-1-1-MR\r\n- BCC-CSM2-MR\r\n- CAMS-CSM1-0\r\n- CESM2-WACCM\r\n- CESM2\r\n- CIESM\r\n- CMCC-CM2-SR5\r\n- CNRM-CM6-1-HR\r\n- CNRM-CM6-1\r\n- CNRM-ESM2-1\r\n- CanESM5\r\n- EC-Earth3-Veg\r\n- FGOALS-f3-L\r\n- FGOALS-g3\r\n- FIO-ESM-2-0\r\n- GISS-E2-1-G\r\n- HadGEM3-GC31-LL\r\n- HadGEM3-GC31-MM\r\n- IITM-ESM\r\n- INM-CM4-8\r\n- INM-CM5-0\r\n- IPSL-CM6A-LR\r\n- KACE-1-0-G\r\n- MCM-UA-1-0\r\n- MIROC-ES2L\r\n- MIROC6\r\n- MPI-ESM1-2-HR\r\n- MPI-ESM1-2-LR\r\n- MRI-ESM2-0\r\n- NESM3\r\n- NorESM2-LM\r\n- NorESM2-MM\r\n- TaiESM1\r\n- UKESM1-0-LL\r\nThis means the _ssp585.nc and _piControl.nc files have 36 versions each, for both CMIP5 and CMIP6 (a total of 144 netCDF files).\r\n\r\nThe above files are from the 'contributed' folder on the 'master' GitHub repository, rather than in data_input or data_output. \r\n\r\nCMIP5 is the fifth stage of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth stage of the Coupled Model Intercomparison Project.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\nACCESS-CM2 is the Australian Community Climate and Earth System Simulator coupled climate model.\r\nACCESS-ESM1-5 is the Australian Community Climate and Earth System Simulator Earth system model version designed to participate in CMIP6 simulations.\r\nAWI-CM-1-1-MR is the Alfred Wegener Institute Climate Model version 1.1 - Medium Resolution, with locally-increased horizontal resolution over energetically active ocean areas.\r\nBCC-CSM2-MR is the Beijing Climate Center Climate System Model version 2 - moderate vertical resolution.\r\nCAMS-CSM1-0 is the Chinese Academy of Meteorological Sciences Climate System Model version 1.\r\nCESM2-WACCM is the Community System Model version 2- Whole Atmosphere Community Climate Model.\r\nCESM2 is the Community Earth System Model version 2.\r\nCIESM is the Community Integrated Earth System Model.\r\nCMCC-CM2-SR5 is the Euro-Mediterranean Centre on Climate Change Coupled Climate Model version 2 - standard configuration.\r\nCNRM-CM6-1-HR is the Centre National de Recherches Météorologiques Climate Model for CMIP6 - altered Horizontal Resolution.\r\nCNRM-CM6-1 is the Centre National de Recherches Météorologiques Climate Model for CMIP6.\r\nCNRM-ESM2-1 is the Centre National de Recherches Météorologiques Earth System Model, derived from CNRM-CM6-1.\r\nCanESM5 is the Canadian Earth System Model version 5.\r\nEC-Earth3-Veg is the European Community Earth-system model version 3, with the Global Circulation Model (GCM) coupled to the dynamic vegetation model.\r\nFGOALS-f3-L is the Flexible Global Ocean-Atmosphere-Land System Model, Finite-volume version 3, low horizontal resolution. \r\nFGOALS-g3 is the Flexible Global Ocean-Atmosphere-Land System Model, Grid-point Version 3.\r\nFIO-ESM-2-0 is the First Institute of Oceanography Earth System Model version 2.0.\r\nGISS-E2-1-G is the Goddard Institute for Space Studies - chemistry-climate model version E2.1, using the GISS Ocean v1 (G01) model.\r\nHadGEM3-GC31-LL is the Met Offfice Hadley Centre Global Environment Model - Global Coupled configuration 3.1 - using an atmosphere/ocean resolution for historical simulation N96/ORCA1.\r\nHadGEM3-GC31-MM is the Met Offfice Hadley Centre Global Environment Model - Global Coupled configuration 3.1 - using an atmosphere/ocean resolution for historical simulation N216/ORCA025.\r\nIITM-ESM is the Indian Institute of Tropical Meteorology Earth System Model.\r\nINM-CM4-8 is the Institute for Numerical Mathematics Climate Model version 4.8. \r\nINM-CM5-0 is the Institute for Numerical Mathematics Climate Model version 5.0. \r\nIPSL-CM6A-LR is the Institut Pierre-Simon Laplace Climate Model for CMIP6 - Low Resolution.\r\nKACE-1-0-G is the Korean Advanced Community Earth system model. \r\nMCM-UA-1-0 is the Manabe Climate Climate - University of Arizona - version 1.0. \r\nMIROC-ES2L is the Model for Interdisciplinary Research on Climate - Earth System version 2 for Long-term simulations.\r\nMIROC6 is the Model for Interdisciplinary Research on Climate version 6.\r\nMPI-ESM1-2-HR is the Max Planck Institute Earth System Model - version 2 - altered Horizontal Resolution.\r\nMPI-ESM1-2-LR is the Max Planck Institute Earth System Model - version 2 - Low Resolution.\r\nMRI-ESM2-0 is the Meteorological Research Institute Earth System Model version 2.0.\r\nNESM3 is the Nanjing University of Information Science and Technology Earth System Model version 3.\r\nNorESM2-LM is the Norwegian Earth System Model version 2 - 2 degree resolution for atmosphere and land components, 1 degree resolution for ocean and sea-ice components.\r\nNorESM2-MM is the Norwegian Earth System Model version 2 - 1 degree resolution for all model components.\r\nTaiESM1 is theTaiwan Earth System Model version 1.\r\nUKESM1-0-LL is the The UK Earth System Model - version 1 - 2 degree resolution for all model components.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. Also listed on the 'master' GitHub page linked in the documentation of this catalogue record are external GitHub repositories and locations within the contributed directory where code for figures have been supplied by other authors. These are provided \"as-is\" and are not guaranteed to be reproducible within this environment. For external GitHub locations, check out the relevant repository READMEs.\r\n\r\nThe notebook used to plot this figure is linked in the 'Related Documents' section. The output figure data is also archived at CEDA.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n - Link to the Jupyter notebook for plotting this figure from the Chapter 7 GitHub repository\r\n- Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-09-11T13:07:49", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, ECS, CMIP6, CMIP5", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-01T11:04:48", "doiPublishedTime": "2023-07-10T15:03:22.110240", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38055, "dataPath": "/badc/ar6_wg1/data/ch_07/inputdata_ch7_faq3_fig1/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8047857, "numberOfFiles": 231, "fileFormat": "XLSX, netCDF, txt" }, "timePeriod": null, "resultQuality": { "ob_id": 4292, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-06-01" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39598, "uuid": "db032a49ac10480c84f3844656f8f7f2", "short_code": "comp", "title": "Caption for FAQ 7.3, Figure 1 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Equilibrium climate sensitivity and future warming. (left) Equilibrium climate sensitivities for the current generation (Coupled Model Intercomparison Project Phase 6, CMIP6) climate models, and the previous (CMIP5) generation. The assessed range in this Report (AR6) is also shown. (right) Climate projections of CMIP5, CMIP6 and AR6 for the very high-emissions scenarios RCP8.5, and SSP5-8.5, respectively. The thick horizontal lines represent the multi-model average and the thin horizontal lines represent the results of individual models. The boxes represent the model ranges for CMIP5 and CMIP6 and the range assessed in AR6." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 50496, 50498, 50559, 50561, 56259, 62368, 62501 ], "vocabularyKeywords": [], "identifier_set": [ 12645 ], "observationcollection_set": [ { "ob_id": 32722, "uuid": "b96ed5cbb8304a82bf1a2a4cad49bf6d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 7.3\r\n- input data for Figure 7.4\r\n- input data for Figure 7.5\r\n- data for Figure 7.6\r\n- input data for Figure 7.7\r\n- data for Figure 7.8\r\n- input data for Figure 7.10\r\n- data for Figure 7.11\r\n- input data for Figure 7.13\r\n- data for Figure 7.16\r\n- data for Figure 7.17\r\n- input data for Figure 7.18\r\n- data for Figure 7.19\r\n- input data for Figure 7.21\r\n- data for FAQ 7.3, Figure 1\r\n- input data for FAQ 7.3, Figure 1\r\n- data for 7.SM.1\r\n- input data for Box 7.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180472, 180473, 180474, 180475, 180476, 180477, 180485, 180616 ], "onlineresource_set": [ 52572, 52573, 83594, 83595, 83596 ] }, { "ob_id": 37828, "uuid": "f0f622f4e9d14f95949a5cc44451e8bb", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 7.SM.1 (v20220721)", "abstract": "Data for Figure 7.SM.1 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.SM.1 shows total effective radiative forcing from SSP scenarios with respect to 1750 for 2000-2500, 14 showing best estimate and 5–95% uncertainty range. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 13 subpanels, with data provided for all panels in the master GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Total effective radiative forcing from SSP scenarios with respect to 1750 for 2000-2500, 14 showing best estimate and 5–95% uncertainty range. \r\n- Graph (top panel) showing radiative forcing trajectories for (shaded regions):\r\n - SSP5-8.5 (brown line)\r\n - SSP3-7.0-lownNTCF (red dashed line)\r\n - SSP3-7.0 (red line)\r\n - SSP3-7.0-lowNTCFCH4 (red dotted line)\r\n - SSP4-6.0 (orange line)\r\n - SSP2-4.5 (yellow line)\r\n - SSP5-3.4-over (early overshoot of purple line)\r\n - SSP4-3.4 (light blue line)\r\n - SSP1-2.6 (purple line)\r\n - SSP1-1.9 (green line)\r\n- Radiative forcing component breakdowns (smaller subpanels):\r\n - CO2 (carbon dioxide)\r\n - CH4 (methane)\r\n - N2O (nitrous oxide)\r\n - Halogenated gases\r\n - O3 (ozone)\r\n - Strat H2O (stratospheric water)\r\n - Contrails and aviation induced cirrus\r\n - Aerosol-radiation interactions\r\n - Aerosol-cloud interactions\r\n - Light absorbing particles on snow and ice\r\n - Land use\r\n - Solar\r\n\r\nUncertainty ranges are not shown for SSP3-7.0-lowNTCF and SSP3-7.0-NTCFCH4 for visual clarity. Bottom matrix shows the best estimate ERF for each anthropogenic component, and solar (volcanic ERF is zero beyond 2024).\r\n\r\nSSP stands for Shared Socioeconomic Pathway.\r\nSSP119 is the Shared Socioeconomic Pathway which represents the lowest scenario of radiative forcing and development scenarios, consistent with RCP1.9.\r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\nSSP245 is the Shared Socioeconomic Pathway which represents the median of radiative forcing and development scenarios, consistent with RCP4.5.\r\nSSP370 is the Shared Socioeconomic Pathway which represents the upper-middle range of radiative forcing and development scenarios, consistent with RCP6.0.\r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\nNTCF stands for Near-Term Climate Forcer.\r\nERF stands for Effective Radiative Forcing.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 7.SM.1:\r\n\r\n - Data file: ERF_%_1750-2500.csv'\r\n - Data file: ERF_%_1750-2500_pc05.csv\r\n - Data file: ERF_%_1750-2500_pc95.csv\r\n - Data file: ERF_%_minorGHGs_1750-2500.csv\r\n\r\nEach % is substituted for one of the following scenarios:\r\nSSP119 - best estimate.\r\nSSP119 - 5th percentile.\r\nSSP119 - 95th percentile.\r\nSSP119 minor GHGs - best estimate.\r\n\r\nSSP126 - best estimate.\r\nSSP126 - 5th percentile.\r\nSSP126 - 95th percentile.\r\nSSP126 minor GHGs - best estimate. \r\n\r\nSSP245 - best estimate.\r\nSSP245 - 5th percentile.\r\nSSP245 - 95th percentile.\r\nSSP245 minor GHGs - best estimate.\r\n\r\nSSP334 - best estimate.\r\n\r\nSSP370 - best estimate.\r\nSSP370 - 5th percentile.\r\nSSP370 - 95th percentile.\r\nSSP370 minor GHGs - best estimate.\r\nSSP370 low NTCF - best estimate.\r\nSSP370 low NTCF - 5th percentile.\r\nSSP370 low NTCF - 95th percentile.\r\nSSP370 low NTCF minor GHGs - best estimate.\r\nSSP370 low NTCFCH4 - best estimate.\r\nSSP370 low NTCFCH4 - 5th percentile.\r\nSSP370 low NTCFCH4 - 95th percentile.\r\nSSP370 low NTCFCH4 minor GHGs - best estimate.\r\n\r\nSSP434 - best estimate.\r\nSSP434 - 5th percentile.\r\nSSP434 - 95th percentile.\r\nSSP434 minor GHGs - best estimate.\r\n\r\nSSP460 - best estimate.\r\nSSP460 - 5th percentile.\r\nSSP460 - 95th percentile.\r\nSSP460 minor GHGs - best estimate.\r\n\r\nSSP534 over - best estimate.\r\nSSP534 over - 5th percentile.\r\nSSP534 over - 95th percentile.\r\nSSP534 over minor GHGs - best estimate.\r\n\r\nSSP585 - best estimate.\r\nSSP585 - 5th percentile.\r\nSSP585 - 95th pecentile.\r\nSSP585 minor GHGs - best estimate.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. Also listed on the 'master' GitHub page linked in the documentation of this catalogue record are external GitHub repositories and locations within the contributed directory where code for figures have been supplied by other authors. These are provided \"as-is\" and are not guaranteed to be reproducible within this environment. For external GitHub locations, check out the relevant repository READMEs.\r\n\r\nWithin the processing chain, every notebook is prefixed by a number. To reproduce all results in the chapter, the notebooks should be run in numerical order, because some later things depend on earlier things (historical temperature attribution requires a constrained ensemble of the two layer climate model, which relies on the generation of the radiative forcing time series). This being said, most notebooks should run standalone, as input data is provided where the datasets are small enough (see the 'master;' GitHub page for these).\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n - Link to the Jupyter notebook for plotting the figure from the Chapter 7 GitHub repository\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-09-11T13:08:24", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Chapter 7, Figure 7.SM.1, effective radiative forcing, ERF", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-01T11:14:26", "doiPublishedTime": "2023-07-10T15:06:54.951011", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38054, "dataPath": "/badc/ar6_wg1/data/ch_07/ch7_SM_1/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 12241439, "numberOfFiles": 44, "fileFormat": "CSV, txt" }, "timePeriod": null, "resultQuality": { "ob_id": 4293, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-06-01" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39599, "uuid": "e3c7a62ce26846a68b86f854286b5eb8", "short_code": "comp", "title": "Caption for Figure 7.SM.1 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Total effective radiative forcing from SSP scenarios with respect to 1750 for 2000-2500, 14 showing best estimate and 5–95% uncertainty range." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12646 ], "observationcollection_set": [ { "ob_id": 32722, "uuid": "b96ed5cbb8304a82bf1a2a4cad49bf6d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 7.3\r\n- input data for Figure 7.4\r\n- input data for Figure 7.5\r\n- data for Figure 7.6\r\n- input data for Figure 7.7\r\n- data for Figure 7.8\r\n- input data for Figure 7.10\r\n- data for Figure 7.11\r\n- input data for Figure 7.13\r\n- data for Figure 7.16\r\n- data for Figure 7.17\r\n- input data for Figure 7.18\r\n- data for Figure 7.19\r\n- input data for Figure 7.21\r\n- data for FAQ 7.3, Figure 1\r\n- input data for FAQ 7.3, Figure 1\r\n- data for 7.SM.1\r\n- input data for Box 7.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180478, 180479, 180480, 180481, 180482, 180483, 180484, 180615 ], "onlineresource_set": [ 52612, 52613, 83597, 83598 ] }, { "ob_id": 37832, "uuid": "45283390b97c4a27861d74b3d915b0bd", "title": "Monthly mean climate data from a transient simulation with the Whole Atmosphere Community Climate Model eXtension (WACCM-X) from 2015 to 2070", "abstract": "This dataset comprises monthly mean data from a global, transient simulation with the Whole Atmosphere Community Climate Model eXtension (WACCM-X) from 2015 to 2070. WACCM-X is a global atmosphere model covering altitudes from the surface up to ~500 km, i.e., including the troposphere, stratosphere, mesosphere and thermosphere. WACCM-X version 2.0 (Liu et al., 2018) was used, part of the Community Earth System Model (CESM) release 2.1.0 (http://www.cesm.ucar.edu/models/cesm2) made available by the National Center for Atmospheric Research. The model was run in free-running mode with a horizontal resolution of 1.9 degrees latitude and 2.5 degrees longitude (giving 96 latitude points and 144 longitude points) and 126 vertical levels. Further description of the model and simulation setup is provided by Cnossen (2022) and references therein. A large number of variables is included on standard monthly mean output files on the model grid, while selected variables are also offered interpolated to a constant height grid or vertically integrated in height (details below). Zonal mean and global mean output files are included as well.\r\n\r\nThe data are provided in NetCDF format and file names have the following structure: \r\n\r\nf.e210.FXHIST.f19_f19.h1a.cam.h0.[YYYY]-[MM][DFT].nc\r\n\r\nwhere [YYYY] gives the year with 4 digits, [MM] gives the month (2 digits) and [DFT] specifies the data file type. The following data file types are included:\r\n\r\n1)\tMonthly mean output on the full grid for the full set of variables; [DFT] = \r\n2)\tZonal mean monthly mean output for the full set of variables; [DFT] = _zm\t\r\n3)\tGlobal mean monthly mean output for the full set of variables; [DFT] = _gm\r\n4)\tHeight-interpolated/-integrated output on the full grid for selected variables; [DFT] = _ht\r\n\r\nA cos(latitude) weighting was used when calculating the global means.\r\n\r\nData were interpolated to a set of constant heights (61 levels in total) using the Z3GM variable (for variables output on midpoints, with 'lev' as the vertical coordinate) or the Z3GMI variable (for variables output on interfaces, with ilev as the vertical coordinate) stored on the original output files (type 1 above). Interpolation was done separately for each longitude, latitude and time. \r\n\r\nMass density (DEN [g/cm3]) was calculated from the M_dens, N2_vmr, O2, and O variables on the original data files before interpolation to constant height levels. \r\n\r\nThe Joule heating power QJ [W/m3] was calculated using \r\nQ_J = (sigma_P*B^2)*((u_i - U_n)^2 + (v_i-v_n)^2 + (w_i-w_n)^2) \r\nwith sigma_P = Pedersen conductivity[S], B = geomagnetic field strength [T], ui, vi, and wi = zonal, meridional, and vertical ion velocities [m/s] and un, vn, and wn = neutral wind velocities [m/s]. QJ was integrated vertically in height (using a 2.5 km height grid spacing rather than the 61 levels on output file type 4) to give the JHH variable on the type 4 data files. The QJOULE variable also given is the Joule heating rate [K/s] at each of the 61 height levels.\r\n\r\nAll data are provided as monthly mean files with one time record per file, giving 672 files for each data file type for the period 2015-2070 (56 years).\r\n\r\nReferences:\r\n\r\nCnossen, I. (2022), A realistic projection of climate change in the upper atmosphere into the 21st century, in preparation.\r\n\r\nLiu, H.-L., C.G. Bardeen, B.T. Foster, et al. (2018), Development and validation of the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X 2.0), Journal of Advances in Modeling Earth Systems, 10(2), 381-402, doi:10.1002/2017ms001232.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:18:05", "updateFrequency": "notPlanned", "dataLineage": "The data were generated with the Whole Atmosphere Community Climate Model eXtension (WACCM-X) version 2.0 (Liu et al., 2018), part of the Community Earth System Model (CESM) release 2.1.0 (http://www.cesm.ucar.edu/models/cesm2) by Ingrid Cnossen. The WACCM-X simulation was run on the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk) in 2019-2020.\r\n\r\nData were then submitted to the CEDA for archival.", "removedDataReason": "", "keywords": "", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-07-29T08:48:18", "doiPublishedTime": "2022-07-29T08:49:03", "removedDataTime": null, "geographicExtent": { "ob_id": 3573, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 37833, "dataPath": "/badc/deposited2022/WACCM-X_2015-2070", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 436008541109, "numberOfFiles": 2691, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 10446, "startTime": "2015-01-01T00:00:00", "endTime": "2070-12-31T00:00:00" }, "resultQuality": { "ob_id": 4034, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-07-21" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37835, "uuid": "0a76ab1544d843ef9b87ff24fc9f861d", "short_code": "comp", "title": "Transient simulation with the Whole Atmosphere Community Climate Model eXtension (WACCM-X) version 2.0, part of the Community Earth System Model (CESM) release 2.1.0, from 1950 to 2015", "abstract": "The data were generated with the Whole Atmosphere Community Climate Model eXtension (WACCM-X) version 2.0 (Liu et al., 2018), part of the Community Earth System Model (CESM) release 2.1.0 (http://www.cesm.ucar.edu/models/cesm2) WACCM-X is a global model of the atmosphere from the surface up to ~500 km altitude and was run in free-running mode with a horizontal resolution of 1.9degrees latitude and 2.5 degrees longitude (giving 96 latitude points and 144 longitude points) and 126 vertical levels from Jan 2015 to 2070. This is an extension of the simulation by Cnossen (2020) from 1950 to 2015. For 2015-2070, lower boundary forcings and chemical emissions followed Shared Socio-economic Pathway (SSP) 2-4.5, a moderate scenario, comparable to Representative Concentration Pathway (RCP) 4.5 (O'Neill et al., 2016). The main magnetic field was specified according to the prediction by Aubert (2015). Solar radiative and particle forcings were specified according to the reference scenario of the Climate Model Intercomparison Project (CMIP) 6 recommendation by Matthes et al. (2017). The simulation therefore includes all known drivers of long-term change in the upper atmosphere and for all of these realistic long-term variations were adopted, offering a plausible, realistic estimate of the future climate of the upper atmosphere. For further details see Cnossen (2022).\r\nThe WACCM-X simulation was run on the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk) in 2019-2020 by Ingrid Cnossen.\r\n\r\n\r\n\r\nReferences: See related documents" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37834, "uuid": "f1bfceeff4e045a588d143fdb9097f2b", "short_code": "proj", "title": "Impacts of climate change in the troposphere, stratosphere and mesosphere on the thermosphere and ionosphere", "abstract": "The data were produced as part of a Natural Environment Research Council (NERC) Independent Research Fellowship (NE/R015651/1) awarded to Ingrid Cnossen, entitled 'Impacts of climate change in the troposphere, stratosphere and mesosphere on the thermosphere and ionosphere'. \r\n\r\nThe project as a whole aims to quantify the importance of man-made climate change in the lower and middle atmosphere in causing long-term changes in the upper atmosphere, both in the past (1950s-2010s) and projected into the future (2050s) according to established emission scenarios. Computer simulations with WACCM-X, a state-of-the-art, global, 3-dimensional climate model, extending from the surface up to ~500 km altitude, are being used to do this. Results from these simulations will be compared to observed long-term changes in the upper atmosphere (e.g., in temperature, density) and to contributions made by other known factors. These include the increase in greenhouse gas concentration within the upper atmosphere itself, which has a cooling effect, and changes in the Earth's magnetic field, which cause more complicated patterns of long-term change. Interactions of changes in the Earth's magnetic field and changes in atmospheric tides due to climate change will also be investigated. This will focus at least initially again on the period of the 1950s to 2050s, but this may be broadened to a larger timespan from 850 to the present-day. \r\n\r\nThe current data set covers the period 1950-2015 and comes from the first long-term transient simulation with WACCM-X. The data set was used by Cnossen (2020) to quantify past climate change in the upper atmosphere, using the same multi-linear regression analysis technique that is often used to extract trends from observational data sets. The data set is also highly suitable for comparisons with observed long-term trends in the upper atmosphere, as the timeframes covered by observational data sets can be matched by selecting the relevant time window from the simulation data and all known drivers of climate change are included. NE/R015651/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 28669, 28670, 52192, 52193, 54920, 54921, 54923, 54924, 54925, 54926, 54927, 54928, 54929, 54930, 54931, 54932, 54933, 54934, 54935, 54936, 54937, 54938, 54939, 54940, 54941, 54943, 54944, 54946, 54947, 54948, 54949, 54952, 54954, 54955, 54956, 54963, 54964, 54965, 54998, 54999, 55000, 55001, 55002, 55003, 55004, 55005, 55006, 55007, 55008, 55009, 55010, 55011, 55012, 55013, 55014, 55015, 55016, 55017, 55018, 55019, 55020, 55021, 55022, 55023, 55024, 55026, 55027, 55028, 55029, 55030, 55031, 55032, 55033, 55034, 55035, 55036, 55037, 55038, 55039, 55042, 55043, 55044, 55045, 55046, 55047, 55048, 55049, 55050, 55051, 55052, 55053, 55054, 55055, 55056, 55057, 55058, 55059, 55062, 55063, 55064, 55065, 55066, 55067, 55068, 55069, 55070, 55071, 55072, 55073, 55076, 55077, 55078, 55080, 55081, 55082, 55083, 55084, 55085, 55086, 55087, 55090, 55091, 55092, 63011, 63012, 63013, 63014, 63015, 63016, 63017, 63018, 63019, 63020, 79993 ], "vocabularyKeywords": [], "identifier_set": [ 12196 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 180520, 180521, 180522, 180523, 180524, 180525, 180530, 180531 ], "onlineresource_set": [ 52625, 52626, 52628, 52629, 52627, 52630 ] }, { "ob_id": 37838, "uuid": "300559d22d9549049017f06bf38db929", "title": "Sentinel 5P: Formaldehyde (HCHO) Total Column level 2 data", "abstract": "This dataset contains total column Formaldehyde (HCHO) data from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel 5P satellite.\r\n\r\nSentinel 5 Precursor (S5P) was launched on the 13th of October 2017 carrying the TROPOspheric Monitoring Instrument (TROPOMI). The TROPOMI is a nadir-viewing, imaging spectrometer covering wavelength bands between the ultraviolet and the shortwave infrared. The instrument uses passive remote sensing techniques to attain its objective by measuring, at the Top Of Atmosphere (TOA), the solar radiation reflected by and radiated from the earth. In addition to the main product results, such as HCHO slant column, vertical column, and air mass factor, the level 2 (geolocated total columns) data files contain several additional parameters and diagnostic information.\r\n\r\nFormaldehyde is an intermediate gas in almost all oxidation chains of Non-Methane Volatile Organic Compounds (NMVOC), leading eventually to CO2. NMVOCs are, together with NOx, CO, and CH4, among the most important precursors of tropospheric O3. The major HCHO source in the remote atmosphere is CH4 oxidation. Over the continents, the oxidation of higher NMVOCs emitted from vegetation, fires, traffic and industrial sources results in important and localised enhancements of the HCHO levels.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-06-26T16:48:48", "updateFrequency": "", "dataLineage": "Data collected and prepared by European Space Agency (ESA). Downloaded from the Collaborative Hub for use by CEDA users.", "removedDataReason": "", "keywords": "Sentinel 5P, Formaldehyde, HCHO, ESA", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-11-14T15:08:41", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 37839, "dataPath": "/neodc/sentinel5p/data/L2_HCHO/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 24321694294927, "numberOfFiles": 68691, "fileFormat": "These data are in NetCDF format." }, "timePeriod": { "ob_id": 10743, "startTime": "2018-05-14T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 1, "explanation": "See dataset associated documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-08-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 38837, "uuid": "7d3ad0d37a4c441ba05205acce4fe5d2", "short_code": "cmppr", "title": "Composite Process for: Level 2 data from the Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI) for Formaldehyde (HCHO) total column data.", "abstract": "Composite process for Level 2 data from the TROPOspheric Monitoring Instrument (TROPOMI) deployed on Sentinel 5P. This consists of the acquisition process for raw imaging data from the Sentinel 5P TROPOMI and the computation component to produce processed Level 2 Formaldehyde (HCHO) total column data." }, "imageDetails": [ 148 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2586, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 49, "licenceURL": "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12321, "uuid": "7896ea1117dc4fa9bb95485ca9b1c6be", "short_code": "proj", "title": "Copernicus Programme", "abstract": "Copernicus, formerly known as the Global Monitoring for Environment and Security (GMES) programme, is headed by the European Commission (EC) in partnership with the European Space Agency (ESA). Within the Copernicus Space Component, ESA is developing a series of Sentinel satellite missions. Data from the Sentinel missions, as well as from Contributing Missions from other space agencies, are made freely available through a unified ground segment. Each Sentinel mission is comprised of a constallation of two or more satellites to fulfil the timeliness and reliability requirements of the Copernicus Services environmental monitoring and civil security activities. As well as operational and monitoring capabilities, the Sentinel missions will provide a wealth of Earth Observation data for scientific exploitation. The Sentinel 1 mission provides all weather, day and night radar imagery with scientific applications in sea-ice measurements, biomass observations and earthquake analysis. Sentinel 2 is a high resolution imaging mission to provide imagery of vegetation, soil and water cover, inland waterways and coastal areas. Sentinel 3 is a multi-instrument mission to measure sea-surface topography, sea- and land-surface temperature, ocean colour and land colour with high-end accuracy and reliability. Sentinel 4 is devoted to atmospheric monitoring and will be flown on a Meteosat Third Generation-Sounder (MTG-S) satellite in geostationary orbit. Sentinel 5 will monitor the atmosphere from polar orbit on board a MetOp Second Generation satellite. The Sentinel 5 precursor satellite mission is being developed to reduce data gaps between Envisat, in particular the Sciamachy instrument, and the launch of Sentinel 5. The Sentinel 5 mission will be dedicated to atmospheric monitoring. Sentinel 6 carries a radar altimeter to measure global sea-surface height, primarily for operational oceanography and for climate studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 55535, 66547, 66548, 66550, 66551, 66552, 66559, 66565, 66571, 66572, 66573, 66574, 66578, 66579, 66580, 66582, 66584, 66587, 66588, 66589, 66590, 66591, 66594, 66595, 66614, 66616, 66623, 66624, 66625, 66626, 66652, 66653, 66661, 66664, 66671, 66673, 66674, 66675, 66676, 66677, 66678, 66679, 66680, 66681, 66682, 66683, 66684, 66685, 66686, 66687, 66688, 66689, 66690, 66691, 66692, 66693, 66694, 66695, 66696, 66697, 66698, 66699, 66700, 66701, 66702, 66703, 66704, 66705, 66706, 66707, 66708, 66709, 66710, 66711, 66712, 66713, 66714, 66715, 66716, 66717, 66718, 66719, 66720, 66721, 66722, 66723, 66724, 66725, 66726, 66727, 66728, 66729, 66730, 66731, 66732, 66733, 66734, 66735, 66736, 66737, 66738, 66739, 66740, 66741, 66742, 66743, 66744, 66745, 66746, 66747, 66748, 66749, 66750 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26459, "uuid": "6accd46663bc4669afaac418f2bf498e", "short_code": "coll", "title": "Sentinel 5 Precursor: Level 2 data", "abstract": "Need some info!" }, { "ob_id": 30129, "uuid": "3b0630c7fa264164868d4da5c9f90bed", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Third Party Data", "abstract": "The National Centre for Earth Observation (NCEO) Third Party data contains a broad range remotely sensed data acquired by satellite for use by the Earth Observation Scientific community supported by NCEO. The Centre for Environmental Data Analysis (CEDA) has archived and provides access to extensive Earth observation datasets under strict licensing conditions. Please see the individual dataset records for conditions of use." } ], "responsiblepartyinfo_set": [ 180554, 180556, 180555, 180560, 180561, 180559, 180558, 180557, 180562, 180563 ], "onlineresource_set": [ 52637, 52638, 52639, 52640, 52641 ] }, { "ob_id": 37840, "uuid": "ba5618b8ad6540c4b16df4877350464c", "title": "Sentinel 5P: Cloud (CLOUD) level 2 data", "abstract": "This dataset contains data that can be used for cloud correction of satellite trace gas retrievals these include: cloud fraction, cloud optical thickness (albedo), and cloud-top pressure (height).\r\n\r\nSentinel 5 Precursor (S5P) was launched on the 13th of October 2017 carrying the TROPOspheric Monitoring Instrument (TROPOMI). Cloud parameters from TROPOMI are not only used for enhancing the accuracy of trace gas retrievals but also to extend the satellite data record of cloud information derived from oxygen A-band measurements initiated with the Global Ozone Monitoring Experiment (GOME).\r\n\r\nThe TROPOMI/S5P cloud properties retrieval is based on the Optical Cloud Recognition Algorithm (OCRA) and Retrieval of Cloud Information using Neural Networks (ROCINN) algorithms currently being used in the operational GOME and GOME-2 products. OCRA retrieves the cloud fraction using measurements in the UV/VIS spectral regions and ROCINN retrieves the cloud height (pressure) and optical thickness (albedo) using measurements in and around the oxygen A-band at 760 nm. For TROPOMI/S5P we use OCRA/ROCINN Version 3.0, which is based on a more realistic treatment of clouds as optically uniform layers of light-scattering particles. Additionally, the cloud parameters are also provided for a cloud model which assumes the cloud to be a Lambertian reflecting boundary.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-06-26T17:51:27", "updateFrequency": "", "dataLineage": "Data collected and prepared by European Space Agency (ESA). Downloaded from the Collaborative Hub for use by CEDA users.", "removedDataReason": "", "keywords": "Sentinel 5P, Cloud, CLOUD, ESA", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-11-10T14:19:46", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 37841, "dataPath": "/neodc/sentinel5p/data/L2_CLOUD/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 17207119367685, "numberOfFiles": 77720, "fileFormat": "These data are netCDF format." }, "timePeriod": { "ob_id": 7138, "startTime": "2018-05-06T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 1, "explanation": "See dataset associated documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-08-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 26492, "uuid": "8b2941793d9b4977a81a02f4aaa92201", "short_code": "cmppr", "title": "Composite Process for: Level 2 data from the Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI) for Cloud data.", "abstract": "Composite process for Level 2 data from the TROPOspheric Monitoring Instrument (TROPOMI) deployed on Sentinel 5P. This consists of the Acquisition process for raw imaging data from the Sentinel 5P TROPOMI and the computation component to produce processed Level 2 cloud data." }, "imageDetails": [ 148 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2586, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 49, "licenceURL": "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12321, "uuid": "7896ea1117dc4fa9bb95485ca9b1c6be", "short_code": "proj", "title": "Copernicus Programme", "abstract": "Copernicus, formerly known as the Global Monitoring for Environment and Security (GMES) programme, is headed by the European Commission (EC) in partnership with the European Space Agency (ESA). Within the Copernicus Space Component, ESA is developing a series of Sentinel satellite missions. Data from the Sentinel missions, as well as from Contributing Missions from other space agencies, are made freely available through a unified ground segment. Each Sentinel mission is comprised of a constallation of two or more satellites to fulfil the timeliness and reliability requirements of the Copernicus Services environmental monitoring and civil security activities. As well as operational and monitoring capabilities, the Sentinel missions will provide a wealth of Earth Observation data for scientific exploitation. The Sentinel 1 mission provides all weather, day and night radar imagery with scientific applications in sea-ice measurements, biomass observations and earthquake analysis. Sentinel 2 is a high resolution imaging mission to provide imagery of vegetation, soil and water cover, inland waterways and coastal areas. Sentinel 3 is a multi-instrument mission to measure sea-surface topography, sea- and land-surface temperature, ocean colour and land colour with high-end accuracy and reliability. Sentinel 4 is devoted to atmospheric monitoring and will be flown on a Meteosat Third Generation-Sounder (MTG-S) satellite in geostationary orbit. Sentinel 5 will monitor the atmosphere from polar orbit on board a MetOp Second Generation satellite. The Sentinel 5 precursor satellite mission is being developed to reduce data gaps between Envisat, in particular the Sciamachy instrument, and the launch of Sentinel 5. The Sentinel 5 mission will be dedicated to atmospheric monitoring. Sentinel 6 carries a radar altimeter to measure global sea-surface height, primarily for operational oceanography and for climate studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 66547, 66548, 66549, 66550, 66551, 66552, 66553, 66554, 66555, 66556, 66557, 66559, 66560, 66561, 66562, 66563, 66564, 66565, 66566, 66567, 66568, 66569, 66570, 66571, 66572, 66573, 66574, 66575, 66576, 66577, 66578, 66579, 66580, 66581, 66582, 66583, 66584, 66585, 66586, 66587, 66588, 66589, 66590, 66591, 66592, 66593, 66594, 66595, 66596, 66597, 66598, 66599, 66600, 66601, 66602, 66603, 66604, 66605, 66606, 66607, 66608, 66609, 66610, 66611, 66612, 66613, 66615, 66616, 66617, 66618, 66619, 66620, 66621, 66622, 66623, 66624, 66625, 66626, 66627, 66628, 66629, 66630, 66631, 66632, 66633, 66634, 66635, 66636, 66637, 66638, 66639, 66640, 66641, 66642, 66643, 66644, 66645, 66646, 66647, 66648, 66649, 66650, 66651, 66652, 66653, 66655, 66656, 66657, 66658, 66659, 66660, 66661, 66662, 66663, 66664, 66665, 66666, 66667, 66668, 66669, 66671, 66678, 66746, 66750, 79919, 79920, 79921, 79922, 79923, 79924, 79925, 79926 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26459, "uuid": "6accd46663bc4669afaac418f2bf498e", "short_code": "coll", "title": "Sentinel 5 Precursor: Level 2 data", "abstract": "Need some info!" }, { "ob_id": 30129, "uuid": "3b0630c7fa264164868d4da5c9f90bed", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Third Party Data", "abstract": "The National Centre for Earth Observation (NCEO) Third Party data contains a broad range remotely sensed data acquired by satellite for use by the Earth Observation Scientific community supported by NCEO. The Centre for Environmental Data Analysis (CEDA) has archived and provides access to extensive Earth observation datasets under strict licensing conditions. Please see the individual dataset records for conditions of use." } ], "responsiblepartyinfo_set": [ 180568, 180565, 180564, 180566, 180571, 180570, 180569, 180567, 180572, 180573 ], "onlineresource_set": [ 52652, 52653, 52654, 52650, 52651 ] }, { "ob_id": 37842, "uuid": "b0beb88af3b748b98afc4b35f77bebf8", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, for global land areas, version 1.2", "abstract": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (CO2), derived from the TANSAT satellite, using the University of Leicester Full-Physics Retrieval Algorithm (UoL-FP, also known as OCFP). This dataset is also referred to as CO2_TAN_OCFP. This version of the dataset provides data globally over land. For further information on the dataset, please see the linked documentation.\r\n\r\nInitially this dataset contains data from the period from March 2017 to May 2018, delivered as part of the GHG_cci Climate Research Data Package 7. Additional time periods may be delivered in the future.\r\n\r\nThis data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-06-25T01:47:19", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI GHG project team and supplied to CEDA as part of the ESA CCI Open Data Portal Project. This data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO).", "removedDataReason": "", "keywords": "ESA, CCI, satellite, TANSAT, atmosphere, carbon dioxide, CO2", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-06-24T12:45:18", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 37843, "dataPath": "/neodc/esacci/ghg/data/cci_plus/CO2_TAN_OCFP/v1.2/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3171872255, "numberOfFiles": 417, "fileFormat": "Data is in NetCDF" }, "timePeriod": { "ob_id": 10448, "startTime": "2017-03-01T00:00:00", "endTime": "2018-05-23T23:59:59" }, "resultQuality": { "ob_id": 3550, "explanation": "For further information see the End to End ECV Uncertainty budget report", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-09-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 31873, "uuid": "1ff83a3b15e44b66a72005043d4cff27", "short_code": "cmppr", "title": "Composite Process for the ESA CCI Greenhouse Gases product: Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, version 1.0", "abstract": "Composite Process for the ESA CCI Greenhouse Gases product: Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, version 1.0" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2564, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 34, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_ghg_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13295, "uuid": "f0c66ffa30514d2daee821286a014b16", "short_code": "proj", "title": "ESA Greenhouse Gases Climate Change Initiative Project", "abstract": "The European Space Agency Greenhouse Gases Climate Change Initiative (GHG CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs)\r\n\r\nCarbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases (GHGs) and a focus of international research activities related to a better understanding of the carbon cycle (see, for example, the Global Carbon Project (GCP)).\r\n \r\nWithin the GHG-CCI project the focus is on satellite data. Satellite observations combined with modelling can add important missing global information on regional CO2 and CH4 (surface) sources and sinks required for better climate prediction. The GHG CCI project started on the 1st September 2010." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1050, 1635, 1636, 11816, 11852, 11853, 11854, 11855, 11856, 11857, 11858, 11859, 11860, 11863, 11864, 11866, 11867, 11869, 11870, 11871, 11872, 13276, 25159, 25160, 25161, 26287 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 180581, 180575, 180576, 180577, 180578, 180579, 180580, 180584, 180582, 180583 ], "onlineresource_set": [ 52658, 52659, 52660, 52663, 52661, 52662 ] }, { "ob_id": 37846, "uuid": "070522ac6a5d4973a95c544beef714b4", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm, version 10", "abstract": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (XCO2), using the fast atmospheric trace gas retrieval for OCO2 (FOCAL-OCO2). The FOCAL-OCO2 algorithm which has been setup to retrieve XCO2 by analysing hyper spectral solar backscattered radiance measurements from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. FOCAL includes a radiative transfer model which has been developed to approximate light scattering effects by multiple scattering at an optically thin scattering layer. This reduces the computational costs by several orders of magnitude. FOCAL's radiative transfer model is utilised to simulate the radiance in all three OCO-2 spectral bands allowing the simultaneous retrieval of CO2, H2O, and solar induced chlorophyll fluorescence. The product is limited to cloud-free scenes on the Earth's day side. This dataset is also referred to as CO2_OC2_FOCA.\r\n\r\nThis version of the data (v10) was produced as part of the European Space Agency's (ESA) \r\nClimate Change Initiative (CCI) Greenhouse Gases (GHG) project (GHG-CCI+, http://cci.esa.int/ghg)\r\nand got co-funding from the Univ. Bremen and EU H2020 projects CHE (grant agreement no. 776186) and VERIFY (grant agreement no. 776810).\r\n\r\nWhen citing this data, please also cite the following peer-reviewed publications:\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, V.Rozanov, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 1: Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup, Remote Sensing, 9(11), 1159; doi:10.3390/rs9111159, 2017\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 2: Application to XCO2 Retrievals from OCO-2, Remote Sensing, 9(11), 1102; doi:10.3390/rs9111102, 2017", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:22:34", "updateFrequency": "notPlanned", "dataLineage": "The FOCAL OCO-2 XCO2 retrieval development, data processing and analysis has received co-funding from ESA’s Climate Change Initiative (CCI+) via project GHG-CCI+ (contract 4000126450/19/I-NB, https://climate.esa.int/en/projects/ghgs) EUMETSAT via the FOCAL-CO2M study (contract EUM/CO/19/4600002372/RL), the European Union via the Horizon 2020 (H2020) projects VERIFY (Grant Agreement No. 776810, http://verify.lsce.ipsl.fr) and CHE (Grant Agreement No. 776186, https://www.che-project.eu) and by the State and the University of Bremen.\r\n\r\nData were supplied to CEDA for archiving as part of the ESA CCI Open Data Portal.", "removedDataReason": "", "keywords": "ESA, CCI, satellite, carbon dioxide, CO2", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-12-22T17:20:27", "doiPublishedTime": "2023-01-17T09:11:44", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38256, "dataPath": "/neodc/esacci/ghg/data/cci_plus/CO2_OC2_FOCA/v10.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 50147202410, "numberOfFiles": 2230, "fileFormat": "Data are in NetCDF format." }, "timePeriod": { "ob_id": 10542, "startTime": "2014-09-06T00:00:00", "endTime": "2021-03-31T23:59:59" }, "resultQuality": { "ob_id": 4155, "explanation": "The dataset has been quality controlled, validated, and uncertainty characterized by the data provider (see related documents).", "passesTest": true, "resultTitle": "CCI GHG", "date": "2023-01-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33058, "uuid": "4d90fa654eda41b982c9b59de3b0fc4e", "short_code": "cmppr", "title": "Composite process for the ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm", "abstract": "The data has been generated from the OCO-2 satellite using the FOCAL-OCO2 algorithm" }, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2564, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 34, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_ghg_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13295, "uuid": "f0c66ffa30514d2daee821286a014b16", "short_code": "proj", "title": "ESA Greenhouse Gases Climate Change Initiative Project", "abstract": "The European Space Agency Greenhouse Gases Climate Change Initiative (GHG CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs)\r\n\r\nCarbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases (GHGs) and a focus of international research activities related to a better understanding of the carbon cycle (see, for example, the Global Carbon Project (GCP)).\r\n \r\nWithin the GHG-CCI project the focus is on satellite data. Satellite observations combined with modelling can add important missing global information on regional CO2 and CH4 (surface) sources and sinks required for better climate prediction. The GHG CCI project started on the 1st September 2010." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1050, 1635, 1636, 11820, 11831, 11841, 11846, 11848, 11895, 23212, 28592, 28594, 28595, 28596, 28597, 50333, 50334, 50335, 50336, 50337, 50338 ], "vocabularyKeywords": [], "identifier_set": [ 12336 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 180633, 180634, 180635, 180636, 180637, 180639, 180640, 180638, 192744, 192745, 180641, 180642, 180643, 180644 ], "onlineresource_set": [ 52671, 52672, 52673, 52669, 52670, 52667, 52674, 52675, 53046 ] }, { "ob_id": 37849, "uuid": "91c218d3a80f4c43ac665d0bdf0ed5e7", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 12.5 (v20220804)", "abstract": "Input Data for Figure 12.5 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 12.5 shows projected changes in selected climatic impact-driver indices for Africa.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Ranasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1767–1926, doi:10.1017/9781009157896.014.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with general data provided in the central directory and specific data in 3 folders (Q100_CMIP5, Q100_CMIP6, Q1000_CORDEX-core).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n - spatial field over Africa of mean change in 1-in-100 year river discharge per unit catchment area (Q100, m3 s-1 km-2) from CORDEX models for 2041-2060 relative to 1995-2014 for RCP8.5\r\n - Shoreline position change over Africa (pointwise) along sandy coasts by the year 2100 relative to 2010 (meters; negative values indicate shoreline retreat) from the CMIP5 based data set presented by Vousdoukas et al. (2020)\r\n - regional averages in Africa of Q100 (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for:\r\n - CMIP6 historical, ssp126 and ssp585\r\n - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n - regional averages in Africa of CMIP5 based projections (mean change estimates and bars the 5th-95th percentile range of associated uncertainty) of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 and RCP4.5 from Vousdoukas et al. (2020)\r\n\r\nSAH, ARP, WAF, CAF, NEAF, SEAF, WSAF, ESAF, MDG, NEU, WCE and MED are domains used in the model. \r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 12.5:\r\n\r\n Panel a:\r\n - Q100_map_panel_a_AFR_less_MED_divdra.nc: Field (colors plotted on the map) of changes of 1-in-100yr river discharge per unit catchment area between 2041-2060 (mid-term) and 1995-2015 (recent past) for CORDEX RCP8.5; the data is from the AFR CORDEX domain, without the MED AR6 region\r\n - Q100_map_panel_a_MED_for_AFR_from_EUR_divdra.nc: same as previous file but for the MED AR6 region, taken from the EUR CORDEX domain\r\n\r\n Panel b:\r\n - CoastalRecession_AFRICA_RCP85_2100.json: pointwise values (color points on the map) for Africa of shoreline position mean changes between 2100 (long-term) and 2010 (recent past) from the CMIP5 based data set presented by Vousdoukas et al. (2020)\r\n\r\n Panel c:\r\n - txt files containing the median and 5th/95th percentiles of each ensemble of the 1-in-100yr river discharge per unit catchment area (Q100) regional averages of time slices: Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.txt, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${scenario}: the name of the scenario : ssp126, ssp585, rcp26, rcp85\r\n - ${period}: the explicit period used to compute the temporal average: 1995-2014 (recent past), 2041-2060 (mid-term) and 2080-2099 (long term)\r\n - ${CORDEX_domain}: the CORDEX domain\r\n - txt files containing the Q100 regional averages of global warming levels: Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${GWL}: the Global Warming Level: 1.5, 2 and 4\r\n - ${CORDEX_domain}: the CORDEX domain\r\n\r\n Panel d:\r\n - globalErosionProjections_by_AR6_region_${scenario}_${horizon).json: regional averages of shoreline position changes for Africa, for the RCP4.5 and RCP8.5 scenarios (${scenario} = RCP45 and ${scenario} = RCP85 respectively) and the 2050 (mid-term, in blue) and 2100 (long-term, in red) future horizons (${horizon}=2050 and ${horizon}=2100 respectively) against the recent past period (2010); the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project. \r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project. \r\nCORDEX is Coordinated Regional Downscaling Experiment from the WCRP. \r\nWCRP is the World Climate Research Programme. SSP stands for Shared Socioeconomic Pathway. \r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6. \r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\nRCP stands for Representative Concentration Pathway. \r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n For panel a, the plotting script ch12_fig12.5_plotting_code_Q100_AFR.py (see data tables and code on Github) draws the rivers and uses a subroutine (dranetwrite) to identify the rivers to plot them individually with lines, using the data from the Q100_map_panel_a_AFR_less_MED_divdra.nc and Q100_map_panel_a_MED_for_AFR_from_EUR_divdra.nc netcdf files; plotting the Q100 netcdf file will produce dots (and not rivers).\r\n\r\n For panel c, the recent past values are plotted as absolute values (left column on each regional subpanel) and the future changes are plotted as differences against the recent past values (differences are computed when plotting the values).\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 12)\r\n - Link to the Supplementary Material for Chapter 12, which contains details on the input data used in Table 12.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n- Link to the Chapter 12 GitHub repository", "creationDate": "2022-08-05T10:57:18.344446", "lastUpdatedDate": "2022-08-05T10:57:18", "latestDataUpdateTime": "2023-04-18T11:59:32", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Africa, regional, river discharge, extreme, shoreline position, CMIP6, CMIP5, CORDEX, global warming levels, GWL, +1.5°C, +2°C, +4°C, ssp126, ssp585, recent past, mid-term, long-term, 1995-2014, 2041-2060, 2081-2100, RCP4.5, RCP8.5", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-04-18T10:42:48", "doiPublishedTime": "2023-06-30T14:25:27.530077", "removedDataTime": null, "geographicExtent": { "ob_id": 3576, "bboxName": "", "eastBoundLongitude": 60.0, "westBoundLongitude": -25.0, "southBoundLatitude": -40.0, "northBoundLatitude": 47.0 }, "verticalExtent": { "ob_id": 168, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38042, "dataPath": "/badc/ar6_wg1/data/ch_12/inputdata_ch12_fig05/v20220804", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 15964042, "numberOfFiles": 73, "fileFormat": "NetCDF, CSV, json, txt" }, "timePeriod": { "ob_id": 10450, "startTime": "1995-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4036, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37851, "uuid": "5cf4987fc61b4974a5f1be25e4b8f238", "short_code": "comp", "title": "Caption for Figure 12.5 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in selected climatic impact-driver indices for Africa. (a) Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m3 s–1 km–2) from CORDEX models for 2041–2060 relative to 1995–2014 for RCP8.5. (b) Shoreline position change along sandy coasts by the year 2100 relative to 2010 (metres; negative values indicate shoreline retreat) from the CMIP5-based dataset presented by Vousdoukas et al. (2020b). (c) Bar plots for Q100 (m3 s–1 km–2) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (GWLs, defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. (d) Bar plots for shoreline position change show CMIP5-based projections of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) and RCP4.5 (blue) from Vousdoukas et al. (2020b). Dots indicate regional mean change estimates and bars show the 5–95th percentile range of associated uncertainty. Note that these shoreline position change projections assume that there are no additional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical Annex VI for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 49807, 50415, 50417, 61147, 80033, 80034 ], "vocabularyKeywords": [], "identifier_set": [ 12538 ], "observationcollection_set": [ { "ob_id": 32728, "uuid": "cab668573a8d45ce9d0efc6b762cf454", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 12.4\r\n- input data for Figure 12.5\r\n- input data for Figure 12.6\r\n- input data for Figure 12.7\r\n- input data for Figure 12.8\r\n- input data for Figure 12.9\r\n- input data for Figure 12.10\r\n- input data for Figure 12.SM.1\r\n- input data for Figure 12.SM.2\r\n- input data for Figure 12.SM.3\r\n- input data for Figure 12.SM.4\r\n- input data for Figure 12.SM.5\r\n- input data for Figure 12.SM.6" } ], "responsiblepartyinfo_set": [ 180649, 180650, 180651, 180652, 180653, 180654, 180655, 180656, 180657, 180658 ], "onlineresource_set": [ 52685, 52684, 82686, 83314, 83315 ] }, { "ob_id": 37852, "uuid": "537b22f0230448fdb9a4ec806ed54d84", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 12.7 (v20220804)", "abstract": "Input Data for Figure 12.7 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 12.7 shows projected changes in selected climatic impact-driver indices for Australasia.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Ranasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1767–1926, doi:10.1017/9781009157896.014.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with general data provided in the central directory and specific data in 3 folders (Q100_CMIP5, Q100_CMIP6, Q1000_CORDEX-core).\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n - spatial field over Australasia of mean change in 1-in-100 year river discharge per unit catchment area (Q100, m3 s-1 km-2) from CORDEX models for 2041-2060 relative to 1995-2014 for RCP8.5\r\n - Shoreline position change over Australasia (pointwise) along sandy coasts by the year 2100 relative to 2010 (meters; negative values indicate shoreline retreat) from the CMIP5 based data set presented by Vousdoukas et al. (2020)\r\n - regional averages in Australasia of Q100 (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for:\r\n - CMIP6 historical, ssp126 and ssp585\r\n - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n - regional averages in Australasia of CMIP5 based projections (mean change estimates and bars the 5th-95th percentile range of associated uncertainty) of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 and RCP4.5 from Vousdoukas et al. (2020)\r\n\r\nNAU, CAU, EAU, SAU and NZ are domains used in the model.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 12.7:\r\n \r\nPanel a:\r\n - Q100_map_panel_a_AUS_divdra.nc: Field (colors plotted on the map) of changes of 1-in-100yr river discharge per unit catchment area between 2041-2060 (mid-term) and 1995-2015 (recent past) for CORDEX RCP8.5; the file contains the data for the regions from the AUS CORDEX domain\r\n\r\nPanel b:\r\n - CoastalRecession_Australasia_RCP85_2100.json: pointwise values (color points on the map) for Australasia of shoreline position mean changes between 2100 (long-term) and 2010 (recent past) from the CMIP5 based data set presented by Vousdoukas et al. (2020)\r\n\r\nPanel c:\r\n - txt files containing the median and 5th/95th percentiles of each ensemble of the 1-in-100yr river discharge per unit catchment area (Q100) regional averages of time slices: Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.txt, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${scenario}: the name of the scenario : ssp126, ssp585, rcp26, rcp85\r\n - ${period}: the explicit period used to compute the temporal average: 1995-2014 (recent past), 2041-2060 (mid-term) and 2080-2099 (long term)\r\n - ${CORDEX_domain}: the CORDEX domain\r\n - txt files containing the Q100 regional averages of global warming levels: Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${GWL}: the Global Warming Level: 1.5, 2 and 4\r\n - ${CORDEX_domain}: the CORDEX domain\r\n\r\nPanel d:\r\n - globalErosionProjections_by_AR6_region_${scenario}_${horizon).json: regional averages of shoreline position changes for Africa, for the RCP4.5 and RCP8.5 scenarios (${scenario} = RCP45 and ${scenario} = RCP85 respectively) and the 2050 (mid-term, in blue) and 2100 (long-term, in red) future horizons (${horizon}=2050 and ${horizon}=2100 respectively) against the recent past period (2010); the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n\r\n\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP. \r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project. \r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project. \r\nSSP stands for Shared Socioeconomic Pathway. \r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6. \r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\nRCP stands for Representative Concentration Pathway. \r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n For panel a, the plotting script (see data tables and code on Github) draws the rivers and uses a subroutine to identify the rivers to plot them individually with lines; plotting the Q100 netcdf file will produce dots (and not rivers).\r\n\r\nFor panel c, the recent past values are plotted as absolute values (left column on each regional subpanel) and the future changes are plotted as differences against the recent past values (differences are computed when plotting the values).\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 12)\r\n - Link to the Supplementary Material for Chapter 12, which contains details on the input data used in Table 12.SM.1\r\n - Link to the code for the figure, archived on Zenodo.\r\n- Link to the Chapter 12 GitHub repository", "creationDate": "2022-08-05T11:01:48.720991", "lastUpdatedDate": "2022-08-05T11:01:48", "latestDataUpdateTime": "2023-04-19T13:42:14", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Australasia, regional, river discharge, extreme, shoreline position, CMIP6, CMIP5, CORDEX, global warming levels, GWL, +1.5°C, +2°C, +4°C, ssp126, ssp585, recent past, mid-term, long-term, 1995-2014, 2041-2060, 2081-2100, RCP4.5, RCP8.5", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-04-19T12:41:11", "doiPublishedTime": "2023-06-30T15:05:55.255984", "removedDataTime": null, "geographicExtent": { "ob_id": 3577, "bboxName": "", "eastBoundLongitude": -175.0, "westBoundLongitude": 105.0, "southBoundLatitude": -57.0, "northBoundLatitude": 0.0 }, "verticalExtent": { "ob_id": 169, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38044, "dataPath": "/badc/ar6_wg1/data/ch_12/inputdata_ch12_fig07/v20220804", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7341861, "numberOfFiles": 42, "fileFormat": "NetCDF, CSV, json, txt" }, "timePeriod": { "ob_id": 10451, "startTime": "1995-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4037, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37854, "uuid": "e969d6921e6c49628bb79295c510e3eb", "short_code": "comp", "title": "Caption for Figure 12.7 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in selected climatic impact-driver indices for Australasia. (a) Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m3 s–1 km–2) from CORDEX models for 2041–2060 relative to 1995–2014 for RCP8.5. (b) Shoreline position change along sandy coasts by the year 2100 relative to 2010 (metres; negative values indicate shoreline retreat) from the CMIP5-based dataset presented by Vousdoukas et al. (2020b). (c) Bar plots for Q100 (m3 s–1 km–2) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. (d) Bar plots for shoreline position change show CMIP5-based projections of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) and RCP4.5 (blue) from Vousdoukas et al. (2020b). Dots indicate regional mean change estimates and bars the 5–95th percentiles ranges of associated uncertainty. Dots indicate regional mean change estimates and bars show the 5–95th percentile range of associated uncertainty. Note that these shoreline position change projections assume that there are no additional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical Annex VI for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6814, 6816, 49805, 49806, 49807 ], "vocabularyKeywords": [], "identifier_set": [ 12540 ], "observationcollection_set": [ { "ob_id": 32728, "uuid": "cab668573a8d45ce9d0efc6b762cf454", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 12.4\r\n- input data for Figure 12.5\r\n- input data for Figure 12.6\r\n- input data for Figure 12.7\r\n- input data for Figure 12.8\r\n- input data for Figure 12.9\r\n- input data for Figure 12.10\r\n- input data for Figure 12.SM.1\r\n- input data for Figure 12.SM.2\r\n- input data for Figure 12.SM.3\r\n- input data for Figure 12.SM.4\r\n- input data for Figure 12.SM.5\r\n- input data for Figure 12.SM.6" } ], "responsiblepartyinfo_set": [ 180661, 180662, 180663, 180664, 180665, 180666, 180667, 180668, 180669, 180670 ], "onlineresource_set": [ 52680, 52681, 82688, 83319, 83320 ] }, { "ob_id": 37855, "uuid": "7c2c37c3c5d14aac87377c7673e35a0b", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 12.9 (v20220804)", "abstract": "Input Data for Figure 12.9 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 12.9 shows projected changes in selected climatic impact-driver indices for Europe.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Ranasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1767–1926, doi:10.1017/9781009157896.014.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with general data provided in the central directory and specific data in 3 folders (Q100_CMIP5, Q100_CMIP6, Q1000_CORDEX-core).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n - spatial field over Europe of mean change in 1-in-100 year river discharge per unit catchment area (Q100, m3 s-1 km-2) from CORDEX models for 2041-2060 relative to 1995-2014 for RCP8.5\r\n - spatial field of changes of number of days per year with snow water equivalent over 100mm (SWE100) from EURO-CORDEX models for 2041-2060 relative to 1995-2014 for RCP8.5; the grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n - the associated mask showing the areas with more than 80% of model agreement in the sign of change\r\n - regional averages in Europe of Q100 (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for:\r\n - CMIP6 historical, ssp126 and ssp585\r\n - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n - regional averages of number of days per year with snow water equivalent over 100mm (SWE100) in Europe for:\r\n - CMIP6 historical, ssp126 and ssp585\r\n - CMIP5 and EURO-CORDEX historical, RCP2.6 and RCP8.5\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n The grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n\r\nNEU, WCE and MED are domains used in the model. \r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 12.9:\r\n \r\nPanel a:\r\n - Q100_map_panel_a_EUR_divdra.nc: Field (colors plotted on the map) of changes of 1-in-100yr river discharge per unit catchment area between 2041-2060 (mid-term) and 1995-2014 (recent past) for CORDEX RCP8.5; the file contains the data for the regions from the EUR CORDEX domain\r\n\r\n Panel b:\r\n - SWE_panel_b_RCP85_mce_minus_baseline.nc: spatial field (colors) of changes of number of days per year with snow water equivalent over 100mm (SWE100) from EURO-CORDEX models for 2041-2060 relative to 1995-2014 for RCP8; the grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero\r\n - mask_80perc-agreement_SWE_panel_b_RCP85_mce_minus_baseline.nc: spatial mask (for hatching) showing where at least 80% of the models agree in terms of sign of change (negative change, positive change or zero change); values are: 1 where true, 0 where false\r\n \r\nPanel c:\r\n - txt files containing the median and 5th/95th percentiles of each ensemble of the 1-in-100yr river discharge per unit catchment area (Q100) regional averages of time slices: Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.txt, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${scenario}: the name of the scenario : ssp126, ssp585, rcp26, rcp85\r\n - ${period}: the explicit period used to compute the temporal average: 1995-2014 (recent past), 2041-2060 (mid-term) and 2080-2099 (long term)\r\n - ${CORDEX_domain}: the CORDEX domain\r\n - txt files containing the Q100 regional averages of global warming levels: Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${GWL}: the Global Warming Level: 1.5, 2 or 4\r\n - ${CORDEX_domain}: the CORDEX domain\r\n \r\nPanel d:\r\n - CMIP5_EUR-11_snw_mask14_AR6_regional_averages.json: regional averages for the CMIP5 multimodel ensemble of number of days per year with snow water equivalent over 100mm (SWE100) in Europe for recent past (1995-2014), mid-term (2041-2060) long-term (2081-2099) for RCP2.6 and RCP8.5, and for three global warming levels: 1.5, 2 and 4; the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n - EURO-CORDEX_SWE_mask14_AR6_regional_averages.json: same as previous file for the EURO-CORDEX multimodel ensemble\r\n - CMIP6_EUR-11_snw_mask14_AR6_regional_averages.json: same as previous file for CMIP6 (ssp126 instead of RCP2.6 and ssp585 instead of RCP8.5)\r\n\r\n\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP. \r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project. \r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project. \r\n\r\nSSP stands for Shared Socioeconomic Pathway. \r\n\r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6. \r\n\r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\n\r\nRCP stands for Representative Concentration Pathway. \r\n\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\n\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n For panel a, the plotting script (see data tables and code on Github) draws the rivers and uses a subroutine to identify the rivers to plot them individually with lines; plotting the Q100 netcdf file will produce dots (and not rivers).\r\n\r\n\r\nFor panel c, the recent past values are plotted as absolute values (left column on each regional subpanel) and the future changes are plotted as differences against the recent past values (differences are computed when plotting the values).\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 12)\r\n - Link to the Supplementary Material for Chapter 12, which contains details on the input data used in Table 12.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-08-05T11:02:14.294715", "lastUpdatedDate": "2022-08-05T11:02:14", "latestDataUpdateTime": "2023-06-08T16:54:31", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Europe, regional, river discharge, extreme, snow water equivalent, CMIP6, CMIP5, EURO-CORDEX, global warming levels, GWL, +1.5°C, +2°C, +4°C, ssp126, ssp585, recent past, mid-term, long-term, 1995-2014, 2041-2060, 2081-2100, RCP4.5, RCP8.5", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-19T08:45:23", "doiPublishedTime": "2023-06-30T15:15:51.081909", "removedDataTime": null, "geographicExtent": { "ob_id": 3578, "bboxName": "", "eastBoundLongitude": 65.0, "westBoundLongitude": -15.0, "southBoundLatitude": 25.0, "northBoundLatitude": 75.0 }, "verticalExtent": { "ob_id": 170, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38046, "dataPath": "/badc/ar6_wg1/data/ch_12/inputdata_ch12_fig09/v20220804", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6793223, "numberOfFiles": 38, "fileFormat": "NetCDF, CSV, json, txt" }, "timePeriod": { "ob_id": 10452, "startTime": "1995-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4038, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37857, "uuid": "723befe56178420f8d8b531a824c0263", "short_code": "comp", "title": "Caption for Figure 12.9 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in selected climatic impact-driver indices for Europe. (a) Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m3 s–1 km–2), and (b) median change in the number of days with snow water equivalent (SWE) over 100 mm (from November to March), from EURO-CORDEX models for 2041–2060 relative to 1995–2014 and RCP8.5. Diagonal lines indicate where less than 80% of models agree on the sign (direction )of change. (c) Bar plots for Q100 (m3 s–1 km–2) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. (d) As for (c) but showing absolute values for number of days with SWE > 100mm, masked to grid cells with at least 14 such days in the recent past. See Technical Annex VI for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6019, 6020, 6023, 6814, 6816, 7767, 10988, 11086, 49805, 49808, 49809 ], "vocabularyKeywords": [], "identifier_set": [ 12542 ], "observationcollection_set": [ { "ob_id": 32728, "uuid": "cab668573a8d45ce9d0efc6b762cf454", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 12.4\r\n- input data for Figure 12.5\r\n- input data for Figure 12.6\r\n- input data for Figure 12.7\r\n- input data for Figure 12.8\r\n- input data for Figure 12.9\r\n- input data for Figure 12.10\r\n- input data for Figure 12.SM.1\r\n- input data for Figure 12.SM.2\r\n- input data for Figure 12.SM.3\r\n- input data for Figure 12.SM.4\r\n- input data for Figure 12.SM.5\r\n- input data for Figure 12.SM.6" } ], "responsiblepartyinfo_set": [ 180673, 180674, 180675, 180676, 180677, 180678, 180679, 180680, 180681, 180682 ], "onlineresource_set": [ 52678, 52679, 82690, 83676 ] }, { "ob_id": 37858, "uuid": "b6a36a7fe12644bfa28bc4ec8bfcb028", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 12.10 (v20220804)", "abstract": "Input Data for Figure 12.10 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 12.10 shows projected changes in selected climatic impact-driver indices for North-America.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Ranasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1767–1926, doi:10.1017/9781009157896.014.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with general data provided in the central directory and specific data in 3 folders (Q100_CMIP5, Q100_CMIP6, Q1000_CORDEX-core).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n - spatial field over North-America of mean change in 1-in-100 year river discharge per unit catchment area (Q100, m3 s-1 km-2) from CORDEX models for 2041-2060 relative to 1995-2014 for RCP8.5\r\n - spatial field of changes of number of days per year with snow water equivalent over 100mm (SWE100) from CORDEX-core models for 2041-2060 relative to 1995-2014 for RCP8.5; the grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n - the associated mask showing the areas with more than 80% of model agreement in the sign of change\r\n - regional averages in North-America of Q100 (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for:\r\n - CMIP6 historical, ssp126 and ssp585\r\n - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n - regional averages of number of days per year with snow water equivalent over 100mm (SWE100) in North-America for:\r\n - CMIP6 historical, ssp126 and ssp585\r\n - CMIP5 and CORDEX-core historical, RCP2.6 and RCP8.5\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n The grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n\r\nCAR, SCA, NWN, NEN, WNA, CNA, ENA and NCA are domains used in the model. \r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 12.9:\r\n \r\nPanel a:\r\n - Q100_map_panel_a_NAM_divdra.nc: Field (colors plotted on the map) of changes of 1-in-100yr river discharge per unit catchment area between 2041-2060 (mid-term) and 1995-2014 (recent past) for CORDEX RCP8.5; the file contains the data for the regions from the NAM CORDEX domain\r\n - Q100_map_panel_a_CAM_for_NAM_divdra.nc: same as above for the CAM CORDEX domain\r\n\r\n Panel b:\r\n - SWE_panel_b_RCP85_2041-2060_minus_1995-2014.nc: spatial field (colors) of changes of number of days per year with snow water equivalent over 100mm (SWE100) from CORDEX-core NAM-22 models for 2041-2060 relative to 1995-2014 for RCP8; the grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero\r\n - mask_80perc-agreement_SWE_panel_b_RCP85_2041-2060_minus_1995-2014.nc: spatial mask (for hatching) showing where at least 80% of the models agree in terms of sign of change (negative change, positive change or zero change); values are: 1 where true, 0 where false\r\n \r\nPanel c:\r\n - txt files containing the median and 5th/95th percentiles of each ensemble of the 1-in-100yr river discharge per unit catchment area (Q100) regional averages of time slices: Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.txt, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${scenario}: the name of the scenario : ssp126, ssp585, rcp26, rcp85\r\n - ${period}: the explicit period used to compute the temporal average: 1995-2014 (recent past), 2041-2060 (mid-term) and 2081-2099 (long term)\r\n - ${CORDEX_domain}: the CORDEX domain\r\n - txt files containing the Q100 regional averages of global warming levels: Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${GWL}: the Global Warming Level: 1.5, 2 and 4\r\n - ${CORDEX_domain}: the CORDEX domain\r\n\r\nPanel d:\r\n- CMIP5_NORTH-AMERICA_snw_mask14_AR6_regional_averages.json: regional averages for the CMIP5 multimodel ensemble of number of days per year with snow water equivalent over 100mm (SWE100) in North-America for recent past (1995-2014), mid-term (2041-2060) long-term (2081-2100) for RCP2.6 and RCP8.5, and for three global warming levels: 1.5, 2 and 4; the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars) - grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n- CMIP6_NORTH-AMERICA_snw_mask14_AR6_regional_averages.json: same as previous file for CMIP6 (ssp126 instead of RCP2.6 and ssp585 instead of RCP8.5) - grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n- NAM-22_CORDEX_NORTH-AMERICA_snw_mask14_AR6_regional_averages.json: same as previous file for the CORDEX-core NAM-22 multimodel ensemble - grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n\r\n- NAM-22_CORDEX_NORTH-AMERICA_snw_mask30_AR6_regional_averages.json: same as previous file for the CORDEX-core NAM-22 multimodel ensemble, but grid points with less than 30 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n- CMIP5_NORTH-AMERICA_snw_mask30_AR6_regional_averages.json: regional averages for the CMIP5 multimodel ensemble of number of days per year with snow water equivalent over 100mm (SWE100) in North-America for recent past (1995-2014), mid-term (2041-2060) long-term (2081-2100) for RCP2.6 and RCP8.5, and for three global warming levels: 1.5, 2 and 4; the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars) - grid points with less than 30 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n- CMIP6_NORTH-AMERICA_snw_mask30_AR6_regional_averages.json: regional averages for the CMIP5 multimodel ensemble of number of days per year with snow water equivalent over 100mm (SWE100) in North-America for recent past (1995-2014), mid-term (2041-2060) long-term (2081-2100) for RCP2.6 and RCP8.5, and for three global warming levels: 1.5, 2 and 4; the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars) - grid points with less than 30 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP. \r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project. \r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project. \r\n\r\nSSP stands for Shared Socioeconomic Pathway. SSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6. \r\n\r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\n\r\nRCP stands for Representative Concentration Pathway. \r\n\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\n\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n For panel a, the plotting script (see data tables and code on Github) draws the rivers and uses a subroutine to identify the rivers to plot them individually with lines; plotting the Q100 netcdf file will produce dots (and not rivers).\r\n\r\n\r\nFor panel c, the recent past values are plotted as absolute values (left column on each regional subpanel) and the future changes are plotted as differences against the recent past values (differences are computed when plotting the values).\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 12)\r\n - Link to the Supplementary Material for Chapter 12, which contains details on the input data used in Table 12.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the Chapter 12 GitHub repository.", "creationDate": "2022-08-05T11:04:03.561985", "lastUpdatedDate": "2022-08-05T11:04:03", "latestDataUpdateTime": "2024-03-09T01:45:08", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, North-America, regional, river discharge, extreme, snow water equivalent, CMIP6, CMIP5, EURO-CORDEX, global warming levels, GWL, +1.5°C, +2°C, +4°C, ssp126, ssp585, recent past, mid-term, long-term, 1995-2014, 2041-2060, 2081-2100, RCP4.5, RCP8.5", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-31T13:36:35", "doiPublishedTime": "2023-06-30T15:21:59.594839", "removedDataTime": null, "geographicExtent": { "ob_id": 3579, "bboxName": "", "eastBoundLongitude": -40.0, "westBoundLongitude": -175.0, "southBoundLatitude": 0.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 171, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 40119, "dataPath": "/badc/ar6_wg1/data/ch_12/inputdata_ch12_fig10/v20220804", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 15117492, "numberOfFiles": 80, "fileFormat": "net-CDF, json, csv, txt" }, "timePeriod": { "ob_id": 10453, "startTime": "1995-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4039, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37860, "uuid": "6374eccfd34d4f9ebd8cdfbbe51f375b", "short_code": "comp", "title": "Caption for Figure 12.10 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in selected climatic impact-driver indices for North America. (a) Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m3 s–1 km–2), and (b) median change in the number of days with snow water equivalent (SWE) over 100 mm (from November to March), from CORDEX-North America models for 2041–2060 relative to 1995–2014 and RCP8.5. Diagonal lines indicate where less than 80% of models agree on the sign (direction) of change. (c) Bar plots for Q100 (m3 s–1 km–2) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. (d) As for (c) but showing absolute values for number of days with SWE > 100 mm, masked to grid cells with at least 14 such days in the recent past. See Technical Annex VI for details of indices. A Caribbean (CAR) Q100 bar plot is included here but assessed in the Small Islands section (Section 12.4.7). Further details on data sources and processing are available in the chapter data table (Table 12.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1050, 6019, 6020, 6023, 6814, 6816, 10988, 49805, 49806, 49807, 49808 ], "vocabularyKeywords": [], "identifier_set": [ 12543 ], "observationcollection_set": [ { "ob_id": 32728, "uuid": "cab668573a8d45ce9d0efc6b762cf454", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 12.4\r\n- input data for Figure 12.5\r\n- input data for Figure 12.6\r\n- input data for Figure 12.7\r\n- input data for Figure 12.8\r\n- input data for Figure 12.9\r\n- input data for Figure 12.10\r\n- input data for Figure 12.SM.1\r\n- input data for Figure 12.SM.2\r\n- input data for Figure 12.SM.3\r\n- input data for Figure 12.SM.4\r\n- input data for Figure 12.SM.5\r\n- input data for Figure 12.SM.6" } ], "responsiblepartyinfo_set": [ 180685, 180686, 180687, 180688, 180689, 180690, 180691, 180692, 180693, 180694 ], "onlineresource_set": [ 52677, 52676, 83449, 83450, 82691 ] }, { "ob_id": 37861, "uuid": "d46d733725d64f45afc1e70054f2f51d", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 12.6 (v20220804)", "abstract": "Input Data for Figure 12.6 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 12.6 shows projected changes in selected climatic impact-driver indices for Asia.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Ranasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1767–1926, doi:10.1017/9781009157896.014.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with general data provided in the central directory and specific data in 3 folders (Q100_CMIP5, Q100_CMIP6, Q1000_CORDEX-core).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n - spatial field over Asia of mean change in 1-in-100 year river discharge per unit catchment area (Q100, m3 s-1 km-2) from CORDEX models for 2041-2060 relative to 1995-2014 for RCP8.5\r\n - Shoreline position change over Asia (pointwise) along sandy coasts by the year 2100 relative to 2010 (meters; negative values indicate shoreline retreat) from the CMIP5 based data set presented by Vousdoukas et al. (2020)\r\n - regional averages in Asia of Q100 (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for:\r\n - CMIP6 historical, ssp126 and ssp585\r\n - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n - regional averages in Asia of CMIP5 based projections (mean change estimates and bars the 5th-95th percentile range of associated uncertainty) of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 and RCP4.5 from Vousdoukas et al. (2020)\r\n\r\nTIB, ECA, EAS, SEA, ARP and SAS are domains used in the model.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 12.6:\r\n \r\nPanel a:\r\n - Q100_map_panel_a_EAS_for_ASIA_divdra.nc: Field (colors plotted on the map) of changes of 1-in-100yr river discharge per unit catchment area between 2041-2060 (mid-term) and 1995-2015 (recent past) for CORDEX RCP8.5; the file contains the data for the regions from the EAS CORDEX domain\r\n - Q100_map_panel_a_SEA_for_ASIA_divdra.nc: same as previous file for the regions from the SEA CORDEX domain\r\n - Q100_map_panel_a_WAS_for_ASIA_divdra.nc: same as previous file for the regions from the WAS CORDEX domain\r\n \r\nPanel b:\r\n - CoastalRecession_ASIA_RCP85_2100.json: pointwise values (color points on the map) for Asia of shoreline position mean changes between 2100 (long-term) and 2010 (recent past) from the CMIP5 based data set presented by Vousdoukas et al. (2020)\r\n\r\nPanel c:\r\n - txt files containing the median and 5th/95th percentiles of each ensemble of the 1-in-100yr river discharge per unit catchment area (Q100) regional averages of time slices: Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.txt, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${scenario}: the name of the scenario : ssp126, ssp585, rcp26, rcp85\r\n - ${period}: the explicit period used to compute the temporal average: 1995-2014 (recent past), 2041-2060 (mid-term) and 2080-2099 (long term)\r\n - ${CORDEX_domain}: the CORDEX domain\r\n - txt files containing the Q100 regional averages of global warming levels: Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${GWL}: the Global Warming Level: 1.5, 2 and 4\r\n - ${CORDEX_domain}: the CORDEX domain\r\n \r\nPanel d:\r\n - globalErosionProjections_by_AR6_region_${scenario}_${horizon).json: regional averages of shoreline position changes for Africa, for the RCP4.5 and RCP8.5 scenarios (${scenario} = RCP45 and ${scenario} = RCP85 respectively) and the 2050 (mid-term, in blue) and 2100 (long-term, in red) future horizons (${horizon}=2050 and ${horizon}=2100 respectively) against the recent past period (2010); the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n\r\n\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP. \r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project. \r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project. \r\nSSP stands for Shared Socioeconomic Pathway. \r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6. \r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\nRCP stands for Representative Concentration Pathway. \r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n For panel a, the plotting script (see data tables and code on Github) draws the rivers and uses a subroutine to identify the rivers to plot them individually with lines; plotting the Q100 netcdf file will produce dots (and not rivers).\r\n\r\n\r\nFor panel c, the recent past values are plotted as absolute values (left column on each regional subpanel) and the future changes are plotted as differences against the recent past values (differences are computed when plotting the values).\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 12)\r\n - Link to the Supplementary Material for Chapter 12, which contains details on the input data used in Table 12.SM.1\r\n - Link to the code for the figure, archived on Zenodo.\r\n- Link to the Chapter 12 GitHub repository", "creationDate": "2022-08-05T11:15:12.639498", "lastUpdatedDate": "2022-08-05T11:15:12", "latestDataUpdateTime": "2023-04-18T11:59:39", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Asia, regional, river discharge, extreme, shoreline position, CMIP6, CMIP5, CORDEX, global warming levels, GWL, +1.5°C, +2°C, +4°C, ssp126, ssp585, recent past, mid-term, long-term, 1995-2014, 2041-2060, 2081-2100, RCP4.5, RCP8.5", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-04-19T12:38:49", "doiPublishedTime": "2023-06-30T14:39:04.744967", "removedDataTime": null, "geographicExtent": { "ob_id": 3580, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": 30.0, "southBoundLatitude": -15.0, "northBoundLatitude": 75.0 }, "verticalExtent": { "ob_id": 172, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38043, "dataPath": "/badc/ar6_wg1/data/ch_12/inputdata_ch12_fig06/v20220804", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 20365611, "numberOfFiles": 104, "fileFormat": "NetCDF, CSV, json, txt" }, "timePeriod": { "ob_id": 10454, "startTime": "1995-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4040, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37863, "uuid": "51d905b64408486fbed837dea056cae9", "short_code": "comp", "title": "Caption for Figure 12.6 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in selected climatic impact-driver indices for Asia. (a) Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m3 s–1 km–2) from CORDEX models for 2041–2060 relative to 1995–2014 for RCP8.5. (b) Shoreline position change along sandy coasts by the year 2100 relative to 2010 (metres; negative values indicate shoreline retreat) from the CMIP5-based dataset presented by Vousdoukas et al. (2020b). (c) Bar plots for Q100 (m3 s–1 km–2) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. (d) Bar plots for shoreline position change show CMIP5-based projections of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) and RCP4.5 (blue) from Vousdoukas et al. (2020b). Dots indicate regional mean change estimates and bars show the 5–95th percentile range of associated uncertainty. Note that these shoreline position change projections assume that there are no additional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical Annex VI for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49807, 50415, 50417, 80033, 80034 ], "vocabularyKeywords": [], "identifier_set": [ 12539 ], "observationcollection_set": [ { "ob_id": 32728, "uuid": "cab668573a8d45ce9d0efc6b762cf454", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 12.4\r\n- input data for Figure 12.5\r\n- input data for Figure 12.6\r\n- input data for Figure 12.7\r\n- input data for Figure 12.8\r\n- input data for Figure 12.9\r\n- input data for Figure 12.10\r\n- input data for Figure 12.SM.1\r\n- input data for Figure 12.SM.2\r\n- input data for Figure 12.SM.3\r\n- input data for Figure 12.SM.4\r\n- input data for Figure 12.SM.5\r\n- input data for Figure 12.SM.6" } ], "responsiblepartyinfo_set": [ 180699, 180700, 180701, 180702, 180703, 180704, 180697, 180698, 180705, 180706 ], "onlineresource_set": [ 52682, 82687, 52683, 83317, 83318 ] }, { "ob_id": 37868, "uuid": "38e7a3f35ced465283debd6cac1cae50", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 12.SM.6 (v20220808)", "abstract": "Data for Figure 12.SM.6 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 12.SM.6 shows regional projections of extreme sea level (1-in-100 year return period extreme total water level). \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nRanasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment Supplementary Material. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Available from https://www.ipcc.ch/\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThis figure has 39 subpanels (AR6 regions). \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- regional averages over 43 AR6 regions of the changes in mean wind speed in percentage of the recent past value (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for:\r\n\r\n - CMIP6 historical, ssp126 and ssp585\r\n\r\n - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5\r\n\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 12.SM.6\r\n \r\nThe regional averages for all the subpanels (AR6 regions) are stored in four json files (Vousdoukas et al 2018 dataset) and one csv file (Kiresci et al 2020:\r\n\r\n- Vousdoukas_ETWL_by_AR6_region_${scenario}_${horizon}.json: contains the regional averages (median and 5th/95th percentiles uncertainty range) for the Vousdoukas et al 2018 dataset for the horizon ${horizon} (2050 or 2100) and the scenario ${scenario} (RCP45 or RCP85)\r\n\r\n- Vousdoukas_ETWL_by_AR6_region_modern.json contains the regional averages for the recent past period (median and 5th/95th percentiles uncertainty range) for the Vousdoukas et al 2018 dataset \r\n\r\n- Kirezci_ESL.csv contains the regional averages of the Kirezci et al (2020) dataset for future horizons and recent past (median and 5th/95th percentiles uncertainty range)\r\n\r\n CMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project\r\n CORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\n SSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\n SSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\n RCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\n RCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nJupyter notebooks containing the data files and code used to plot this figure are stored in the 'scripts' GitHub repository linked in the documentation. \r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 12)\r\n - Link to the Supplementary Material for Chapter 12, which contains details on the input data used in Table 12.SM.1\r\n- Link to the master GitHub repository containing the Juptyer notebooks to run the code for the figure, as well as the other figures in Chapter 12.\r\n- Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-08-08T10:47:41.148074", "lastUpdatedDate": "2022-08-08T08:09:50", "latestDataUpdateTime": "2023-02-10T12:13:15", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Global, regional, AR6 regions, extreme total water level, sea level, CMIP6, CMIP5, CORDEX, global warming levels, GWL, +1.5°C, +2°C, +4°C, ssp126, ssp585, recent past, mid-term, long-term, 1995-2014, 2041-2060, 2081-2100, RCP4.5, RCP8.5", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-11T13:02:59", "doiPublishedTime": "2023-06-30T16:30:18.386883", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38053, "dataPath": "/badc/ar6_wg1/data/ch_12/ch12_sm_06/v20220808", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 70789, "numberOfFiles": 9, "fileFormat": "csv, json" }, "timePeriod": { "ob_id": 10456, "startTime": "1979-01-01T00:00:00", "endTime": "2105-12-31T23:59:59" }, "resultQuality": { "ob_id": 2, "explanation": "See dataset associated documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-08-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40235, "uuid": "4d909eabbd2446ca84f4d749b66b810c", "short_code": "comp", "title": "Caption for Figure 12.SM.6 from Chapter 12 Supplementary Material of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional projections of extreme sea level (1-in-100-year return period Extreme Total Water Level (ETWL). The bar plots show projections of regionally averaged ETWL from the CMIP5-based datasets presented in Vousdoukas et al. (2018b; filled circles, ‘V’ in legend), and Kirezci et al. (2020; open circles, ‘K’ in legend), for the AR6 WGI Reference Regions, for RCP8.5 (red) and RCP4.5 (blue). Dots represent the median estimate and bars the 5th–95th percentiles representing the uncertainty associated with the projections for the mid-term (2050), long term (2100) and the recent past (black, 1979/1980–2014). Units are metres. See Technical Annex VI for details about the index. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12549 ], "observationcollection_set": [ { "ob_id": 32728, "uuid": "cab668573a8d45ce9d0efc6b762cf454", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 12.4\r\n- input data for Figure 12.5\r\n- input data for Figure 12.6\r\n- input data for Figure 12.7\r\n- input data for Figure 12.8\r\n- input data for Figure 12.9\r\n- input data for Figure 12.10\r\n- input data for Figure 12.SM.1\r\n- input data for Figure 12.SM.2\r\n- input data for Figure 12.SM.3\r\n- input data for Figure 12.SM.4\r\n- input data for Figure 12.SM.5\r\n- input data for Figure 12.SM.6" } ], "responsiblepartyinfo_set": [ 180741, 180742, 180743, 180744, 180745, 180746, 180747, 180748, 180749 ], "onlineresource_set": [ 52714, 52755, 52713, 83454 ] }, { "ob_id": 37869, "uuid": "8418e1f0c9d64a758490a14daeb22574", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 12.SM.5 (v20220808)", "abstract": "Data for Figure 12.SM.5 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 12.SM.5 shows regional projections for changes in mean wind speed for different scenarios, time horizons and global warming levels. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nRanasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment Supplementary Material. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Available from https://www.ipcc.ch/\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThis figure has 43 subpanels (AR6 regions). \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- regional averages over 43 AR6 regions of the changes in mean wind speed in percentage of the recent past value (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for:\r\n\r\n - CMIP6 historical, ssp126 and ssp585\r\n\r\n - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5\r\n\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 12.SM.5\r\n \r\nThe regional averages of the changes in mean wind speed in percentage of the recent past value for all the subpanels (AR6 regions) are stored in three json files:\r\n\r\n- CMIP5_sfcWind_diff-perc-baseline_AR6_regional_averages.json: data for the CMIP5 multi-model ensemble\r\n\r\n- CMIP6_sfcWind_diff-perc-baseline_AR6_regional_averages.json: data for the CMIP6 multi-model ensemble\r\n\r\n- CORDEX_sfcWind_diff-perc-baseline_AR6_regional_averages.json: data for the CORDEX multi-model ensemble\r\n\r\nThe content of the files is organized as follows:\r\n\r\n - level 1 key:\r\n - GWL: string: 1.5, 2, 3, 4\r\n or\r\n - name of the time slice: baseline or ${scenario}_${horizon}, with:\r\n - ${scenario}: the scenario: ssp126 or ssp585 for CMIP6, rcp26 or rcp85 for CMIP5 and CORDEX\r\n - ${horizon}: mid (mid-term) or far (long-term)\r\n - level 2 keys: name of the AR6 region\r\n - value: list with:\r\n - first element: the multi-model ensemble 10th percentile (lower bounds of the vertical lines)\r\n - second element: the multi-model ensemble median (the dots)\r\n - third element: the multi-model ensemble 90th percentile (upper bounds of the vertical lines)\r\n\r\n CMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project\r\n CORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\n SSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\n SSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\n RCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\n RCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n\r\nJupyter notebooks containing the data files and code used to plot this figure are stored in the 'scripts' GitHub repository linked in the documentation. \r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 12)\r\n - Link to the Supplementary Material for Chapter 12, which contains details on the input data used in Table 12.SM.1\r\n- Link to the master GitHub repository containing the Juptyer notebooks to run the code for the figure, as well as the other figures in Chapter 12.\r\n- Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-08-08T10:47:44.677752", "lastUpdatedDate": "2022-08-08T08:09:48", "latestDataUpdateTime": "2023-02-10T12:07:00", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Global, regional, AR6 regions, mean wind speed, CMIP6, CMIP5, CORDEX, global warming levels, GWL, +1.5°C, +2°C, +4°C, ssp126, ssp585, recent past, mid-term, long-term, 1995-2014, 2041-2060, 2081-2100, RCP4.5, RCP8.5", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-11T12:58:13", "doiPublishedTime": "2023-06-30T16:13:24.112250", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38052, "dataPath": "/badc/ar6_wg1/data/ch_12/ch12_sm_05/v20220808", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 131854, "numberOfFiles": 6, "fileFormat": "json" }, "timePeriod": { "ob_id": 10413, "startTime": "1995-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4261, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-11" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40030, "uuid": "1c33edd56c064616bcf3f82f264eae89", "short_code": "comp", "title": "Caption for Figure 12.SM.5 from Chapter 12 Supplementary Material of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional projections for changes in mean wind speed for different scenarios, time horizons and global warming levels. The bar plots show projections of wind speed as percentage changes relative to the \r\nrecent past (1994–2015) for the mid-term (2041–2060) and long term (2081–2100), and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown), using CMIP6 (darkest colours), CMIP5 (medium colours) and CORDEX (lightest colours) ensembles. RCP8.5/SSP5-8.5 is shown in red and RCP2.6/SSP1-2.6 in blue. The median (dots) and the 10th–90th percentile range of model ensemble values across each model ensemble and each time period are shown for the regional mean over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The CORDEX ensemble is missing in regions that are not fully covered by the CORDEX domain (EEU, ESB, RAR, RFE and WSB). See Technical Annex VI for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12548 ], "observationcollection_set": [ { "ob_id": 32728, "uuid": "cab668573a8d45ce9d0efc6b762cf454", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 12.4\r\n- input data for Figure 12.5\r\n- input data for Figure 12.6\r\n- input data for Figure 12.7\r\n- input data for Figure 12.8\r\n- input data for Figure 12.9\r\n- input data for Figure 12.10\r\n- input data for Figure 12.SM.1\r\n- input data for Figure 12.SM.2\r\n- input data for Figure 12.SM.3\r\n- input data for Figure 12.SM.4\r\n- input data for Figure 12.SM.5\r\n- input data for Figure 12.SM.6" } ], "responsiblepartyinfo_set": [ 180750, 180751, 180752, 180753, 180754, 180755, 180756, 180757, 180758, 180759, 180760, 180761 ], "onlineresource_set": [ 52712, 52756, 52711, 83453 ] }, { "ob_id": 37871, "uuid": "8c58b12e8fd541428b1b25b0d572ce94", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 12.SM.4 (v20220808)", "abstract": "Data for Figure 12.SM.4 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 12.SM.4 shows regional projections for changes in soil moisture for different scenarios, time horizons and global warming levels. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nRanasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment Supplementary Material. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Available from https://www.ipcc.ch/\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n This figure has 41 subpanels (AR6 regions). \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\nThis figure has 41 subpanels (AR6 regions). \r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 12.SM.4\r\n \r\nThe regional averages of the changes in soil moisture in percentage of the recent past value for all the subpanels (AR6 regions) are stored in three json files:\r\n\r\n- CMIP5_SM_diff_perc2020_AR6_regional_averages.json: data for the CMIP5 multi-model ensemble\r\n\r\n- CMIP6_SM_diff_perc2020_AR6_regional_averages.json: data for the CMIP6 multi-model ensemble\r\n\r\n- CORDEX_SM_diff_perc2020_AR6_regional_averages.json: data for the CORDEX multi-model ensemble\r\n\r\nThe content of the files is organized as follows:\r\n\r\n - level 1 key:\r\n - GWL: string: 1.5, 2, 3, 4\r\n or\r\n - name of the time slice: baseline or ${scenario}_${horizon}, with:\r\n - ${scenario}: the scenario: ssp126 or ssp585 for CMIP6, rcp26 or rcp85 for CMIP5 and CORDEX\r\n - ${horizon}: mid (mid-term) or far (long-term)\r\n - level 2 keys: name of the AR6 region\r\n - value: list with:\r\n - first element: the multi-model ensemble 10th percentile (lower bounds of the vertical lines)\r\n - second element: the multi-model ensemble median (the dots)\r\n - third element: the multi-model ensemble 90th percentile (upper bounds of the vertical lines)\r\n\r\n CMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project\r\n CORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\n SSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\n SSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\n RCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\n RCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nJupyter notebooks containing the data files and code used to plot this figure are stored in the 'scripts' GitHub repository linked in the documentation. \r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 12)\r\n - Link to the Supplementary Material for Chapter 12, which contains details on the input data used in Table 12.SM.1\r\n- Link to the master GitHub repository containing the Juptyer notebooks to run the code for the figure, as well as the other figures in Chapter 12.\r\n- Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-08-08T10:47:47.527749", "lastUpdatedDate": "2022-08-08T08:09:46", "latestDataUpdateTime": "2023-06-08T16:44:59", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Global, regional, AR6 regions, soil moisture, CMIP6, CMIP5, CORDEX, global warming levels, GWL, +1.5°C, +2°C, +4°C, ssp126, ssp585, recent past, mid-term, long-term, 1995-2014, 2041-2060, 2081-2100, RCP4.5, RCP8.5", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-19T10:01:37", "doiPublishedTime": "2023-06-30T16:07:01.562979", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38051, "dataPath": "/badc/ar6_wg1/data/ch_12/ch12_sm_04/v20220808", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 127417, "numberOfFiles": 6, "fileFormat": "CSV, json, txt" }, "timePeriod": { "ob_id": 10413, "startTime": "1995-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4316, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-06-19" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40179, "uuid": "3c27822f6442450999a593c1ff6895f5", "short_code": "comp", "title": "Caption for Figure 12.SM.4 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional projections for changes in soil moisture for different scenarios, time horizons and global warming levels. The bar plots show projections of soil moisture as percentage changes relative to the recent past (1994–2015) for the mid-term (2041–2060) and long term (2081–2100), and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown), using CMIP6 (darkest colours), CMIP5 (medium colours) and CORDEX (lightest colours) ensembles. RCP8.5/SSP5-8.5 is shown in red and RCP2.6/SSP1-2.6 in blue. The median (dots) and the 10th–90th percentile range of model ensemble values across each model ensemble and each time period are shown for the regional mean over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The CORDEX ensemble is missing in regions that are not fully covered by the CORDEX domain (EEU, ESB, RAR, RFE and WSB) or because less than five simulations were available (NWN, NEN, WNA, CAN, ENA and NCA). See Technical Annex VI for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12547 ], "observationcollection_set": [ { "ob_id": 32728, "uuid": "cab668573a8d45ce9d0efc6b762cf454", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 12.4\r\n- input data for Figure 12.5\r\n- input data for Figure 12.6\r\n- input data for Figure 12.7\r\n- input data for Figure 12.8\r\n- input data for Figure 12.9\r\n- input data for Figure 12.10\r\n- input data for Figure 12.SM.1\r\n- input data for Figure 12.SM.2\r\n- input data for Figure 12.SM.3\r\n- input data for Figure 12.SM.4\r\n- input data for Figure 12.SM.5\r\n- input data for Figure 12.SM.6" } ], "responsiblepartyinfo_set": [ 180762, 180763, 180764, 180765, 180766, 180767, 180768, 180769, 180770, 180771 ], "onlineresource_set": [ 52710, 52709, 52757, 83719 ] }, { "ob_id": 37872, "uuid": "e8975cc3195b487d9ec482cb8ef5f07d", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 12.SM.3 (v20220808)", "abstract": "Data for Figure 12.SM.3 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 12.SM.3 shows regional projections for the number of negative precipitation anomaly events per decade using the 6-month Standardised Precipitation Index for different scenarios, time horizons and global warming levels.. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nRanasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment Supplementary Material. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Available from https://www.ipcc.ch/\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThis figure has 43 subpanels (AR6 regions). \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- regional averages over 43 AR6 regions of the number of negative precipitation anomaly events per decade (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for:\r\n\r\n - CMIP6 historical, ssp126 and ssp585\r\n\r\n - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5\r\n\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 12.SM.3\r\n \r\nThe regional averages for all the subpanels (AR6 regions) are stored in three json files:\r\n\r\n- CMIP5_DF6_AR6_regional_averages.json: data for the CMIP5 multi-model ensemble\r\n\r\n- CMIP6_DF6_AR6_regional_averages.json: data for the CMIP6 multi-model ensemble\r\n\r\n- CORDEX_DF6_AR6_regional_averages.json: data for the CORDEX multi-model ensemble\r\n\r\nThe content of the files is organized as follows:\r\n\r\n - level 1 key:\r\n - GWL: string: 1.5, 2, 3, 4\r\n or\r\n - name of the time slice: baseline or ${scenario}_${horizon}, with:\r\n - ${scenario}: the scenario: ssp126 or ssp585 for CMIP6, rcp26 or rcp85 for CMIP5 and CORDEX\r\n - ${horizon}: mid (mid-term) or far (long-term)\r\n - level 2 keys: name of the AR6 region\r\n - value: list with:\r\n - first element: the multi-model ensemble 10th percentile (lower bounds of the vertical lines)\r\n - second element: the multi-model ensemble median (the dots)\r\n - third element: the multi-model ensemble 90th percentile (upper bounds of the vertical lines)\r\n\r\n CMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project\r\n CORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\n SSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\n SSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\n RCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\n RCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nJupyter notebooks containing the data files and code used to plot this figure are stored in the 'scripts' GitHub repository linked in the documentation. \r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 12)\r\n - Link to the Supplementary Material for Chapter 12, which contains details on the input data used in Table 12.SM.1\r\n- Link to the master GitHub repository containing the Juptyer notebooks to run the code for the figure, as well as the other figures in Chapter 12.\r\n- Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-08-08T10:47:49.934548", "lastUpdatedDate": "2022-08-08T08:25:31", "latestDataUpdateTime": "2023-06-08T16:44:45", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Global, regional, AR6 regions, negative precipitation anomaly, CMIP6, CMIP5, CORDEX, global warming levels, GWL, +1.5°C, +2°C, +4°C, ssp126, ssp585, recent past, mid-term, long-term, 1995-2014, 2041-2060, 2081-2100, RCP4.5, RCP8.5", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-19T09:51:13", "doiPublishedTime": "2023-06-30T16:01:10.878175", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38050, "dataPath": "/badc/ar6_wg1/data/ch_12/ch12_sm_03/v20220808", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 145672, "numberOfFiles": 6, "fileFormat": "json, txt" }, "timePeriod": { "ob_id": 10413, "startTime": "1995-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4315, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-06-19" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40178, "uuid": "1c81595aa99c4a9b814f7666c82405de", "short_code": "comp", "title": "Caption for Figure 12.SM.3 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional projections for the number of negative precipitation anomaly events per decade using the six-month Standardized Precipitation Index for different scenarios, time horizons and global warming levels. The bar plots show projections from CMIP6 (darkest colours), CMIP5 (medium colours) and CORDEX (lightest colours) ensembles, for RCP8.5/SSP5-8.5 (red) and RCP2.6/SSP1-2.6 (blue), for the mid-term (2041–2060), long term (2081–2100) and the recent past (grey, 1995–2014). Results for global warming levels (defined relative to the pre-industrial period 1850–1900) are shown in purple for 1.5°C, yellow for 2°C and brown for 4°C. The median (dots) and the 10th–90th percentile range of model ensemble values across each model ensemble and each time period are shown for the regional mean over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). Units are events per decade. The CORDEX ensemble is missing in regions that are not fully covered by the CORDEX domain (EEU, ESB, RAR, RFE and WSB). See Technical Annex VI for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12546 ], "observationcollection_set": [ { "ob_id": 32728, "uuid": "cab668573a8d45ce9d0efc6b762cf454", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 12.4\r\n- input data for Figure 12.5\r\n- input data for Figure 12.6\r\n- input data for Figure 12.7\r\n- input data for Figure 12.8\r\n- input data for Figure 12.9\r\n- input data for Figure 12.10\r\n- input data for Figure 12.SM.1\r\n- input data for Figure 12.SM.2\r\n- input data for Figure 12.SM.3\r\n- input data for Figure 12.SM.4\r\n- input data for Figure 12.SM.5\r\n- input data for Figure 12.SM.6" } ], "responsiblepartyinfo_set": [ 180772, 180773, 180774, 180775, 180776, 180777, 180778, 180779, 180780, 180781, 180782 ], "onlineresource_set": [ 52708, 52758, 52707, 83718 ] }, { "ob_id": 37873, "uuid": "660a0224eee04d0880b78f538510f416", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 12.SM.2 (v20220808)", "abstract": "Data for Figure 12.SM.2 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 12.SM.2 shows regional projections for the number of days per year with NOAA Heat Index exceeding 41°C for different scenarios, time horizons and global warming levels. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nRanasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment Supplementary Material. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Available from https://www.ipcc.ch/\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThis figure has 43 subpanels (AR6 regions).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- regional averages over 43 AR6 regions of the number of days per year with NOAA Heat Index exceeding 41°C (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for:\r\n\r\n - CMIP6 historical, ssp126 and ssp585\r\n\r\n - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5\r\n\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 12.SM.2\r\n \r\nThe regional averages for all the subpanels (AR6 regions) are stored in three json files:\r\n\r\n- CMIP5_HI41_AR6_regional_averages.json: data for the CMIP5 multi-model ensemble\r\n\r\n- CMIP6_HI41_AR6_regional_averages.json: data for the CMIP6 multi-model ensemble\r\n\r\n- CORDEX_HI41_AR6_regional_averages.json: data for the CORDEX multi-model ensemble\r\n\r\nThe content of the files is organized as follows:\r\n\r\n - level 1 key:\r\n - GWL: string: 1.5, 2, 3, 4\r\n or\r\n - name of the time slice: baseline or ${scenario}_${horizon}, with:\r\n - ${scenario}: the scenario: ssp126 or ssp585 for CMIP6, rcp26 or rcp85 for CMIP5 and CORDEX\r\n - ${horizon}: mid (mid-term) or far (long-term)\r\n - level 2 keys: name of the AR6 region\r\n - value: list with:\r\n - first element: the multi-model ensemble 10th percentile (lower bounds of the vertical lines)\r\n - second element: the multi-model ensemble median (the dots)\r\n - third element: the multi-model ensemble 90th percentile (upper bounds of the vertical lines)\r\n\r\n NOAA is the National Oceanic and Atmospheric Administration.\r\n CMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project\r\n CORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\n SSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\n SSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\n RCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\n RCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nJupyter notebooks containing the data files and code used to plot this figure are stored in the 'scripts' GitHub repository linked in the documentation. \r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 12)\r\n - Link to the Supplementary Material for Chapter 12, which contains details on the input data used in Table 12.SM.1\r\n - Link to the master GitHub repository containing the Juptyer notebooks to run the code for the figure, as well as the other figures in Chapter 12.\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-08-08T10:47:54.396217", "lastUpdatedDate": "2022-08-08T08:25:29", "latestDataUpdateTime": "2024-03-09T01:45:09", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, NOAA Heat Index, 41°C, CMIP6, CMIP5, CORDEX, global warming levels, GWL, +1.5°C, +2°C, +4°C, ssp126, ssp585, recent past, mid-term, long-term, 1995-2014, 2041-2060, 2081-2100, RCP4.5, RCP8.5", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-19T09:11:32", "doiPublishedTime": "2023-06-30T15:46:55.092099", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38049, "dataPath": "/badc/ar6_wg1/data/ch_12/ch12_sm_02/v20220808", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 139570, "numberOfFiles": 6, "fileFormat": "json, txt" }, "timePeriod": { "ob_id": 10413, "startTime": "1995-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4314, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-06-19" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40025, "uuid": "c96f38f9239f4f0b9a9b56f4a096af3e", "short_code": "comp", "title": "Caption for Figure 12.SM.2 from Chapter 12 Supplementary Material of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional projections for the number of days per year with the NOAA Heat Index exceeding 41°C for different scenarios, time horizons and global warming levels. 41°C corresponds to conditions that the US National Weather Service classifies into the category of ‘Danger’ (Blazejczyk et al., 2012). The bar plots show projections from CMIP6 (darkest colours), CMIP5 (medium colours) and CORDEX (lightest colours) ensembles, for RCP8.5/SSP5-8.5 (red) and RCP2.6/SSP1-2.6 (blue), for the mid-term (2041–2060), long term (2081–2100) and the recent past (grey, 1995–2014). Results for global warming levels (defined relative to the pre-industrial period 1850–1900) are shown in purple for 1.5°C, yellow for 2°C and brown for 4°C. The median (dots) and the 10th–90th percentile range of model ensemble values across each model ensemble and each time period are shown for the regional mean over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). Bias adjustment is applied. The CORDEX ensemble is missing in regions that are not fully covered by the CORDEX domain (EEU, ESB, RAR, RFE and WSB). See Technical Annex VI for details of indices and bias adjustment. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12545 ], "observationcollection_set": [ { "ob_id": 32728, "uuid": "cab668573a8d45ce9d0efc6b762cf454", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 12.4\r\n- input data for Figure 12.5\r\n- input data for Figure 12.6\r\n- input data for Figure 12.7\r\n- input data for Figure 12.8\r\n- input data for Figure 12.9\r\n- input data for Figure 12.10\r\n- input data for Figure 12.SM.1\r\n- input data for Figure 12.SM.2\r\n- input data for Figure 12.SM.3\r\n- input data for Figure 12.SM.4\r\n- input data for Figure 12.SM.5\r\n- input data for Figure 12.SM.6" } ], "responsiblepartyinfo_set": [ 180783, 180784, 180785, 180786, 180787, 180788, 180789, 180790, 180791 ], "onlineresource_set": [ 52706, 52705, 52759, 83452 ] }, { "ob_id": 37875, "uuid": "156e4a10ddfb418aa24aadf244fbadf6", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 12.SM.1 (v20220808)", "abstract": "Data for Figure 12.SM.1 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 12.SM.1 shows regional projections for the number of days per year with maximum temperature exceeding 35°C for different scenarios, time horizons and global warming levels. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nRanasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment Supplementary Material. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Available from https://www.ipcc.ch/\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThis figure has 43 subpanels (AR6 regions).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- regional averages over 43 AR6 regions of the number of days per year with maximum daily temperature exceeding 35°C (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for:\r\n\r\n - CMIP6 historical, ssp126 and ssp585\r\n\r\n - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5\r\n\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 12.SM.1:\r\n \r\nThe regional averages for all the subpanels (AR6 regions) are stored in three json files:\r\n\r\n- CMIP5_tx35isimip_AR6_regional_averages.json: data for the CMIP5 multi-model ensemble\r\n\r\n- CMIP6_tx35isimip_AR6_regional_averages.json: data for the CMIP6 multi-model ensemble\r\n\r\n- CORDEX_tx35isimip_AR6_regional_averages.json: data for the CORDEX multi-model ensemble\r\n\r\nThe content of the files is organized as follows:\r\n\r\n - level 1 key:\r\n - GWL: string: 1.5, 2, 3, 4\r\n or\r\n - name of the time slice: baseline or ${scenario}_${horizon}, with:\r\n - ${scenario}: the scenario: ssp126 or ssp585 for CMIP6, rcp26 or rcp85 for CMIP5 and CORDEX\r\n - ${horizon}: mid (mid-term) or far (long-term)\r\n - level 2 keys: name of the AR6 region\r\n - value: list with:\r\n - first element: the multi-model ensemble 10th percentile (lower bounds of the vertical lines)\r\n - second element: the multi-model ensemble median (the dots)\r\n - third element: the multi-model ensemble 90th percentile (upper bounds of the vertical lines)\r\n\r\n CMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project\r\n CORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\n SSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\n SSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\n RCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\n RCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nJupyter notebooks containing the data files and code used to plot this figure are stored in the 'scripts' GitHub repository linked in the documentation. \r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 12)\r\n - Link to the Supplementary Material for Chapter 12, which contains details on the input data used in Table 12.SM.1\r\n- Link to the master GitHub repository containing the Juptyer notebooks to run the code for the figure, as well as the other figures in Chapter 12.\r\n- Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-08-08T11:01:56.703459", "lastUpdatedDate": "2022-08-08T09:57:48", "latestDataUpdateTime": "2023-05-31T14:31:51", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Global, regional, AR6 regions, tx35, extreme, temperature, number of days with maximum temperature above 35°C, CMIP6, CMIP5, CORDEX, global warming levels, GWL, +1.5°C, +2°C, +4°C, ssp126, ssp585, recent past, mid-term, long-term, 1995-2014, 2041-2060, 2081-2100, RCP4.5, RCP8.5", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-10T16:02:38", "doiPublishedTime": "2023-06-30T15:34:41.242003", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38048, "dataPath": "/badc/ar6_wg1/data/ch_12/ch12_sm_01/v20220808", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 146406, "numberOfFiles": 6, "fileFormat": "CSV, json, txt" }, "timePeriod": { "ob_id": 10413, "startTime": "1995-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4287, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-31" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40023, "uuid": "bd277710c77d441cb3bb6251f7d30ec4", "short_code": "comp", "title": "Caption for Figure 12.SM.1 from Chapter 12 Supplementary Material of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional projections for the number of days per year with maximum temperature exceeding 35°C for different scenarios, time horizons and global warming levels. The bar plots show projections from CMIP6 (darkest colours), CMIP5 (medium colours) and CORDEX (lightest colours) ensembles, for RCP8.5/SSP5-8.5 (red) and CP2.6/SSP1-2.6 (blue), for the mid-term (2041–2060), long term (2081–2100) and the recent past (grey, 1995–2014). Results for global warming levels (defined relative to the pre-industrial period 1850–1900) are shown in purple for 1.5°C, yellow for 2°C and brown for 4°C. The median (dots) and the 10th–90th percentile range of model ensemble values across each model ensemble and each time period are shown for the regional mean over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). Bias adjustment is applied (see Atlas.1.4.5). The CORDEX ensemble is missing in regions that are not fully covered by the CORDEX domain (EEU, ESB, RAR, RFE and WSB). See Technical Annex VI for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12544 ], "observationcollection_set": [ { "ob_id": 32728, "uuid": "cab668573a8d45ce9d0efc6b762cf454", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 12.4\r\n- input data for Figure 12.5\r\n- input data for Figure 12.6\r\n- input data for Figure 12.7\r\n- input data for Figure 12.8\r\n- input data for Figure 12.9\r\n- input data for Figure 12.10\r\n- input data for Figure 12.SM.1\r\n- input data for Figure 12.SM.2\r\n- input data for Figure 12.SM.3\r\n- input data for Figure 12.SM.4\r\n- input data for Figure 12.SM.5\r\n- input data for Figure 12.SM.6" } ], "responsiblepartyinfo_set": [ 180792, 180793, 180794, 180795, 180796, 180797, 180798, 180799, 180800 ], "onlineresource_set": [ 52703, 52760, 83451, 52704 ] }, { "ob_id": 37886, "uuid": "288d2cfa740f4e60a369b5778064bd5a", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.22 (v20220815)", "abstract": "Data for Figure 6.22 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 6.22 shows time evolution of the effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-economic Pathways (SSPs). \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nSzopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 817–922, doi:10.1017/9781009157896.008.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 1 panel, with data provided for this panel.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years), and the sum of these, relative to the year 2019 and to the year 1750. \r\n\r\nThe GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 (feedback parameter of –1.31 W m–2 °C–1, see Cross-Chapter Box 7.1). The vertical bars to the right in each panel show the uncertainties (5–95% ranges) for the GSAT change between 2019 and 2100. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 6.SM.3).\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 6.22:\r\n \r\n - Data file: fig_timeseries_dT_p95-p50_HFCs_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_p95-p50_Sum_SLCF_Aerosols_Methane_Ozone_HFCs_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_p95-p50_aerosol-total-with_bc-snow_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_p95-p50_ch4_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_p95-p50_o3_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_recommendation_HFCs_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_recommendation_Sum_SLCF_Aerosols_Methane_Ozone_HFCs_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_recommendation_aerosol-total-with_bc-snow_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_recommendation_ch4_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_recommendation_o3_2019-2100_refyear2019.csv\r\n\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nPanels were plotted using Python and the code has been embedded in Jupyter notebooks for reproducibility - code is available in the GitHub repository linked in the documentation.\r\n\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n- Link to the GitHub repository containing the Jupyter notebooks used to run the code associated with this figure.\r\n- Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-08-15T14:20:47.447926", "lastUpdatedDate": "2022-08-15T12:24:24", "latestDataUpdateTime": "2022-08-22T13:02:13", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, scenario warming, short-lived climate forcers, aerosol, methane, ozone, HFCs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-24T16:38:41", "doiPublishedTime": "2023-03-22T10:58:32.566124", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38037, "dataPath": "/badc/ar6_wg1/data/ch_06/ch6_fig22/v20220815", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 216577, "numberOfFiles": 13, "fileFormat": "Data are csv formatted" }, "timePeriod": { "ob_id": 10458, "startTime": "2019-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 2, "explanation": "See dataset associated documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-08-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39578, "uuid": "33ffe137cbed458994d2c0cd88e049d3", "short_code": "comp", "title": "Caption for Figure 6.22 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Time evolution of the effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-Economic Pathways (SSPs). Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years), and the sum of these, relative to the year 2019 and to the year 1750. The GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 (feedback parameter of –1.31 W m–2°C–1, see Cross-Chapter Box 7.1). The vertical bars to the right in each panel show the uncertainties (5–95% ranges) for the GSAT change between 2019 and 2100. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 20655, 49830, 49831, 49832, 49833, 49834, 49835, 49836, 49837, 49838, 49839, 49840 ], "vocabularyKeywords": [], "identifier_set": [ 12427 ], "observationcollection_set": [ { "ob_id": 32721, "uuid": "18cf830c350e451a9002105294861dc0", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 6.3\r\n- data for Figure 6.12\r\n- input data for Figure 6.12\r\n- input data for Figure 6.14\r\n- data for Figure 6.16\r\n- data for Figure 6.17\r\n- data for Figure 6.20\r\n- data for Figure 6.21\r\n- data for Figure 6.22\r\n- input data for Figure 6.22\r\n- data for Figure 6.23\r\n- data for figure 6.24\r\n- input data for Figure 6.24\r\n- data for Figure 6.SM.3\r\n- data for Figure 6.SM.4\r\n- data for Figure 6.SM.5" } ], "responsiblepartyinfo_set": [ 180863, 180864, 180865, 180866, 180867, 180868, 180942, 180949, 180950 ], "onlineresource_set": [ 52721, 52720, 52727, 52751, 82742 ] }, { "ob_id": 37887, "uuid": "8855e410adf547b4afd039a5b88487f4", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.12 (v20220815)", "abstract": "Data for Figure 6.12 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 6.12 shows contribution to effective radiative forcing (ERF) and global mean surface air temperature (GSAT) change from component emissions between 1750 to 2019 based on CMIP6 models. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nSzopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 817–922, doi:10.1017/9781009157896.008.\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Contribution to effective radiative forcing (ERF) (a) and global mean surface air temperature (GSAT) change (b) from component emissions between 1750 to 2019 based on CMIP6 models\r\n\r\nERFs for the direct effect of well-mixed greenhouse gases (WMGHGs) are from the analytical formulae in section 7.3.2, H2O (strat) is from Table 7.8. ERFs for other components are multi-model means from Thornhill et al. (2021b) and are based on ESM simulations in which emissions of one species at a time are increased from 1850 to 2014 levels. The derived emissions-based ERFs are rescaled to match the concentration-based ERFs in Figure 7.6.\r\n\r\nError bars are 5–95% and for the ERF account for uncertainty in radiative efficiencies and multi-model error in the means. ERFs due to aerosol–radiation (ERFari) and cloud effects are calculated from separate radiation calls for clear-sky and aerosol-free conditions (Ghan, 2013; Thornhill et al., 2021b). \r\n\r\n‘Cloud’ includes cloud adjustments (semi-direct effect) and ERF from indirect aerosol-cloud to –0.22 W m–2 for ERFari and –0.84 W m–2 interactions (ERFaci). The aerosol components (SO2, organic carbon and black carbon) are scaled to sum to –0.22 W m–2 for ERFari and –0.84 W m–2 for ‘cloud’ (Section 7.3.3). \r\n\r\nFor GSAT estimates, time series (1750–2019) for the ERFs have been estimated by scaling with concentrations for WMGHGs and with historical emissions for SLCFs. The time variation of ERFaci for aerosols is from Chapter 7. The global mean temperature response is calculated from the ERF time series using an impulse response function (Cross-Chapter Box 7.1) with a climate feedback parameter of –1.31 W m–2 °C–1. \r\n\r\nContributions to ERF and GSAT change from contrails and light-absorbing particles on snow and ice are not represented, but their estimates can be seen on Figure 7.6 and 7.7, respectively. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 6.SM.3)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 6.12:\r\n \r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_ERF.csv\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_ERF_uncertainty.csv\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_dT.csv\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_dT_uncertainty.csv\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nERFari stands for Effective Radiative Forcing of aerosol-radiation interactions.\r\nERFaci stands for Effective Radiative Forcing of aerosol-cloud interactions. \r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nPanels were plotted using Python and the code has been embedded in Jupyter notebooks for reproducibility - code is available in the GitHub repository linked in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n- Link to the GitHub repository containing the Jupyter notebooks used to run the code associated with this figure.\r\n- Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-08-15T14:20:48.537953", "lastUpdatedDate": "2022-08-15T12:24:23", "latestDataUpdateTime": "2022-08-22T13:02:06", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, historical effective radiative forcing, attributed historical warming", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-24T16:37:46", "doiPublishedTime": "2023-03-22T10:36:33.811580", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38035, "dataPath": "/badc/ar6_wg1/data/ch_06/ch6_fig12/v20220815", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 13255, "numberOfFiles": 7, "fileFormat": "Data are csv formatted" }, "timePeriod": { "ob_id": 10459, "startTime": "1750-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 1, "explanation": "See dataset associated documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-08-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39577, "uuid": "94f26e92d6e9469a8828bdd83eedafcc", "short_code": "comp", "title": "Caption for Figure 6.12 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Contribution to effective radiative forcing (ERF) (a) and global mean surface air temperature (GSAT) change (b) from component emissions between 1750 to 2019 based on CMIP6 models (Thornhill et al. , 2021b). ERFs for the direct effect of well-mixed greenhouse gases (WMGHGs) are from the analytical formulae in section 7.3.2, H2O (strat) is from Table 7.8. ERFs for other components are multi-model means from Thornhill et al. (2021b) and are based on ESM simulations in which emissions of one species at a time are increased from 1850 to 2014 levels. The derived emissions-based ERFs are rescaled to match the concentration-based ERFs in Figure 7.6. Error bars are 5–95% and for the ERF account for uncertainty in radiative efficiencies and multi-model error in the means. ERFs due to aerosol–radiation (ERFari) and cloud effects are calculated from separate radiation calls for clear-sky and aerosol-free conditions (Ghan, 2013; Thornhill et al., 2021b). ‘Cloud’ includes cloud adjustments (semi-direct effect) and ERF from indirect aerosol-cloud to –0.22 W m–2 for ERFari and –0.84 W m–2 interactions (ERFaci). The aerosol components (SO2, organic carbon and black carbon) are scaled to sum to –0.22 W m–2 for ERFari and –0.84 W m–2 for ‘cloud’ Section 7.3.3). For GSAT estimates, time series (1750–2019) for the ERFs have been estimated by scaling with concentrations for WMGHGs and with historical emissions for SLCFs. The time variation of ERFaci for aerosols is from Chapter 7. The global mean temperature response is calculated from the ERF time series using an impulse response function (Cross-Chapter Box 7.1) with a climate feedback parameter of –1.31 W m–2°C–1. Contributions to ERF and GSAT change from contrails and light-absorbing particles on snow and ice are not represented, but their estimates can be seen on Figure 7.6 and 7.7, respectively. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49810, 49811, 49812, 49813, 49814, 49815, 49816, 49817, 49818, 49819, 49820, 49821, 49822, 49823, 49824, 49825, 49826, 49827, 49828, 49829 ], "vocabularyKeywords": [], "identifier_set": [ 12424 ], "observationcollection_set": [ { "ob_id": 32721, "uuid": "18cf830c350e451a9002105294861dc0", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 6.3\r\n- data for Figure 6.12\r\n- input data for Figure 6.12\r\n- input data for Figure 6.14\r\n- data for Figure 6.16\r\n- data for Figure 6.17\r\n- data for Figure 6.20\r\n- data for Figure 6.21\r\n- data for Figure 6.22\r\n- input data for Figure 6.22\r\n- data for Figure 6.23\r\n- data for figure 6.24\r\n- input data for Figure 6.24\r\n- data for Figure 6.SM.3\r\n- data for Figure 6.SM.4\r\n- data for Figure 6.SM.5" } ], "responsiblepartyinfo_set": [ 180869, 180870, 180871, 180872, 180873, 180874, 180941, 180947, 180948 ], "onlineresource_set": [ 82737, 52725, 52728, 52752, 52724 ] }, { "ob_id": 37888, "uuid": "ca4127fb1be14ea68fefd5643fe3677f", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.24 (v20220815)", "abstract": "Data for Figure 6.24 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 6.24 shows effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-economic Pathways (SSPs). \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nSzopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 817–922, doi:10.1017/9781009157896.008.\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years) compared with those of total anthropogenic forcing for 2040 and 2100 relative to the year 2019. \r\n\r\nThe GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 (feedback parameter of –1.31 W m–2 °C–1; Cross-Chapter Box 7.1). Uncertainties are 5–95% ranges. The scenario total (grey bar) includes all anthropogenic forcings (long- and short-lived climate forcers, and land-use changes) whereas the white diamonds and bars show the net effects of SLCFs and HFCs and their uncertainties. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 6.SM.3)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 6.24:\r\n \r\n - Data file: fig_dT_2040_2100_stacked_bar_5th-50th__2020-2040_refyear2020.csv\r\n - Data file: fig_dT_2040_2100_stacked_bar_5th-50th__2020-2100_refyear2020.csv\r\n - Data file: fig_dT_2040_2100_stacked_bar_95th-50th__2020-2040_refyear2020.csv\r\n - Data file: fig_dT_2040_2100_stacked_bar_95th-50th__2020-2100_refyear2020.csv\r\n - Data file: fig_dT_2040_2100_stacked_bar_mean__2019-2040_refyear2019.csv\r\n - Data file: fig_dT_2040_2100_stacked_bar_mean__2019-2100_refyear2019.csv\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nPanels were plotted using Python and the code has been embedded in Jupyter notebooks for reproducibility - code is available in the GitHub repository linked in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n- Link to the GitHub repository containing the Jupyter notebooks used to run the code associated with this figure.\r\n- Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-08-15T14:24:18.777420", "lastUpdatedDate": "2022-08-15T12:24:25", "latestDataUpdateTime": "2024-03-09T01:42:36", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, scenario warming, short versus long term warming, short-lived climate forcers, aerosol, methane, ozone, HFCs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-24T16:42:48", "doiPublishedTime": "2023-03-22T11:02:24.708472", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38038, "dataPath": "/badc/ar6_wg1/data/ch_06/ch6_fig24/v20220815", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 13863, "numberOfFiles": 9, "fileFormat": "Data are csv formatted" }, "timePeriod": { "ob_id": 10458, "startTime": "2019-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 1, "explanation": "See dataset associated documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-08-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39579, "uuid": "848b8a79298f48d3a55c4c313ff603fa", "short_code": "comp", "title": "Caption for Figure 6.24 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-economic Pathways (SSPs). Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years), are compared with those of total anthropogenic forcing for 2040 and 2100 relative to the year 2019. The GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 (feedback parameter of –1.31 W m–2°C–1; Cross-Chapter Box 7.1). Uncertainties are 5–95% ranges. The scenario total (grey bar) includes all anthropogenic forcings (long- and short-lived climate forcers, and land-use changes) whereas the white diamonds and bars show the net effects of SLCFs and HFCs and their uncertainties. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49815, 49841, 49842, 49843, 49844, 49845, 49846 ], "vocabularyKeywords": [], "identifier_set": [ 12429 ], "observationcollection_set": [ { "ob_id": 32721, "uuid": "18cf830c350e451a9002105294861dc0", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 6.3\r\n- data for Figure 6.12\r\n- input data for Figure 6.12\r\n- input data for Figure 6.14\r\n- data for Figure 6.16\r\n- data for Figure 6.17\r\n- data for Figure 6.20\r\n- data for Figure 6.21\r\n- data for Figure 6.22\r\n- input data for Figure 6.22\r\n- data for Figure 6.23\r\n- data for figure 6.24\r\n- input data for Figure 6.24\r\n- data for Figure 6.SM.3\r\n- data for Figure 6.SM.4\r\n- data for Figure 6.SM.5" } ], "responsiblepartyinfo_set": [ 180875, 180876, 180877, 180878, 180879, 180880, 180943, 180951, 180952 ], "onlineresource_set": [ 52722, 52723, 52726, 52750, 82744 ] }, { "ob_id": 37889, "uuid": "b96e2225918348e1ae47b1fedee881a6", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 12.4 (v20220623)", "abstract": "Data for Figure 12.4 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 12.4 shows median projected changes in selected climatic impact-driver indices based on CMIP6 models for ssp126 and ssp585 scenarios, and RCP4.5 and RCP8.5 scenarios (for extreme total water level), for mid-term and long-term (relative to recent past).\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Ranasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1767–1926, doi:10.1017/9781009157896.014.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 18 panels (panel a to panel r), with data provided for all panels in the 12.4 figure directory; for each panel, the panel name is indicated in the file name with panel_X (with X being panel letter between a and r).\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains median projected changes in selected climatic impact-driver indices based on CMIP6 models for ssp126 and ssp585 scenarios for mid-term (2041-2060) and long-term (2081-2100), relative to recent past (1995-2014), and their associated masks of model agreement (with values -1 where at least 80% of the models agree in the sign of change, 0 elsewhere) for:\r\n \r\n - the mean number of days per year with maximum temperature exceeding 35°C\r\n - the mean number of days per year with NOAA Heat Index over 41°C\r\n - the number of negative precipitation anomaly events per decade using the 6-month Standardised Precipitation Index\r\n - the mean soil moisture\r\n - the mean surface wind speed\r\n \r\n It also contains the files of global projected median extreme total water level for CMIP5 RCP4.5 and RCP8.5 scenarios covering both mid-term (2041-2060) and long-term (2081-2100) horizons, and one for the recent past. The data is organized as points with their associated lon/lat coordinates.\r\n\r\nNOAA stands for the US National Oceanic and Atmospheric Administration.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nPlease note the following filenames have been changed to ensure continuity with filenames on GitHub repository:\r\npanel p (ETWL):\r\n- panel_p_globalTWL_RCP45.nc -> globalTWL_RCP45.nc\r\n- panel_p_q_r_globalTWL_baseline.nc -> globalTWL_baseline.nc\r\npanel q (ETWL):\r\n- panel_q_globalTWL_RCP45.nc -> globalTWL_RCP85.nc\r\n- panel_p_q_r_globalTWL_baseline.nc -> globalTWL_baseline.nc\r\npanel r (ETWL):\r\n- panel_r_globalTWL_RCP45.nc -> globalTWL_RCP85.nc\r\n- panel_p_q_r_globalTWL_baseline.nc -> globalTWL_baseline.nc\r\n\r\nData provided in relation to Figure 12.4:\r\n\r\nPanels a-c (tx35) where X is replaced with a,b or c:\r\n- 'tx35_panel_X_ssp126_2081-2100_minus_baseline.nc' :\r\nglobal spatial field of changes in mean number of days per year with maximum temperature exceeding 35°C for CMIP6 ssp126 ensemble median, long-term (colors)\r\n- 'mask_80perc-agreement_tx35_panel_X_ssp126_2081-2100_minus_baseline.nc' : \r\nspatial mask (for hatching) showing where at least 80% of the models agree in terms of sign of change (negative change, positive change or zero change); values are: -1 where true, 0 where false\r\n\r\n\r\nPanels d-f (HI41) where X is replaced with d,e or f:\r\n- 'HI41_panel_X_ssp126_2081-2100_minus_baseline.nc' :\r\nglobal spatial field of changes in mean number of days per year with NOAA Heat Index over 41°C for CMIP6 ssp126 ensemble median, long-term (colors)\r\n- 'mask_80perc-agreement_HI41_panel_X_ssp126_2081-2100_minus_baseline.nc' : \r\nspatial mask (for hatching) showing where at least 80% of the models agree in terms of sign of change (negative change, positive change or zero change); values are: -1 where true, 0 where false\r\n\r\n\r\nPanel g-i (DF6) where X is replaced with g, h or i:\r\n- 'DF6_panel_X_ssp126_farch_minus_baseline.nc' : \r\nglobal spatial field of changes in number of negative precipitation anomaly events per decade using the 6-month Standardised Precipitation Index for CMIP6 ssp126 ensemble median, long-term (colors)\r\n- 'mask_80perc-agreement_DF6_panel_X_ssp126_farch_minus_baseline.nc' : \r\nspatial mask (for hatching) showing where at least :80% of the models agree in terms of sign of change (negative change, positive change or zero change); values are: -1 where true, 0 where false\r\n\r\n\r\nPanel j-l (SM) where X is replaced with j, k or l:\r\n- 'SM_panel_X_ssp126_2081-2100_minus_baseline.nc' : \r\nglobal spatial field of changes in mean soil moisture for CMIP6 ssp126 ensemble median, long-term (colors)\r\n- 'mask_80perc-agreement_SM_panel_X_ssp126_2081-2100_minus_baseline.nc': \r\nspatial mask (for hatching) showing where at least 80% of the models agree in terms of sign of change (negative change, positive change or zero change); values are: -1 where true, 0 where false\r\n\r\n\r\n\r\nPanels m-o (sfcWind) where X is replaced with m, n or o:\r\n- 'sfcWind_panel_X_ssp126_2081-2100_minus_baseline.nc': \r\nglobal spatial field of changes in mean surface wind speed for CMIP6 ssp126 ensemble median, long-term (colors)\r\n- 'mask_80perc-agreement_sfcWind_panel_X_ssp126_2081-2100_minus_baseline.nc': \r\nspatial mask (for hatching) showing where at least 80% of the models agree in terms of sign of change (negative change, positive change or zero change); values are: -1 where true, 0 where false\r\n\r\n\r\nPanel p (ETWL):\r\n- globalTWL_RCP45.nc; global spatial field of median extreme sea level for CMIP5 RCP4.5, long-term (colors); long-term corresponds to decades=2100 in the file\r\n\r\nPanels q and r (ETWL):\r\n- globalTWL_RCP85.nc; global spatial field of median extreme sea level for RCP8.5, mid-term and long-term (colors)\r\n\r\nPanels p, q and r (ETWL):\r\n- globalTWL_baseline.nc; global spatial field of median extreme sea level for baseline\r\n\r\nFor panels p, q and r:\r\n- the data shown on the plot is the difference between the future projections (globalTWL_RCP45.nc and globalTWL_RCP85.nc) and the baseline (globalTWL_baseline.nc)\r\n- the variable used is TWL; it is three dimensional: npoints, npercentiles (value given by variable percentile(npercentiles)), nsdec (value given by variable decades(nsdec))\r\n- we use percentile=50\r\n- mid-term corresponds to decades=2050\r\n- long-term corresponds to decades=2100\r\n- the baseline file has no time (decades) dimension\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\nRCP4.5 is the Representative Concentration Pathway for 4.5 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\nNotes on reproducing the figure\r\n---------------------------------------------------\r\nScripts for plotting figure panels can be found in the dedicated Chapter 12 GitHub repository which is linked in the Related Documents section of this catalogue record. Code used for the figure is archived on Zenodo.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 12)\r\n - Link to the Supplementary Material for Chapter 12, which contains details on the input data used in Table 12.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the Chapter 12 GitHub repository", "creationDate": "2022-08-15T15:20:07.388919", "lastUpdatedDate": "2022-08-15T15:20:07", "latestDataUpdateTime": "2024-03-09T03:20:23", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, temperature, extreme, heat, tx35, CMIP6, ssp126, ssp585, long term, mid term, HI41, SM, soil moisture, DF6, drought frequency, sfcWind, surface wind speed, extreme total water level, RCP4.5, RCP8.5", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-04-18T10:26:12", "doiPublishedTime": "2023-06-30T14:10:55.691844", "removedDataTime": null, "geographicExtent": { "ob_id": 3591, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 173, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37890, "dataPath": "/badc/ar6_wg1/data/ch_12/ch12_fig04/v20220623", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 49171533, "numberOfFiles": 36, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10457, "startTime": "1995-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4042, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37891, "uuid": "0a16f7c82f3040bd87b2488e0368aa05", "short_code": "comp", "title": "Caption for Figure 12.4 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Median projected changes in selected climatic impact-driver indices based on CMIP6 models. (a–c) Mean number of days per year with maximum temperature exceeding 35°C; (d–f) mean number of days per year with the NOAA Heat Index (HI) exceeding 41°C; (g–i) number of negative precipitation anomaly events per decade using the six-month Standardized Precipitation Index; (j–l) mean soil moisture (%) and (m–o) mean wind speed (%). (p–r) shows change in extreme sea level (1-in-100-year return period total water level from Vousdoukas et al. (2018)’s CMIP5 based dataset; metres). Left-hand column is for SSP1-2.6, 2081–2100; middle column is for SSP5-8.5 2041–2060; and right-hand column SSP5-8.5, 2081–2100, all expressed as changes relative to 1995–2014. Exception is extreme total water level which is for (p) RCP4.5 2100, (q) RCP8.5 2050 and (r) RCP8.5 2100, each relative to 1980–2014. Bias correction is applied to daily maximum temperature and HI data (Annex VI and Atlas.1.4.5). Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on the sign (direction) of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. See Annex VI for details of indices. Figures 12. SM.1–12.SM.6 show regionally averaged values of these indices for the AR6 WGI Reference Regions for various model ensembles, scenarios, time horizons and global warming levels.Further details on data sources and processing are available in the chapter data table (Table 12.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 50542, 50543, 62501, 63553, 63554, 63555, 63556, 63557, 63899, 63900, 63901, 63902, 63903, 63904 ], "vocabularyKeywords": [], "identifier_set": [ 12537 ], "observationcollection_set": [ { "ob_id": 32728, "uuid": "cab668573a8d45ce9d0efc6b762cf454", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 12.4\r\n- input data for Figure 12.5\r\n- input data for Figure 12.6\r\n- input data for Figure 12.7\r\n- input data for Figure 12.8\r\n- input data for Figure 12.9\r\n- input data for Figure 12.10\r\n- input data for Figure 12.SM.1\r\n- input data for Figure 12.SM.2\r\n- input data for Figure 12.SM.3\r\n- input data for Figure 12.SM.4\r\n- input data for Figure 12.SM.5\r\n- input data for Figure 12.SM.6" } ], "responsiblepartyinfo_set": [ 180881, 180882, 180883, 180884, 180885, 180886, 180887, 180888, 180889, 180890, 180891, 180892 ], "onlineresource_set": [ 52729, 52730, 82685, 83068, 83313 ] }, { "ob_id": 37892, "uuid": "568fb4b2e6464a50a30c7140bb88a497", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Box 7.2, Figure 1. (v20220817)", "abstract": "Input Data for Box 7.2, Figure 1 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nBox 7.2, Figure 1 shows estimates of the net cumulative energy change (ZJ = 1021 Joules) for the period 1971–2018 associated with: (a) observations of changes in the Global Energy Inventory (b) Integrated Radiative Forcing; (c) Integrated Radiative Response. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 6 subpanels, with input data provided for panels a-f.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Estimates of the net cumulative energy change (ZJ = 1021 Joules) for the period 1971–2018 associated with: \r\n(a) observations of changes in the Global Energy Inventory \r\n(b) Integrated Radiative Forcing; \r\n(c) Integrated Radiative Response.\r\n\r\nBlack dotted lines indicate the central estimate with likely and very likely ranges as indicated in the legend. The grey dotted lines indicate the energy change associated with an estimated pre-industrial Earth energy imbalance of 0.2 W m–2 (a), and an illustration of an assumed pattern effect of –0.5 W m–2 °C–1 (c). \r\n\r\nBackground grey lines indicate equivalent heating rates in W m–2 per unit area of Earth’s surface. \r\nPanels (d) and (e) show the breakdown of components, as indicated in the legend, for the global energy inventory and integrated radiative forcing, respectively. Panel (f) shows the global energy budget assessed for the period 1971–2018, that is, the consistency between the change in the global energy inventory relative to pre-industrial and the implied energy change from integrated radiative forcing plus integrated radiative response under a number of different assumptions, as indicated in the legend, including assumptions of correlated and uncorrelated uncertainties in forcing plus response. \r\n\r\nShading represents the very likely range for observed energy change relative to pre-industrial levels and likely range for all other quantities. \r\nForcing and response time series are expressed relative to a baseline period of 1850–1900. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Box 7.2, Figure 1:\r\n \r\n - Data file: AR6_ERF_1750-2019.csv\r\n - Data file: AR6_energy_GMSL_timeseries_FGD_1971to2018_IMBIEupdate.csv\r\n - Data file: AR6_energy_GMSL_timeseries_FGD_1971to2018_corrigendum.csv\r\n - Data file: Box7.2_ERF_ZJ_percentiles_FGD_1971to2018.csv\r\n - Data file: Box7.2_Response_ZJ_percentiles_FGD_1971to2018.csv\r\n - Data file: Box7.2_ERFResp_uncorrelated_ZJ_percentiles_FGD_1971to2018.csv\r\n - Data file: Box7.2_ERFResp_correlated_ZJ_percentiles_FGD_1971to2018.csv\r\n\r\nData files are converted to csv from pickle format for archival. A link to the original files on GitHub is provided in the 'Related Documents' section.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. Also listed on the 'master' GitHub page linked in the documentation of this catalogue record are external GitHub repositories and locations within the contributed directory where code for figures have been supplied by other authors. These are provided \"as-is\" and are not guaranteed to be reproducible within this environment. For external GitHub locations, check out the relevant repository READMEs.\r\n\r\nThe data provided here is converted from pickle files which are used in the plotting script. The link to the original pickle files on GitHub is provided. To reproduce the figure from the input data provided, you will need to edit the filepaths within the notebook based on your local directory structure.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n - Link to the notebook to plot the figure on the Chapter 7 GitHub repository\r\n - Link to the original pickle files on GitHub\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to Cross-Chapter Box 9.1, Figure 1", "creationDate": "2022-08-17T08:13:06.523921", "lastUpdatedDate": "2022-08-17T07:58:56", "latestDataUpdateTime": "2023-06-07T10:39:21", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).\r\nData converted to CSV from pickle format for archival by CEDA staff.", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, ocean heat content, energy budget, ocean, cryosphere, land, net cumulative energy change, radiative forcing, radiative response", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-07T09:38:05", "doiPublishedTime": "2023-07-10T15:14:20.512246", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38057, "dataPath": "/badc/ar6_wg1/data/ch_07/inputdata_ch7_BOX2_fig1/v20220817", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 190006, "numberOfFiles": 10, "fileFormat": "CSV" }, "timePeriod": { "ob_id": 10474, "startTime": "1971-01-01T00:00:00", "endTime": "2018-12-31T23:59:59" }, "resultQuality": { "ob_id": 4297, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-06-01" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39600, "uuid": "1a4e8f4fb6974644aa5dd4da7e1c8bca", "short_code": "comp", "title": "Caption for Box 7.2, Figure 1 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Estimates of the net cumulative energy change (ZJ = 1021Joules) for the period 1971–2018 associated with: (a) observations of changes in the global energy inventory; (b) integrated radiative forcing; and (c) integrated radiative response. Black dotted lines indicate the central estimate with likely and very likely ranges as indicated in the legend. The grey dotted lines indicate the energy change associated with an estimated pre-industrial Earth energy imbalance of 0.2 W m–2 (a), and an illustration of an assumed pattern effect of –0.5 W m–2°C–1 (c). Background grey lines indicate equivalent heating rates in W m–2 per unit area of Earth’s surface. Panels (d) and (e) show the breakdown of components, as indicated in the legend, for the global energy inventory and integrated radiative forcing, respectively. Panel (f) shows the global energy budget assessed for the period 1971–2018, that is, the consistency between the change in the global energy inventory relative to pre-industrial and the implied energy change from integrated radiative forcing plus integrated radiative response under a number of different assumptions, as indicated in the legend, including assumptions of correlated and uncorrelated uncertainties in forcing plus response. Shading represents the very likely range for observed energy change relative to pre-industrial levels and likely range for all other quantities. Forcing and response time series are expressed relative to a baseline period of 1850–1900. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12647 ], "observationcollection_set": [ { "ob_id": 32722, "uuid": "b96ed5cbb8304a82bf1a2a4cad49bf6d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 7.3\r\n- input data for Figure 7.4\r\n- input data for Figure 7.5\r\n- data for Figure 7.6\r\n- input data for Figure 7.7\r\n- data for Figure 7.8\r\n- input data for Figure 7.10\r\n- data for Figure 7.11\r\n- input data for Figure 7.13\r\n- data for Figure 7.16\r\n- data for Figure 7.17\r\n- input data for Figure 7.18\r\n- data for Figure 7.19\r\n- input data for Figure 7.21\r\n- data for FAQ 7.3, Figure 1\r\n- input data for FAQ 7.3, Figure 1\r\n- data for 7.SM.1\r\n- input data for Box 7.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180895, 180896, 180897, 180898, 180899, 180900, 180946, 180968, 180969, 195481 ], "onlineresource_set": [ 52754, 52753, 82711, 83599, 83600, 83601, 83674 ] }, { "ob_id": 37893, "uuid": "923b94820acd42a1888eaae24de328f8", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Box TS4, Figure 1 (v20220817)", "abstract": "Data for Box TS4 from Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nBox TS4, Figure 1 shows global mean sea level change on different time scales and under different scenarios.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nArias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.\r\n\r\nWhen citing the SSP-based sea-level projections, please also include the following citation:\r\nGarner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Global Mean Sea-Level Rise Projections. Version 20210809. https://doi.org/10.5281/zenodo.5914710.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThe figure has three panels. Panel a shows global mean sea level (GMSL) change from 1900 to 2150, observed (1900–2018) and projected under the Shared Socioeconomic Pathway (SSP) scenarios (2000–2150). Panel b shows GMSL change on 100-, 2,000-, and 10,000-year time scales as a function of global surface temperature. Panel c shows timing of exceedance of different GMSL thresholds under different SSPs. \r\n\r\nFinal data is only available for panel c. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\nGlobal mean sea level change time-series from 1901-2150 for:\r\n- Observed global mean sea level change (1901-2018).\r\n- Projected global mean sea level change (2005-2150).\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Box TS4, Figure 1:\r\n\r\nSSP-based global mean sea level projections are archived as\r\n\r\nGarner, G. G., Hermans, T., Kopp, R. E., Slangen, A. B. A., Edwards, T. L., Levermann, A., Nowicki, S., Palmer, M. D., Smith, C., Fox-Kemper, B., Hewitt, H. T., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S. S., Edwards, T. L., Golledge, N. R., Hemer, M., Krinner, G., Mix, A., … Pearson, B. (2021). IPCC AR6 Sea Level Projections (Version 20210809) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5914710\r\n\r\nPanel c:\r\n\r\n- FigTS4-1c-milestone_ssp119_data.nc\r\n- FigTS4-1c-milestone_ssp126_data.nc\r\n- FigTS4-1c-milestone_ssp126_data.nc\r\n- FigTS4-1c-milestone_ssp126_data.nc\r\n- FigTS4-1c-milestone_ssp126_data.nc\r\n\r\nSee sections 9.6.3.2 and 9.6.3.3 for detailed information on the SSP-based global mean sea level projections and their production.\r\n\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nPanel c data were plotted using standard open-source R software - code is available via the link in the documentation.\r\n\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n- Link to the code for the figure, archived on Zenodo.\r\n- Link to the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report, archived on Zenodo.", "creationDate": "2022-08-17T08:13:45.771859", "lastUpdatedDate": "2022-08-17T07:58:54", "latestDataUpdateTime": "2024-03-09T03:05:51", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, sea level", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-06T14:12:45", "doiPublishedTime": "2023-08-02T12:56:38.612730", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38007, "dataPath": "/badc/ar6_wg1/data/TS/BOX_ts4_fig1/v20220817", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 11599, "numberOfFiles": 8, "fileFormat": "NetCDF, txt" }, "timePeriod": { "ob_id": 10464, "startTime": "1901-01-01T00:00:00", "endTime": "2300-12-31T23:59:59" }, "resultQuality": { "ob_id": 4301, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-06-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40142, "uuid": "8210545e1f564114a80d30d99ec1d73b", "short_code": "comp", "title": "Caption for Box TS4, Figure 1 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global mean sea level (GMSL) change on different time scales and under different scenarios. The intent of this figure is to (i) show the century-scale GMSL projections in the context of the 20th century observations, (ii) illustrate ‘deep uncertainty’ in projections by considering the timing of GMSL rise milestones, and (iii) show the long-term commitment associated with different warming levels, including the paleo evidence to support this. (a) GMSL change from 1900 to 2150, observed (1900–2018) and projected under the SSP scenarios (2000–2150), relative to a 1995–2014 baseline. Solid lines show median projections. Shaded regions showlikely ranges for SSP1-2.6 and SSP3-7.0. Dotted and dashed lines show respectively the 83rd and 95th percentilelow confidence projections for SSP5-8.5. Bars at right showlikely ranges for SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 in 2150. Lightly shaded thick/thin bars show 17th–83rd/5th–95th percentile low-confidence ranges in 2150 for SSP1-2.6 and SSP5-8.5, based upon projection methods incorporating structured expert judgement and marine ice cliff instability. Low confidence range for SSP5-8.5 in 2150 extends to 4.8/5.4 m at the 83rd/95th percentile. (b) GMSL change on 100- (blue), 2000- (green) and 10,000-year (magenta) time scales as a function of global surface temperature, relative to 1850–1900. For 100-year projections, GMSL is projected for the year 2100, relative to a 1995–2014 baseline, and temperature anomalies are average values over 2081–2100. For longer-term commitments, warming is indexed by peak warming above 1850–1900 reached after cessation of emissions. Shaded regions show paleo-constraints on global surface temperature and GMSL for the Last Interglacial and mid-Pliocene Warm Period. Lightly shaded thick/thin blue bars show 17th–83rd/5th–95th percentile low confidence ranges for SSP1-2.6 and SSP5-8.5 in 2100, plotted at 2°C and 5°C. (c) Timing of exceedance of GMSL thresholds of 0.5, 1.0, 1.5 and 2.0 m, under different SSPs. Lightly shaded thick/thin bars show 17th–83rd/5th–95th percentile low-confidence ranges for SSP1-2.6 and SSP5-8.5. {4.3.2, 9.6.1, 9.6.2, 9.6.3, Box 9.4}" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 63007, 63008, 63009, 63010 ], "vocabularyKeywords": [], "identifier_set": [ 12666 ], "observationcollection_set": [ { "ob_id": 39213, "uuid": "7409c7ea8a5045f8ab08e44db9cccb1a", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Technical Summary", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Technical Summary.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure TS.1\r\n- data for Figure TS.9\r\n- input data for Figure TS.12 \r\n- data for Figure TS.13\r\n- data for Figure TS.15\r\n- data for Figure TS.17\r\n- data for Figure TS.19\r\n- data for Figure TS.22\r\n- input data for Figure TS.24\r\n- data for Figure TS.25\r\n- data for Box TS.2, Figure 1\r\n- data for Box TS.2, Figure 2\r\n- data for Box TS.4, Figure 1\r\n- input data for Box Ts.4, Figure 1\r\n- input data for Box TS.5, Figure 1\r\n- data for Box TS.6, Figure 1\r\n- data for Box TS.7, Figure 1\r\n- data for Box TS.13, Figure 1\r\n- data for Cross-Section Box TS.1, Figure 1" } ], "responsiblepartyinfo_set": [ 180901, 180902, 180903, 180904, 180905, 180906, 180945, 193607 ], "onlineresource_set": [ 52744, 52747, 82923, 82732 ] }, { "ob_id": 37894, "uuid": "2727525bf80c4798b7319116a0c15353", "title": "Coral larval dispersal simulations for the southwestern Indian Ocean (1993-2020)", "abstract": "This dataset, contributing towards the Marine dispersal and retention in the western Indian Ocean project, contains raw coral larval connectivity data computed by the SECoW (Simulating Ecosystem Connectivity with WINDS) dispersal model. 1024 virtual coral larvae are generated every day from 1993-2020 at c. 8000 coral reef sites across the southwestern Indian Ocean, and advected for 120 days following surface-currents from the 1/50° WINDS-M simulation (http://dx.doi.org/10.5285/BF6F0CFBD09E47498572F21081376702) using OceanParcels. A larval settling ‘event’ is defined as a time interval during which a virtual coral larva is continuously within a 1/50° coral reef cell, between 1-120 days after spawning. SECoW records the arrival time, event duration, and cell index for every settling event (up to a maximum of 60). From these data, larval settling fluxes, incorporating larval mortality and competency, can be computed through post-processing with SECoW. These data can therefore be adapted to a variety of biological parameters at a fraction of the computational cost required to recompute larval trajectories. The resulting coral reef connectivity predictions will be useful for marine practitioners working on coral reef conservation across the southwestern Indian Ocean. The data were produced as part of the Marine Dispersal and Retention in the Western Indian Ocean project funded by the Natural Environment Research Council (NERC) grant NE/S007474/1. See linked online references on this record for cited items given above.", "creationDate": "2022-08-17T12:13:53.045154", "lastUpdatedDate": "2022-08-17T12:13:53", "latestDataUpdateTime": "2024-03-09T01:42:40", "updateFrequency": "notPlanned", "dataLineage": "This dataset consists of modelled large scale dispersal experiments across the southwestern Indian Ocean. The simulations were funded through PhD project NE/S007474/1, Marine dispersal and retention in the western Indian Ocean, held by Noam Vogt-Vincent who was supervised by Helen Johnson, both at University of Oxford. The outputs are archived on the British Oceanographic Data Centre (BODC)'s space at the Centre for Environmental Data Analysis (CEDA) and assigned a DOI. No quality control procedures were applied by BODC.", "removedDataReason": "", "keywords": "Indian Ocean, Coral, Reef, Larvae, Dispersal, Connectivity, Lagrangian, Monsoon, Model", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-06-06T12:28:55", "doiPublishedTime": "2023-06-06T12:30:32", "removedDataTime": null, "geographicExtent": { "ob_id": 3592, "bboxName": "", "eastBoundLongitude": 77.3, "westBoundLongitude": 34.37, "southBoundLatitude": -23.3, "northBoundLatitude": 0.0 }, "verticalExtent": null, "result_field": { "ob_id": 37895, "dataPath": "/bodc/UOX220127/WIO_coral_larval_dispersal", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5658878197561, "numberOfFiles": 10106, "fileFormat": "Data are CF-Compliant NetCDF formatted data files" }, "timePeriod": { "ob_id": 10460, "startTime": "1993-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 4043, "explanation": "The data are provided as-is with no quality control undertaken by the British Oceanographic Data Centre (BODC). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "BODC Data Quality Statement", "date": "2022-08-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37896, "uuid": "3b5fa422fe5344699cd0e223e127da64", "short_code": "comp", "title": "Ocean Parcels", "abstract": "The OceanParcels project develops Parcels (Probably A Really Computationally Efficient Lagrangian Simulator), a set of Python classes and methods to create customisable particle tracking simulations using output from Ocean Circulation models. Parcels can be used to track passive and active particulates such as water, plankton, plastic and fish. The code from the OceanParcels project is licensed under an open source MIT license and can be downloaded from github.com/OceanParcels/parcels or installed via anaconda.org/conda-forge/parcels." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37624, "uuid": "499d381a047140cf84798a36a8d3d65d", "short_code": "proj", "title": "Marine dispersal and retention in the western Indian Ocean", "abstract": "Ocean currents transport many particulates throughout the ocean, from larvae to pollutants. Understanding these transport pathways, and their variability, is essential for many applications, such as marine conservation and pollution attribution. However, many types of particulates are too small to be tracked directly. This project is a numerical model-based investigation of marine dispersal in the western Indian Ocean, one of the least-studied parts of the global ocean. It specifically looked at two applications of marine dispersal: marine plastics accumulating at remote islands, and coral reef connectivity. To predict the sources of debris (both terrestrial and marine in origin) accumulating at remote western Indian Ocean islands, large-scale Lagrangian analyses were carried out incorporating beaching, sinking, and the effects of ocean currents, winds, and waves. The raw data from these Lagrangian analyses are archived here, from which source analyses can be carried out for various parameters using the associated scripts. To assess the connectivity of coral reefs across the southwestern Indian Ocean, we firstly ran a high (c. 2 km) resolution ocean simulation in a 28-year realistic, and 10-year climatological, configurations. The model output is available here in full, including high (30 minute) temporal resolution surface velocities. Daily Lagrangian larval dispersal simulations from all reefs in the southwestern Indian Ocean from 1993-2020 were also carried out. The raw output is archived here, and can be converted into larval fluxes (incorporating biological parameters such as mortality and competency) with the associated scripts. This work was funded by NERC Doctoral Training Partnership in Environmental Research grant NE/S007474/1 awarded to Noam Vogt-Vincent." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62821, 62823, 62824, 62825, 62826, 62827, 62828, 62829, 62830, 62831, 62832, 62833, 62834, 62835, 62836, 62837, 62838, 62839, 62840, 62841, 62842, 62843, 62844, 62845, 62846, 62847, 62848, 62849, 62850, 62851, 62852, 62853, 62854, 62855, 62856, 62857, 62858, 62859, 62860, 62861, 62862, 62863, 62864, 62865, 62866, 62867, 62868, 62869, 62870, 62871, 62872, 62873, 62874, 62875, 62876, 62877, 62878, 62879, 62880, 62881, 62882, 62883, 62884, 62885, 62886, 62887, 62888, 62889, 62890, 62891, 62892, 62893, 62894, 62895, 62896, 62897, 62898, 62899, 62900, 62901, 62902, 62903, 62904, 62905, 62906, 62907, 62908, 62909, 62910, 62911, 62912, 62913, 62914, 62915, 62916, 62917, 62918, 62919, 62920, 62921, 62922, 62923, 62924, 62925, 62926, 62927, 62928, 62929, 62930, 62931, 62932, 62933, 62934, 62935, 62936, 62937, 62938, 62939, 62940, 62941, 62942, 62943, 62944, 62945, 62946, 62947, 62948, 62949, 62950, 62951, 62952, 62953, 62954, 62955, 62956, 62957, 62958, 62959, 62960, 62961, 62962, 62963, 62964, 62965, 62966, 62967, 62968, 62969, 62970, 62971, 62972, 62973, 62974, 62975, 62976, 62977, 62978, 62979, 62980, 62981, 62982, 62983, 62984, 62985, 62986, 62987, 62988, 62989, 62990, 62991, 62992, 62993, 62994, 62995, 62996, 62997, 62998, 62999, 63000, 63001, 63002, 63003, 63004, 63005, 63006 ], "vocabularyKeywords": [], "identifier_set": [ 12527 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 180910, 180913, 180914, 180907, 180911, 180909, 180908, 180912, 180915 ], "onlineresource_set": [ 52731, 52732, 82471, 82472 ] }, { "ob_id": 37899, "uuid": "fc001b104fe6458e92ab6a0be314e68e", "title": "Model output for marine debris accumulating at Seychelles and other remote islands in the western Indian Ocean (1993-2019)", "abstract": "This dataset contains raw beaching data computed by marine debris simulations (run using OceanParcels) for a range of physical scenarios (surface currents from GLORYS12V1 (https://doi.org/10.3389/feart.2021.698876) Stokes drift from WAVERYS (https://doi.org/10.1007/s10236-020-01433-w) and surface winds from ERA5 (https://doi.org/10.1002/qj.3803)) as described in the accompanying manuscript. Through postprocessing, debris ‘connectivity’ matrices can be computed, providing predictions for the main terrestrial and marine source regions of plastic debris accumulating at remote islands in the western Indian Ocean. These simulations include beaching and sinking processes, and a set of example matrices is provided here (https://doi.org/10.5287/bodleian:DEdqwXZQw) However, these matrices can be recomputed for different sinking and beaching rates using the scripts archived here (https://doi.org/10.5281/zenodo.7351695) or see here (https://github.com/nvogtvincent/WIO_Marine_Debris/) for the live version with documentation. These predictions will be useful for environmental practitioners in the western Indian Ocean to assess source regions for marine debris accumulating at islands of interest, and when this debris is likely to beach. The data were produced as part of the Marine Dispersal and Retention in the Western Indian Ocean project funded by the Natural Environment Research Council (NERC) grant NE/S007474/1. See linked online references on this record for cited items given above.", "creationDate": "2022-08-17T12:32:02.470797", "lastUpdatedDate": "2022-08-17T12:32:02", "latestDataUpdateTime": "2025-01-07T01:52:37", "updateFrequency": "notPlanned", "dataLineage": "This dataset consists of modelled large scale dispersal experiments across the southwestern Indian Ocean. The simulations were funded through PhD project NE/S007474/1, Marine dispersal and retention in the western Indian Ocean, held by Noam Vogt-Vincent who was supervised by Helen Johnson, both at University of Oxford. The outputs are archived on the British Oceanographic Data Centre (BODC)'s space at the Centre for Environmental Data Analysis (CEDA) and assigned a DOI. No quality control procedures were applied by BODC.", "removedDataReason": "", "keywords": "Indian Ocean, Plastic, Pollution, Marine debris, Seychelles, Fisheries, ALDFG, Lagrangian, Monsoon, Model", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-12-13T16:01:29", "doiPublishedTime": "2022-12-13T16:15:35", "removedDataTime": null, "geographicExtent": { "ob_id": 3594, "bboxName": "", "eastBoundLongitude": 74.0, "westBoundLongitude": 39.0, "southBoundLatitude": -22.0, "northBoundLatitude": 13.0 }, "verticalExtent": null, "result_field": { "ob_id": 37900, "dataPath": "/bodc/UOX220126/WIO_marine_debris_accumulation", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1039187221309, "numberOfFiles": 12001, "fileFormat": "Data are CF-Compliant NetCDF formatted data files" }, "timePeriod": { "ob_id": 10462, "startTime": "1993-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 4045, "explanation": "The data are provided as-is with no quality control undertaken by the British Oceanographic Data Centre (BODC). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "BODC Data Quality Statement", "date": "2022-08-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37896, "uuid": "3b5fa422fe5344699cd0e223e127da64", "short_code": "comp", "title": "Ocean Parcels", "abstract": "The OceanParcels project develops Parcels (Probably A Really Computationally Efficient Lagrangian Simulator), a set of Python classes and methods to create customisable particle tracking simulations using output from Ocean Circulation models. Parcels can be used to track passive and active particulates such as water, plankton, plastic and fish. The code from the OceanParcels project is licensed under an open source MIT license and can be downloaded from github.com/OceanParcels/parcels or installed via anaconda.org/conda-forge/parcels." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37624, "uuid": "499d381a047140cf84798a36a8d3d65d", "short_code": "proj", "title": "Marine dispersal and retention in the western Indian Ocean", "abstract": "Ocean currents transport many particulates throughout the ocean, from larvae to pollutants. Understanding these transport pathways, and their variability, is essential for many applications, such as marine conservation and pollution attribution. However, many types of particulates are too small to be tracked directly. This project is a numerical model-based investigation of marine dispersal in the western Indian Ocean, one of the least-studied parts of the global ocean. It specifically looked at two applications of marine dispersal: marine plastics accumulating at remote islands, and coral reef connectivity. To predict the sources of debris (both terrestrial and marine in origin) accumulating at remote western Indian Ocean islands, large-scale Lagrangian analyses were carried out incorporating beaching, sinking, and the effects of ocean currents, winds, and waves. The raw data from these Lagrangian analyses are archived here, from which source analyses can be carried out for various parameters using the associated scripts. To assess the connectivity of coral reefs across the southwestern Indian Ocean, we firstly ran a high (c. 2 km) resolution ocean simulation in a 28-year realistic, and 10-year climatological, configurations. The model output is available here in full, including high (30 minute) temporal resolution surface velocities. Daily Lagrangian larval dispersal simulations from all reefs in the southwestern Indian Ocean from 1993-2020 were also carried out. The raw output is archived here, and can be converted into larval fluxes (incorporating biological parameters such as mortality and competency) with the associated scripts. This work was funded by NERC Doctoral Training Partnership in Environmental Research grant NE/S007474/1 awarded to Noam Vogt-Vincent." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 12334, 12335, 12336, 62799, 62800, 62801, 62802, 62803, 62804, 62805, 62806, 62807, 62808, 62809, 62810, 62811, 62812, 62813, 62814, 62815, 62816, 62817, 62818, 62819, 62820, 62821, 62822, 62823, 62824, 62825, 63958, 63959, 63960, 63961, 63962, 63963, 63964, 63965, 63966, 63967, 63968, 63969, 63970 ], "vocabularyKeywords": [], "identifier_set": [ 12313 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 180929, 180932, 180933, 180926, 180928, 180927, 180931, 180930, 180934 ], "onlineresource_set": [ 52739, 52740, 52741, 52742, 52743, 82468, 82469, 82470 ] }, { "ob_id": 37901, "uuid": "df7d665d7b7c4cadbd08558ea2e103c8", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Box TS4, Figure 1 (v20220817)", "abstract": "Input Data for Box TS4 from Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nBox TS4, Figure 1 shows global mean sea level change on different time scales and under different scenarios.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nArias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.\r\n\r\nWhen citing the SSP-based sea-level projections, please also include the following citation:\r\nGarner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Global Mean Sea-Level Rise Projections. Version 20210809. https://doi.org/10.5281/zenodo.5914710.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThe figure has three panels. Panel a shows global mean sea level (GMSL) change from 1900 to 2150, observed (1900–2018) and projected under the Shared Socioeconomic Pathway (SSP) scenarios (2000–2150). Panel b shows GMSL change on 100-, 2,000-, and 10,000-year time scales as a function of global surface temperature. Panel c shows timing of exceedance of different GMSL thresholds under different SSPs. \r\n\r\nInput data is only available for panel a and c. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\nGlobal mean sea level change time-series from 1901-2150 for:\r\n- Observed global mean sea level change (1901-2018).\r\n- Projected global mean sea level change (2005-2150).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Box TS4, Figure 1:\r\n\r\nSSP-based global mean sea level projections are archived as\r\n\r\nGarner, G. G., Hermans, T., Kopp, R. E., Slangen, A. B. A., Edwards, T. L., Levermann, A., Nowicki, S., Palmer, M. D., Smith, C., Fox-Kemper, B., Hewitt, H. T., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S. S., Edwards, T. L., Golledge, N. R., Hemer, M., Krinner, G., Mix, A., … Pearson, B. (2021). IPCC AR6 Sea Level Projections (Version 20210809) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5914710\r\n\r\nPanel a:\r\n\r\nConsensus observational GMSL curve: gmsl_altimeter+TG_ensemble_28012021.mat\r\nThis file is not provided but a link to the Chapter 9 GitHub repository which contains this file is provided.\r\n\r\nSSP-based GMSL projections through 2100, medium confidence:\r\n- pbox1e_total_ssp119_globalsl_figuredata.nc\r\n- pbox1e_total_ssp126_globalsl_figuredata.nc\r\n- pbox1e_total_ssp245_globalsl_figuredata.nc\r\n- pbox1e_total_ssp370_globalsl_figuredata.nc\r\n- pbox1e_total_ssp585_globalsl_figuredata.nc\r\n\r\nSSP-based GMSL projections after 2100, medium confidence:\r\n- pbox1f_total_ssp119_globalsl_figuredata.nc\r\n- pbox1f_total_ssp126_globalsl_figuredata.nc\r\n- pbox1f_total_ssp245_globalsl_figuredata.nc\r\n- pbox1f_total_ssp370_globalsl_figuredata.nc\r\n- pbox1f_total_ssp585_globalsl_figuredata.nc\r\n\r\nSSP-based GMSL projections through 2100, low confidence:\r\n- pbox2e_total_ssp126_globalsl_figuredata.nc\r\n- pbox2e_total_ssp585_globalsl_figuredata.nc\r\n\r\nSSP-based GMSL projections after 2100, low confidence:\r\n- pbox2f_total_ssp126_globalsl_figuredata.nc\r\n- pbox2f_total_ssp245_globalsl_figuredata.nc\r\n- pbox2f_total_ssp585_globalsl_figuredata.nc\r\n\r\nPanel c:\r\n\r\nThreshold exceedance timing under different SSPs, medium confidence, with parametric emulator for Antarctic ice sheet:\r\n- wf_1f_ssp119_milestone_figuredata.nc\r\n- wf_1f_ssp126_milestone_figuredata.nc\r\n- wf_1f_ssp245_milestone_figuredata.nc\r\n- wf_1f_ssp370_milestone_figuredata.nc\r\n- wf_1f_ssp585_milestone_figuredata.nc\r\n\r\nThreshold exceedance timing under different SSPs, medium confidence, with LARMIP-2 emulator for Antarctic ice sheet:\r\n- wf_2f_ssp119_milestone_figuredata.nc\r\n- wf_2f_ssp126_milestone_figuredata.nc\r\n- wf_2f_ssp245_milestone_figuredata.nc\r\n- wf_2f_ssp370_milestone_figuredata.nc\r\n- wf_2f_ssp585_milestone_figuredata.nc\r\n\r\nThreshold exceedance timing under different SSPs, low confidence, with DeConto et al. 2021-based Antarctic ice sheet projections incorporating Marine Ice Cliff Instability:\r\n- wf_3f_ssp126_milestone_figuredata.nc\r\n- wf_3f_ssp585_milestone_figuredata.nc\r\n\r\nThreshold exceedance timing under different SSPs, low confidence, with Bamber et al. 2019-based structured expert judgement ice sheet projections:\r\n- wf_4_ssp126_milestone_figuredata.nc\r\n- wf_4_ssp585_milestone_figuredata.nc\r\n\r\nSee sections 9.6.3.2 and 9.6.3.3 for detailed information on the SSP-based global mean sea level projections and their production.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nPanel a data were plotted using standard matplotlib software and a Linux shell script, - code is available via the link in the documentation. The code requires the input data provided here and the additional gmsl_altimeter+TG_ensemble_28012021.mat file from Figure 9.27. The link to this file from the Chapter 9 GitHub is provided. \r\n\r\nPanel c data were plotted using standard open-source R software - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n- Link to the code for the figure, archived on Zenodo.\r\n- Link to the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report, archived on Zenodo.\r\n - Link to figure data in matlab format on github", "creationDate": "2022-08-17T14:25:42.408149", "lastUpdatedDate": "2022-08-17T14:03:20", "latestDataUpdateTime": "2023-06-06T15:06:11", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, sea level", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-06T14:14:47", "doiPublishedTime": "2023-08-02T12:44:08.623033", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38008, "dataPath": "/badc/ar6_wg1/data/TS/inputdata_BOX_ts4_fig1/v20220817", "oldDataPath": [ 38008 ], "storageLocation": "internal", "storageStatus": "online", "volume": 403412, "numberOfFiles": 33, "fileFormat": "NetCDF, txt" }, "timePeriod": { "ob_id": 10464, "startTime": "1901-01-01T00:00:00", "endTime": "2300-12-31T23:59:59" }, "resultQuality": { "ob_id": 4303, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-06-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40142, "uuid": "8210545e1f564114a80d30d99ec1d73b", "short_code": "comp", "title": "Caption for Box TS4, Figure 1 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global mean sea level (GMSL) change on different time scales and under different scenarios. The intent of this figure is to (i) show the century-scale GMSL projections in the context of the 20th century observations, (ii) illustrate ‘deep uncertainty’ in projections by considering the timing of GMSL rise milestones, and (iii) show the long-term commitment associated with different warming levels, including the paleo evidence to support this. (a) GMSL change from 1900 to 2150, observed (1900–2018) and projected under the SSP scenarios (2000–2150), relative to a 1995–2014 baseline. Solid lines show median projections. Shaded regions showlikely ranges for SSP1-2.6 and SSP3-7.0. Dotted and dashed lines show respectively the 83rd and 95th percentilelow confidence projections for SSP5-8.5. Bars at right showlikely ranges for SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 in 2150. Lightly shaded thick/thin bars show 17th–83rd/5th–95th percentile low-confidence ranges in 2150 for SSP1-2.6 and SSP5-8.5, based upon projection methods incorporating structured expert judgement and marine ice cliff instability. Low confidence range for SSP5-8.5 in 2150 extends to 4.8/5.4 m at the 83rd/95th percentile. (b) GMSL change on 100- (blue), 2000- (green) and 10,000-year (magenta) time scales as a function of global surface temperature, relative to 1850–1900. For 100-year projections, GMSL is projected for the year 2100, relative to a 1995–2014 baseline, and temperature anomalies are average values over 2081–2100. For longer-term commitments, warming is indexed by peak warming above 1850–1900 reached after cessation of emissions. Shaded regions show paleo-constraints on global surface temperature and GMSL for the Last Interglacial and mid-Pliocene Warm Period. Lightly shaded thick/thin blue bars show 17th–83rd/5th–95th percentile low confidence ranges for SSP1-2.6 and SSP5-8.5 in 2100, plotted at 2°C and 5°C. (c) Timing of exceedance of GMSL thresholds of 0.5, 1.0, 1.5 and 2.0 m, under different SSPs. Lightly shaded thick/thin bars show 17th–83rd/5th–95th percentile low-confidence ranges for SSP1-2.6 and SSP5-8.5. {4.3.2, 9.6.1, 9.6.2, 9.6.3, Box 9.4}" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62793, 62794, 62795, 62796, 62797, 62798, 63905, 63906, 63907 ], "vocabularyKeywords": [], "identifier_set": [ 12665 ], "observationcollection_set": [ { "ob_id": 39213, "uuid": "7409c7ea8a5045f8ab08e44db9cccb1a", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Technical Summary", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Technical Summary.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure TS.1\r\n- data for Figure TS.9\r\n- input data for Figure TS.12 \r\n- data for Figure TS.13\r\n- data for Figure TS.15\r\n- data for Figure TS.17\r\n- data for Figure TS.19\r\n- data for Figure TS.22\r\n- input data for Figure TS.24\r\n- data for Figure TS.25\r\n- data for Box TS.2, Figure 1\r\n- data for Box TS.2, Figure 2\r\n- data for Box TS.4, Figure 1\r\n- input data for Box Ts.4, Figure 1\r\n- input data for Box TS.5, Figure 1\r\n- data for Box TS.6, Figure 1\r\n- data for Box TS.7, Figure 1\r\n- data for Box TS.13, Figure 1\r\n- data for Cross-Section Box TS.1, Figure 1" } ], "responsiblepartyinfo_set": [ 180935, 180936, 180937, 180938, 180939, 180940, 180944, 193608, 193609, 193610, 193611, 193612, 193613, 193614, 193615, 193616, 193617, 193618, 193619, 193620, 193621, 193622, 193623, 193624, 193625, 193626, 193627, 193628, 193629, 193630, 193631, 193632, 193633 ], "onlineresource_set": [ 52745, 52746, 82924, 82731, 83663 ] }, { "ob_id": 37903, "uuid": "0b5c980aa58447508eccdda79554b2b7", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 12.8 (v20220804)", "abstract": "Input Data for Figure 12.8 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 12.8 shows projected changes in selected climatic impact-driver indices for Central and South America.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Ranasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1767–1926, doi:10.1017/9781009157896.014.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with general data provided in the central directory and specific data in 3 folders (Q100_CMIP5, Q100_CMIP6, Q1000_CORDEX-core).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n - spatial field over South-America and Central-America of mean change in 1-in-100 year river discharge per unit catchment area (Q100, m3 s-1 km-2) from CORDEX models for 2041-2060 relative to 1995-2014 for RCP8.5\r\n\r\n- Shoreline position change over South-America (pointwise) along sandy coasts by the year 2100 relative to 2010 (meters; negative values indicate shoreline retreat) from the CMIP5 based data set presented by Vousdoukas et al. (2020)\r\n\r\n- regional averages in South-America and Central-America of Q100 (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for:\r\n\r\n - CMIP6 historical, ssp126 and ssp585\r\n\r\n - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5\r\n\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n\r\n- regional averages in South-America and Central-America of CMIP5 based projections (mean change estimates and bars the 5th-95th percentile range of associated uncertainty) of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 and RCP4.5 from Vousdoukas et al. (2020)\r\n\r\nNWS, NSA, SAM, NES, SWS, SES, SSA, CAR and SCA are domains used in the model. \r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 12.8:\r\n\r\nPanel a:\r\n\r\n- Q100_map_panel_a_SAM_divdra.nc: Field (colors plotted on the map) of changes of 1-in-100yr river discharge per unit catchment area between 2041-2060 (mid-term) and 1995-2015 (recent past) for CORDEX RCP8.5; the file contains the data for the regions from the SAM CORDEX domain\r\n\r\n- Q100_map_panel_a_CAM_for_SAM_divdra.nc: same as previous file for the regions from the CAM CORDEX domain\r\n\r\nPanel b:\r\n\r\n- CoastalRecession_SOUTH-AMERICA_RCP85_2100.json: pointwise values (color points on the map) for South-America and Central-America of shoreline position mean changes between 2100 (long-term) and 2010 (recent past) from the CMIP5 based data set presented by Vousdoukas et al. (2020)\r\n\r\nPanel c:\r\n\r\n- txt files containing the median and 5th/95th percentiles of each ensemble of the 1-in-100yr river discharge per unit catchment area (Q100) regional averages of time slices: Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.txt, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${scenario}: the name of the scenario : ssp126, ssp585, rcp26, rcp85\r\n - ${period}: the explicit period used to compute the temporal average: 1995-2014 (recent past), 2041-2060 (mid-term) and 2081-2099 (long term)\r\n - ${CORDEX_domain}: the CORDEX domain\r\n\r\n- txt files containing the Q100 regional averages of global warming levels: Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${GWL}: the Global Warming Level: 1.5, 2 and 4\r\n - ${CORDEX_domain}: the CORDEX domain\r\n\r\nPanel d:\r\n\r\n- globalErosionProjections_by_AR6_region_${scenario}_${horizon).json: regional averages of shoreline position changes for Africa, for the RCP4.5 and RCP8.5 scenarios (${scenario} = RCP45 and ${scenario} = RCP85 respectively) and the 2050 (mid-term, in blue) and 2100 (long-term, in red) future horizons (${horizon}=2050 and ${horizon}=2100 respectively) against the recent past period (2010); the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project. \r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project. \r\nCORDEX is Coordinated Regional Downscaling Experiment from the WCRP. \r\nWCRP is the World Climate Research Programme. SSP stands for Shared Socioeconomic Pathway. \r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6. \r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\nRCP stands for Representative Concentration Pathway. \r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nFor panel a, the plotting script (see data tables and code on Github) draws the rivers and uses a subroutine to identify the rivers to plot them individually with lines; plotting the Q100 netcdf file will produce dots (and not rivers).\r\n\r\nFor panel c, the recent past values are plotted as absolute values (left column on each regional subpanel) and the future changes are plotted as differences against the recent past values (differences are computed when plotting the values).\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 12)\r\n - Link to the Supplementary Material for Chapter 12, which contains details on the input data used in Table 12.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the Chapter 12GitHub repository", "creationDate": "2022-08-19T15:20:39.468128", "lastUpdatedDate": "2022-08-19T15:19:55", "latestDataUpdateTime": "2023-04-19T13:44:00", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, South-America, Central-America, regional, river discharge, extreme, shoreline position, CMIP6, CMIP5, CORDEX, global warming levels, GWL, +1.5°C, +2°C, +4°C, ssp126, ssp585, recent past, mid-term, long-term, 1995-2014, 2041-2060, 2081-2100, RCP4.5, RCP8.5", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-04-19T12:57:15", "doiPublishedTime": "2023-06-30T15:11:27.971731", "removedDataTime": null, "geographicExtent": { "ob_id": 3596, "bboxName": "", "eastBoundLongitude": -30.0, "westBoundLongitude": -110.0, "southBoundLatitude": -60.0, "northBoundLatitude": 27.0 }, "verticalExtent": null, "result_field": { "ob_id": 38045, "dataPath": "/badc/ar6_wg1/data/ch_12/inputdata_ch12_fig08/v20220804", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 10729091, "numberOfFiles": 73, "fileFormat": "NetCDF, CSV, json, txt" }, "timePeriod": { "ob_id": 10413, "startTime": "1995-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4244, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-04-19" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39939, "uuid": "fc61a060aace4d10b06f5b4ae1b91866", "short_code": "comp", "title": "Caption for Figure 12.8 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "(a) Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m3 s–1 km–2) from CORDEX-South and Central America models for 2041–2060 relative to 1995–2014 for RCP8.5. (b) Shoreline position change along sandy coasts by the year 2100 relative to 2010 for RCP8.5 (metres; negative values indicate shoreline retreat) from the CMIP5-based dataset presented by Vousdoukas et al. (2020b). (c) Bar plots for Q100 (m3 s–1 km–2) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. (d) Bar plots for shoreline position change show CMIP5-based projections of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) and RCP4.5 (blue) from Vousdoukas et al. (2020b). Dots indicate regional mean change estimates and bars show the 5–95th percentile range of associated uncertainty. Note that these shoreline position change projections assume that there are no additional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical Annex VI for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1)" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49807, 50415, 50417, 80033, 80034 ], "vocabularyKeywords": [], "identifier_set": [ 12541 ], "observationcollection_set": [ { "ob_id": 32728, "uuid": "cab668573a8d45ce9d0efc6b762cf454", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 12: Climate change information for regional impact and for risk assessment.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 12.4\r\n- input data for Figure 12.5\r\n- input data for Figure 12.6\r\n- input data for Figure 12.7\r\n- input data for Figure 12.8\r\n- input data for Figure 12.9\r\n- input data for Figure 12.10\r\n- input data for Figure 12.SM.1\r\n- input data for Figure 12.SM.2\r\n- input data for Figure 12.SM.3\r\n- input data for Figure 12.SM.4\r\n- input data for Figure 12.SM.5\r\n- input data for Figure 12.SM.6" } ], "responsiblepartyinfo_set": [ 180959, 180960, 180961, 180962, 180963, 180964, 180965, 180966, 180967 ], "onlineresource_set": [ 52748, 52749, 82689, 83321, 83322 ] }, { "ob_id": 37906, "uuid": "3069833ab06a4968a90fa9f649b87ed7", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 7.7 (v20220721)", "abstract": "Input Data for Figure 7.7 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.7 shows the contribution of forcing agents to 2019 temperature change relative to 1750 produced using the two-layer emulator (Supplementary Material 7.SM.2), constrained to assessed ranges for key climate metrics described in Cross-Chapter Box 7.1.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 1 panel, with data provided for this panel in the master GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Contribution of forcing agents to 2019 temperature change relative to 1750 produced using the two-layer emulator (Supplementary Material 7.SM.2), constrained to assessed ranges for key climate metrics described in Cross-Chapter Box 7.1. The forcing agents represented are the following:\r\n - carbon dioxide\r\n - other well-mixed greenhouse gases (WMGHGs)\r\n - ozone\r\n - stratospheric water vapour\r\n - surface albedo\r\n - contrails and aviation-induced cirrus\r\n - aerosols\r\n - solar\r\n - volcanic\r\n - total\r\n\r\nThe results are from a 2237-member ensemble. \r\nSolid bars represent best estimates, and very likely (5–95%) ranges are given by error bars. Dashed error bars show the contribution of forcing uncertainty alone, using best estimates of ECS (3.0°C), TCR (1.8°C) and two-layer model parameters representing the CMIP6 multi-model mean. \r\nSolid error bars show the combined effects of forcing and climate response uncertainty using the distribution of ECS and TCR from Tables 7.13 and 7.14, and the distribution of calibrated model parameters from 44 CMIP6 models. \r\nNon-CO2 WMGHGs are further broken down into contributions from methane (CH4), nitrous oxide (N2O) and halogenated compounds. \r\nSurface albedo is broken down into land-use changes and light-absorbing particles on snow and ice. \r\nAerosols are broken down into contributions from aerosol–cloud interactions (ERFaci) and aerosol–radiation interactions (ERFari). \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 7.7:\r\n \r\n - Data file: AR6 FGD assessment time series - GMST and GSAT.xlsx\r\n\r\nECS stands for Equilibrium Climate Sensitivity.\r\nTCR stands for Transient Climate Response.\r\nERFaci stands for Effective Radiative Forcing of aerosol-cloud interaction.\r\nERFari stands for Effective Radiative Forcing of aerosol-radiation interaction.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. Also listed on the 'master' GitHub page linked in the documentation of this catalogue record are external GitHub repositories and locations within the contributed directory where code for figures have been supplied by other authors. These are provided \"as-is\" and are not guaranteed to be reproducible within this environment. For external GitHub locations, check out the relevant repository READMEs.\r\n\r\nWithin the processing chain, every notebook is prefixed by a number. To reproduce all results in the chapter, the notebooks should be run in numerical order, because some later things depend on earlier things (historical temperature attribution requires a constrained ensemble of the two layer climate model, which relies on the generation of the radiative forcing time series). This being said, most notebooks should run standalone, as input data is provided where the datasets are small enough (see the 'master;' GitHub page for these).\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n- Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, temperature, anthropogenic, natural, greenhouse gases, aerosols", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38062, "dataPath": "/badc/ar6_wg1/data/ch_07/inputdata_ch7_fig07/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 304000, "numberOfFiles": 2, "fileFormat": "XLSX, txt" }, "timePeriod": null, "resultQuality": { "ob_id": 8, "explanation": "Research data from the OP3 campaign.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-01-26" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 32722, "uuid": "b96ed5cbb8304a82bf1a2a4cad49bf6d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 7.3\r\n- input data for Figure 7.4\r\n- input data for Figure 7.5\r\n- data for Figure 7.6\r\n- input data for Figure 7.7\r\n- data for Figure 7.8\r\n- input data for Figure 7.10\r\n- data for Figure 7.11\r\n- input data for Figure 7.13\r\n- data for Figure 7.16\r\n- data for Figure 7.17\r\n- input data for Figure 7.18\r\n- data for Figure 7.19\r\n- input data for Figure 7.21\r\n- data for FAQ 7.3, Figure 1\r\n- input data for FAQ 7.3, Figure 1\r\n- data for 7.SM.1\r\n- input data for Box 7.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180980, 180981, 180982, 180983, 180984, 180985, 180986, 180987 ], "onlineresource_set": [ 52764, 52763 ] }, { "ob_id": 37924, "uuid": "62af3c6051044460aa0a716e2204bffc", "title": "Meteorological Data from Palas FIDAS 200 Instrument at Manchester Air Quality Site, 2019 onwards", "abstract": "Meteorological data including Temperature, Pressure and Humidity from a Palas FIDAS 200 Instrument at Manchester Air Quality Site (MAQS) for the Integrated Research Observation System for Clean Air (OSCA) project from 2019-onwards.", "creationDate": "2022-08-23T13:40:32.347057", "lastUpdatedDate": "2022-08-23T13:40:32", "latestDataUpdateTime": "2023-10-05T15:31:40", "updateFrequency": "monthly", "dataLineage": "Data is transfered directly from the instrument to the computer database, before being transferred to the general database, where it is processed, averaged over minute time intervals and flagged where data is suspect.Data processing software found in: https://github.com/ncasuk/AMF_CVs/releases/tag/v1.0.0", "removedDataReason": "", "keywords": "OSCA, maqs, air quality, OSCA-AQ", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-17T12:31:55", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3598, "bboxName": "", "eastBoundLongitude": -2.214244, "westBoundLongitude": -2.214244, "southBoundLatitude": 53.456636, "northBoundLatitude": 53.456636 }, "verticalExtent": null, "result_field": { "ob_id": 37925, "dataPath": "/badc/osca/data/manchester/OSCA_Manc_FIDAS_Met", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 248623645, "numberOfFiles": 106, "fileFormat": "NetCDF and CSV format" }, "timePeriod": { "ob_id": 10470, "startTime": "2019-07-19T00:00:00", "endTime": "2021-12-31T00:00:00" }, "resultQuality": { "ob_id": 4052, "explanation": "If Temeprature falls outside the -25 to 50 degrees c range it is flagged as outside the operational range. If any pressure falls below 950 mbar, it is flagged as outside the operational range. If humidity is outside 0 to 100 per cent range it is flagged as outside the operational rang.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37927, "uuid": "6273ecd8935947b192690f55cff48f9a", "short_code": "acq", "title": "Acquisition for: Meteorological Data from Palas FIDAS 200 Instrument at Manchester Air Quality Site 2019-21", "abstract": "Acquisition for: Meteorological Data from Palas FIDAS 200 Instrument at Manchester Air Quality Site 2019-21" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37963, "uuid": "ab605b618884401c91afd0274c92144e", "short_code": "proj", "title": "Integrated Research Observation System for Clean Air (OSCA)", "abstract": "The Integrated Research Observation System for Clean Air (OSCA) is a multidisciplinary research project, combining atmospheric observations, laboratory studies, data processing development and integrated scientific synthesis to deliver improved understanding of urban air pollution in the UK, and enable delivery of key objectives of the Clean Air: Analysis and Solutions Programme. OSCA exploits recent significant UK investment in air pollution measurement infrastructure - the air pollution supersites in London, Birmingham and Manchester - and other new UKRI-funded capability developed for field, modelling and laboratory studies of air pollution processes. \r\n\r\nThe project brings together a research team spanning 5 UK HEIs and 2 NERC centres across disciplines of atmospheric science, engineering, mathematics, chemistry, physics and computer science and includes investigators with direct involvement in the provision of science advice in support of policy development - through established links with (e.g.) Department for Environment, Food and Rural Affairs (Defra), Department for Transport (DfT), Department of Health (DoH), regional policymakers, and international bodies. OSCA will provide scientific insights that will inform implementation of the new UK Clean Air Strategy, contribute to development and evaluation of regional air quality policy measures, and enable the development and optimisation of emission abatement measures, for the protection of human health. The project provides a definitive assessment of the current state of UK urban air quality, and of trends in air pollutants - both those expected in response to policy and changing behaviour, and unanticipated consequences of these - and provides data and infrastructure to underpin other proposed projects in the Clean Air Programme. NE/T001984/1, \tNE/T001917/1, NE/T001909/2, NE/T001798/1, NE/T001925/1, NE/T001798/2, NE/T001976/1, NE/T001909/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58082, 58083, 65554, 65555, 65556, 65557, 65558, 65564, 65565, 65566, 65922, 65923, 65924, 65925, 65926, 65927 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 37907, "uuid": "65b50d3348cb4745bb7acfcf6f2057b8", "short_code": "coll", "title": "Integrated Research Observation System for Clean Air (OCSA): Birmingham, Manchester and London air quality supersites data collection", "abstract": "A collection of ground-based observation datasets made at 3 air quality supersites in Manchester, Birmingham and London for the Integrated Research Observation System for Clean Air (OSCA) project which is part of the Strategic Priorities Fund (SPF) Clean Air Programme. \r\n\r\nMeasurements were made of a broad range of atmospheric components by similar comprehensive instrument suites at the 3 sites on a longterm basis beginning at the end of 2018. Additional instrumentation was temporarily added for intensive operation periods in June 2021 and January 2022.\r\n\r\nThis project was a collaboration between University of Manchester, University of Birmingham, Kings College London, University of Cambridge, University of York, Centre for Ecology and Hydrology, National Centre for Atmospheric Science (NCAS) and University of Edinburgh." }, { "ob_id": 45670, "uuid": "824c0c058de442ab885431eba938dc03", "short_code": "coll", "title": "NCAS archive of Air quality measurements from Manchester, Birmingham and London air quality supersites", "abstract": "This collection contains maeasurements of air quality parameters from a suite of instruments deployed at 3 urban supesites located in Manchester, Birmingham and London Honor Oak Park.\r\n\r\nMore details to follow." } ], "responsiblepartyinfo_set": [ 181043, 181044, 181045, 181046, 181047, 181048, 181053, 181054 ], "onlineresource_set": [] }, { "ob_id": 37938, "uuid": "c5a629e1012b472c9b1fa130d2432fe1", "title": "Particulate Matter Data from Palas FIDAS 200 Instrument at Manchester Air Quality Site, 2019 onwards", "abstract": "Particulate matter data measured at 7m above ground level by a Palas FIDAS 200 Instrument at Manchester Air Quality Site (MAQS) for the Integrated Research Observation System for Clean Air (OSCA) project.\r\nMeasurements include the abundance of mass concentration of PM1 ambient aerosol in air, mass concentration of PM2.5 ambient aerosol in air, mass concentration of PM10 ambient aerosol in Air, and the concentration of ambient aerosol particles.", "creationDate": "2022-08-23T15:52:33.482984", "lastUpdatedDate": "2022-08-23T15:52:33", "latestDataUpdateTime": "2024-07-25T16:14:10", "updateFrequency": "monthly", "dataLineage": "Data are collected by the project team and transfered directly from the instrument to the computer database, before being transferred to the general database, where it is processed, averaged over minute time intervals and flagged where data is suspect. Data processing software can be found in: https://github.com/ncasuk/AMF_CVs/releases/tag/v1.0.0\r\nOnce complete, data are uploaded to CEDA for archiving.", "removedDataReason": "", "keywords": "PM10, PM2.5, PM1, OSCA, maqs, air quality, OSCA-AQ", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-19T12:32:54", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3600, "bboxName": "", "eastBoundLongitude": -2.214244, "westBoundLongitude": -2.214244, "southBoundLatitude": 53.456636, "northBoundLatitude": 53.456636 }, "verticalExtent": null, "result_field": { "ob_id": 37939, "dataPath": "/badc/osca/data/manchester/OSCA_Manc_FIDAS_PM", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 241876749, "numberOfFiles": 111, "fileFormat": "NetCDF and CSV format" }, "timePeriod": { "ob_id": 10476, "startTime": "2019-07-19T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4055, "explanation": "If PM10 falls outside the 0 to 250 ug/m3 range it is flagged as outside the operational range. If any other PM data falls below 0 ug/m3, it is flagged as outside the operational range.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 39508, "uuid": "b5935f7cdd87406da1a736e66e2888c9", "short_code": "acq", "title": "Acquisition for Particulate Matter Data from Palas FIDAS 200 Instrument at Manchester Air Quality Site", "abstract": "PM1, PM10 and PM2.5 measurements by FIDAS 200 instrument at the Manchester air-quality supersite" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37963, "uuid": "ab605b618884401c91afd0274c92144e", "short_code": "proj", "title": "Integrated Research Observation System for Clean Air (OSCA)", "abstract": "The Integrated Research Observation System for Clean Air (OSCA) is a multidisciplinary research project, combining atmospheric observations, laboratory studies, data processing development and integrated scientific synthesis to deliver improved understanding of urban air pollution in the UK, and enable delivery of key objectives of the Clean Air: Analysis and Solutions Programme. OSCA exploits recent significant UK investment in air pollution measurement infrastructure - the air pollution supersites in London, Birmingham and Manchester - and other new UKRI-funded capability developed for field, modelling and laboratory studies of air pollution processes. \r\n\r\nThe project brings together a research team spanning 5 UK HEIs and 2 NERC centres across disciplines of atmospheric science, engineering, mathematics, chemistry, physics and computer science and includes investigators with direct involvement in the provision of science advice in support of policy development - through established links with (e.g.) Department for Environment, Food and Rural Affairs (Defra), Department for Transport (DfT), Department of Health (DoH), regional policymakers, and international bodies. OSCA will provide scientific insights that will inform implementation of the new UK Clean Air Strategy, contribute to development and evaluation of regional air quality policy measures, and enable the development and optimisation of emission abatement measures, for the protection of human health. The project provides a definitive assessment of the current state of UK urban air quality, and of trends in air pollutants - both those expected in response to policy and changing behaviour, and unanticipated consequences of these - and provides data and infrastructure to underpin other proposed projects in the Clean Air Programme. NE/T001984/1, \tNE/T001917/1, NE/T001909/2, NE/T001798/1, NE/T001925/1, NE/T001798/2, NE/T001976/1, NE/T001909/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58082, 58083, 65554, 65555, 65556, 65557, 65558, 65564, 65565, 65566, 65972, 65973, 65974, 65975, 65976, 65977, 65978, 65979 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 37907, "uuid": "65b50d3348cb4745bb7acfcf6f2057b8", "short_code": "coll", "title": "Integrated Research Observation System for Clean Air (OCSA): Birmingham, Manchester and London air quality supersites data collection", "abstract": "A collection of ground-based observation datasets made at 3 air quality supersites in Manchester, Birmingham and London for the Integrated Research Observation System for Clean Air (OSCA) project which is part of the Strategic Priorities Fund (SPF) Clean Air Programme. \r\n\r\nMeasurements were made of a broad range of atmospheric components by similar comprehensive instrument suites at the 3 sites on a longterm basis beginning at the end of 2018. Additional instrumentation was temporarily added for intensive operation periods in June 2021 and January 2022.\r\n\r\nThis project was a collaboration between University of Manchester, University of Birmingham, Kings College London, University of Cambridge, University of York, Centre for Ecology and Hydrology, National Centre for Atmospheric Science (NCAS) and University of Edinburgh." }, { "ob_id": 45670, "uuid": "824c0c058de442ab885431eba938dc03", "short_code": "coll", "title": "NCAS archive of Air quality measurements from Manchester, Birmingham and London air quality supersites", "abstract": "This collection contains maeasurements of air quality parameters from a suite of instruments deployed at 3 urban supesites located in Manchester, Birmingham and London Honor Oak Park.\r\n\r\nMore details to follow." } ], "responsiblepartyinfo_set": [ 181093, 181094, 181095, 181096, 181097, 181098, 181103, 181104 ], "onlineresource_set": [ 82646 ] }, { "ob_id": 37941, "uuid": "03fd47b671b9482293ba122d3b7044da", "title": "Black Carbon and Ultraviolet Particulate Matter Levels detected from a Magee Scientific Aethalometer Model AE33 Instrument at Manchester Air Quality Site, 2019 onwards", "abstract": "Use of Magee Scientific Aethalometer Model AE33 Instrument to measure the Black Carbon and Ultraviolet Particulate Matter Levels from the Particlate Matter in Air Data at Manchester Air Quality Site (MAQS) from 2019 onwards. Black Carbon measurements were obtained using electromagnetic wavelengths 370, 470, 520, 590, 660, 880, 950 nm.", "creationDate": "2022-08-23T15:54:29.635295", "lastUpdatedDate": "2022-08-23T15:54:29", "latestDataUpdateTime": "2024-03-09T03:04:04", "updateFrequency": "monthly", "dataLineage": "Data is transfered directly from the instrument to the computer database, before being transferred to the general database, where it is processed, averaged over minute time intervals and flagged where data is suspect. Data processing software found in: https://github.com/ncasuk/AMF_CVs/releases/tag/v1.0.0\r\nAetholometer Data is obtained between July 2019 and April 2022.", "removedDataReason": "", "keywords": "OSCA, maqs, air quality, OSCA-AQ, BC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-17T12:06:05", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3601, "bboxName": "", "eastBoundLongitude": -2.214244, "westBoundLongitude": -2.214244, "southBoundLatitude": 53.456636, "northBoundLatitude": 53.456636 }, "verticalExtent": null, "result_field": { "ob_id": 39751, "dataPath": "/badc/osca/data/manchester/OSCA_MAQS_AE33_BC_UVPM", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 536639586, "numberOfFiles": 175, "fileFormat": "NetCDF and BADC-CSV" }, "timePeriod": { "ob_id": 10477, "startTime": "2019-07-03T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4056, "explanation": "No response or zero factor implimented. Intercomparison to be obtained once Total Carbon Analyser is obtained. Will be uploaded to GitHub folder in due course.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37944, "uuid": "cc5fefff8b45454886ae5c3fb4c6f812", "short_code": "acq", "title": "Acquisition for: Black Carbon and Ultraviolet Particulate Matter Levels detected from a Magee Scientific Aethalometer Model AE33 Instrument at Manchester Air Quality Site 2019-onwards", "abstract": "Acquisition for: Black Carbon and Ultraviolet Particulate Matter Levels detected from a Magee Scientific Aethalometer Model AE33 Instrument at Manchester Air Quality Site" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37963, "uuid": "ab605b618884401c91afd0274c92144e", "short_code": "proj", "title": "Integrated Research Observation System for Clean Air (OSCA)", "abstract": "The Integrated Research Observation System for Clean Air (OSCA) is a multidisciplinary research project, combining atmospheric observations, laboratory studies, data processing development and integrated scientific synthesis to deliver improved understanding of urban air pollution in the UK, and enable delivery of key objectives of the Clean Air: Analysis and Solutions Programme. OSCA exploits recent significant UK investment in air pollution measurement infrastructure - the air pollution supersites in London, Birmingham and Manchester - and other new UKRI-funded capability developed for field, modelling and laboratory studies of air pollution processes. \r\n\r\nThe project brings together a research team spanning 5 UK HEIs and 2 NERC centres across disciplines of atmospheric science, engineering, mathematics, chemistry, physics and computer science and includes investigators with direct involvement in the provision of science advice in support of policy development - through established links with (e.g.) Department for Environment, Food and Rural Affairs (Defra), Department for Transport (DfT), Department of Health (DoH), regional policymakers, and international bodies. OSCA will provide scientific insights that will inform implementation of the new UK Clean Air Strategy, contribute to development and evaluation of regional air quality policy measures, and enable the development and optimisation of emission abatement measures, for the protection of human health. The project provides a definitive assessment of the current state of UK urban air quality, and of trends in air pollutants - both those expected in response to policy and changing behaviour, and unanticipated consequences of these - and provides data and infrastructure to underpin other proposed projects in the Clean Air Programme. NE/T001984/1, \tNE/T001917/1, NE/T001909/2, NE/T001798/1, NE/T001925/1, NE/T001798/2, NE/T001976/1, NE/T001909/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58082, 58083, 65554, 65555, 65556, 65557, 65558, 65564, 65565, 65566, 65712 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 37907, "uuid": "65b50d3348cb4745bb7acfcf6f2057b8", "short_code": "coll", "title": "Integrated Research Observation System for Clean Air (OCSA): Birmingham, Manchester and London air quality supersites data collection", "abstract": "A collection of ground-based observation datasets made at 3 air quality supersites in Manchester, Birmingham and London for the Integrated Research Observation System for Clean Air (OSCA) project which is part of the Strategic Priorities Fund (SPF) Clean Air Programme. \r\n\r\nMeasurements were made of a broad range of atmospheric components by similar comprehensive instrument suites at the 3 sites on a longterm basis beginning at the end of 2018. Additional instrumentation was temporarily added for intensive operation periods in June 2021 and January 2022.\r\n\r\nThis project was a collaboration between University of Manchester, University of Birmingham, Kings College London, University of Cambridge, University of York, Centre for Ecology and Hydrology, National Centre for Atmospheric Science (NCAS) and University of Edinburgh." }, { "ob_id": 45670, "uuid": "824c0c058de442ab885431eba938dc03", "short_code": "coll", "title": "NCAS archive of Air quality measurements from Manchester, Birmingham and London air quality supersites", "abstract": "This collection contains maeasurements of air quality parameters from a suite of instruments deployed at 3 urban supesites located in Manchester, Birmingham and London Honor Oak Park.\r\n\r\nMore details to follow." } ], "responsiblepartyinfo_set": [ 181107, 181108, 181109, 181110, 181111, 181112, 181117, 181118, 181119, 181334, 181120 ], "onlineresource_set": [] }, { "ob_id": 37946, "uuid": "e0f62422d38d419ca9384e017d351acc", "title": "Number Concentration of Ambient Aerosol Particles in Air Data from Cloud Particle Condenser Instrument at Manchester Air Quality Site, 2019 onwards.", "abstract": "Number concentration of ambient aerosol particles in air data measured at 7m above ground level by Cloud Particle Condenser (CPC) Instruments at at the Manchester Air Quality Site (MAQS) for the Integrated Research Observation System for Clean Air (OSCA) project.\r\n\r\nCloud Particle Condenser instrument 3750 was used (June 2019 - February 2021 and December 2021 - February 2022) and was switched over to Cloud Particle Condenser instrument 3772 (February - December 2021)", "creationDate": "2022-08-23T15:55:35.039480", "lastUpdatedDate": "2022-08-23T15:55:35", "latestDataUpdateTime": "2023-10-03T15:29:36", "updateFrequency": "monthly", "dataLineage": "Data are collected by the project team and transferred directly from the instrument to the computer database, before being transferred to the general database, where it is processed, averaged over minute time intervals and flagged where data is suspect.Data processing software found in: https://github.com/ncasuk/AMF_CVs/releases/tag/v1.0.0\r\nData obtained from 3750 CPC for June 2019 - February 2021 and December 2021 - February 2022\r\nData obtained from 3772 CPC for February - September 2021\r\nIntercomparison between CPCs Data obtained from 3750 CPC and 3772 CPC.\r\nOnce complete, data are uploaded to CEDA for archiving.", "removedDataReason": "", "keywords": "CPC, OSCA, maqs, air quality, OSCA-AQ", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-19T12:04:10", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3602, "bboxName": "", "eastBoundLongitude": -2.214244, "westBoundLongitude": -2.214244, "southBoundLatitude": 53.456636, "northBoundLatitude": 53.456636 }, "verticalExtent": null, "result_field": { "ob_id": 37942, "dataPath": "/badc/osca/data/manchester/OSCA_MAQS_CPC_aerosol_conc", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 181804451, "numberOfFiles": 119, "fileFormat": "NetCDF and CSV format" }, "timePeriod": { "ob_id": 10478, "startTime": "2019-07-19T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4057, "explanation": "No response or zero factor implimented but intercomparison between CPCs Data obtained from 3750 CPC and 3772 CPC shows 3750 CPC to be accurate. For intercomparison document, see GitHub folder.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37949, "uuid": "589332d093c248e9985e164a221534a1", "short_code": "acq", "title": "Acquisition for: Number Concentration of Ambient Aerosol Particles in Air Data from Cloud Particle Condenser Instrument at Manchester Air Quality Site 2019-22", "abstract": "CPC at Manchester AQ supersite" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37963, "uuid": "ab605b618884401c91afd0274c92144e", "short_code": "proj", "title": "Integrated Research Observation System for Clean Air (OSCA)", "abstract": "The Integrated Research Observation System for Clean Air (OSCA) is a multidisciplinary research project, combining atmospheric observations, laboratory studies, data processing development and integrated scientific synthesis to deliver improved understanding of urban air pollution in the UK, and enable delivery of key objectives of the Clean Air: Analysis and Solutions Programme. OSCA exploits recent significant UK investment in air pollution measurement infrastructure - the air pollution supersites in London, Birmingham and Manchester - and other new UKRI-funded capability developed for field, modelling and laboratory studies of air pollution processes. \r\n\r\nThe project brings together a research team spanning 5 UK HEIs and 2 NERC centres across disciplines of atmospheric science, engineering, mathematics, chemistry, physics and computer science and includes investigators with direct involvement in the provision of science advice in support of policy development - through established links with (e.g.) Department for Environment, Food and Rural Affairs (Defra), Department for Transport (DfT), Department of Health (DoH), regional policymakers, and international bodies. OSCA will provide scientific insights that will inform implementation of the new UK Clean Air Strategy, contribute to development and evaluation of regional air quality policy measures, and enable the development and optimisation of emission abatement measures, for the protection of human health. The project provides a definitive assessment of the current state of UK urban air quality, and of trends in air pollutants - both those expected in response to policy and changing behaviour, and unanticipated consequences of these - and provides data and infrastructure to underpin other proposed projects in the Clean Air Programme. NE/T001984/1, \tNE/T001917/1, NE/T001909/2, NE/T001798/1, NE/T001925/1, NE/T001798/2, NE/T001976/1, NE/T001909/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58082, 58083, 65554, 65555, 65556, 65557, 65558, 65564, 65565, 65566, 65996, 65997 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 37907, "uuid": "65b50d3348cb4745bb7acfcf6f2057b8", "short_code": "coll", "title": "Integrated Research Observation System for Clean Air (OCSA): Birmingham, Manchester and London air quality supersites data collection", "abstract": "A collection of ground-based observation datasets made at 3 air quality supersites in Manchester, Birmingham and London for the Integrated Research Observation System for Clean Air (OSCA) project which is part of the Strategic Priorities Fund (SPF) Clean Air Programme. \r\n\r\nMeasurements were made of a broad range of atmospheric components by similar comprehensive instrument suites at the 3 sites on a longterm basis beginning at the end of 2018. Additional instrumentation was temporarily added for intensive operation periods in June 2021 and January 2022.\r\n\r\nThis project was a collaboration between University of Manchester, University of Birmingham, Kings College London, University of Cambridge, University of York, Centre for Ecology and Hydrology, National Centre for Atmospheric Science (NCAS) and University of Edinburgh." }, { "ob_id": 45670, "uuid": "824c0c058de442ab885431eba938dc03", "short_code": "coll", "title": "NCAS archive of Air quality measurements from Manchester, Birmingham and London air quality supersites", "abstract": "This collection contains maeasurements of air quality parameters from a suite of instruments deployed at 3 urban supesites located in Manchester, Birmingham and London Honor Oak Park.\r\n\r\nMore details to follow." } ], "responsiblepartyinfo_set": [ 181126, 181127, 181128, 181129, 181130, 181125, 181135, 181136, 181137, 181138 ], "onlineresource_set": [ 82619 ] }, { "ob_id": 37951, "uuid": "0a948ff688f24a9f87aed71ae38a4fc0", "title": "Number and Speed of Drops in Precipitation per Minute from Disdrometer at Manchester Air Quality Site 2019- onwards", "abstract": "Data from Laser Precipitation Monitor Model 5.4110.00.000 to measure the frequency, speed and other factors for Solid and Liquid Precipitation at Manchester Air Quality Site (MAQS), 2019 onwards.", "creationDate": "2022-08-23T15:56:34.183910", "lastUpdatedDate": "2022-08-23T15:56:34", "latestDataUpdateTime": "2023-10-03T15:29:43", "updateFrequency": "monthly", "dataLineage": "Data is transfered directly from the instrument to the computer database, before being transferred to the general database, where it is processed, averaged over minute time intervals and flagged where data is suspect.Data processing software found in: https://github.com/ncasuk/AMF_CVs/releases/tag/v1.0.0\r\nThis data is extracted and sorted into columns.", "removedDataReason": "", "keywords": "OSCA, maqs, air quality, OSCA-AQ", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-17T11:52:57", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3603, "bboxName": "", "eastBoundLongitude": -2.214244, "westBoundLongitude": -2.214244, "southBoundLatitude": 53.456636, "northBoundLatitude": 53.456636 }, "verticalExtent": null, "result_field": { "ob_id": 37952, "dataPath": "/badc/osca/data/manchester/OSCA_MAQS_Distro_Precipitation", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 14185445457, "numberOfFiles": 218, "fileFormat": "NetCDF and CSV format" }, "timePeriod": { "ob_id": 10479, "startTime": "2019-07-04T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4058, "explanation": "No response or zero factor implimented but data is analysed set by set, to check for anomylous times.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37954, "uuid": "9dda6fcd8ffa46cc8df99ea25c05b29b", "short_code": "acq", "title": "Acquisition for: Number and Speed of Drops in Precipitation per Minute from Disdrometre at Manchester Air Quality Site 2019-onwards", "abstract": "Acquisition for: Number and Speed of Drops in Precipitation per Minute from Disdrometre at Manchester Air Quality Site 2019-onwards" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37963, "uuid": "ab605b618884401c91afd0274c92144e", "short_code": "proj", "title": "Integrated Research Observation System for Clean Air (OSCA)", "abstract": "The Integrated Research Observation System for Clean Air (OSCA) is a multidisciplinary research project, combining atmospheric observations, laboratory studies, data processing development and integrated scientific synthesis to deliver improved understanding of urban air pollution in the UK, and enable delivery of key objectives of the Clean Air: Analysis and Solutions Programme. OSCA exploits recent significant UK investment in air pollution measurement infrastructure - the air pollution supersites in London, Birmingham and Manchester - and other new UKRI-funded capability developed for field, modelling and laboratory studies of air pollution processes. \r\n\r\nThe project brings together a research team spanning 5 UK HEIs and 2 NERC centres across disciplines of atmospheric science, engineering, mathematics, chemistry, physics and computer science and includes investigators with direct involvement in the provision of science advice in support of policy development - through established links with (e.g.) Department for Environment, Food and Rural Affairs (Defra), Department for Transport (DfT), Department of Health (DoH), regional policymakers, and international bodies. OSCA will provide scientific insights that will inform implementation of the new UK Clean Air Strategy, contribute to development and evaluation of regional air quality policy measures, and enable the development and optimisation of emission abatement measures, for the protection of human health. The project provides a definitive assessment of the current state of UK urban air quality, and of trends in air pollutants - both those expected in response to policy and changing behaviour, and unanticipated consequences of these - and provides data and infrastructure to underpin other proposed projects in the Clean Air Programme. NE/T001984/1, \tNE/T001917/1, NE/T001909/2, NE/T001798/1, NE/T001925/1, NE/T001798/2, NE/T001976/1, NE/T001909/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58079, 58082, 58083, 65554, 65555, 65556, 65557, 65558, 65564, 65565, 65566, 65665, 65666, 65668, 65669, 65670, 65671, 65672, 65673, 65674, 65675, 65676, 65677, 65678, 65679, 65680, 65681, 65682, 65683, 65684, 65685, 65686, 65687, 65688, 65689, 65690, 65691, 65692, 65693, 65694, 65695, 65696, 65697, 65698, 65699, 65700, 65701, 65702, 65703, 65704, 65705, 65706, 65707, 65708, 65709, 65710, 65711, 80467, 80468, 80469, 80470, 80471, 80472, 80473, 80474, 80475, 80476, 80477, 80478, 80479, 80480, 80481, 80482, 80483, 80484, 80485, 80486, 80487, 80488, 80489, 80490, 80491, 80492, 80493, 80494, 80495, 80496, 80497, 80498, 80499, 80500, 80501, 80502, 80503, 80504, 80505, 80506, 80507, 80508, 80509, 80510, 80511, 80512, 80513, 80514, 80515, 80516, 80517, 80518, 80519, 80520, 80521, 80522, 80523, 80524, 80525, 80526, 80527, 80528, 80529, 80530, 80531, 80532, 80533, 80534, 80535, 80536, 80537, 80538, 80539, 80540, 80541, 80542, 80543, 80544, 80545, 80546, 80547, 80548, 80549, 80550, 80551, 80552, 80553, 80554, 80555, 80556, 80557, 80558, 80559, 80560, 80561, 80562, 80563, 80564, 80565, 80566, 80567, 80568, 80569, 80570, 80571, 80572, 80573, 80574, 80575, 80576, 80577, 80578, 80579, 80580, 80581, 80582, 80583, 80584, 80585, 80586, 80587, 80588, 80589, 80590, 80591, 80592, 80593, 80594, 80595, 80596, 80597, 80598, 80599, 80600, 80601, 80602, 80603, 80604, 80605, 80606, 80607, 80608, 80609, 80610, 80611, 80612, 80613, 80614, 80615, 80616, 80617, 80618, 80619, 80620, 80621, 80622, 80623, 80624, 80625, 80626, 80627, 80628, 80629, 80630, 80631, 80632, 80633, 80634, 80635, 80636, 80637, 80638, 80639, 80640, 80641, 80642, 80643, 80644, 80645, 80646, 80647, 80648, 80649, 80650, 80651, 80652, 80653, 80654, 80655, 80656, 80657, 80658, 80659, 80660, 80661, 80662, 80663, 80664, 80665, 80666, 80667, 80668, 80669, 80670, 80671, 80672, 80673, 80674, 80675, 80676, 80677, 80678, 80679, 80680, 80681, 80682, 80683, 80684, 80685, 80686, 80687, 80688, 80689, 80690, 80691, 80692, 80693, 80694, 80695, 80696, 80697, 80698, 80699, 80700, 80701, 80702, 80703, 80704, 80705, 80706, 80707, 80708, 80709, 80710, 80711, 80712, 80713, 80714, 80715, 80716, 80717, 80718, 80719, 80720, 80721, 80722, 80723, 80724, 80725, 80726, 80727, 80728, 80729, 80730, 80731, 80732, 80733, 80734, 80735, 80736, 80737, 80738, 80739, 80740, 80741, 80742, 80743, 80744, 80745, 80746, 80747, 80748, 80749, 80750, 80751, 80752, 80753, 80754, 80755, 80756, 80757, 80758, 80759, 80760, 80761, 80762, 80763, 80764, 80765, 80766, 80767, 80768, 80769, 80770, 80771, 80772, 80773, 80774, 80775, 80776, 80777, 80778, 80779, 80780, 80781, 80782, 80783, 80784, 80785, 80786, 80787, 80788, 80789, 80790, 80791, 80792, 80793, 80794, 80795, 80796, 80797, 80798, 80799, 80800, 80801, 80802, 80803, 80804, 80805, 80806, 80807, 80808, 80809, 80810, 80811, 80812, 80813, 80814, 80815, 80816, 80817, 80818, 80819, 80820, 80821, 80822, 80823, 80824, 80825, 80826, 80827, 80828, 80829, 80830, 80831, 80832, 80833, 80834, 80835, 80836, 80837, 80838, 80839, 80840, 80841, 80842, 80843, 80844, 80845, 80846, 80847, 80848, 80849, 80850, 80851, 80852, 80853, 80854, 80855, 80856, 80857, 80858, 80859, 80860, 80861, 80862, 80863, 80864, 80865, 80866, 80867, 80868, 80869, 80870, 80871, 80872, 80873, 80874, 80875, 80876, 80877, 80878, 80879, 80880, 80881, 80882, 80883, 80884, 80885, 80886, 80887, 80888, 80889, 80890, 80891, 80892, 80893, 80894, 80895, 80896, 80897 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 37907, "uuid": "65b50d3348cb4745bb7acfcf6f2057b8", "short_code": "coll", "title": "Integrated Research Observation System for Clean Air (OCSA): Birmingham, Manchester and London air quality supersites data collection", "abstract": "A collection of ground-based observation datasets made at 3 air quality supersites in Manchester, Birmingham and London for the Integrated Research Observation System for Clean Air (OSCA) project which is part of the Strategic Priorities Fund (SPF) Clean Air Programme. \r\n\r\nMeasurements were made of a broad range of atmospheric components by similar comprehensive instrument suites at the 3 sites on a longterm basis beginning at the end of 2018. Additional instrumentation was temporarily added for intensive operation periods in June 2021 and January 2022.\r\n\r\nThis project was a collaboration between University of Manchester, University of Birmingham, Kings College London, University of Cambridge, University of York, Centre for Ecology and Hydrology, National Centre for Atmospheric Science (NCAS) and University of Edinburgh." }, { "ob_id": 45670, "uuid": "824c0c058de442ab885431eba938dc03", "short_code": "coll", "title": "NCAS archive of Air quality measurements from Manchester, Birmingham and London air quality supersites", "abstract": "This collection contains maeasurements of air quality parameters from a suite of instruments deployed at 3 urban supesites located in Manchester, Birmingham and London Honor Oak Park.\r\n\r\nMore details to follow." } ], "responsiblepartyinfo_set": [ 181143, 181144, 181145, 181146, 181147, 181148, 181153, 181154, 181155, 181156 ], "onlineresource_set": [] }, { "ob_id": 37956, "uuid": "5fc811f707f54415b129882a38889501", "title": "Ammonia and Water Abundance Measurements from Los Gatos Research Ammonia Analyzer Instrument at Manchester Air Quality Site 2019 onwards.", "abstract": "Ammonia (NH3) and water (H20) abundance measurements made at 7 metres above ground level by a Los Gatos research ammonia analyzer instrument at Manchester air quality site (MAQS) for the Integrated Research Observation System for Clean Air (OSCA) project.", "creationDate": "2022-08-23T15:58:54.961850", "lastUpdatedDate": "2022-08-23T15:58:54", "latestDataUpdateTime": "2024-03-09T03:20:14", "updateFrequency": "monthly", "dataLineage": "Data are transferred directly from the instrument to the computer database, before being transferred to the general database, where it is processed, averaged over minute time intervals and flagged where data is suspect. Data processing software found in: https://github.com/ncasuk/AMF_CVs/releases/tag/v1.0.0\r\nOnce complete, data are uploaded to CEDA for archiving.", "removedDataReason": "", "keywords": "NH3, H20,OSCA, maqs, air quality, OSCA-AQ", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T15:09:44", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3604, "bboxName": "", "eastBoundLongitude": -2.214244, "westBoundLongitude": -2.214244, "southBoundLatitude": 53.456636, "northBoundLatitude": 53.456636 }, "verticalExtent": null, "result_field": { "ob_id": 37957, "dataPath": "/badc/osca/data/manchester/OSCA_MAQS_LGR_EAA-22_NH3", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 404430833, "numberOfFiles": 193, "fileFormat": "NetCDF and CSV format" }, "timePeriod": { "ob_id": 10480, "startTime": "2018-12-20T00:00:00", "endTime": "2022-04-30T00:00:00" }, "resultQuality": { "ob_id": 4059, "explanation": "The baseline zero for the NH3 measurement has been checked using a zero-cylinder on 26th-27th July 2021. If the zero-corrected NH3 level falls below 0.5ppb or above 300 ppb it is flagged. 0.5 ppb is quoted by Los Gatos Research as the lower limit for the reliability of the Ammonia Analyser.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37959, "uuid": "caa871e0ab2f4ff6a26cb1bf654737a5", "short_code": "acq", "title": "Acquisition for: Ammonia and Water Abundance Measurements from Los Gatos Research Ammonia Analyzer Instrument at Manchester Air Quality Site 2019-22", "abstract": "Ammonia and Water Abundance Measurements from Los Gatos Research Ammonia Analyzer Instrument at Manchester Air Quality Site" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37963, "uuid": "ab605b618884401c91afd0274c92144e", "short_code": "proj", "title": "Integrated Research Observation System for Clean Air (OSCA)", "abstract": "The Integrated Research Observation System for Clean Air (OSCA) is a multidisciplinary research project, combining atmospheric observations, laboratory studies, data processing development and integrated scientific synthesis to deliver improved understanding of urban air pollution in the UK, and enable delivery of key objectives of the Clean Air: Analysis and Solutions Programme. OSCA exploits recent significant UK investment in air pollution measurement infrastructure - the air pollution supersites in London, Birmingham and Manchester - and other new UKRI-funded capability developed for field, modelling and laboratory studies of air pollution processes. \r\n\r\nThe project brings together a research team spanning 5 UK HEIs and 2 NERC centres across disciplines of atmospheric science, engineering, mathematics, chemistry, physics and computer science and includes investigators with direct involvement in the provision of science advice in support of policy development - through established links with (e.g.) Department for Environment, Food and Rural Affairs (Defra), Department for Transport (DfT), Department of Health (DoH), regional policymakers, and international bodies. OSCA will provide scientific insights that will inform implementation of the new UK Clean Air Strategy, contribute to development and evaluation of regional air quality policy measures, and enable the development and optimisation of emission abatement measures, for the protection of human health. The project provides a definitive assessment of the current state of UK urban air quality, and of trends in air pollutants - both those expected in response to policy and changing behaviour, and unanticipated consequences of these - and provides data and infrastructure to underpin other proposed projects in the Clean Air Programme. NE/T001984/1, \tNE/T001917/1, NE/T001909/2, NE/T001798/1, NE/T001925/1, NE/T001798/2, NE/T001976/1, NE/T001909/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58082, 58083, 65554, 65555, 65556, 65557, 65558, 65564, 65565, 65566, 65663, 65993, 65994, 65995 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 37907, "uuid": "65b50d3348cb4745bb7acfcf6f2057b8", "short_code": "coll", "title": "Integrated Research Observation System for Clean Air (OCSA): Birmingham, Manchester and London air quality supersites data collection", "abstract": "A collection of ground-based observation datasets made at 3 air quality supersites in Manchester, Birmingham and London for the Integrated Research Observation System for Clean Air (OSCA) project which is part of the Strategic Priorities Fund (SPF) Clean Air Programme. \r\n\r\nMeasurements were made of a broad range of atmospheric components by similar comprehensive instrument suites at the 3 sites on a longterm basis beginning at the end of 2018. Additional instrumentation was temporarily added for intensive operation periods in June 2021 and January 2022.\r\n\r\nThis project was a collaboration between University of Manchester, University of Birmingham, Kings College London, University of Cambridge, University of York, Centre for Ecology and Hydrology, National Centre for Atmospheric Science (NCAS) and University of Edinburgh." }, { "ob_id": 45670, "uuid": "824c0c058de442ab885431eba938dc03", "short_code": "coll", "title": "NCAS archive of Air quality measurements from Manchester, Birmingham and London air quality supersites", "abstract": "This collection contains maeasurements of air quality parameters from a suite of instruments deployed at 3 urban supesites located in Manchester, Birmingham and London Honor Oak Park.\r\n\r\nMore details to follow." } ], "responsiblepartyinfo_set": [ 181161, 181162, 181163, 181164, 181165, 181166, 181171, 181172 ], "onlineresource_set": [ 82623 ] }, { "ob_id": 37961, "uuid": "671628b8f4474599ba074607e65c4bcc", "title": "Methane, Carbon Monoxide, Carbon Dioxide and Water Abundance Measurements from a Los Gatos Research Ammonia Analyzer Instrument at the Manchester Air Quality Site, 2019 onwards", "abstract": "Methane, carbon monoxide, carbon dioxide and water abundance measurements made at 7 metres above ground level by a Los Gatos Research Multi-Carbon Analyzer Instrument at the Manchester Air Quality Site (MAQS) for the Integrated Research Observation System for Clean Air (OSCA) project.", "creationDate": "2022-08-23T16:03:28.163529", "lastUpdatedDate": "2022-08-23T16:03:28", "latestDataUpdateTime": "2023-10-06T09:29:35", "updateFrequency": "monthly", "dataLineage": "Data are collected by the project team and transfered directly from the instrument to the computer database, before being transferred to the general database, where it is processed, averaged over minute time intervals and flagged where data is suspect. Once complete, data are uploaded to CEDA for archiving.\r\nData processing software found in: https://github.com/ncasuk/AMF_CVs/releases/tag/v1.0.0", "removedDataReason": "", "keywords": "OSCA, air quality, supersite, CH4, CO, CO2, H2O, maqs,OSCA-AQ", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T15:08:07", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3605, "bboxName": "", "eastBoundLongitude": -2.214244, "westBoundLongitude": -2.214244, "southBoundLatitude": 53.456636, "northBoundLatitude": 53.456636 }, "verticalExtent": null, "result_field": { "ob_id": 37962, "dataPath": "/badc/osca/data/manchester/OSCA_MAQS_LGR_CH4_CO_CO2_H2O", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 614325128, "numberOfFiles": 191, "fileFormat": "NetCDF and CSV format" }, "timePeriod": { "ob_id": 10481, "startTime": "2018-12-20T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4060, "explanation": "The baseline zero and response values for CH4 and CO2 are checked every six months and changes are incorporated into the data. All reports of errors by the LGR result in flagged data. CO2 values are flagged as anomolous if they fall below 350 ppm, significantly below the background CO2 level, or above 1000 ppm, very high for an urban setting in most contexts. H2O data is outside the boundaries of 20 ppm to 70,000 ppm as outside these boundaries the accuracy of the instrument, according to Los Gatos Research, becomes suspect. CH4 levels are flagged if they are below 1.5 ppm, to account for the background level of methane already in the atmosphere, or above 30 ppm, a level rarely reached in an urban environment. CO levels are flagged if they are below 50 ppb, to account for the background level of CO already in the atmosphere, or above 1400 ppb, a level rarely reached in an urban environment.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37964, "uuid": "57d5ab9e544c4c80be316d6eee3429df", "short_code": "acq", "title": "Acquisition for: Methane, Carbon Monoxide, Carbon Dioxide and Water Abundance Measurements from Los Gatos Research Ammonia Analyzer Instrument at Manchester Air Quality Site 2019-22", "abstract": "Acquisition for: Methane, Carbon Monoxide, Carbon Dioxide and Water Abundance Measurements from Los Gatos Research Ammonia Analyzer Instrument at Manchester Air Quality Site 2019-22" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37963, "uuid": "ab605b618884401c91afd0274c92144e", "short_code": "proj", "title": "Integrated Research Observation System for Clean Air (OSCA)", "abstract": "The Integrated Research Observation System for Clean Air (OSCA) is a multidisciplinary research project, combining atmospheric observations, laboratory studies, data processing development and integrated scientific synthesis to deliver improved understanding of urban air pollution in the UK, and enable delivery of key objectives of the Clean Air: Analysis and Solutions Programme. OSCA exploits recent significant UK investment in air pollution measurement infrastructure - the air pollution supersites in London, Birmingham and Manchester - and other new UKRI-funded capability developed for field, modelling and laboratory studies of air pollution processes. \r\n\r\nThe project brings together a research team spanning 5 UK HEIs and 2 NERC centres across disciplines of atmospheric science, engineering, mathematics, chemistry, physics and computer science and includes investigators with direct involvement in the provision of science advice in support of policy development - through established links with (e.g.) Department for Environment, Food and Rural Affairs (Defra), Department for Transport (DfT), Department of Health (DoH), regional policymakers, and international bodies. OSCA will provide scientific insights that will inform implementation of the new UK Clean Air Strategy, contribute to development and evaluation of regional air quality policy measures, and enable the development and optimisation of emission abatement measures, for the protection of human health. The project provides a definitive assessment of the current state of UK urban air quality, and of trends in air pollutants - both those expected in response to policy and changing behaviour, and unanticipated consequences of these - and provides data and infrastructure to underpin other proposed projects in the Clean Air Programme. NE/T001984/1, \tNE/T001917/1, NE/T001909/2, NE/T001798/1, NE/T001925/1, NE/T001798/2, NE/T001976/1, NE/T001909/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58079, 58082, 58083, 65554, 65555, 65556, 65557, 65558, 65564, 65565, 65566, 65660, 65661, 65662, 65663, 65971 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 37907, "uuid": "65b50d3348cb4745bb7acfcf6f2057b8", "short_code": "coll", "title": "Integrated Research Observation System for Clean Air (OCSA): Birmingham, Manchester and London air quality supersites data collection", "abstract": "A collection of ground-based observation datasets made at 3 air quality supersites in Manchester, Birmingham and London for the Integrated Research Observation System for Clean Air (OSCA) project which is part of the Strategic Priorities Fund (SPF) Clean Air Programme. \r\n\r\nMeasurements were made of a broad range of atmospheric components by similar comprehensive instrument suites at the 3 sites on a longterm basis beginning at the end of 2018. Additional instrumentation was temporarily added for intensive operation periods in June 2021 and January 2022.\r\n\r\nThis project was a collaboration between University of Manchester, University of Birmingham, Kings College London, University of Cambridge, University of York, Centre for Ecology and Hydrology, National Centre for Atmospheric Science (NCAS) and University of Edinburgh." }, { "ob_id": 45670, "uuid": "824c0c058de442ab885431eba938dc03", "short_code": "coll", "title": "NCAS archive of Air quality measurements from Manchester, Birmingham and London air quality supersites", "abstract": "This collection contains maeasurements of air quality parameters from a suite of instruments deployed at 3 urban supesites located in Manchester, Birmingham and London Honor Oak Park.\r\n\r\nMore details to follow." } ], "responsiblepartyinfo_set": [ 181179, 181180, 181181, 181182, 181183, 181184, 181189, 181190 ], "onlineresource_set": [ 82610 ] }, { "ob_id": 37967, "uuid": "6f6366eb78c44875b2c92d3b8fe403c1", "title": "Number concentration and size distribution of ambient aerosol particles in air measurements from a Cloud Particle Condenser instrument (SMPS) at the Manchester Air Quality Site, 2019 onwards", "abstract": "Number concentration and size distribution of ambient aerosol particles in air measurements made at 7m above ground level using a 3082 Scanning Mobility Particle Sizer (SMPS) in combination with a 3750 Cloud Particle Condenser (CPC) at the Manchester Air Quality Site (MAQS) for the Integrated Research Observation System for Clean Air (OSCA) project.", "creationDate": "2022-08-23T16:17:15.600009", "lastUpdatedDate": "2022-08-23T16:17:15", "latestDataUpdateTime": "2023-08-02T09:29:53", "updateFrequency": "monthly", "dataLineage": "Data are collected by the project team and transfered directly from the instrument to the computer database, before being transferred to the general database, where it is processed, averaged over minute time intervals and flagged where data is suspect. CPC data were checked using intercomparison with other CPC instruments. Once complete, data are uploaded to CEDA for archiving.\r\nData processing software found in: https://github.com/ncasuk/AMF_CVs/releases/tag/v1.0.0", "removedDataReason": "", "keywords": "OSCA, air quality, supersite, maqs, aerosol,OSCA-AQ", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T14:39:51", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3606, "bboxName": "", "eastBoundLongitude": -2.214244, "westBoundLongitude": -2.214244, "southBoundLatitude": 53.456636, "northBoundLatitude": 53.456636 }, "verticalExtent": null, "result_field": { "ob_id": 37968, "dataPath": "/badc/osca/data/manchester/OSCA_MAQS_SMPS-CPC_aerosol", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 600775615, "numberOfFiles": 99, "fileFormat": "NetCDF and CSV format" }, "timePeriod": { "ob_id": 10482, "startTime": "2019-07-01T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4061, "explanation": "No response or zero factor implimented but intercomparison between CPCs Data obtained and PSI is passed through inlet to check for alignment on various occasions. For intercomparison document, see GitHub folder.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37970, "uuid": "8b005221580a41d49660c568d6b3a523", "short_code": "acq", "title": "Acquisition for: Number Concentration of Ambient Aerosol Particles in Air Data from Cloud Particle Condenser Instrument at Manchester Air Quality Site 2019-onwards (SMPS)", "abstract": "Acquisition for: Number Concentration of Ambient Aerosol Particles in Air Data from Cloud Particle Condenser Instrument at Manchester Air Quality Site 2019-onwards (SMPS)" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37963, "uuid": "ab605b618884401c91afd0274c92144e", "short_code": "proj", "title": "Integrated Research Observation System for Clean Air (OSCA)", "abstract": "The Integrated Research Observation System for Clean Air (OSCA) is a multidisciplinary research project, combining atmospheric observations, laboratory studies, data processing development and integrated scientific synthesis to deliver improved understanding of urban air pollution in the UK, and enable delivery of key objectives of the Clean Air: Analysis and Solutions Programme. OSCA exploits recent significant UK investment in air pollution measurement infrastructure - the air pollution supersites in London, Birmingham and Manchester - and other new UKRI-funded capability developed for field, modelling and laboratory studies of air pollution processes. \r\n\r\nThe project brings together a research team spanning 5 UK HEIs and 2 NERC centres across disciplines of atmospheric science, engineering, mathematics, chemistry, physics and computer science and includes investigators with direct involvement in the provision of science advice in support of policy development - through established links with (e.g.) Department for Environment, Food and Rural Affairs (Defra), Department for Transport (DfT), Department of Health (DoH), regional policymakers, and international bodies. OSCA will provide scientific insights that will inform implementation of the new UK Clean Air Strategy, contribute to development and evaluation of regional air quality policy measures, and enable the development and optimisation of emission abatement measures, for the protection of human health. The project provides a definitive assessment of the current state of UK urban air quality, and of trends in air pollutants - both those expected in response to policy and changing behaviour, and unanticipated consequences of these - and provides data and infrastructure to underpin other proposed projects in the Clean Air Programme. NE/T001984/1, \tNE/T001917/1, NE/T001909/2, NE/T001798/1, NE/T001925/1, NE/T001798/2, NE/T001976/1, NE/T001909/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58082, 58083, 65554, 65555, 65556, 65557, 65558, 65564, 65565, 65566, 65568, 65570, 65653, 65654, 65655, 65656, 65657, 65658, 65659 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 37907, "uuid": "65b50d3348cb4745bb7acfcf6f2057b8", "short_code": "coll", "title": "Integrated Research Observation System for Clean Air (OCSA): Birmingham, Manchester and London air quality supersites data collection", "abstract": "A collection of ground-based observation datasets made at 3 air quality supersites in Manchester, Birmingham and London for the Integrated Research Observation System for Clean Air (OSCA) project which is part of the Strategic Priorities Fund (SPF) Clean Air Programme. \r\n\r\nMeasurements were made of a broad range of atmospheric components by similar comprehensive instrument suites at the 3 sites on a longterm basis beginning at the end of 2018. Additional instrumentation was temporarily added for intensive operation periods in June 2021 and January 2022.\r\n\r\nThis project was a collaboration between University of Manchester, University of Birmingham, Kings College London, University of Cambridge, University of York, Centre for Ecology and Hydrology, National Centre for Atmospheric Science (NCAS) and University of Edinburgh." }, { "ob_id": 45670, "uuid": "824c0c058de442ab885431eba938dc03", "short_code": "coll", "title": "NCAS archive of Air quality measurements from Manchester, Birmingham and London air quality supersites", "abstract": "This collection contains maeasurements of air quality parameters from a suite of instruments deployed at 3 urban supesites located in Manchester, Birmingham and London Honor Oak Park.\r\n\r\nMore details to follow." } ], "responsiblepartyinfo_set": [ 181197, 181198, 181199, 181200, 181201, 181202, 181207, 181208, 181209, 181210 ], "onlineresource_set": [ 82611 ] }, { "ob_id": 37972, "uuid": "91625bdf73944c5e896eb56d1fec35ee", "title": "Wind speed and direction data from Gill sonic windmaster instrument at the Manchester Air Quality Site, 2019 onwards", "abstract": "Quality tested wind speed and wind direction data at 7m above ground level from a Gill Sonic Windmaster instruments at the Manchester Air Quality Site (MAQS) for the Integrated Research Observation System for Clean Air (OSCA) project.", "creationDate": "2022-08-23T16:20:05.886204", "lastUpdatedDate": "2022-08-23T16:20:05", "latestDataUpdateTime": "2023-10-03T15:29:45", "updateFrequency": "monthly", "dataLineage": "Data are collected by the project team and transfered directly from the instrument to the computer database, before being transferred to the general database, where it is processed, averaged over minute time intervals and flagged where data is suspect. Once complete, data are uploaded to CEDA for archiving.\r\nData processing software found in: https://github.com/ncasuk/AMF_CVs/releases/tag/v1.0.0", "removedDataReason": "", "keywords": "OSCA, air quality, supersite, maqs, wind,OSCA-AQ", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T14:39:32", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3607, "bboxName": "", "eastBoundLongitude": -2.214244, "westBoundLongitude": -2.214244, "southBoundLatitude": 53.456636, "northBoundLatitude": 53.456636 }, "verticalExtent": null, "result_field": { "ob_id": 37973, "dataPath": "/badc/osca/data/manchester/OSCA_MAQS_Sonic_Windmaster", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 460630863, "numberOfFiles": 189, "fileFormat": "NetCDF and CSV format" }, "timePeriod": { "ob_id": 10483, "startTime": "2019-07-03T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4062, "explanation": "If Wind Speed falls outside the 0 to 50 m/s range it is flagged as outside the operational range\nIf wind direction gives a value outside 0 to 360 degrees it is flagged as outside the operational range", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37975, "uuid": "1166db4db1c1489eb5bbc847e2a261fe", "short_code": "acq", "title": "Acquisition for: OSCA_MAQS_SonicWindmaster_2019-onwards", "abstract": "Acquisition for: OSCA_MAQS_SonicWindmaster_2019-onwards" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37963, "uuid": "ab605b618884401c91afd0274c92144e", "short_code": "proj", "title": "Integrated Research Observation System for Clean Air (OSCA)", "abstract": "The Integrated Research Observation System for Clean Air (OSCA) is a multidisciplinary research project, combining atmospheric observations, laboratory studies, data processing development and integrated scientific synthesis to deliver improved understanding of urban air pollution in the UK, and enable delivery of key objectives of the Clean Air: Analysis and Solutions Programme. OSCA exploits recent significant UK investment in air pollution measurement infrastructure - the air pollution supersites in London, Birmingham and Manchester - and other new UKRI-funded capability developed for field, modelling and laboratory studies of air pollution processes. \r\n\r\nThe project brings together a research team spanning 5 UK HEIs and 2 NERC centres across disciplines of atmospheric science, engineering, mathematics, chemistry, physics and computer science and includes investigators with direct involvement in the provision of science advice in support of policy development - through established links with (e.g.) Department for Environment, Food and Rural Affairs (Defra), Department for Transport (DfT), Department of Health (DoH), regional policymakers, and international bodies. OSCA will provide scientific insights that will inform implementation of the new UK Clean Air Strategy, contribute to development and evaluation of regional air quality policy measures, and enable the development and optimisation of emission abatement measures, for the protection of human health. The project provides a definitive assessment of the current state of UK urban air quality, and of trends in air pollutants - both those expected in response to policy and changing behaviour, and unanticipated consequences of these - and provides data and infrastructure to underpin other proposed projects in the Clean Air Programme. NE/T001984/1, \tNE/T001917/1, NE/T001909/2, NE/T001798/1, NE/T001925/1, NE/T001798/2, NE/T001976/1, NE/T001909/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58082, 58083, 65554, 65555, 65556, 65557, 65558, 65564, 65565, 65566, 65648, 65649, 65650, 65651, 65652 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 37907, "uuid": "65b50d3348cb4745bb7acfcf6f2057b8", "short_code": "coll", "title": "Integrated Research Observation System for Clean Air (OCSA): Birmingham, Manchester and London air quality supersites data collection", "abstract": "A collection of ground-based observation datasets made at 3 air quality supersites in Manchester, Birmingham and London for the Integrated Research Observation System for Clean Air (OSCA) project which is part of the Strategic Priorities Fund (SPF) Clean Air Programme. \r\n\r\nMeasurements were made of a broad range of atmospheric components by similar comprehensive instrument suites at the 3 sites on a longterm basis beginning at the end of 2018. Additional instrumentation was temporarily added for intensive operation periods in June 2021 and January 2022.\r\n\r\nThis project was a collaboration between University of Manchester, University of Birmingham, Kings College London, University of Cambridge, University of York, Centre for Ecology and Hydrology, National Centre for Atmospheric Science (NCAS) and University of Edinburgh." }, { "ob_id": 45670, "uuid": "824c0c058de442ab885431eba938dc03", "short_code": "coll", "title": "NCAS archive of Air quality measurements from Manchester, Birmingham and London air quality supersites", "abstract": "This collection contains maeasurements of air quality parameters from a suite of instruments deployed at 3 urban supesites located in Manchester, Birmingham and London Honor Oak Park.\r\n\r\nMore details to follow." } ], "responsiblepartyinfo_set": [ 181215, 181216, 181217, 181218, 181219, 181220, 181225, 181226 ], "onlineresource_set": [ 82608 ] }, { "ob_id": 37977, "uuid": "f60761f3279042859e5c2902dfa0f2ef", "title": "Nitrogen Dioxide Abundance Data from Teledyne Model T500U Instrument at the Manchester Air Quality Site, 2019 onwards", "abstract": "Mass fraction of Nitrogen Dioxide (NO2) in air measured at 7m above ground level by a Teledyne Model T500U Instrument at the Manchester Air Quality Site (MAQS) for the Integrated Research Observation System for Clean Air (OSCA) project.", "creationDate": "2022-08-23T16:21:19.270603", "lastUpdatedDate": "2022-08-23T16:21:19", "latestDataUpdateTime": "2023-10-06T09:29:44", "updateFrequency": "monthly", "dataLineage": "Data are collected by the project team and transfered directly from the instrument to the computer database, before being transferred to the general database, where it is processed, averaged over minute time intervals and flagged where data is suspect. Once complete, data are uploaded to CEDA for archiving.\r\nData processing software found in: https://github.com/ncasuk/AMF_CVs/releases/tag/v1.0.0", "removedDataReason": "", "keywords": "OSCA, air quality, supersite, maqs, NO2,OSCA-AQ", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T14:41:46", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3608, "bboxName": "", "eastBoundLongitude": -2.214244, "westBoundLongitude": -2.214244, "southBoundLatitude": 53.456636, "northBoundLatitude": 53.456636 }, "verticalExtent": null, "result_field": { "ob_id": 37978, "dataPath": "/badc/osca/data/manchester/OSCA_MAQS_Teledyne_T500U_NO2", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 308625426, "numberOfFiles": 195, "fileFormat": "Data are CSV formatted" }, "timePeriod": { "ob_id": 10484, "startTime": "2018-12-20T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4063, "explanation": "The baseline zero level and the response factor analyser NO2 cylinder, and this technique is verified every 6 month audit. If the zero-corrected NO2 level falls below 0 or above 300 ppb it is flagged. ", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37980, "uuid": "32bfbde18a544ed6911d7f170a73596e", "short_code": "acq", "title": "Acquisition for: Nitrogen Dioxide Abundance Data from Teledyne Model T500U Instrument at Manchester Air Quality Site 2019-onwards", "abstract": "Acquisition for: Nitrogen Dioxide Abundance Data from Teledyne Model T500U Instrument at Manchester Air Quality Site 2019-onwards" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37963, "uuid": "ab605b618884401c91afd0274c92144e", "short_code": "proj", "title": "Integrated Research Observation System for Clean Air (OSCA)", "abstract": "The Integrated Research Observation System for Clean Air (OSCA) is a multidisciplinary research project, combining atmospheric observations, laboratory studies, data processing development and integrated scientific synthesis to deliver improved understanding of urban air pollution in the UK, and enable delivery of key objectives of the Clean Air: Analysis and Solutions Programme. OSCA exploits recent significant UK investment in air pollution measurement infrastructure - the air pollution supersites in London, Birmingham and Manchester - and other new UKRI-funded capability developed for field, modelling and laboratory studies of air pollution processes. \r\n\r\nThe project brings together a research team spanning 5 UK HEIs and 2 NERC centres across disciplines of atmospheric science, engineering, mathematics, chemistry, physics and computer science and includes investigators with direct involvement in the provision of science advice in support of policy development - through established links with (e.g.) Department for Environment, Food and Rural Affairs (Defra), Department for Transport (DfT), Department of Health (DoH), regional policymakers, and international bodies. OSCA will provide scientific insights that will inform implementation of the new UK Clean Air Strategy, contribute to development and evaluation of regional air quality policy measures, and enable the development and optimisation of emission abatement measures, for the protection of human health. The project provides a definitive assessment of the current state of UK urban air quality, and of trends in air pollutants - both those expected in response to policy and changing behaviour, and unanticipated consequences of these - and provides data and infrastructure to underpin other proposed projects in the Clean Air Programme. NE/T001984/1, \tNE/T001917/1, NE/T001909/2, NE/T001798/1, NE/T001925/1, NE/T001798/2, NE/T001976/1, NE/T001909/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58079, 58082, 58083, 65554, 65555, 65556, 65557, 65558, 65564, 65565, 65566, 65971 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 37907, "uuid": "65b50d3348cb4745bb7acfcf6f2057b8", "short_code": "coll", "title": "Integrated Research Observation System for Clean Air (OCSA): Birmingham, Manchester and London air quality supersites data collection", "abstract": "A collection of ground-based observation datasets made at 3 air quality supersites in Manchester, Birmingham and London for the Integrated Research Observation System for Clean Air (OSCA) project which is part of the Strategic Priorities Fund (SPF) Clean Air Programme. \r\n\r\nMeasurements were made of a broad range of atmospheric components by similar comprehensive instrument suites at the 3 sites on a longterm basis beginning at the end of 2018. Additional instrumentation was temporarily added for intensive operation periods in June 2021 and January 2022.\r\n\r\nThis project was a collaboration between University of Manchester, University of Birmingham, Kings College London, University of Cambridge, University of York, Centre for Ecology and Hydrology, National Centre for Atmospheric Science (NCAS) and University of Edinburgh." }, { "ob_id": 45670, "uuid": "824c0c058de442ab885431eba938dc03", "short_code": "coll", "title": "NCAS archive of Air quality measurements from Manchester, Birmingham and London air quality supersites", "abstract": "This collection contains maeasurements of air quality parameters from a suite of instruments deployed at 3 urban supesites located in Manchester, Birmingham and London Honor Oak Park.\r\n\r\nMore details to follow." } ], "responsiblepartyinfo_set": [ 181233, 181234, 181235, 181236, 181237, 181238, 181243, 181244 ], "onlineresource_set": [ 82607 ] }, { "ob_id": 37982, "uuid": "dd3a958c68074e9fbad07e4922b7b011", "title": "CO Abundance Data from Thermo 48i Carbon Monoxide Analyser Instrument at the Manchester Air Quality Site, 2019 onwards", "abstract": "Mass fraction of carbon monoxide (CO) in air measured at 7m above ground level by a Thermo 48i Carbon Monoxide Analyser Instrument at the Manchester Air Quality Site (MAQS) from 2019 onwards for the Integrated Research Observation System for Clean Air (OSCA) project.", "creationDate": "2022-08-23T16:25:15.819219", "lastUpdatedDate": "2022-08-23T16:25:15", "latestDataUpdateTime": "2023-10-06T09:29:48", "updateFrequency": "monthly", "dataLineage": "Data are collected by the project team and transfered directly from the instrument to the computer database, before being transferred to the general database, where it is processed, averaged over minute time intervals and flagged where data is suspect. Once complete, data are uploaded to CEDA for archiving.\r\nData processing software found in: https://github.com/ncasuk/AMF_CVs/releases/tag/v1.0.0", "removedDataReason": "", "keywords": "OSCA, air quality, supersite, CO, maqs, OSCA-AQ", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-17T12:36:51", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3609, "bboxName": "", "eastBoundLongitude": -2.214244, "westBoundLongitude": -2.214244, "southBoundLatitude": 53.456636, "northBoundLatitude": 53.456636 }, "verticalExtent": null, "result_field": { "ob_id": 37983, "dataPath": "/badc/osca/data/manchester/OSCA_MAQS_Thermo_48i_CO", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 200037229, "numberOfFiles": 134, "fileFormat": "NetCDF and CSV format" }, "timePeriod": { "ob_id": 10485, "startTime": "2018-12-20T00:00:00", "endTime": "2022-04-30T00:00:00" }, "resultQuality": { "ob_id": 4064, "explanation": "The baseline level for the CO analyser is tested every 3 hours using a CO zero-cylinder, and the response factor is optained from every 6 month audit. If the zero-corrected CO level falls below 0 or above 300 ppb it is flagged.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37985, "uuid": "aa8e0a5908284ee6a533075794b2cfc7", "short_code": "acq", "title": "Acquisition for: CO Abundance Data from Thermo 48i Carbon Monoxide Analyser Instrument at Manchester Air Quality Site 2019-onwards", "abstract": "Acquisition for: CO Abundance Data from Thermo 48i Carbon Monoxide Analyser Instrument at Manchester Air Quality Site 2019-onwards" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37963, "uuid": "ab605b618884401c91afd0274c92144e", "short_code": "proj", "title": "Integrated Research Observation System for Clean Air (OSCA)", "abstract": "The Integrated Research Observation System for Clean Air (OSCA) is a multidisciplinary research project, combining atmospheric observations, laboratory studies, data processing development and integrated scientific synthesis to deliver improved understanding of urban air pollution in the UK, and enable delivery of key objectives of the Clean Air: Analysis and Solutions Programme. OSCA exploits recent significant UK investment in air pollution measurement infrastructure - the air pollution supersites in London, Birmingham and Manchester - and other new UKRI-funded capability developed for field, modelling and laboratory studies of air pollution processes. \r\n\r\nThe project brings together a research team spanning 5 UK HEIs and 2 NERC centres across disciplines of atmospheric science, engineering, mathematics, chemistry, physics and computer science and includes investigators with direct involvement in the provision of science advice in support of policy development - through established links with (e.g.) Department for Environment, Food and Rural Affairs (Defra), Department for Transport (DfT), Department of Health (DoH), regional policymakers, and international bodies. OSCA will provide scientific insights that will inform implementation of the new UK Clean Air Strategy, contribute to development and evaluation of regional air quality policy measures, and enable the development and optimisation of emission abatement measures, for the protection of human health. The project provides a definitive assessment of the current state of UK urban air quality, and of trends in air pollutants - both those expected in response to policy and changing behaviour, and unanticipated consequences of these - and provides data and infrastructure to underpin other proposed projects in the Clean Air Programme. NE/T001984/1, \tNE/T001917/1, NE/T001909/2, NE/T001798/1, NE/T001925/1, NE/T001798/2, NE/T001976/1, NE/T001909/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58082, 58083, 65554, 65555, 65556, 65557, 65558, 65564, 65565, 65566, 65928, 65929 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 37907, "uuid": "65b50d3348cb4745bb7acfcf6f2057b8", "short_code": "coll", "title": "Integrated Research Observation System for Clean Air (OCSA): Birmingham, Manchester and London air quality supersites data collection", "abstract": "A collection of ground-based observation datasets made at 3 air quality supersites in Manchester, Birmingham and London for the Integrated Research Observation System for Clean Air (OSCA) project which is part of the Strategic Priorities Fund (SPF) Clean Air Programme. \r\n\r\nMeasurements were made of a broad range of atmospheric components by similar comprehensive instrument suites at the 3 sites on a longterm basis beginning at the end of 2018. Additional instrumentation was temporarily added for intensive operation periods in June 2021 and January 2022.\r\n\r\nThis project was a collaboration between University of Manchester, University of Birmingham, Kings College London, University of Cambridge, University of York, Centre for Ecology and Hydrology, National Centre for Atmospheric Science (NCAS) and University of Edinburgh." }, { "ob_id": 45670, "uuid": "824c0c058de442ab885431eba938dc03", "short_code": "coll", "title": "NCAS archive of Air quality measurements from Manchester, Birmingham and London air quality supersites", "abstract": "This collection contains maeasurements of air quality parameters from a suite of instruments deployed at 3 urban supesites located in Manchester, Birmingham and London Honor Oak Park.\r\n\r\nMore details to follow." } ], "responsiblepartyinfo_set": [ 181251, 181252, 181253, 181254, 181255, 181256, 181261, 181262 ], "onlineresource_set": [ 82606 ] }, { "ob_id": 37987, "uuid": "966423f87a304c8b99a80f66c1b8e6fd", "title": "Ozone Abundance Data from Model 49i Ozone Analyzer Instrument at the Manchester Air Quality Site, 2019 onwards", "abstract": "Mass Fraction of Ozone (O3) in air measured at 7 metres above ground level by a Thermo Model 49i Ozone Analyzer Instrument at the Manchester Air Quality Site (MAQS) for the Integrated Research Observation System for Clean Air (OSCA) project.", "creationDate": "2022-08-23T16:26:27.509628", "lastUpdatedDate": "2022-08-23T16:26:27", "latestDataUpdateTime": "2023-10-06T09:29:53", "updateFrequency": "monthly", "dataLineage": "Data are collected by the project team and transfered directly from the instrument to the computer database, before being transferred to the general database, where it is processed, averaged over minute time intervals and flagged where data is suspect. Once complete, data are uploaded to CEDA for archiving.\r\nData processing software found in: https://github.com/ncasuk/AMF_CVs/releases/tag/v1.0.0", "removedDataReason": "", "keywords": "OSCA, air quality, supersite, O3,maqs,OSCA-AQ", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T15:05:49", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3610, "bboxName": "", "eastBoundLongitude": -2.214244, "westBoundLongitude": -2.214244, "southBoundLatitude": 53.456636, "northBoundLatitude": 53.456636 }, "verticalExtent": null, "result_field": { "ob_id": 37988, "dataPath": "/badc/osca/data/manchester/OSCA_MAQS_Thermo_49i_O3", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 312610964, "numberOfFiles": 195, "fileFormat": "NetCDF and CSV format" }, "timePeriod": { "ob_id": 10486, "startTime": "2018-12-20T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4065, "explanation": "This data is then audited using a baseline zero level and the response factor every 6 month audit. If the zero-corrected O3 level falls below 0 or above 300 ppb it is flagged. ", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37990, "uuid": "d8344f7805eb436593fc39ab4be2cca9", "short_code": "acq", "title": "Acquisition for: Ozone Abundance Data from Model 49i Ozone Analyzer Instrument at Manchester Air Quality Site 2019-22", "abstract": "Acquisition for: Ozone Abundance Data from Model 49i Ozone Analyzer Instrument at Manchester Air Quality Site 2019-22" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37963, "uuid": "ab605b618884401c91afd0274c92144e", "short_code": "proj", "title": "Integrated Research Observation System for Clean Air (OSCA)", "abstract": "The Integrated Research Observation System for Clean Air (OSCA) is a multidisciplinary research project, combining atmospheric observations, laboratory studies, data processing development and integrated scientific synthesis to deliver improved understanding of urban air pollution in the UK, and enable delivery of key objectives of the Clean Air: Analysis and Solutions Programme. OSCA exploits recent significant UK investment in air pollution measurement infrastructure - the air pollution supersites in London, Birmingham and Manchester - and other new UKRI-funded capability developed for field, modelling and laboratory studies of air pollution processes. \r\n\r\nThe project brings together a research team spanning 5 UK HEIs and 2 NERC centres across disciplines of atmospheric science, engineering, mathematics, chemistry, physics and computer science and includes investigators with direct involvement in the provision of science advice in support of policy development - through established links with (e.g.) Department for Environment, Food and Rural Affairs (Defra), Department for Transport (DfT), Department of Health (DoH), regional policymakers, and international bodies. OSCA will provide scientific insights that will inform implementation of the new UK Clean Air Strategy, contribute to development and evaluation of regional air quality policy measures, and enable the development and optimisation of emission abatement measures, for the protection of human health. The project provides a definitive assessment of the current state of UK urban air quality, and of trends in air pollutants - both those expected in response to policy and changing behaviour, and unanticipated consequences of these - and provides data and infrastructure to underpin other proposed projects in the Clean Air Programme. NE/T001984/1, \tNE/T001917/1, NE/T001909/2, NE/T001798/1, NE/T001925/1, NE/T001798/2, NE/T001976/1, NE/T001909/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58079, 58082, 58083, 65554, 65555, 65556, 65557, 65558, 65564, 65565, 65566, 65647 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 37907, "uuid": "65b50d3348cb4745bb7acfcf6f2057b8", "short_code": "coll", "title": "Integrated Research Observation System for Clean Air (OCSA): Birmingham, Manchester and London air quality supersites data collection", "abstract": "A collection of ground-based observation datasets made at 3 air quality supersites in Manchester, Birmingham and London for the Integrated Research Observation System for Clean Air (OSCA) project which is part of the Strategic Priorities Fund (SPF) Clean Air Programme. \r\n\r\nMeasurements were made of a broad range of atmospheric components by similar comprehensive instrument suites at the 3 sites on a longterm basis beginning at the end of 2018. Additional instrumentation was temporarily added for intensive operation periods in June 2021 and January 2022.\r\n\r\nThis project was a collaboration between University of Manchester, University of Birmingham, Kings College London, University of Cambridge, University of York, Centre for Ecology and Hydrology, National Centre for Atmospheric Science (NCAS) and University of Edinburgh." }, { "ob_id": 45670, "uuid": "824c0c058de442ab885431eba938dc03", "short_code": "coll", "title": "NCAS archive of Air quality measurements from Manchester, Birmingham and London air quality supersites", "abstract": "This collection contains maeasurements of air quality parameters from a suite of instruments deployed at 3 urban supesites located in Manchester, Birmingham and London Honor Oak Park.\r\n\r\nMore details to follow." } ], "responsiblepartyinfo_set": [ 181269, 181270, 181271, 181272, 181273, 181274, 181279, 181280 ], "onlineresource_set": [ 82613 ] }, { "ob_id": 37992, "uuid": "1d58f2f5e7874e55a83ca57311dcfb9a", "title": "NO and NOy Abundance Data from Thermo Model 42i-Y NOY Analyzer Instrument at the Manchester Air Quality Site, 2019 onwards", "abstract": "Oxides of nitrogen (NO and NOy) rates measured at 7 metres above ground level by a Thermo Model 42i-Y NOY Analyzer Instrument at the Manchester Air Quality Site (MAQS) for the Integrated Research Observation System for Clean Air (OSCA) project.", "creationDate": "2022-08-23T16:27:35.658595", "lastUpdatedDate": "2022-08-23T16:27:35", "latestDataUpdateTime": "2024-03-09T03:04:08", "updateFrequency": "monthly", "dataLineage": "Data are collected by the project team and transfered directly from the instrument to the computer database, before being transferred to the general database, where it is processed, averaged over minute time intervals and flagged where data is suspect. Once complete, data are uploaded to CEDA for archiving.\r\nData processing software found in: https://github.com/ncasuk/AMF_CVs/releases/tag/v1.0.0", "removedDataReason": "", "keywords": "OSCA, air quality, supersite, maqs, OSCA-AQ", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T15:08:56", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3611, "bboxName": "", "eastBoundLongitude": -2.214244, "westBoundLongitude": -2.214244, "southBoundLatitude": 53.456636, "northBoundLatitude": 53.456636 }, "verticalExtent": null, "result_field": { "ob_id": 37993, "dataPath": "/badc/osca/data/manchester/OSCA_MAQS_Thermo_49iY_NO-NOy", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 492458331, "numberOfFiles": 195, "fileFormat": "NetCDF and CSV format" }, "timePeriod": { "ob_id": 10487, "startTime": "2018-12-20T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4066, "explanation": "The baseline zero level and the response factor analyser NO2 cylinder, and this technique is verified every 6 month audit. If the zero-corrected NO or NOy level falls below 0 or above 300 ppb it is flagged. Also if the NO level exceeds the NOy data, it has been flagged", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37995, "uuid": "e230e834ff0a40c299f52204591bda21", "short_code": "acq", "title": "Acquisition for: NO and NOy Abundance Data from Thermo Model 42i-Y NOY Analyzer Instrument at Manchester Air Quality Site 2019-22", "abstract": "Acquisition for: NO and NOy Abundance Data from Thermo Model 42i-Y NOY Analyzer Instrument at Manchester Air Quality Site 2019-22" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37963, "uuid": "ab605b618884401c91afd0274c92144e", "short_code": "proj", "title": "Integrated Research Observation System for Clean Air (OSCA)", "abstract": "The Integrated Research Observation System for Clean Air (OSCA) is a multidisciplinary research project, combining atmospheric observations, laboratory studies, data processing development and integrated scientific synthesis to deliver improved understanding of urban air pollution in the UK, and enable delivery of key objectives of the Clean Air: Analysis and Solutions Programme. OSCA exploits recent significant UK investment in air pollution measurement infrastructure - the air pollution supersites in London, Birmingham and Manchester - and other new UKRI-funded capability developed for field, modelling and laboratory studies of air pollution processes. \r\n\r\nThe project brings together a research team spanning 5 UK HEIs and 2 NERC centres across disciplines of atmospheric science, engineering, mathematics, chemistry, physics and computer science and includes investigators with direct involvement in the provision of science advice in support of policy development - through established links with (e.g.) Department for Environment, Food and Rural Affairs (Defra), Department for Transport (DfT), Department of Health (DoH), regional policymakers, and international bodies. OSCA will provide scientific insights that will inform implementation of the new UK Clean Air Strategy, contribute to development and evaluation of regional air quality policy measures, and enable the development and optimisation of emission abatement measures, for the protection of human health. The project provides a definitive assessment of the current state of UK urban air quality, and of trends in air pollutants - both those expected in response to policy and changing behaviour, and unanticipated consequences of these - and provides data and infrastructure to underpin other proposed projects in the Clean Air Programme. NE/T001984/1, \tNE/T001917/1, NE/T001909/2, NE/T001798/1, NE/T001925/1, NE/T001798/2, NE/T001976/1, NE/T001909/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58082, 58083, 65554, 65555, 65556, 65557, 65558, 65564, 65565, 65566, 65643, 65644, 65645, 65646 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 37907, "uuid": "65b50d3348cb4745bb7acfcf6f2057b8", "short_code": "coll", "title": "Integrated Research Observation System for Clean Air (OCSA): Birmingham, Manchester and London air quality supersites data collection", "abstract": "A collection of ground-based observation datasets made at 3 air quality supersites in Manchester, Birmingham and London for the Integrated Research Observation System for Clean Air (OSCA) project which is part of the Strategic Priorities Fund (SPF) Clean Air Programme. \r\n\r\nMeasurements were made of a broad range of atmospheric components by similar comprehensive instrument suites at the 3 sites on a longterm basis beginning at the end of 2018. Additional instrumentation was temporarily added for intensive operation periods in June 2021 and January 2022.\r\n\r\nThis project was a collaboration between University of Manchester, University of Birmingham, Kings College London, University of Cambridge, University of York, Centre for Ecology and Hydrology, National Centre for Atmospheric Science (NCAS) and University of Edinburgh." }, { "ob_id": 45670, "uuid": "824c0c058de442ab885431eba938dc03", "short_code": "coll", "title": "NCAS archive of Air quality measurements from Manchester, Birmingham and London air quality supersites", "abstract": "This collection contains maeasurements of air quality parameters from a suite of instruments deployed at 3 urban supesites located in Manchester, Birmingham and London Honor Oak Park.\r\n\r\nMore details to follow." } ], "responsiblepartyinfo_set": [ 181287, 181288, 181289, 181290, 181291, 181292, 181297, 181298 ], "onlineresource_set": [ 82612 ] }, { "ob_id": 37997, "uuid": "21723af1f2a640c6990ce9cb3b70aaef", "title": "Elemental Composition Data of Ambient PM2.5 and PM10 Aerosol Particles in Air from a Cooper XACT 625i Instrument at the Manchester Air Quality Site, 2019 onwards", "abstract": "Elemental Composition of Ambient Aerosol Particles in Air Data measured at 7m above ground level using a Cooper XACT 625i Instrument at the Manchester Air Quality Site for the Integrated Research Observation System for Clean Air (OSCA) project. Only PM10 Aerosols are analysed prior to December 2020, and both PM2.5 and PM10 are analysed from December 2020 onwards.", "creationDate": "2022-08-23T16:28:40.237209", "lastUpdatedDate": "2022-08-23T16:28:40", "latestDataUpdateTime": "2023-07-21T15:45:37", "updateFrequency": "monthly", "dataLineage": "Data are collected by the project team and transfered directly from the instrument to the computer database, before being transferred to the general database, where it is processed, averaged over minute time intervals and flagged where data is suspect. Data processing software found in: https://github.com/ncasuk/AMF_CVs/releases/tag/v1.0.0\r\nXACT obtains data exists for PM10 Aerosols only for data prior to December 2020, and both PM2.5 and PM10 are analysed between December 2020 and Febuary 2022.\r\nOnce complete, data are uploaded to CEDA for archiving.", "removedDataReason": "", "keywords": "OSCA, air quality, supersite, maqs, PM10, PM2.5,OSCA-AQ", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T14:40:26", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3612, "bboxName": "", "eastBoundLongitude": -2.214244, "westBoundLongitude": -2.214244, "southBoundLatitude": 53.456636, "northBoundLatitude": 53.456636 }, "verticalExtent": null, "result_field": { "ob_id": 37998, "dataPath": "/badc/osca/data/manchester/OSCA_MAQS_XACT_elemental_comp", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 71000701, "numberOfFiles": 155, "fileFormat": "NetCDF and CSV format" }, "timePeriod": { "ob_id": 10488, "startTime": "2019-07-03T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4067, "explanation": "No response or zero factor implimented. Intercomparison to be obtained once ACSM data is obtained. Will be uploaded to GitHub folder in due course.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-08-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38000, "uuid": "c7275f9381d845feb568a4b2f3f97580", "short_code": "acq", "title": "Acquisition for: Elemental Composition Data of Ambient PM2.5 and PM10 Aerosol Particles in Air from a Cooper XACT 625i Instrument at Manchester Air Quality Site 2019-22", "abstract": "Acquisition for: Elemental Composition Data of Ambient PM2.5 and PM10 Aerosol Particles in Air from a Cooper XACT 625i Instrument at Manchester Air Quality Site 2019-22" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37963, "uuid": "ab605b618884401c91afd0274c92144e", "short_code": "proj", "title": "Integrated Research Observation System for Clean Air (OSCA)", "abstract": "The Integrated Research Observation System for Clean Air (OSCA) is a multidisciplinary research project, combining atmospheric observations, laboratory studies, data processing development and integrated scientific synthesis to deliver improved understanding of urban air pollution in the UK, and enable delivery of key objectives of the Clean Air: Analysis and Solutions Programme. OSCA exploits recent significant UK investment in air pollution measurement infrastructure - the air pollution supersites in London, Birmingham and Manchester - and other new UKRI-funded capability developed for field, modelling and laboratory studies of air pollution processes. \r\n\r\nThe project brings together a research team spanning 5 UK HEIs and 2 NERC centres across disciplines of atmospheric science, engineering, mathematics, chemistry, physics and computer science and includes investigators with direct involvement in the provision of science advice in support of policy development - through established links with (e.g.) Department for Environment, Food and Rural Affairs (Defra), Department for Transport (DfT), Department of Health (DoH), regional policymakers, and international bodies. OSCA will provide scientific insights that will inform implementation of the new UK Clean Air Strategy, contribute to development and evaluation of regional air quality policy measures, and enable the development and optimisation of emission abatement measures, for the protection of human health. The project provides a definitive assessment of the current state of UK urban air quality, and of trends in air pollutants - both those expected in response to policy and changing behaviour, and unanticipated consequences of these - and provides data and infrastructure to underpin other proposed projects in the Clean Air Programme. NE/T001984/1, \tNE/T001917/1, NE/T001909/2, NE/T001798/1, NE/T001925/1, NE/T001798/2, NE/T001976/1, NE/T001909/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58082, 58083, 65554, 65555, 65556, 65557, 65558, 65564, 65565, 65566, 65571, 65572, 65573, 65574, 65575, 65576, 65577, 65578, 65579, 65580, 65581, 65582, 65583, 65584, 65585, 65586, 65587, 65588, 65589, 65590, 65591, 65592, 65593, 65594, 65595, 65596, 65597, 65598, 65599, 65600, 65601, 65602, 65603, 65604, 65605, 65606, 65607, 65608, 65609, 65610, 65611, 65612, 65613, 65614, 65615, 65616, 65617, 65618, 65619, 65620, 65621, 65622, 65623, 65624, 65625, 65626, 65627, 65628, 65629, 65630, 65631, 65632, 65633, 65634, 65635, 65636, 65637, 65638, 65639, 65640, 65641, 65642, 66004, 66005, 66006, 66007, 66008, 66009, 66010, 66011, 66012, 66013, 66014, 66015, 66016, 66017, 66018, 66019, 66020, 66021, 66022, 66023, 66024, 66025, 66026, 66027, 66028, 66029, 66030, 66031, 66032, 66033, 66034, 66035, 66036, 66037, 66038, 66039, 66040, 66041, 66042, 66043, 66044, 66045, 66046, 66047, 66048, 66049, 66050, 66051, 66052, 66053, 66054, 66055, 66056, 66057, 66058, 66059, 66060, 66061, 66062, 66063, 66064, 66065, 66066, 66067, 66068, 66069, 66070, 66071, 66072, 66073, 66074 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 37907, "uuid": "65b50d3348cb4745bb7acfcf6f2057b8", "short_code": "coll", "title": "Integrated Research Observation System for Clean Air (OCSA): Birmingham, Manchester and London air quality supersites data collection", "abstract": "A collection of ground-based observation datasets made at 3 air quality supersites in Manchester, Birmingham and London for the Integrated Research Observation System for Clean Air (OSCA) project which is part of the Strategic Priorities Fund (SPF) Clean Air Programme. \r\n\r\nMeasurements were made of a broad range of atmospheric components by similar comprehensive instrument suites at the 3 sites on a longterm basis beginning at the end of 2018. Additional instrumentation was temporarily added for intensive operation periods in June 2021 and January 2022.\r\n\r\nThis project was a collaboration between University of Manchester, University of Birmingham, Kings College London, University of Cambridge, University of York, Centre for Ecology and Hydrology, National Centre for Atmospheric Science (NCAS) and University of Edinburgh." }, { "ob_id": 45670, "uuid": "824c0c058de442ab885431eba938dc03", "short_code": "coll", "title": "NCAS archive of Air quality measurements from Manchester, Birmingham and London air quality supersites", "abstract": "This collection contains maeasurements of air quality parameters from a suite of instruments deployed at 3 urban supesites located in Manchester, Birmingham and London Honor Oak Park.\r\n\r\nMore details to follow." } ], "responsiblepartyinfo_set": [ 181305, 181306, 181307, 181308, 181309, 181310, 181315, 181316, 181317, 181318 ], "onlineresource_set": [ 82609 ] }, { "ob_id": 38002, "uuid": "c6b366dabf9b4536b5500e5f1f7a7235", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 6.12 (v20220824)", "abstract": "Input Data for Figure 6.12 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 6.12 shows contribution to effective radiative forcing (ERF) and global mean surface air temperature (GSAT) change from component emissions between 1750 to 2019 based on CMIP6 models. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nSzopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 817–922, doi:10.1017/9781009157896.008.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Contribution to effective radiative forcing (ERF) (a) and global mean surface air temperature (GSAT) change (b) from component emissions between 1750 to 2019 based on CMIP6 models\r\n\r\nERFs for the direct effect of well-mixed greenhouse gases (WMGHGs) are from the analytical formulae in section 7.3.2, H2O (strat) is from Table 7.8. ERFs for other components are multi-model means from Thornhill et al. (2021b) and are based on ESM simulations in which emissions of one species at a time are increased from 1850 to 2014 levels. The derived emissions-based ERFs are rescaled to match the concentration-based ERFs in Figure 7.6.\r\n\r\nError bars are 5–95% and for the ERF account for uncertainty in radiative efficiencies and multi-model error in the means. ERFs due to aerosol–radiation (ERFari) and cloud effects are calculated from separate radiation calls for clear-sky and aerosol-free conditions (Ghan, 2013; Thornhill et al., 2021b). \r\n\r\n‘Cloud’ includes cloud adjustments (semi-direct effect) and ERF from indirect aerosol-cloud to –0.22 W m–2 for ERFari and –0.84 W m–2 interactions (ERFaci). The aerosol components (SO2, organic carbon and black carbon) are scaled to sum to –0.22 W m–2 for ERFari and –0.84 W m–2 for ‘cloud’ (Section 7.3.3). \r\n\r\nFor GSAT estimates, time series (1750–2019) for the ERFs have been estimated by scaling with concentrations for WMGHGs and with historical emissions for SLCFs. The time variation of ERFaci for aerosols is from Chapter 7. The global mean temperature response is calculated from the ERF time series using an impulse response function (Cross-Chapter Box 7.1) with a climate feedback parameter of –1.31 W m–2 °C–1. \r\n\r\nContributions to ERF and GSAT change from contrails and light-absorbing particles on snow and ice are not represented, but their estimates can be seen on Figure 7.6 and 7.7, respectively. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 6.SM.3)\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nERFari stands for Effective Radiative Forcing of aerosol-radiation interactions.\r\nERFaci stands for Effective Radiative Forcing of aerosol-cloud interactions. \r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 6.12:\r\n \r\n - Data file: hodnebrog_tab3.csv: Radiative forcing for HFCs from Hodnebrog et al (2020)\r\n - Data file: recommended_irf_from_2xCO2_2021_02_25_222758.csv: Impulse response function (IRF) from AR6\r\n - Data file: table2_thornhill2020.csv: ERF from Thornhill et al (2021)\r\n - Data file: attribution_input.csv\r\n - Data file: attribution_input_sd.csv\r\n\r\nThe folder: 'LLGHG_history_AR6_v9_updated' - contains csv files for each sheet in excel file 'LLGHG_history_AR6_v9_updated.xlsx' which gives historical concentrations from AR6.\r\n\r\nThe folder CEDS_v2021-02-05_emissions (historical emissions of SLCFs from CEDS) contains the following file formats:\r\n\r\n${component}$_${region}$_CEDS_emissions_by${category}$_${type}$_2021_02_05.csv, with:\r\n\r\n- ${component}: BC, CH4, CO2, CO, N2O, NH3, NMVOC, NOx, OC, SO2\r\n- ${region}: blank, or 'global'\r\n- ${category}: sector, country, sector and country\r\n- ${type}: blank, or 'fuel'\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nPanels were plotted using Python and the code has been embedded in Jupyter notebooks for reproducibility - code is available in the GitHub repository linked in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n - Link to the GitHub repository containing the Jupyter notebooks used to run the code associated with this figure.\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-08-15T14:20:48.537953", "lastUpdatedDate": "2022-12-15T20:21:18", "latestDataUpdateTime": "2022-12-16T09:09:02", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, historical effective radiative forcing, attributed historical warming", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-10T14:51:20", "doiPublishedTime": "2023-07-03T14:31:37.517526", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38039, "dataPath": "/badc/ar6_wg1/data/ch_06/inputdata_ch6_fig12/v20220824", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 168322757, "numberOfFiles": 58, "fileFormat": "CSV, XLSX, txt, net-CDF" }, "timePeriod": { "ob_id": 10459, "startTime": "1750-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 4259, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-10" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39580, "uuid": "c7624ed5d9f1410b86820236c4346ac6", "short_code": "comp", "title": "Caption for Figure 6.12 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Contribution to effective radiative forcing (ERF) (a) and global mean surface air temperature (GSAT) change (b) from component emissions between 1750 to 2019 based on CMIP6 models (Thornhill et al. , 2021b). ERFs for the direct effect of well-mixed greenhouse gases (WMGHGs) are from the analytical formulae in section 7.3.2, H2O (strat) is from Table 7.8. ERFs for other components are multi-model means from Thornhill et al. (2021b) and are based on ESM simulations in which emissions of one species at a time are increased from 1850 to 2014 levels. The derived emissions-based ERFs are rescaled to match the concentration-based ERFs in Figure 7.6. Error bars are 5–95% and for the ERF account for uncertainty in radiative efficiencies and multi-model error in the means. ERFs due to aerosol–radiation (ERFari) and cloud effects are calculated from separate radiation calls for clear-sky and aerosol-free conditions (Ghan, 2013; Thornhill et al., 2021b). ‘Cloud’ includes cloud adjustments (semi-direct effect) and ERF from indirect aerosol-cloud to –0.22 W m–2 for ERFari and –0.84 W m–2 interactions (ERFaci). The aerosol components (SO2, organic carbon and black carbon) are scaled to sum to –0.22 W m–2 for ERFari and –0.84 W m–2 for ‘cloud’ Section 7.3.3). For GSAT estimates, time series (1750–2019) for the ERFs have been estimated by scaling with concentrations for WMGHGs and with historical emissions for SLCFs. The time variation of ERFaci for aerosols is from Chapter 7. The global mean temperature response is calculated from the ERF time series using an impulse response function (Cross-Chapter Box 7.1) with a climate feedback parameter of –1.31 W m–2°C–1. Contributions to ERF and GSAT change from contrails and light-absorbing particles on snow and ice are not represented, but their estimates can be seen on Figure 7.6 and 7.7, respectively. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12570 ], "observationcollection_set": [ { "ob_id": 32721, "uuid": "18cf830c350e451a9002105294861dc0", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 6.3\r\n- data for Figure 6.12\r\n- input data for Figure 6.12\r\n- input data for Figure 6.14\r\n- data for Figure 6.16\r\n- data for Figure 6.17\r\n- data for Figure 6.20\r\n- data for Figure 6.21\r\n- data for Figure 6.22\r\n- input data for Figure 6.22\r\n- data for Figure 6.23\r\n- data for figure 6.24\r\n- input data for Figure 6.24\r\n- data for Figure 6.SM.3\r\n- data for Figure 6.SM.4\r\n- data for Figure 6.SM.5" } ], "responsiblepartyinfo_set": [ 181338, 181339, 181340, 181341, 181342, 181343, 181344, 181345, 181346, 192697 ], "onlineresource_set": [ 52768, 52770, 52771, 82651, 82652 ] }, { "ob_id": 38003, "uuid": "0ca27ce794324ec086d6a6c60d5567ac", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 6.22 and Figure 6.24 (v20220824)", "abstract": "Input data for figures 6.22 and 6.24 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 6.22 shows time evolution of the effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-economic Pathways (SSPs). \r\n\r\nFigure 6.24 shows effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-economic Pathways (SSPs).\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nSzopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 817–922, doi:10.1017/9781009157896.008.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nFigure 6.22 has 1 panel, with input data provided for this panel.\r\n\r\nFigure 6.24 has 2 subpanels, with input data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years), and the sum of these, relative to the year 2019 and to the year 1750. \r\n\r\n- The GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 (feedback parameter of –1.31 W m–2 °C–1, see Cross-Chapter Box 7.1). The vertical bars to the right in each panel show the uncertainties (5–95% ranges) for the GSAT change between 2019 and 2100. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 6.SM.3).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figures\r\n ---------------------------------------------------\r\n Data provided in relation to figures 6.22 and 6.24:\r\n \r\n - Data file: AR6_ERF_1750-2019.csv: ERF derived from FAiR\r\n - Data file: AR6_ERF_minorGHGs_1750-2019.csv: ERF derived from FAiR\r\n - Data file: recommended_irf_from_2xCO2_2021_02_25_222758.csv: Impulse response function (IRF) from AR6\r\n\r\nThe folder SSPs (SSP scenario ERF from FAIR) contains the following file formats:\r\n\r\nERF_${scenario}$_${component}$_1750-2500.csv, with:\r\n\r\n- $(scenario): the name of the scenario : ssp119, ssp126, ssp245, ssp334, ssp370, ssp370-low-nTCF-aerchemmip, ssp370-low-nTCF-gidden, ssp434, ssp460, ssp534-over, ssp585\r\n- $(component): blank, or 'minor GHGs'\r\n\r\nThe folder slcf_warming_ranges (uncertainties in dGSAT from FAIR) contains the following file formats:\r\n\r\nslcf_warming_ranges_${scenario)_$(uncertainty).csv, with:\r\n\r\n- ${scenario}: the name of the scenario : ssp119, ssp126, ssp245, ssp334, ssp370, ssp370-lowNTCF-aerchemmip, ssp370-lowNTCF-gidden, ssp434, ssp460, ssp534-over, ssp585\r\n- ${uncertainty}: percentiles of warming: p05, p16, p50, p84, p95\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figures from the provided data\r\n ---------------------------------------------------\r\nPanels were plotted using Python and the code has been embedded in Jupyter notebooks for reproducibility - code is available in the GitHub repository linked in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to Figure 6.22 on the IPCC AR6 website\r\n - Link to Figure 6.24 on the IPCC AR6 website\r\n - Link to the report component containing the figures (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n - Link to the GitHub repository containing the Jupyter notebooks used to run the code associated with these figures.\r\n - Link to the code for the figures, archived on Zenodo.", "creationDate": "2022-08-15T14:20:47.447926", "lastUpdatedDate": "2022-12-16T09:25:32", "latestDataUpdateTime": "2023-05-10T16:07:02", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, scenario warming, short-lived climate forcers, aerosol, methane, ozone, HFCs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-10T15:13:03", "doiPublishedTime": "2023-07-03T14:42:39.190510", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38040, "dataPath": "/badc/ar6_wg1/data/ch_06/inputdata_ch6_fig22/v20220824", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5507225, "numberOfFiles": 82, "fileFormat": "CSV, txt" }, "timePeriod": { "ob_id": 10458, "startTime": "2019-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4191, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39631, "uuid": "08fddd2bb51440b58583938b715dd6d0", "short_code": "comp", "title": "Caption for Figure 6.22 and 6.24 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Figure 6.22 | Time evolution of the effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-Economic Pathways (SSPs). Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years), and the sum of these, relative to the year 2019 and to the year 1750. The GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 (feedback parameter of –1.31 W m–2°C–1, see Cross-Chapter Box 7.1). The vertical bars to the right in each panel show the uncertainties (5–95% ranges) for the GSAT change between 2019 and 2100. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).\r\n\r\nFigure 6.24 | Effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-economic Pathways (SSPs). Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years), are compared with those of total anthropogenic forcing for 2040 and 2100 relative to the year 2019. The GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 (feedback parameter of –1.31 W m–2°C–1; Cross-Chapter Box 7.1). Uncertainties are 5–95% ranges. The scenario total (grey bar) includes all anthropogenic forcings (long- and short-lived climate forcers, and land-use changes) whereas the white diamonds and bars show the net effects of SLCFs and HFCs and their uncertainties. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64273, 64274, 64275, 64276, 64277, 64278, 64279, 64280, 64281, 64282, 64283, 64284, 64285, 64286, 64287, 64288, 64289, 64290, 64291, 64292, 64293, 64294, 64295, 64296, 64297, 64298, 64299, 64300, 64301, 64302, 64303, 64304, 64305, 64306, 64307, 64308, 64309, 64310, 64311, 64312, 64313, 64314, 64315, 64316, 64317, 64318, 64319, 64320, 64321, 64322, 64323, 64324, 64325, 64326, 64327, 64328, 64329, 64330, 64331, 64332, 64333, 64334, 64335, 64336, 64337, 64338, 64340, 64341, 64342, 87103, 87104, 87105 ], "vocabularyKeywords": [], "identifier_set": [ 12572 ], "observationcollection_set": [ { "ob_id": 32721, "uuid": "18cf830c350e451a9002105294861dc0", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 6.3\r\n- data for Figure 6.12\r\n- input data for Figure 6.12\r\n- input data for Figure 6.14\r\n- data for Figure 6.16\r\n- data for Figure 6.17\r\n- data for Figure 6.20\r\n- data for Figure 6.21\r\n- data for Figure 6.22\r\n- input data for Figure 6.22\r\n- data for Figure 6.23\r\n- data for figure 6.24\r\n- input data for Figure 6.24\r\n- data for Figure 6.SM.3\r\n- data for Figure 6.SM.4\r\n- data for Figure 6.SM.5" } ], "responsiblepartyinfo_set": [ 181347, 181348, 181349, 181350, 181351, 181352, 181353, 181354, 181355, 192698 ], "onlineresource_set": [ 52773, 52772, 52774, 52775, 82653, 82654 ] }, { "ob_id": 38067, "uuid": "8bcf6925cddc4681b96f94d424537b9e", "title": "MIDAS Open: UK daily temperature data, v202207", "abstract": "The UK daily temperature data contain maximum and minimum temperatures (air, grass and concrete slab) measured over a period of up to 24 hours. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM, DLY3208 or AWSDLY messages. The data span from 1853 to 2021. For details on measurement techniques, including calibration information and changes in measurements, see section 5.2 of the MIDAS User Guide linked to from this record. Soil temperature data may be found in the UK soil temperature datasets linked from this record.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2021.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily temperature observations within the full MIDAS collection.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-09-01T15:46:12", "updateFrequency": "notPlanned", "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. Data are extracted from a sub-set of available tables and delivered to Centre for Environmental Data Analysis (CEDA) approximately on a yearly basis.", "removedDataReason": "", "keywords": "Met Office, MIDAS, UK, meteorology, temperature, diurnal, daily, monthly", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2022-09-09T13:46:08", "doiPublishedTime": "2022-09-22T15:46:48", "removedDataTime": null, "geographicExtent": { "ob_id": 18, "bboxName": "MIDAS UK table s geographic domain", "eastBoundLongitude": 1.74002, "westBoundLongitude": -5.54236, "southBoundLatitude": 50.1172, "northBoundLatitude": 60.7592 }, "verticalExtent": null, "result_field": { "ob_id": 38078, "dataPath": "/badc/ukmo-midas-open/data/uk-daily-temperature-obs/dataset-version-202207/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2137232187, "numberOfFiles": 62933, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 10496, "startTime": "1853-01-01T09:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 305, "explanation": "Data undergo quality checking by the Met Office. State of the data in the quality control process and level of data quality are indicated using version numbers and quality control flagging with the data. See documentation about how to use the quality control flagging and version numbers.\n\nThere are also some known data from commissioning trials in the data, which are given a src_id of 99999. These should be ignored by the user.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-11" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 1243, "uuid": "93916103ebf54c5fad1e24fee0255e91", "short_code": "acq", "title": "Acquisition Process for: UK Daily Temperature Data, Part of the Met Office Integrated Data Archive System (MIDAS)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Thermometer; PLATFORMS: NCM (National Climate Message) Station Network, DLY3208 (Daily observations from Metform 3208) Station Network, AWSDLY (Automatic Weather Station Daily values) Station Network; " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 1186, "uuid": "245df050d57a500c183b88df509f5f5a", "short_code": "proj", "title": "Met Office Integrated Data Archive System (MIDAS)", "abstract": "Since the early days of this century the Met Office has been responsible for maintaining the public memory of the weather. All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. The MIDAS database contains the following general types of meteorological data: surface observations over land areas of the UK as far back as the digital record extends, a selection of global surface observations for the last 20 years, global surface marine observations from national and international sources as far back as the digital record extends, radiosonde observations over the UK, and at overseas stations operated by the Met Office, as far back as the digital record extends, a selection of global radiosonde observations for the last 10 years." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 76672, 76674, 76675, 76678, 76680, 76690, 76694, 76695, 76696, 76697, 76724, 76725, 76726, 76727, 76728, 76729, 76730, 76731, 76732, 76733, 76734, 76735, 76736, 76737, 76738, 76739 ], "vocabularyKeywords": [], "identifier_set": [ 12199 ], "observationcollection_set": [ { "ob_id": 26184, "uuid": "dbd451271eb04662beade68da43546e1", "short_code": "coll", "title": "Met Office MIDAS Open: UK Land Surface Stations Data (1853-current)", "abstract": "MIDAS Open is the open data version of the Met Office Integrated Data Archive System (MIDAS) containing land surface station data starting from 1853 and ending at the of the previous complete year. This collection comprises of hourly and daily weather measurements and observations of parameters relating to temperature, rainfall, sunshine, radiation, wind and weather observations such as present weather codes, cloud cover, snow etc.\r\n\r\nThe collection contains land surface observations data from those stations where the data have been designated as public sector information. Prior to version v202407 this consisted of stations operated by the Met Office only, but from version v202407, daily and hourly rainfall observations from stations with gauges owned by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) have also been included in the collection. Since then, stations owned by other third-party organisations where approval for inclusion has been reached have also been added to the product.\r\n\r\nAll of these data are provided under an Open Government Licence. \r\n\r\nThe current collection contains the following proportions of the fuller MIDAS dataset collection:\r\n\r\n96% of daily temperature observations\r\n96% of daily weather observations\r\n92% of hourly weather observations\r\n94% of daily rainfall observations\r\n96% of hourly rainfall observations\r\n98% of soil temperature observations\r\n96% of solar radiation observations\r\n93% of mean wind observations\r\n\r\nDaily rainfall: Versions up until MIDAS Open v202407 only have about 13% coverage of observations. In version v202407, the coverage was increased to 58% with the inclusion of the third-party hydrological agency stations. In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. The collection includes stations which are currently operational as well as stations which were operational in the past and have since closed.\r\n\r\nEach version of the dataset will include data up until the end of the previous complete year relative to the year in the version number of the dataset (e.g. v202407 included data up until the end of 2023).\r\n\r\nNote: This collection does not supersede the full MIDAS collection which is also archived at CEDA." } ], "responsiblepartyinfo_set": [ 181389, 181390, 181391, 181392, 181393, 181394, 181395, 181396, 181397 ], "onlineresource_set": [ 52787, 52784, 52785, 52786, 52790, 52791, 52788, 90622, 90623, 90624, 90625, 90626, 90627, 90628, 90629, 90630, 90631, 90632, 90633, 90634, 90635, 90636, 90637, 90638, 90639, 90640, 90641, 90642, 90643, 90644, 90645, 52789 ] }, { "ob_id": 38068, "uuid": "fa83484e57854d6fbde16ff945ff6dc0", "title": "MIDAS Open: UK mean wind data, v202207", "abstract": "The UK mean wind data contain the mean wind speed and direction, and the direction, speed and time of the maximum gust, all during 1 or more hours, ending at the stated time and date. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, DLY3208, HWNDAUTO and HWND6910. The data spans from 1949 to 2021.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2021.\r\n\r\nFor further details on observing practice, including measurement accuracies for the message types, see relevant sections of the MIDAS User Guide linked from this record (e.g. section 3.3 details the wind network in the UK, section 5.5 covers wind measurements in general and section 4 details message type information).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-09-01T12:25:07", "updateFrequency": "notPlanned", "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. Data are extracted from a sub-set of available tables and delivered to Centre for Environmental Data Analysis (CEDA) approximately on a yearly basis.", "removedDataReason": "", "keywords": "Met Office, MIDAS, UK, meteorology, wind speed, wind direction, gust", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2022-09-09T13:48:31", "doiPublishedTime": "2022-09-22T15:47:11", "removedDataTime": null, "geographicExtent": { "ob_id": 17, "bboxName": "", "eastBoundLongitude": 1.74002, "westBoundLongitude": -8.5636, "southBoundLatitude": 49.914, "northBoundLatitude": 60.8562 }, "verticalExtent": null, "result_field": { "ob_id": 38080, "dataPath": "/badc/ukmo-midas-open/data/uk-mean-wind-obs/dataset-version-202207/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7743361027, "numberOfFiles": 14644, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 10491, "startTime": "1949-01-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 296, "explanation": "Data undergo quality checking by the Met Office. State of the data in the quality control process and level of data quality are indicated using version numbers and quality control flagging with the data. See documentation about how to use the quality control flagging and version numbers.\n\nThere are also some known data from commissioning trials in the data, which are given a src_id of 99999. These should be ignored by the user.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-11" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 1194, "uuid": "38b9698404d64725bc8a0b9b6b6f15ae", "short_code": "acq", "title": "Acquisition Process for: UK Mean Wind Data, Part of the Met Office Integrated Data Archive System (MIDAS)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Anemometer; PLATFORMS: Land SYNOP (surface synoptic observations) Station Network, HCM (Hourly Climate Messages) Station Network, AWSHRLY (Automatic Weather Station Hourly values) Station Network, DLY3208 (Daily observations from Metform 3208) Station Network, HWND6910 (Hourly WIND from Metform 6910) Station Network, HWNDAUTO (Hourly WiND from AUTOmatic recording devices) Station Network; " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 1186, "uuid": "245df050d57a500c183b88df509f5f5a", "short_code": "proj", "title": "Met Office Integrated Data Archive System (MIDAS)", "abstract": "Since the early days of this century the Met Office has been responsible for maintaining the public memory of the weather. All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. The MIDAS database contains the following general types of meteorological data: surface observations over land areas of the UK as far back as the digital record extends, a selection of global surface observations for the last 20 years, global surface marine observations from national and international sources as far back as the digital record extends, radiosonde observations over the UK, and at overseas stations operated by the Met Office, as far back as the digital record extends, a selection of global radiosonde observations for the last 10 years." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 76672, 76674, 76675, 76678, 76679, 76680, 76682, 76683, 76686, 76690, 76694, 76695, 76696, 76697, 76838, 76839, 76840, 76841, 76842, 76843, 76844, 76845, 76846, 76847, 76848, 76849, 76850, 76851 ], "vocabularyKeywords": [], "identifier_set": [ 12202 ], "observationcollection_set": [ { "ob_id": 26184, "uuid": "dbd451271eb04662beade68da43546e1", "short_code": "coll", "title": "Met Office MIDAS Open: UK Land Surface Stations Data (1853-current)", "abstract": "MIDAS Open is the open data version of the Met Office Integrated Data Archive System (MIDAS) containing land surface station data starting from 1853 and ending at the of the previous complete year. This collection comprises of hourly and daily weather measurements and observations of parameters relating to temperature, rainfall, sunshine, radiation, wind and weather observations such as present weather codes, cloud cover, snow etc.\r\n\r\nThe collection contains land surface observations data from those stations where the data have been designated as public sector information. Prior to version v202407 this consisted of stations operated by the Met Office only, but from version v202407, daily and hourly rainfall observations from stations with gauges owned by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) have also been included in the collection. Since then, stations owned by other third-party organisations where approval for inclusion has been reached have also been added to the product.\r\n\r\nAll of these data are provided under an Open Government Licence. \r\n\r\nThe current collection contains the following proportions of the fuller MIDAS dataset collection:\r\n\r\n96% of daily temperature observations\r\n96% of daily weather observations\r\n92% of hourly weather observations\r\n94% of daily rainfall observations\r\n96% of hourly rainfall observations\r\n98% of soil temperature observations\r\n96% of solar radiation observations\r\n93% of mean wind observations\r\n\r\nDaily rainfall: Versions up until MIDAS Open v202407 only have about 13% coverage of observations. In version v202407, the coverage was increased to 58% with the inclusion of the third-party hydrological agency stations. In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. The collection includes stations which are currently operational as well as stations which were operational in the past and have since closed.\r\n\r\nEach version of the dataset will include data up until the end of the previous complete year relative to the year in the version number of the dataset (e.g. v202407 included data up until the end of 2023).\r\n\r\nNote: This collection does not supersede the full MIDAS collection which is also archived at CEDA." } ], "responsiblepartyinfo_set": [ 181398, 181399, 181400, 181401, 181402, 181403, 181404, 181405, 181406 ], "onlineresource_set": [ 52795, 52792, 52793, 52794, 52796, 52798, 52799, 52797, 90573, 90574, 90575, 90576, 90577, 90578, 90579, 90580, 90581, 90582, 90583, 90584, 90585, 90586, 90587, 90588, 90589, 90590, 90591, 90592, 90593, 90594 ] }, { "ob_id": 38069, "uuid": "4b44cec2f9a846f39d5007983b7eaaab", "title": "MIDAS Open: UK daily weather observation data, v202207", "abstract": "The UK daily weather observation data contain meteorological values measured on a 24 hour time scale. The measurements of sunshine duration, concrete state, snow depth, fresh snow depth, and days of snow, hail, thunder and gail were attained by observation stations operated by the Met Office across the UK operated and transmitted within DLY3208, NCM, AWSDLY and SYNOP messages. The data span from 1887 to 2021. For details of observations see the relevant sections of the MIDAS User Guide linked from this record for the various message types.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2021, and additional historical data for Sheffield (South Yorkshire, 1898-1935).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily weather observations within the full MIDAS collection.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-09-01T15:53:18", "updateFrequency": "notPlanned", "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. Data are extracted from a sub-set of available tables and delivered to Centre for Environmental Data Analysis (CEDA) approximately on a yearly basis.", "removedDataReason": "", "keywords": "Met Office, MIDAS, UK, meteorology, daily, diurnal, monthly", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2022-09-09T13:50:54", "doiPublishedTime": "2022-09-22T15:47:02", "removedDataTime": null, "geographicExtent": { "ob_id": 18, "bboxName": "MIDAS UK table s geographic domain", "eastBoundLongitude": 1.74002, "westBoundLongitude": -5.54236, "southBoundLatitude": 50.1172, "northBoundLatitude": 60.7592 }, "verticalExtent": null, "result_field": { "ob_id": 38076, "dataPath": "/badc/ukmo-midas-open/data/uk-daily-weather-obs/dataset-version-202207/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3037385885, "numberOfFiles": 47397, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 10495, "startTime": "1887-01-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 306, "explanation": "Data undergo quality checking by the Met Office. State of the data in the quality control process and level of data quality are indicated using version numbers and quality control flagging with the data. See documentation about how to use the quality control flagging and version numbers.\n\nThere are also some known data from commissioning trials in the data, which are given a src_id of 99999. These should be ignored by the user.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-11" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 1246, "uuid": "1efa07092762447189d030fe5d41e1fa", "short_code": "acq", "title": "Acquisition Process for: UK Daily Weather Observation Data, Part of the Met Office Integrated Data Archive System (MIDAS)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Station Observer, Sunshine Recorder, Cloud Recorder, Snow Depth Sensor, Present (and Past) Weather Sensor; PLATFORMS: Land SYNOP (surface synoptic observations) Station Network, NCM (National Climate Message) Station Network, DLY3208 (Daily observations from Metform 3208) Station Network, AWSDLY (Automatic Weather Station Daily values) Station Network; " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 1186, "uuid": "245df050d57a500c183b88df509f5f5a", "short_code": "proj", "title": "Met Office Integrated Data Archive System (MIDAS)", "abstract": "Since the early days of this century the Met Office has been responsible for maintaining the public memory of the weather. All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. The MIDAS database contains the following general types of meteorological data: surface observations over land areas of the UK as far back as the digital record extends, a selection of global surface observations for the last 20 years, global surface marine observations from national and international sources as far back as the digital record extends, radiosonde observations over the UK, and at overseas stations operated by the Met Office, as far back as the digital record extends, a selection of global radiosonde observations for the last 10 years." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 31142, 31144, 31146, 31150, 31161, 31162, 31164, 31165, 31166, 31167, 31168, 31169, 31170, 31171, 31172, 31173, 31174, 31175, 31176, 31217, 31231, 31242, 31299, 31364, 31365, 31366, 31367, 31368, 31369, 31370, 31371, 31372, 31373, 31374, 31375, 31376, 31377, 31378, 31379, 31380, 31381, 31382, 31383, 31384, 31385, 31386, 31387 ], "vocabularyKeywords": [], "identifier_set": [ 12201 ], "observationcollection_set": [ { "ob_id": 26184, "uuid": "dbd451271eb04662beade68da43546e1", "short_code": "coll", "title": "Met Office MIDAS Open: UK Land Surface Stations Data (1853-current)", "abstract": "MIDAS Open is the open data version of the Met Office Integrated Data Archive System (MIDAS) containing land surface station data starting from 1853 and ending at the of the previous complete year. This collection comprises of hourly and daily weather measurements and observations of parameters relating to temperature, rainfall, sunshine, radiation, wind and weather observations such as present weather codes, cloud cover, snow etc.\r\n\r\nThe collection contains land surface observations data from those stations where the data have been designated as public sector information. Prior to version v202407 this consisted of stations operated by the Met Office only, but from version v202407, daily and hourly rainfall observations from stations with gauges owned by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) have also been included in the collection. Since then, stations owned by other third-party organisations where approval for inclusion has been reached have also been added to the product.\r\n\r\nAll of these data are provided under an Open Government Licence. \r\n\r\nThe current collection contains the following proportions of the fuller MIDAS dataset collection:\r\n\r\n96% of daily temperature observations\r\n96% of daily weather observations\r\n92% of hourly weather observations\r\n94% of daily rainfall observations\r\n96% of hourly rainfall observations\r\n98% of soil temperature observations\r\n96% of solar radiation observations\r\n93% of mean wind observations\r\n\r\nDaily rainfall: Versions up until MIDAS Open v202407 only have about 13% coverage of observations. In version v202407, the coverage was increased to 58% with the inclusion of the third-party hydrological agency stations. In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. The collection includes stations which are currently operational as well as stations which were operational in the past and have since closed.\r\n\r\nEach version of the dataset will include data up until the end of the previous complete year relative to the year in the version number of the dataset (e.g. v202407 included data up until the end of 2023).\r\n\r\nNote: This collection does not supersede the full MIDAS collection which is also archived at CEDA." } ], "responsiblepartyinfo_set": [ 181407, 181408, 181409, 181410, 181411, 181412, 181413, 181414, 181415 ], "onlineresource_set": [ 52803, 52802, 52804, 52800, 52805, 52806, 52807, 52801, 90595, 90596, 90597, 90598, 90599, 90600, 90601, 90602, 90603 ] }, { "ob_id": 38070, "uuid": "6180fb7ed76a442eb1b8f3f152fd08d7", "title": "MIDAS Open: UK hourly weather observation data, v202207", "abstract": "The UK hourly weather observation data contain meteorological values measured on an hourly time scale. The measurements of the concrete state, wind speed and direction, cloud type and amount, visibility, and temperature were recorded by observation stations operated by the Met Office across the UK and transmitted within SYNOP, DLY3208, AWSHRLY and NCM messages. The sunshine duration measurements were transmitted in the HSUN3445 message. The data spans from 1875 to 2021.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2021, and additional historical data for Sheffield (South Yorkshire, 1882-1935).\r\n\r\nFor details on observing practice see the message type information in the MIDAS User Guide linked from this record and relevant sections for parameter types.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Note, METAR message types are not included in the Open version of this dataset. Those data may be accessed via the full MIDAS hourly weather data.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-09-01T17:16:10", "updateFrequency": "notPlanned", "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. Data are extracted from a sub-set of available tables and delivered to Centre for Environmental Data Analysis (CEDA) approximately on a yearly basis.", "removedDataReason": "", "keywords": "Met Office, MIDAS, UK, meteorology, hourly", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2022-09-09T13:52:59", "doiPublishedTime": "2022-09-22T15:47:43", "removedDataTime": null, "geographicExtent": { "ob_id": 18, "bboxName": "MIDAS UK table s geographic domain", "eastBoundLongitude": 1.74002, "westBoundLongitude": -5.54236, "southBoundLatitude": 50.1172, "northBoundLatitude": 60.7592 }, "verticalExtent": null, "result_field": { "ob_id": 38079, "dataPath": "/badc/ukmo-midas-open/data/uk-hourly-weather-obs/dataset-version-202207/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 31273999098, "numberOfFiles": 51431, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 10492, "startTime": "1875-01-01T09:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 300, "explanation": "Data undergo quality checking by the Met Office. State of the data in the quality control process and level of data quality are indicated using version numbers and quality control flagging with the data. See documentation about how to use the quality control flagging and version numbers.\n\nThere are also some known data from commissioning trials in the data, which are given a src_id of 99999. These should be ignored by the user.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-11" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 27186, "uuid": "336b071077af4805a8394f1402c5c26b", "short_code": "acq", "title": "Acquisition Process for: UK Hourly Weather Observation Data, Part of the Met Office Integrated Data Archive System (MIDAS) Open version (excluded METARS)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Thermometer, Visiometer, Station Observer, Sunshine Recorder, Raingauge, Cloud Recorder, Snow Depth Sensor, Present (and Past) Weather Sensor; PLATFORMS: Land SYNOP (surface synoptic observations) Station Network, METAR (MEteorological Terminal Aviation Routine Weather Report) Station Network, NCM (National Climate Message) Station Network, DLY3208 (Daily observations from Metform 3208) Station Network, AWSHRLY (Automatic Weather Station Hourly values) Station Network, HSUN3445 (Hourly values of SUNshine duration from Metform 3445) Station Network;" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 1186, "uuid": "245df050d57a500c183b88df509f5f5a", "short_code": "proj", "title": "Met Office Integrated Data Archive System (MIDAS)", "abstract": "Since the early days of this century the Met Office has been responsible for maintaining the public memory of the weather. All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. The MIDAS database contains the following general types of meteorological data: surface observations over land areas of the UK as far back as the digital record extends, a selection of global surface observations for the last 20 years, global surface marine observations from national and international sources as far back as the digital record extends, radiosonde observations over the UK, and at overseas stations operated by the Met Office, as far back as the digital record extends, a selection of global radiosonde observations for the last 10 years." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 31142, 31146, 31150, 31161, 31162, 31164, 31165, 31166, 31167, 31168, 31169, 31170, 31171, 31172, 31173, 31174, 31175, 31176, 31217, 31224, 31231, 31238, 31239, 31240, 31241, 31242, 31243, 31244, 31245, 31246, 31247, 31248, 31249, 31250, 31251, 31252, 31253, 31254, 31255, 31256, 31257, 31258, 31259, 31260, 31261, 31262, 31263, 31264, 31265, 31266, 31267, 31268, 31269, 31270, 31271, 31272, 31273, 31274, 31275, 31276, 31277, 31278, 31279, 31280, 31281, 31282, 31283, 31284, 31285, 31286, 31287, 31288, 31289, 31290, 31291, 31292, 31293, 31294, 31295, 31296, 31297, 31298, 31299, 31300, 31301, 31302, 31303, 31304, 31305, 31306, 31307, 31308, 31309, 31310, 31311, 31312, 31313, 31314, 31315, 31316, 31317, 31318, 31319, 31320, 31321, 31322, 31323, 31324, 31325, 31326, 31327, 31328, 31329, 31330, 31331, 31332, 31333 ], "vocabularyKeywords": [], "identifier_set": [ 12206 ], "observationcollection_set": [ { "ob_id": 26184, "uuid": "dbd451271eb04662beade68da43546e1", "short_code": "coll", "title": "Met Office MIDAS Open: UK Land Surface Stations Data (1853-current)", "abstract": "MIDAS Open is the open data version of the Met Office Integrated Data Archive System (MIDAS) containing land surface station data starting from 1853 and ending at the of the previous complete year. This collection comprises of hourly and daily weather measurements and observations of parameters relating to temperature, rainfall, sunshine, radiation, wind and weather observations such as present weather codes, cloud cover, snow etc.\r\n\r\nThe collection contains land surface observations data from those stations where the data have been designated as public sector information. Prior to version v202407 this consisted of stations operated by the Met Office only, but from version v202407, daily and hourly rainfall observations from stations with gauges owned by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) have also been included in the collection. Since then, stations owned by other third-party organisations where approval for inclusion has been reached have also been added to the product.\r\n\r\nAll of these data are provided under an Open Government Licence. \r\n\r\nThe current collection contains the following proportions of the fuller MIDAS dataset collection:\r\n\r\n96% of daily temperature observations\r\n96% of daily weather observations\r\n92% of hourly weather observations\r\n94% of daily rainfall observations\r\n96% of hourly rainfall observations\r\n98% of soil temperature observations\r\n96% of solar radiation observations\r\n93% of mean wind observations\r\n\r\nDaily rainfall: Versions up until MIDAS Open v202407 only have about 13% coverage of observations. In version v202407, the coverage was increased to 58% with the inclusion of the third-party hydrological agency stations. In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. The collection includes stations which are currently operational as well as stations which were operational in the past and have since closed.\r\n\r\nEach version of the dataset will include data up until the end of the previous complete year relative to the year in the version number of the dataset (e.g. v202407 included data up until the end of 2023).\r\n\r\nNote: This collection does not supersede the full MIDAS collection which is also archived at CEDA." } ], "responsiblepartyinfo_set": [ 181421, 181422, 181423, 181416, 181417, 181418, 181419, 181420, 181424 ], "onlineresource_set": [ 52811, 52812, 52809, 52810, 52808, 52813, 52814, 52815, 90507, 90508, 90509, 90510, 90511, 90512, 90513, 90514, 90515, 90516, 90517, 90518, 90519, 90520, 90521, 90522, 90523, 90524, 90525, 90526, 90527 ] }, { "ob_id": 38071, "uuid": "15deeb29cdcd4524b07560e5aad45ded", "title": "MIDAS Open: UK daily rainfall data, v202207", "abstract": "The UK daily rainfall data contain rainfall accumulation and precipitation amounts over a 24 hour period. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSDLY, DLY3208 and SSER. The data spans from 1853 to 2021. Over time a range of rain gauges have been used - see section 5.6 and the relevant message type information in the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version (202107) of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2021, and additional historical data for Colmonell (Ayrshire, 1924-1960), Camps Reservoir (Lanarkshire, 1934-1960), and Greenock (Renfrewshire, 1910-1960).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset. Currently this represents approximately 13% of available daily rainfall observations within the full MIDAS collection.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-09-01T15:11:56", "updateFrequency": "notPlanned", "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. Data are extracted from a sub-set of available tables and delivered to Centre for Environmental Data Analysis (CEDA) approximately on a yearly basis.", "removedDataReason": "", "keywords": "Met Office, MIDAS, UK, meteorology, rainfall, diurnal, daily, monthly", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2022-09-09T14:34:57", "doiPublishedTime": "2022-09-22T15:47:34", "removedDataTime": null, "geographicExtent": { "ob_id": 18, "bboxName": "MIDAS UK table s geographic domain", "eastBoundLongitude": 1.74002, "westBoundLongitude": -5.54236, "southBoundLatitude": 50.1172, "northBoundLatitude": 60.7592 }, "verticalExtent": null, "result_field": { "ob_id": 38075, "dataPath": "/badc/ukmo-midas-open/data/uk-daily-rain-obs/dataset-version-202207/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1084616388, "numberOfFiles": 51646, "fileFormat": "Data are BADC-CSV formatted" }, "timePeriod": { "ob_id": 10497, "startTime": "1853-01-02T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 297, "explanation": "Data undergo quality checking by the Met Office. State of the data in the quality control process and level of data quality are indicated using version numbers and quality control flagging with the data. See documentation about how to use the quality control flagging and version numbers.\n\nThere are also some known data from commissioning trials in the data, which are given a src_id of 99999. These should be ignored by the user.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-11" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 27187, "uuid": "037f3eff9db941a583b252e800a94979", "short_code": "acq", "title": "Acquisition Process for: UK Daily Rainfall Data, Part of the Met Office Integrated Data Archive System (MIDAS) Open - excludes WADRAIN and WAMRAIN", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Raingauge; PLATFORMS: DLY3208 (Daily observations from Metform 3208) Station Network, SSER (Solid State Event Recorder) Station Network, WADRAIN (Water Authorities Daily RAINfall) Station Network, WAMRAIN (Water Authorities Monthly RAINfall value) Station Network, NCM (National Climate Message) Station Network, AWSDLY (Automatic Weather Station Daily values) Station Network;" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 1186, "uuid": "245df050d57a500c183b88df509f5f5a", "short_code": "proj", "title": "Met Office Integrated Data Archive System (MIDAS)", "abstract": "Since the early days of this century the Met Office has been responsible for maintaining the public memory of the weather. All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. The MIDAS database contains the following general types of meteorological data: surface observations over land areas of the UK as far back as the digital record extends, a selection of global surface observations for the last 20 years, global surface marine observations from national and international sources as far back as the digital record extends, radiosonde observations over the UK, and at overseas stations operated by the Met Office, as far back as the digital record extends, a selection of global radiosonde observations for the last 10 years." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 31142, 31144, 31146, 31150, 31161, 31162, 31164, 31165, 31166, 31167, 31168, 31169, 31170, 31171, 31172, 31173, 31174, 31175, 31176, 31209, 31210, 31211, 31212, 31213, 31214, 31215, 31216, 31217 ], "vocabularyKeywords": [], "identifier_set": [ 12205 ], "observationcollection_set": [ { "ob_id": 26184, "uuid": "dbd451271eb04662beade68da43546e1", "short_code": "coll", "title": "Met Office MIDAS Open: UK Land Surface Stations Data (1853-current)", "abstract": "MIDAS Open is the open data version of the Met Office Integrated Data Archive System (MIDAS) containing land surface station data starting from 1853 and ending at the of the previous complete year. This collection comprises of hourly and daily weather measurements and observations of parameters relating to temperature, rainfall, sunshine, radiation, wind and weather observations such as present weather codes, cloud cover, snow etc.\r\n\r\nThe collection contains land surface observations data from those stations where the data have been designated as public sector information. Prior to version v202407 this consisted of stations operated by the Met Office only, but from version v202407, daily and hourly rainfall observations from stations with gauges owned by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) have also been included in the collection. Since then, stations owned by other third-party organisations where approval for inclusion has been reached have also been added to the product.\r\n\r\nAll of these data are provided under an Open Government Licence. \r\n\r\nThe current collection contains the following proportions of the fuller MIDAS dataset collection:\r\n\r\n96% of daily temperature observations\r\n96% of daily weather observations\r\n92% of hourly weather observations\r\n94% of daily rainfall observations\r\n96% of hourly rainfall observations\r\n98% of soil temperature observations\r\n96% of solar radiation observations\r\n93% of mean wind observations\r\n\r\nDaily rainfall: Versions up until MIDAS Open v202407 only have about 13% coverage of observations. In version v202407, the coverage was increased to 58% with the inclusion of the third-party hydrological agency stations. In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. The collection includes stations which are currently operational as well as stations which were operational in the past and have since closed.\r\n\r\nEach version of the dataset will include data up until the end of the previous complete year relative to the year in the version number of the dataset (e.g. v202407 included data up until the end of 2023).\r\n\r\nNote: This collection does not supersede the full MIDAS collection which is also archived at CEDA." } ], "responsiblepartyinfo_set": [ 181425, 181426, 181427, 181428, 181429, 181430, 181431, 181432, 181433 ], "onlineresource_set": [ 52817, 52816, 52819, 52820, 52821, 52822, 52823, 52818, 90528, 90529, 90530, 90531, 90532, 90533, 90534, 90535, 90536 ] }, { "ob_id": 38072, "uuid": "64f5d7be890a4ac08cb2b4e78eb5fcc1", "title": "MIDAS Open: UK hourly rainfall data, v202207", "abstract": "The UK hourly rainfall data contain the rainfall amount (and duration from tilting syphon gauges) during the hour (or hours) ending at the specified time. The data also contains precipitation amounts, however precipitation measured over 24 hours are not stored. Over time a range of rain gauges have been used - see the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSHRLY, DLY3208, SREW and SSER. The data spans from 1915 to 2021.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-09-01T12:02:36", "updateFrequency": "notPlanned", "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. Data are extracted from a sub-set of available tables and delivered to Centre for Environmental Data Analysis (CEDA) approximately on a yearly basis.", "removedDataReason": "", "keywords": "Met Office, MIDAS, UK, meteorology, rainfall, hourly", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2022-09-09T14:37:21", "doiPublishedTime": "2022-09-22T15:47:25", "removedDataTime": null, "geographicExtent": { "ob_id": 17, "bboxName": "", "eastBoundLongitude": 1.74002, "westBoundLongitude": -8.5636, "southBoundLatitude": 49.914, "northBoundLatitude": 60.8562 }, "verticalExtent": null, "result_field": { "ob_id": 38077, "dataPath": "/badc/ukmo-midas-open/data/uk-hourly-rain-obs/dataset-version-202207/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5143758435, "numberOfFiles": 15675, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 10494, "startTime": "1915-01-01T18:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 308, "explanation": "Data undergo quality checking by the Met Office. State of the data in the quality control process and level of data quality are indicated using version numbers and quality control flagging with the data. See documentation about how to use the quality control flagging and version numbers.\n\nThere are also some known data from commissioning trials in the data, which are given a src_id of 99999. These should be ignored by the user.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-11" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 27188, "uuid": "b0418372412d4d79ba08d65681fc6f5d", "short_code": "acq", "title": "Acquisition Process for: UK Hourly Rainfall Data, Part of the Met Office Integrated Data Archive System (MIDAS) Open (excludes WAHRAIN)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Raingauge; PLATFORMS: SSER (Solid State Event Recorder) Station Network, AWSHRLY (Automatic Weather Station Hourly values) Station Network, SREW (Synoptic Rainfall Europe West) Station Network, DLY3208 (Daily observations from Metform 3208) Station Network, NCM (National Climate Message) Station Network;" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 1186, "uuid": "245df050d57a500c183b88df509f5f5a", "short_code": "proj", "title": "Met Office Integrated Data Archive System (MIDAS)", "abstract": "Since the early days of this century the Met Office has been responsible for maintaining the public memory of the weather. All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. The MIDAS database contains the following general types of meteorological data: surface observations over land areas of the UK as far back as the digital record extends, a selection of global surface observations for the last 20 years, global surface marine observations from national and international sources as far back as the digital record extends, radiosonde observations over the UK, and at overseas stations operated by the Met Office, as far back as the digital record extends, a selection of global radiosonde observations for the last 10 years." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 31142, 31144, 31146, 31150, 31161, 31162, 31164, 31165, 31166, 31167, 31168, 31169, 31170, 31171, 31172, 31173, 31174, 31175, 31176, 31210, 31211, 31213, 31215, 31217, 31334, 31335, 31336, 31337 ], "vocabularyKeywords": [], "identifier_set": [ 12204 ], "observationcollection_set": [ { "ob_id": 26184, "uuid": "dbd451271eb04662beade68da43546e1", "short_code": "coll", "title": "Met Office MIDAS Open: UK Land Surface Stations Data (1853-current)", "abstract": "MIDAS Open is the open data version of the Met Office Integrated Data Archive System (MIDAS) containing land surface station data starting from 1853 and ending at the of the previous complete year. This collection comprises of hourly and daily weather measurements and observations of parameters relating to temperature, rainfall, sunshine, radiation, wind and weather observations such as present weather codes, cloud cover, snow etc.\r\n\r\nThe collection contains land surface observations data from those stations where the data have been designated as public sector information. Prior to version v202407 this consisted of stations operated by the Met Office only, but from version v202407, daily and hourly rainfall observations from stations with gauges owned by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) have also been included in the collection. Since then, stations owned by other third-party organisations where approval for inclusion has been reached have also been added to the product.\r\n\r\nAll of these data are provided under an Open Government Licence. \r\n\r\nThe current collection contains the following proportions of the fuller MIDAS dataset collection:\r\n\r\n96% of daily temperature observations\r\n96% of daily weather observations\r\n92% of hourly weather observations\r\n94% of daily rainfall observations\r\n96% of hourly rainfall observations\r\n98% of soil temperature observations\r\n96% of solar radiation observations\r\n93% of mean wind observations\r\n\r\nDaily rainfall: Versions up until MIDAS Open v202407 only have about 13% coverage of observations. In version v202407, the coverage was increased to 58% with the inclusion of the third-party hydrological agency stations. In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. The collection includes stations which are currently operational as well as stations which were operational in the past and have since closed.\r\n\r\nEach version of the dataset will include data up until the end of the previous complete year relative to the year in the version number of the dataset (e.g. v202407 included data up until the end of 2023).\r\n\r\nNote: This collection does not supersede the full MIDAS collection which is also archived at CEDA." } ], "responsiblepartyinfo_set": [ 181434, 181435, 181436, 181437, 181438, 181439, 181440, 181441, 181442 ], "onlineresource_set": [ 52830, 52824, 52825, 52826, 52828, 52827, 52829, 52831, 90537, 90538, 90539, 90540, 90541, 90542, 90543, 90544, 90545, 90546, 90547, 90548, 90549, 90550, 90551, 90552, 90553, 90554, 90555, 90556, 90557, 90558, 90559 ] }, { "ob_id": 38073, "uuid": "4ecbf3fa1b084c5a9080248433275124", "title": "MIDAS Open: UK soil temperature data, v202207", "abstract": "The UK soil temperature data contain daily and hourly values of soil temperatures at depths of 5, 10, 20, 30, 50, and 100 centimetres. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM or DLY3208 messages. The data spans from 1900 to 2021.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2021.\r\n\r\nAt many stations temperatures below the surface are measured at various depths. The depths used today are 5, 10, 20, 30 and 100cm, although measurements are not necessarily made at all these depths at a station and exceptionally measurements may be made at other depths. When imperial units were in general use, typically before 1961, the normal depths of measurement were 4, 8, 12, 24 and 48 inches.\r\n\r\nLiquid-in-glass soil thermometers at a depth of 20 cm or less are unsheathed and have a bend in the stem between the bulb and the lowest graduation. At greater depths the thermometer is suspended in a steel tube and has its bulb encased in wax.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-09-01T12:10:12", "updateFrequency": "notPlanned", "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. Data are extracted from a sub-set of available tables and delivered to Centre for Environmental Data Analysis (CEDA) approximately on a yearly basis.", "removedDataReason": "", "keywords": "Met Office, MIDAS, UK, meteorology, soil temperatures", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2022-09-09T14:39:44", "doiPublishedTime": "2022-09-22T15:47:18", "removedDataTime": null, "geographicExtent": { "ob_id": 18, "bboxName": "MIDAS UK table s geographic domain", "eastBoundLongitude": 1.74002, "westBoundLongitude": -5.54236, "southBoundLatitude": 50.1172, "northBoundLatitude": 60.7592 }, "verticalExtent": null, "result_field": { "ob_id": 38081, "dataPath": "/badc/ukmo-midas-open/data/uk-soil-temperature-obs/dataset-version-202207/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4046211314, "numberOfFiles": 23510, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 10490, "startTime": "1900-01-01T09:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 301, "explanation": "Data undergo quality checking by the Met Office. State of the data in the quality control process and level of data quality are indicated using version numbers and quality control flagging with the data. See documentation about how to use the quality control flagging and version numbers.\n\nThere are also some known data from commissioning trials in the data, which are given a src_id of 99999. These should be ignored by the user.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-11" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 1227, "uuid": "0c787dde18584b3293d4f55daf6ef431", "short_code": "acq", "title": "Acquisition Process for: UK Soil Temperature Data, Part of the Met Office Integrated Data Archive System (MIDAS)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Thermometer; PLATFORMS: NCM (National Climate Message) Station Network, HCM (Hourly Climate Messages) Station Network, DLY3208 (Daily observations from Metform 3208) Station Network, AWSHRLY (Automatic Weather Station Hourly values) Station Network;" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 1186, "uuid": "245df050d57a500c183b88df509f5f5a", "short_code": "proj", "title": "Met Office Integrated Data Archive System (MIDAS)", "abstract": "Since the early days of this century the Met Office has been responsible for maintaining the public memory of the weather. All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. The MIDAS database contains the following general types of meteorological data: surface observations over land areas of the UK as far back as the digital record extends, a selection of global surface observations for the last 20 years, global surface marine observations from national and international sources as far back as the digital record extends, radiosonde observations over the UK, and at overseas stations operated by the Met Office, as far back as the digital record extends, a selection of global radiosonde observations for the last 10 years." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 31142, 31144, 31146, 31150, 31161, 31162, 31164, 31165, 31166, 31167, 31168, 31169, 31170, 31171, 31172, 31173, 31174, 31175, 31176, 31217, 31218, 31219, 31220, 31221, 31222, 31223, 31224, 31225, 31226, 31227, 31228, 31229, 31230, 31231, 31232, 31233, 31234, 31235, 31236, 31237 ], "vocabularyKeywords": [], "identifier_set": [ 12203 ], "observationcollection_set": [ { "ob_id": 26184, "uuid": "dbd451271eb04662beade68da43546e1", "short_code": "coll", "title": "Met Office MIDAS Open: UK Land Surface Stations Data (1853-current)", "abstract": "MIDAS Open is the open data version of the Met Office Integrated Data Archive System (MIDAS) containing land surface station data starting from 1853 and ending at the of the previous complete year. This collection comprises of hourly and daily weather measurements and observations of parameters relating to temperature, rainfall, sunshine, radiation, wind and weather observations such as present weather codes, cloud cover, snow etc.\r\n\r\nThe collection contains land surface observations data from those stations where the data have been designated as public sector information. Prior to version v202407 this consisted of stations operated by the Met Office only, but from version v202407, daily and hourly rainfall observations from stations with gauges owned by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) have also been included in the collection. Since then, stations owned by other third-party organisations where approval for inclusion has been reached have also been added to the product.\r\n\r\nAll of these data are provided under an Open Government Licence. \r\n\r\nThe current collection contains the following proportions of the fuller MIDAS dataset collection:\r\n\r\n96% of daily temperature observations\r\n96% of daily weather observations\r\n92% of hourly weather observations\r\n94% of daily rainfall observations\r\n96% of hourly rainfall observations\r\n98% of soil temperature observations\r\n96% of solar radiation observations\r\n93% of mean wind observations\r\n\r\nDaily rainfall: Versions up until MIDAS Open v202407 only have about 13% coverage of observations. In version v202407, the coverage was increased to 58% with the inclusion of the third-party hydrological agency stations. In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. The collection includes stations which are currently operational as well as stations which were operational in the past and have since closed.\r\n\r\nEach version of the dataset will include data up until the end of the previous complete year relative to the year in the version number of the dataset (e.g. v202407 included data up until the end of 2023).\r\n\r\nNote: This collection does not supersede the full MIDAS collection which is also archived at CEDA." } ], "responsiblepartyinfo_set": [ 181443, 181444, 181445, 181446, 181447, 181448, 181449, 181450, 181451 ], "onlineresource_set": [ 52832, 52834, 52836, 52837, 52838, 52839, 52833, 52835, 90560, 90561, 90562, 90563, 90564, 90565, 90566, 90567, 90568, 90569, 90570, 90571, 90572 ] }, { "ob_id": 38074, "uuid": "e3a7f3336ff8464f9ae6534a8e8676e5", "title": "MIDAS Open: UK hourly solar radiation data, v202207", "abstract": "The UK hourly solar radiation data contain the amount of solar irradiance received during the hour ending at the specified time. All sites report 'global' radiation amounts. This is also known as 'total sky radiation' as it includes both direct solar irradiance and 'diffuse' irradiance as a result of light scattering. Some sites also provide separate diffuse and direct irradiation amounts, depending on the instrumentation at the site. For these the sun's path is tracked with two pyrometers - one where the path to the sun is blocked by a suitable disc to allow the scattered sunlight to be measured to give the diffuse measurement, while the other has a tube pointing at the sun to measure direct solar irradiance whilst blanking out scattered sun light. \r\n\r\nFor details about the different measurements made and the limited number of sites making them please see the MIDAS Solar Irradiance table linked to in the online resources section of this record.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2021.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, MODLERAD, ESAWRADT and DRADR35 messages. The data spans from 1947 to 2021.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-09-01T10:29:33", "updateFrequency": "notPlanned", "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. Data are extracted from a sub-set of available tables and delivered to Centre for Environmental Data Analysis (CEDA) approximately on a yearly basis.", "removedDataReason": "", "keywords": "Met Office, MIDAS, UK, meteorology, solar irradiance, hourly, global, diffuse, direct", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2022-09-09T13:42:54", "doiPublishedTime": "2022-09-22T15:46:54", "removedDataTime": null, "geographicExtent": { "ob_id": 17, "bboxName": "", "eastBoundLongitude": 1.74002, "westBoundLongitude": -8.5636, "southBoundLatitude": 49.914, "northBoundLatitude": 60.8562 }, "verticalExtent": null, "result_field": { "ob_id": 38082, "dataPath": "/badc/ukmo-midas-open/data/uk-radiation-obs/dataset-version-202207/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2844877616, "numberOfFiles": 5369, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 10493, "startTime": "1947-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 308, "explanation": "Data undergo quality checking by the Met Office. State of the data in the quality control process and level of data quality are indicated using version numbers and quality control flagging with the data. See documentation about how to use the quality control flagging and version numbers.\n\nThere are also some known data from commissioning trials in the data, which are given a src_id of 99999. These should be ignored by the user.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-11" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 1240, "uuid": "051afeb5c56f46469683e9b8b0bb38b1", "short_code": "acq", "title": "Acquisition Process for: Global Radiation Observations, Part of the Met Office Integrated Data Archive System (MIDAS)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Pyranometer, Sunshine Recorder; PLATFORMS: DRADR35 (Daily Radiation Form 35) Station Network, AWSHRLY (Automatic Weather Station Hourly values) Station Network, MODLERAD (Hourly radiation values from Met Office Data Logging Equipment) Station Network, ESAWRADT (Enhanced Synoptic Automatic Weather station RADiaTion) Station Network, HCM (Hourly Climate Messages) Station Network, Land SYNOP (surface synoptic observations) Station Network; " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 1186, "uuid": "245df050d57a500c183b88df509f5f5a", "short_code": "proj", "title": "Met Office Integrated Data Archive System (MIDAS)", "abstract": "Since the early days of this century the Met Office has been responsible for maintaining the public memory of the weather. All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. The MIDAS database contains the following general types of meteorological data: surface observations over land areas of the UK as far back as the digital record extends, a selection of global surface observations for the last 20 years, global surface marine observations from national and international sources as far back as the digital record extends, radiosonde observations over the UK, and at overseas stations operated by the Met Office, as far back as the digital record extends, a selection of global radiosonde observations for the last 10 years." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 31142, 31144, 31146, 31150, 31161, 31162, 31164, 31165, 31166, 31167, 31168, 31169, 31170, 31171, 31172, 31173, 31174, 31175, 31176, 31217, 31231, 31334, 31335, 31338, 31339, 31340, 31341, 31342, 31343, 31344, 31345, 31346, 31347, 31348, 31349 ], "vocabularyKeywords": [], "identifier_set": [ 12200 ], "observationcollection_set": [ { "ob_id": 26184, "uuid": "dbd451271eb04662beade68da43546e1", "short_code": "coll", "title": "Met Office MIDAS Open: UK Land Surface Stations Data (1853-current)", "abstract": "MIDAS Open is the open data version of the Met Office Integrated Data Archive System (MIDAS) containing land surface station data starting from 1853 and ending at the of the previous complete year. This collection comprises of hourly and daily weather measurements and observations of parameters relating to temperature, rainfall, sunshine, radiation, wind and weather observations such as present weather codes, cloud cover, snow etc.\r\n\r\nThe collection contains land surface observations data from those stations where the data have been designated as public sector information. Prior to version v202407 this consisted of stations operated by the Met Office only, but from version v202407, daily and hourly rainfall observations from stations with gauges owned by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) have also been included in the collection. Since then, stations owned by other third-party organisations where approval for inclusion has been reached have also been added to the product.\r\n\r\nAll of these data are provided under an Open Government Licence. \r\n\r\nThe current collection contains the following proportions of the fuller MIDAS dataset collection:\r\n\r\n96% of daily temperature observations\r\n96% of daily weather observations\r\n92% of hourly weather observations\r\n94% of daily rainfall observations\r\n96% of hourly rainfall observations\r\n98% of soil temperature observations\r\n96% of solar radiation observations\r\n93% of mean wind observations\r\n\r\nDaily rainfall: Versions up until MIDAS Open v202407 only have about 13% coverage of observations. In version v202407, the coverage was increased to 58% with the inclusion of the third-party hydrological agency stations. In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. The collection includes stations which are currently operational as well as stations which were operational in the past and have since closed.\r\n\r\nEach version of the dataset will include data up until the end of the previous complete year relative to the year in the version number of the dataset (e.g. v202407 included data up until the end of 2023).\r\n\r\nNote: This collection does not supersede the full MIDAS collection which is also archived at CEDA." } ], "responsiblepartyinfo_set": [ 181454, 181455, 181456, 181457, 181458, 181459, 181452, 181453, 181460 ], "onlineresource_set": [ 52840, 52847, 52844, 52841, 52845, 52846, 83123, 52842, 90604, 90605, 90606, 90607, 90608, 90609, 90610, 90611, 90612, 90613, 90614, 90615, 90616, 90617, 90618, 90619, 90620, 90621, 52843 ] }, { "ob_id": 38092, "uuid": "aff921a9f2a34f008744342f0baaa9a5", "title": "HadCM3 general circulation model forced simulations of oscillating last glacial maximum forced with deglacial meltwater", "abstract": "This dataset contains HadCM3 model global forced simulations of the last glacial maximum, inputs and outputs, as used in the publication Rome et al. 2022 (DOI:10.1002/essoar.10511015.1). Seven simulations outputs are included under the names XOUPA, TFGBD_XOUPD, XOUPH, TFGBR_XOUPL, TFGBI, XOUPF and TFGBJ. The simulations were created using the general circulation model HadCM3 over the entire globe under last glacial maximum (21,000 years ago) conditions and forced with snapshots of meltwater derived from the early deglaciation (21,500 to 17,800 years ago). The outputs consist of sea-ice concentration, ocean overturning circulation, mixed layer depth, sea surface salinity, sea surface temperature, precipitation and surface air temperature. For reproducibility, the meltwater input files used for the forcing (lgm_inputs), the model input files (mw_inputs), the pre-industrial climatologies (pi_climate) and the crash diagnostics (crash_data) are included.", "creationDate": "2022-09-07T09:27:26.817577", "lastUpdatedDate": "2022-09-07T09:27:26", "latestDataUpdateTime": "2022-09-07T15:05:44", "updateFrequency": "notPlanned", "dataLineage": "Data were generated using the HadCM3 climate model with a 19 layers x 2.5 degrees x 3.75 degrees atmosphere model coupled to a 20 layers x 1.25 degrees x 1.25 degrees ocean model, and land and vegetation models. Data were converted to the netCDF format and compiled the field of interest in time series for each field and simulation. The data were then archived at CEDA", "removedDataReason": "", "keywords": "last glacial maximum, last deglaciation, HadCM3", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-27T14:35:34", "doiPublishedTime": "2022-09-27T14:36:40", "removedDataTime": null, "geographicExtent": { "ob_id": 3615, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38093, "dataPath": "/badc/deposited2022/lgm_oscillations", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 53536747877, "numberOfFiles": 107, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 10500, "startTime": "0001-01-01T00:00:00", "endTime": "9999-12-31T00:00:00" }, "resultQuality": { "ob_id": 4071, "explanation": "", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-09-07" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 38095, "uuid": "cd0ab0a5aa3447138a63c6178280bbdd", "short_code": "comp", "title": "Climate models inputs and outputs introduced in Rome et al. 2022 (DOI:10.1002/essoar.10511015.1)", "abstract": "Seven simulations outputs were uploaded under the names XOUPA, TFGBD_XOUPD, XOUPH, TFGBR_XOUPL, TFGBI, XOUPF and TFGBJ. The simulations were created using the general circulation model HadCM3 over the entire globe under last glacial maximum (21,000 years ago) conditions and forced with snapshots of meltwater derived from the early deglaciation (21,500 to 17,800 years ago). The outputs consists of sea-ice concentration, ocean overturning circulation, mixed layer depth, sea surface salinity, sea surface temperature, precipitation and surface air temperature. For reproducibility, we also uploaded the meltwater input files used for the forcing (lgm_inputs), the model input files (mw_inputs), the pre-industrial climatologies (pi_climate) and the crash diagnostics (crash_data), all discussed in the article. \"" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38094, "uuid": "ae07ec2a537145e9ac05d923d9a6f891", "short_code": "proj", "title": "Abrupt climate changes during the last ice age: a study of millennial-scale variability in climate simulations", "abstract": "This PhD project by Yvan Romé explored the occurrence of millennial-scale variability in the HadCM3 general circulation model during the last glacial period and, in particular, the last deglaciation. To identify the range of climate conditions and the mechanisms behind glacial millennial-scale variability, new sets of multi-millennial HadCM3 simulations forced with deglacial patterns of meltwater forcing were created. This includes simulations of the last glacial maximum forced with fixed meltwater discharges derived from the ice sheet melting history showing a pseudo-oscillating behaviour under the right balance of magnitude and location of the freshwater forcing. The oscillating simulations were analysed in more detail through multiple sensitivity analyses to map the salinity fluxes in the critical convection regions. Finally, the meltwater discharge protocol was applied to transient simulations of the last glacial maximum using two different ice sheet reconstructions.\r\nThe data included in this project's dataset consists mainly of model outputs that focus on the main mechanisms at stake in glacial climate variability, namely the changes in AMOC regimes, the reorganisation of ocean masses and the effect of the ice sheet reconstruction. They provide unique support for studying the occurrence of climate variability on different timescales in general circulation models and the potential chain of events of the last deglaciation.\r\nThis work was funded by the Natural Environment Research Council's Panorama doctoral training partnership." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1321, 1322, 1323, 3053, 3063, 3064, 3143, 3574, 3576, 3695, 3697, 3699, 3700, 7018, 11282, 23902, 49894, 49895, 49896, 49897, 49898, 49899, 49900, 49901, 49902, 49903, 49904, 49905, 49906, 49907, 49908, 49909, 49910, 49911, 49912, 49913, 49914, 49915, 49916, 49917, 49918, 49919, 49920, 49921, 49922, 49923, 49924, 49925, 49926, 49927, 49928, 49929, 49930, 49931, 49932, 49933, 49934, 49935, 49936, 49937, 49938, 49939, 49940, 49941, 49942, 49943, 49944, 49945, 49946, 49947, 49948, 49949, 49950, 49951, 49952, 49953, 49954, 49955, 49956, 49957, 49958, 49959, 49960, 49961, 49962, 49963, 49964, 49965, 49966, 49967, 49968, 49969, 49970, 49971, 49972, 49973, 49974, 49975, 49976, 49977, 49978, 49979, 49980, 49981, 49982 ], "vocabularyKeywords": [], "identifier_set": [ 12207 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 181499, 181500, 181501, 181502, 181503, 181504, 181509, 181510, 181511, 181512, 181513 ], "onlineresource_set": [ 52851, 87729 ] }, { "ob_id": 38099, "uuid": "605bdfe77b0a4c7493c0477f5a95fc0a", "title": "CALIOP and OMPS-LP satellite retrievals of the aerosol extinction coefficient for the Australian wildfires stratospheric impact study, Southern Hemisphere Data Set 2020", "abstract": "This dataset contains zonally averaged data from NASA's Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and NASA's Ozone Mapping and Profiler Suite -Limb Profiler (OMPS-LP) satellite retrievals of the aerosol extinction coefficient and ozone anomalies during 2020. These data were used for the experiments of Damany-Pearce et al. in the publication \"Australian Wildfires cause the largest stratospheric warming since Pinatubo and extends the lifetime of the Antarctic ozone hole\" 2022. \r\n\r\nThe ozone anomalies are given at monthly intervals, throughout all of 2020, at 1˚ latitude bands over the whole Southern Hemisphere, based on OMPS-LP retrievals. The aerosol extinction coefficient anomalies are given at weekly intervals, from 2020-12-29 through to the end of 2020, at 10˚ latitude bands from 20˚S to 70˚S. These are given as a combined dataset based off both CALIOP and OMPS-LP retrievals, the full details of how the retrievals were combined and all anomalies were calculated is outlined in Damany-Pearce et al., (2020). NetCDF files of the total uncertainty in both the CALIOP and OMPS-LP retrieved aerosol extinction coefficient anomalies are also included.\r\n\r\nThe dataset also includes 8 files that would be needed to reproduce the experiments of Damany-Pearce et al. (2022) on the (UK Earth System Model) UKESM1 platform, these are the satellite retrievals of aerosol extinction coefficient and ozone anomalies, that have been converted into the input required by UKESM1 and full descriptions of these are provided in the readme.txt file.", "creationDate": "2022-09-09T15:18:43.693205", "lastUpdatedDate": "2022-09-09T15:18:43", "latestDataUpdateTime": "2024-03-09T02:49:15", "updateFrequency": "notPlanned", "dataLineage": "These data were downloads of CALIOP and OMPS-LP satellite retrievals that have been zonally averaged and placed into subsets and then converted into the products required as input by the UKESM1 model by the project team. Data were then uploaded to CEDA for archival", "removedDataReason": "", "keywords": "SASSO, NE/S00212X/1", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-30T11:18:45", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3617, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 0.0 }, "verticalExtent": null, "result_field": { "ob_id": 38100, "dataPath": "/badc/deposited2022/sasso_aus_wildfires_stratosphere", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1521084402, "numberOfFiles": 15, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 10502, "startTime": "2019-12-29T00:00:00", "endTime": "2020-12-31T00:00:00" }, "resultQuality": { "ob_id": 4073, "explanation": "Data-quality procedures have been applied to the satellite data, most are advised by the user guides for the CALIOP and OMPS-LP products, full details of these procedures are provided in Damany-Pearce et al., (2020). ", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-09-09" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 38108, "uuid": "dbd00d9ac8fa4c7f80a953f3c46d95c5", "short_code": "cmppr", "title": "Composite process for SASSO Australian Wildfires", "abstract": "Data were collected by the CALIOP and OMPS-LS instruments and zonally averaged and placed into subsets and then converted into the products required as input by the UKESM1 model by the project team" }, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38101, "uuid": "7a4feaf0fd624dacaf34a0129f074fcc", "short_code": "proj", "title": "Soot Aerodynamic Size Selection for Optical properties (SASSO)", "abstract": "Black carbon (BC) and brown carbon (BrC) aerosols are widely studied components of atmospheric aerosol because they can absorb solar radiation and heat the atmosphere, causing positive radiative forcing of the climate. Although BC and BrC are very important for climate, they are poorly represented in atmospheric models. This is in part due to the complex microphysical properties of BC and the lack of accurate refractive index (RI) descriptions for both BC and BrC.\r\nSASSO capitalized on a unique combination of new technical and methodological developments at the University of Manchester and the University of Exeter to generate experimental data on a variety of soot types, providing much greater constraints on optical properties than previously possible. Specifically, SASSO used the Aerodynamic Aerosol Classifier (AAC) for size selection of emissions from wood burning, diesel combustion, and secondary organic aerosol (SOA) formation, prior to optical measurements using cavity ring-down and photoacoustic spectroscopy with the EXtinction, SCattering and Absorption of Light for AirBorne Aerosol Research (EXSCALABAR) instrumentation, custom-built by the Met Office.\r\nIn addition, to test the \"lensing effect,\" non-absorbing and weakly absorbing organic materials were condensed onto BC to form coated soot particles. The mass concentration of black carbon aerosol was measured using the Single Particle Soot Photometer (SP2), and chemical information was obtained via Aerosol Mass Spectrometry (AMS), allowing different optical models for mixed particles to be evaluated." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1633, 6023, 7026, 7028, 9042, 9043, 19043, 21576, 21990, 22196, 49983, 49984, 49985, 49986, 49987, 49988, 49989, 49990, 49991, 49992, 49993, 49994, 49995, 49996, 49997 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 181535, 181536, 181537, 181538, 181539, 181540, 181545, 181546, 181547, 181548, 181549, 181550, 181551, 181552, 181553 ], "onlineresource_set": [ 52856, 52857, 52855, 52974 ] }, { "ob_id": 38103, "uuid": "e0b4e1e56c1c4460b796073a31366980", "title": "CRU TS4.06: Climatic Research Unit (CRU) Time-Series (TS) version 4.06 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2021)", "abstract": "The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.06 data are month-by-month variations in climate over the period 1901-2021, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia and funded by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre.\r\n\r\nThe CRU TS4.06 variables are cloud cover, diurnal temperature range, frost day frequency, wet day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2021.\r\n\r\nThe CRU TS4.06 data were produced using angular-distance weighting (ADW) interpolation. All versions prior to 4.00 used triangulation routines in IDL. Please see the release notes for full details of this version update. \r\n\r\nThe CRU TS4.06 data are monthly gridded fields based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. The NetCDF versions contain an additional integer variable, ’stn’, which provides, for each datum in the main variable, a count (between 0 and 8) of the number of stations used in that interpolation. The missing value code for 'stn' is -999.\r\n\r\nAll CRU TS output files are actual values - NOT anomalies.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-09-28T11:18:10", "updateFrequency": "notPlanned", "dataLineage": "The CRU TS data are produced by the Climatic Research Unit (CRU) at the University of East Anglia and are passed to the Centre for Environmental Data Analysis (CEDA) for long-term archival and distribution. Previous releases of the CRU TS data include:\r\n\r\nCRU TS 4.06 was provided to CEDA for archival in May 2022.\r\n\r\nCRU TS 4.05 was provided to CEDA for archival in June 2021.\r\n\r\nCRU TS 4.04 was provided to CEDA for archival in April 2020.\r\n\r\nCRU TS 4.03 was provided to CEDA for archival in May 2019. \r\n\r\nCRU TS 4.02 was provided to CEDA for archival in December 2018. \r\n\r\nCRU TS 4.01 was provided to CEDA for archival in September 2017. \r\n\r\nCRU TS 4.00 was provided to CEDA for archival in March 2017. \r\n\r\nCRU TS 3.24.01 was provided to CEDA for archival in January 2017. This is the latest version available and is a replacement for the withdrawn dataset 3.24, it supersedes all previous data versions (which are available to allow user comparisons)\r\n\r\nCRU TS 3.24 was provided to CEDA for archival in July 2016. This is the latest version available, superseding all previous data versions (which are available to allow user comparisons), v3.24 has been withdrawn.\r\n\r\nCRU TS 3.23 was provided to CEDA in October 2015 by CRU. This is the latest version available, superseding all previous data versions (which are available to allow user comparisons).\r\n\r\nCRU TS 3.22 was provided to CEDA for archival in July 2014 by CRU.\r\n\r\nCRU TS 3.21 was provided to CEDA for archival in July 2013 by CRU.\r\n\r\nCRU TS 3.20 was produced in December 2012.\r\nIn March 2013, CRU TS observation databases for TMP and PRE variables were provided by CRU. Others are in preparation. In July 2013, two errors were found in the PRE and WET variables of CRU TS v3.20. These have been repaired in CRU TS v3.21. Details of the errors found are available in the Release Notes in the archive.\r\n\r\nCRU TS 3.10.01 In July 2012, systematic errors were discovered in the CRUTS v3.10 process. The effect was, in some cases, to reduce the gridded values for PRE and therefore WET. Values of FRS were found to be unrealistic in some areas due to the algorithms used for synthetic generation. The files (pre, frs and wet) were immediately removed from BADC. The corrected run for precipitation, based on the v3.10 precipitation station data, was generated as a direct replacement and given the version number 3.10.01. There were no corrected runs produced for wet and frs.\r\n\r\nCRU TS 3.00 data files acquired directly from CRU in 2007. CRU provided the BADC with software to generate the CRU datasets in 2010, and this was used to produce CRU TS 3.10 at the BADC in early 2011.", "removedDataReason": "", "keywords": "CRU, CRU TS, 4.06, CRU TS4.06, CRU TS 4, CRU TS 4.06,atmosphere, earth science, climate,", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "0.5x0.5 degree grid", "status": "ongoing", "dataPublishedTime": "2022-09-30T09:09:34", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 513, "bboxName": "CRU High Resolution Grid", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -60.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38104, "dataPath": "/badc/cru/data/cru_ts/cru_ts_4.06", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7033543050, "numberOfFiles": 409, "fileFormat": "Data are provided in ASCII and NetCDF formats." }, "timePeriod": { "ob_id": 10503, "startTime": "1901-01-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 3416, "explanation": "The data are quality controlled by the Climatic Research Unit (CRU) at the University of East Anglia. Details are given in the paper Harris et al. 2020 and the release notes, links to both can be found in the documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-05-21" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 20388, "uuid": "81842aa686174647ae132a4c841d73b6", "short_code": "comp", "title": "UEA Climatic Research Unit (CRU) high resolution gridding software deployed on UEA CRU computer system for v4.00", "abstract": "This computation involved: UEA Climate Research Unit (CRU) High Resolution gridding software deployed on UEA Climate Research Unit (CRU) computer system. For details about the production of CRU TS and CRU CY datasets, please refer to Harris et al. (2020) - see Details/Docs tab, moderated by the Release Notes for v4.00 (which outline the new gridding process)" }, "procedureCompositeProcess": null, "imageDetails": [ 103 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 6672, "uuid": "b6c783922d1ce68c4293d90caede5bb9", "short_code": "proj", "title": "UEA Climatic Research Unit (CRU) Gridded Datasets production project", "abstract": "The Climatic Research Unit at the University of East Anglia is producing various resolution gridded datasets.\r\nSome of those datasets are stored at CEDA-BADC." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 27513, "uuid": "3587430e588b491e8a795664466a27d1", "short_code": "coll", "title": "Climatic Research Unit (CRU): Time-series (TS) datasets of variations in climate with variations in other phenomena v4", "abstract": "Time-series (TS) datasets are month-by-month variation in climate over the last century or so as produced by the Climatic Research Unit (CRU) at the University of East Anglia. These are calculated on high-resolution (0.5x0.5 degree) grids, which are based on an archive of monthly mean temperatures provided by more than 4000 weather stations distributed around the world. They allow variations in climate to be studied, and include variables such as cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum temperature, vapour pressure, potential evapo-transpiration and wet day frequency.\r\n\r\nThe CRU TS data are monthly gridded fields based on daily values -hence the ASCII and netcdf files both contain monthly mean values for the various parameters." } ], "responsiblepartyinfo_set": [ 181556, 181557, 181558, 181559, 181560, 181561, 181562, 181563, 181564, 181565, 181566, 181568, 181567, 181569, 181570 ], "onlineresource_set": [ 52858, 52859, 52860, 52861, 52862, 52863, 52864, 52865, 52867, 52868, 52866, 52963 ] }, { "ob_id": 38111, "uuid": "dd4d20c8626f4b9d99bc14358b1b50fe", "title": "Airborne LiDAR and RGB imagery from Sepilok Reserve and Danum Valley in Malaysia in 2020", "abstract": "This dataset contains LiDAR and RedGreenBlue (RGB) Imagery data collected from a helicopter over two forest sites in Sabah, Malaysia in February 2020.\r\n\r\nPoint cloud data are included in LAS (LASer) format as well as RGB data summary rasters in .tif format. The raster images were processed with LAStools using default parameters. Canopy Height Model (CHM), Digital Surface Model (DSM), Digital Terrain Model (DTM) and pulse density (pd) are also present. The RGB data are provided as jpgs and are organised by flight julian day (JD). \r\n\r\nThe Sepilok Reserve was scanned in full between 15 February 2020 (julian day 46). This is a total area of 27 square kilometres. In Danum Valley the scanning was distributed into two contiguous areas, the protected area (20 square kilometres) and the reduced impact logging area (9 square kilometres) on the 19-22 February 2020 (julian day 50-53). Importantly, these areas were chosen because of the availability of prior airborne LiDAR data collected by NERC in 2014 and by Ground Data Solutions in 2013. \r\n\r\nThe helicopter flew at approximately 350 m altitude above the forest canopy and at a speed of approximately 100 km/hr. The data were georeferenced using ground control points and are provided in the UTM 50N coordinate system.", "creationDate": "2022-09-15T13:56:22.236781", "lastUpdatedDate": "2022-10-03T08:53:57", "latestDataUpdateTime": "2023-06-08T16:43:23", "updateFrequency": "notPlanned", "dataLineage": "All data was collected by Ground Data Solutions R&D Sdn Bhd from a helicopter. The raw LiDAR waveform data were then discretized into point cloud data (in LAS1.2 format). Data were prepared by the project team before uploading to CEDA.", "removedDataReason": "", "keywords": "Lidar, Sepilok, Danum Valley", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-29T14:21:15", "doiPublishedTime": "2022-10-03T08:57:31", "removedDataTime": null, "geographicExtent": { "ob_id": 3618, "bboxName": "", "eastBoundLongitude": 117.99498, "westBoundLongitude": 117.600395, "southBoundLatitude": 4.80042, "northBoundLatitude": 5.864044 }, "verticalExtent": null, "result_field": { "ob_id": 38112, "dataPath": "/badc/deposited2022/Airborne_Imagery_Sabah_2020", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1010598480479, "numberOfFiles": 9982, "fileFormat": "Data are tif images, shape files and LASer (.las) formatted." }, "timePeriod": { "ob_id": 10504, "startTime": "2020-02-14T00:00:00", "endTime": "2020-02-20T00:00:00" }, "resultQuality": { "ob_id": 4075, "explanation": "LiDAR\nVertical accuracy ±0.15 m (95% confidence or 2 sigma) in flat open terrain\nHorizontal accuracy 0.5 m (95% confidence or 2 sigma) in flat open terrain\nAverage point density 10 pts/m2\nAverage point spacing 0.31m (Open terrain) 1-3m (vegetated terrain)\nLaser points relative accuracy 2 to 3 cm RMSE\n\nRGB\nAverage pixel resolution 10 cm\n\n\n", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-09-15" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38114, "uuid": "12c4b096482d442784ea51171fef1267", "short_code": "acq", "title": "Acquisition for: Airborne LiDAR and RGB data from Sepilok Reserve and Danum Valley in Malaysia in 2020", "abstract": "Lidar and RGB Imagery operated by Ground Data Solutions R&D Sdn Bhd from a helicopter" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38113, "uuid": "ff6cc7ada8824ce3bbec1e777a5e0850", "short_code": "proj", "title": "A 3D perspective of the effects of topography and wind on forest height and dynamics", "abstract": "This project aims to track how forest canopy height (and therefore biomass) is changing over time and whether this is correlated to local topography or wind patterns. We collected LiDAR and RGB data in areas with similar pre-existing data sets in order to detect changes over time. NE/S010750/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12208 ], "observationcollection_set": [ { "ob_id": 40623, "uuid": "88a8620229014e0ebacf0606b302112d", "short_code": "coll", "title": "ForestScan Collection", "abstract": "This collection is part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates. The collection contains a multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unoccupied aerial vehicle LiDAR scanning (UAV-LS), airborne laser scanning (ALS), and in-situ tree census and ancillary data. ForestScan was conceived to evaluate new technologies for characterising forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for the first three Forest Biomass Research Monitoring Sites in Paracou Research Station in French Guiana; Station d'Etudes des Gorilles et Chimpanzes, Lopé National Park in Gabon; and Kabili-Sepilok Forest Reserve, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, unmanned aerial vehicle-based laser scanning (UAV-LS) and airborne laser scanning (ALS) where possible, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection.\r\n\r\nWe also provide detailed field data collection protocols for TLS, UAV-LS, and ALS measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates.\r\n\r\nThe ForestScan project is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and GEO-TREES, a new Group on Earth Observations (GEO) initiative aimed at establishing a network of FBRM sites. ForestScan is the first demonstration of what could be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of EO-derived AGB estimates" } ], "responsiblepartyinfo_set": [ 181584, 181585, 181586, 181587, 181588, 181589, 181594, 181595, 181596 ], "onlineresource_set": [ 94257 ] }, { "ob_id": 38128, "uuid": "1f359da21c4041b4ab0977d05c7d38f0", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.15 (v20220916)", "abstract": "Data for Figure TS.15 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure TS.15 shows contribution to ERF and global surface temperature change from component emissions between 1750 to 2019 based on CMIP6 models, and net aerosol effective radiative forcing (ERF) from different lines of evidence.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three panels with data provided for all panels in the underlying chapter figures (6.12 and 7.5).\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n Figure 6.12:\r\n - Contribution to effective radiative forcing (ERF) (a) and global mean surface air temperature (GSAT) change (b) from component emissions between 1750 to 2019 based on CMIP6 models\r\n\r\n\r\nFigure 7.5:\r\n - Net aerosol effective radiative forcing (ERF), in W m-2, from:\r\n - AR5 assessment\r\n - AR6 assessment comprising the following:\r\n (Energy balance constraints [–2 to 0 W m–2 with no best estimate])\r\n (Observational evidence from satellite retrievals of –1.4 [–2.2 to –0.6] W m–2)\r\n (Combined model-based evidence of –1.25 [–2.1 to –0.4] W m–2)\r\n\r\n\r\nDetails about the dataset in the catalogue records of the underlying chapter figures (6.12 and 7.5)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a and panel b:\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_ERF.csv.\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_ERF_uncertainty.csv.\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_dT.csv.\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_dT_uncertainty.csv.\r\n \r\n Panel c:\r\n - Data file: table7.6.csv: input data for figure 7.5\r\n\r\n CMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n ERFari stands for Effective Radiative Forcing of aerosol-radiation interaction.\r\n ERFaci stands for Effective Radiative Forcing of aerosol-cloud interaction.\r\n IRFari stands for Instantaneous Radiative Forcing of aerosol-radiation interaction.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Panel a and panel b are identical to panel a and panel b of figure 6.12. Panel c is identical to figure 7.5.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to underlying chapter figures from which the figure was generated (Figure 6.12, Figure 7.5)\r\n - Link to code used to produce figure 7.5 on the Chapter 7 GitHub repository.", "creationDate": "2022-09-20T14:26:20.761853", "lastUpdatedDate": "2022-09-20T14:26:20", "latestDataUpdateTime": "2023-06-07T15:19:53", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, historical effective radiative forcing, attributed historical warming", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-07T14:25:45", "doiPublishedTime": "2023-07-27T14:05:29.637068", "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 180, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38129, "dataPath": "/badc/ar6_wg1/data/TS/ts_15/v20220916", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 18403, "numberOfFiles": 9, "fileFormat": "Data are CSV formatted" }, "timePeriod": { "ob_id": 10509, "startTime": "1750-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 4080, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-09-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 38130, "uuid": "cd7d7823afce4a089ba78fc65c0ebcc2", "short_code": "comp", "title": "Caption for Figure TS.15 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Contrbution to ERF and b) global surface temperature change from component emissions between 1750 to 2019 based on CMIP6 models and c) net aerosol effective radiative forcing (ERF) from different lines of evidence. The intent of the figure is to show advances since AR5 in the understanding of a) aerosol ERF from different lines of evidence as assessed in Chapter 7, b) emissions-based ERF and c) global surface temperature response for SLCFs as estimated in Chapter 6. In panel a), ERFs for well-mixed greenhouse gases (WMGHGs) are from the analytical formulae. ERFs for other components are multi-model means based on ESM simulations that quantify the effect of individual components. The derived emission-based ERFs are rescaled to match the concentration- based ERFs in Figure 7.6. Error bars are 5-95% and for the ERF account for uncertainty in radiative efficiencies and multi-model error in the means. In panel b), the global mean temperature response is calculated from the ERF time series using an impulse response function. In panel c), the AR6 assessment is based on energy balance constraints, observational evidence from satellite retrievals, and climate model-based evidence. For each line of evidence, the assessed best-estimate contributions from ERF due to ERFari and ERFaci are shown with darker and paler shading, respectively. Estimates from individual CMIP5 and CMIP6 models are depicted by blue and red crosses, respectively. The observational assessment for ERFari is taken from the instantaneous forcing due to aerosol-radiation interactions (IRFari). Uncertainty ranges are given in black bars for the total aerosol ERF and depict very likely ranges. {Sections 7.3.3, 6.4.2, Cross-Chapter Box 7.1, Figures 6.12, 7.5 ; Table 7.8}" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64257, 64258, 64259, 64260, 64261, 64262, 64263, 64264, 64265, 64266, 64267, 64268, 64269, 64270, 64271, 64272 ], "vocabularyKeywords": [], "identifier_set": [ 12657 ], "observationcollection_set": [ { "ob_id": 39213, "uuid": "7409c7ea8a5045f8ab08e44db9cccb1a", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Technical Summary", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Technical Summary.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure TS.1\r\n- data for Figure TS.9\r\n- input data for Figure TS.12 \r\n- data for Figure TS.13\r\n- data for Figure TS.15\r\n- data for Figure TS.17\r\n- data for Figure TS.19\r\n- data for Figure TS.22\r\n- input data for Figure TS.24\r\n- data for Figure TS.25\r\n- data for Box TS.2, Figure 1\r\n- data for Box TS.2, Figure 2\r\n- data for Box TS.4, Figure 1\r\n- input data for Box Ts.4, Figure 1\r\n- input data for Box TS.5, Figure 1\r\n- data for Box TS.6, Figure 1\r\n- data for Box TS.7, Figure 1\r\n- data for Box TS.13, Figure 1\r\n- data for Cross-Section Box TS.1, Figure 1" } ], "responsiblepartyinfo_set": [ 181643, 181644, 181645, 181646, 181647, 181648, 193064, 193067, 193065, 193066 ], "onlineresource_set": [ 52869, 82354, 82355, 82356, 82725 ] }, { "ob_id": 38136, "uuid": "6ef666573b7247ce974a5574b3ee7dcf", "title": "FAAM C297 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C297 took place on 16th July 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:54:26.448607", "lastUpdatedDate": "2022-09-20T15:54:26", "latestDataUpdateTime": "2024-02-01T15:40:23", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:44", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3625, "bboxName": "", "eastBoundLongitude": -106.51201629638672, "westBoundLongitude": -107.59278106689453, "southBoundLatitude": 33.470699310302734, "northBoundLatitude": 35.0447883605957 }, "verticalExtent": null, "result_field": { "ob_id": 38135, "dataPath": "/badc/faam/data/2022/c297-jul-16", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5793870313, "numberOfFiles": 48, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10511, "startTime": "2022-07-16T14:09:46.667969", "endTime": "2022-07-16T19:20:32.531250" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38138, "uuid": "1951058427604cc29186c367e14ae93a", "short_code": "acq", "title": "FAAM Flight C297 Acquisition", "abstract": "FAAM Flight C297 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 51124, 51129, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60200, 60201, 60307, 60308, 60309, 62679, 62680, 62681, 62682 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181671, 181672, 181673, 181674, 181675, 181676, 181677, 181678, 181679 ], "onlineresource_set": [ 52872, 52873 ] }, { "ob_id": 38141, "uuid": "9c94557cd6214a0986ad6af3d17e5d87", "title": "FAAM C298 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C298 took place on 19th July 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:56:08.125144", "lastUpdatedDate": "2022-09-20T15:56:08", "latestDataUpdateTime": "2025-05-08T16:46:06", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:45", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3626, "bboxName": "", "eastBoundLongitude": -106.57572174072266, "westBoundLongitude": -107.46529388427734, "southBoundLatitude": 33.51112747192383, "northBoundLatitude": 35.15516662597656 }, "verticalExtent": null, "result_field": { "ob_id": 38140, "dataPath": "/badc/faam/data/2022/c298-jul-19", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 10406286170, "numberOfFiles": 55, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10512, "startTime": "2022-07-19T13:40:27", "endTime": "2022-07-19T20:07:55" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38142, "uuid": "4d582994c71c4477aea35fb30ad36483", "short_code": "acq", "title": "FAAM Flight C298 Acquisition", "abstract": "FAAM Flight C298 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 50851, 50853, 50856, 50857, 50934, 50936, 50937, 50942, 50949, 50950, 50952, 50953, 50965, 50968, 50969, 50972, 50973, 50980, 50981, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51124, 51129, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51279, 51280, 53949, 53951, 53954, 59964, 59965, 59966, 59967, 59968, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60071, 60073, 60074, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60172, 60173, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60200, 60201, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60213, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 60307, 60308, 60309, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62658, 62659, 62660, 62661, 62662, 62663, 62664, 62665, 62666, 62667, 62668, 62669, 62670, 62671, 62672, 62673, 62674, 62675, 62676, 62677, 62678, 62679, 62680, 62681, 62682 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181697, 181698, 181699, 181700, 181701, 181702, 181703, 181704, 181705 ], "onlineresource_set": [ 52875, 52876 ] }, { "ob_id": 38145, "uuid": "4112c04290fd43879a48ac38c500c67a", "title": "FAAM C299 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C299 took place on 20th July 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:56:14.139701", "lastUpdatedDate": "2022-09-20T15:56:14", "latestDataUpdateTime": "2025-05-08T17:14:25", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:45", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3627, "bboxName": "", "eastBoundLongitude": -106.51851654052734, "westBoundLongitude": -107.7066650390625, "southBoundLatitude": 33.40574264526367, "northBoundLatitude": 35.04587173461914 }, "verticalExtent": null, "result_field": { "ob_id": 38144, "dataPath": "/badc/faam/data/2022/c299-jul-20", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7479616303, "numberOfFiles": 57, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10513, "startTime": "2022-07-20T14:14:02", "endTime": "2022-07-20T20:19:02" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38146, "uuid": "01def96ed9364c988d45e8d177a21cb2", "short_code": "acq", "title": "FAAM Flight C299 Acquisition", "abstract": "FAAM Flight C299 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 50842, 50851, 50853, 50856, 50857, 50865, 50880, 50888, 50889, 50905, 50907, 50911, 50932, 50933, 50934, 50936, 50937, 50942, 50947, 50948, 50949, 50950, 50952, 50953, 50965, 50967, 50969, 50970, 50971, 50973, 50978, 50979, 50982, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51044, 51045, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51098, 51124, 51129, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 54967, 54971, 54975, 54976, 59964, 59965, 59966, 59967, 59968, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60119, 60172, 60173, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60200, 60201, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62658, 62659, 62660, 62661, 62662, 62663, 62664, 62665, 62666, 62669, 62671, 62678, 62679, 62680, 62681, 62682, 65806, 71784, 72012, 74055, 74056, 74057, 74058, 74059, 74060, 74061, 74063, 74064, 74065, 74066, 74067, 74068, 74069, 74070, 74071, 74072, 74073, 74074, 74075, 74076, 74077, 74078, 74079, 74080, 74081, 74082, 74083, 74084, 74085, 74086, 74087, 74088, 74089, 74090, 74092, 74093, 74097, 74099, 74100, 74101, 74102, 74103, 74104, 74105, 74160, 74161, 74504, 74505, 74506, 74507, 74508, 74509, 74510, 74511, 74512, 74513, 74514, 74515, 74516, 75052, 75061, 76041, 76042, 76043, 76044, 76045, 76046, 76047, 76048, 76049, 76050, 76051, 76052, 76053, 76054, 82188, 82190, 82192, 82194, 82198, 82200, 82202, 82203, 82204, 82208, 82209, 82210, 82212, 82217, 82219, 82220, 82233, 82235, 82237, 82238, 82241, 82242, 82243, 82248, 87696, 87697 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181713, 181714, 181715, 181716, 181717, 181718, 181719, 181720, 181721 ], "onlineresource_set": [ 52878, 52879 ] }, { "ob_id": 38149, "uuid": "5ca07feaa3504929adafc3a77d3cf5de", "title": "FAAM C300 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C300 took place on 22 July 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:56:23.126973", "lastUpdatedDate": "2022-09-20T15:56:23", "latestDataUpdateTime": "2025-05-08T17:11:11", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:46", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3628, "bboxName": "", "eastBoundLongitude": -106.517333984375, "westBoundLongitude": -107.43806457519531, "southBoundLatitude": 33.37639236450195, "northBoundLatitude": 35.04453659057617 }, "verticalExtent": null, "result_field": { "ob_id": 38148, "dataPath": "/badc/faam/data/2022/c300-jul-22", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7199383646, "numberOfFiles": 57, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10514, "startTime": "2022-07-22T13:40:33", "endTime": "2022-07-22T20:15:40" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38150, "uuid": "1b0cdda9fb6a4dab9ad454aa251f7663", "short_code": "acq", "title": "FAAM Flight C300 Acquisition", "abstract": "FAAM Flight C300 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 50842, 50851, 50853, 50856, 50857, 50865, 50880, 50888, 50889, 50905, 50907, 50911, 50932, 50933, 50934, 50936, 50937, 50942, 50947, 50948, 50949, 50950, 50952, 50953, 50965, 50967, 50969, 50970, 50971, 50973, 50978, 50979, 50982, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51044, 51045, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51098, 51124, 51129, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 54967, 54971, 54975, 54976, 59964, 59965, 59966, 59967, 59968, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60119, 60172, 60173, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60201, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62658, 62659, 62660, 62661, 62662, 62663, 62664, 62665, 62666, 62669, 62671, 62678, 62679, 62680, 62681, 62682, 65806, 71784, 72012, 74055, 74056, 74057, 74058, 74059, 74060, 74061, 74062, 74063, 74064, 74065, 74066, 74067, 74068, 74069, 74070, 74071, 74072, 74073, 74074, 74075, 74076, 74077, 74078, 74079, 74080, 74081, 74082, 74083, 74084, 74085, 74086, 74087, 74088, 74089, 74090, 74092, 74093, 74096, 74097, 74098, 74099, 74100, 74101, 74102, 74103, 74104, 74105, 74160, 74161, 74506, 75052, 75061, 76041, 76042, 76043, 76044, 76045, 76046, 76047, 76048, 76049, 76050, 76051, 76052, 76053, 76054, 82188, 82190, 82192, 82194, 82195, 82198, 82200, 82202, 82203, 82204, 82208, 82209, 82210, 82212, 82217, 82218, 82219, 82220, 82223, 82233, 82235, 82237, 82238, 82241, 82242, 82243, 82248, 89695, 89696, 89697 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181730, 181731, 181732, 181733, 181734, 181735, 181736, 181729, 181737 ], "onlineresource_set": [ 52881, 52882 ] }, { "ob_id": 38153, "uuid": "6c7a6047f3b44d45b0c0055d1f3def76", "title": "FAAM C301 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C301 took place on 23rd July 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:56:55.351438", "lastUpdatedDate": "2022-09-20T15:56:55", "latestDataUpdateTime": "2025-05-08T17:11:25", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:46", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3629, "bboxName": "", "eastBoundLongitude": -106.57572937011719, "westBoundLongitude": -107.57290649414062, "southBoundLatitude": 33.27363967895508, "northBoundLatitude": 35.04486846923828 }, "verticalExtent": null, "result_field": { "ob_id": 38152, "dataPath": "/badc/faam/data/2022/c301-jul-23", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9191104906, "numberOfFiles": 59, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10515, "startTime": "2022-07-23T13:26:44", "endTime": "2022-07-23T20:13:07" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38154, "uuid": "ed999a39c39e46c5a6cbb589cd9644de", "short_code": "acq", "title": "FAAM Flight C301 Acquisition", "abstract": "FAAM Flight C301 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 50842, 50851, 50853, 50856, 50857, 50865, 50880, 50888, 50889, 50905, 50907, 50911, 50932, 50933, 50934, 50936, 50937, 50942, 50947, 50948, 50949, 50950, 50952, 50953, 50965, 50967, 50969, 50970, 50971, 50973, 50978, 50979, 50982, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51044, 51045, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51098, 51124, 51129, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 54967, 54971, 54975, 54976, 59964, 59965, 59966, 59967, 59968, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60119, 60172, 60173, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60201, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62658, 62659, 62660, 62661, 62662, 62663, 62664, 62665, 62666, 62669, 62671, 62678, 62679, 62680, 62681, 62682, 65806, 71784, 72012, 74055, 74056, 74057, 74058, 74059, 74061, 74062, 74063, 74064, 74065, 74066, 74067, 74068, 74069, 74070, 74071, 74072, 74075, 74077, 74078, 74079, 74080, 74081, 74082, 74084, 74085, 74086, 74087, 74088, 74090, 74092, 74093, 74096, 74097, 74099, 74100, 74101, 74102, 74103, 74104, 74105, 74160, 74161, 74506, 75052, 75061, 76041, 76042, 76043, 76044, 76045, 76046, 76047, 76048, 76049, 76050, 76051, 76052, 76053, 76054, 79240, 79241, 79243, 79244, 79245, 79246, 82188, 82190, 82192, 82194, 82195, 82198, 82200, 82202, 82203, 82204, 82208, 82209, 82210, 82212, 82217, 82218, 82219, 82220, 82223, 82233, 82235, 82237, 82238, 82241, 82242, 82243, 83059, 89695, 91584, 91585, 91586 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181745, 181746, 181747, 181748, 181749, 181750, 181751, 181752, 181753 ], "onlineresource_set": [ 52884, 52885 ] }, { "ob_id": 38157, "uuid": "6ea24f0f12754cda99e12f108eafff72", "title": "FAAM C302 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C302 took place on 24th July 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:57:01.884667", "lastUpdatedDate": "2022-09-20T15:57:01", "latestDataUpdateTime": "2025-05-08T17:11:38", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:47", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3630, "bboxName": "", "eastBoundLongitude": -106.57569122314453, "westBoundLongitude": -107.63233947753906, "southBoundLatitude": 33.00108337402344, "northBoundLatitude": 35.04588317871094 }, "verticalExtent": null, "result_field": { "ob_id": 38156, "dataPath": "/badc/faam/data/2022/c302-jul-24", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6751225153, "numberOfFiles": 58, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10516, "startTime": "2022-07-24T13:28:42", "endTime": "2022-07-24T19:34:07" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38158, "uuid": "e468514b8e30442fa516bc7978361c5c", "short_code": "acq", "title": "FAAM Flight C302 Acquisition", "abstract": "FAAM Flight C302 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 50851, 50853, 50856, 50857, 50934, 50936, 50937, 50942, 50949, 50950, 50952, 50953, 50965, 50968, 50969, 50972, 50973, 50980, 50981, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51124, 51129, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 59964, 59965, 59966, 59967, 59968, 59969, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60076, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60172, 60173, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60200, 60201, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60214, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62658, 62659, 62660, 62661, 62662, 62663, 62664, 62665, 62666, 62667, 62668, 62669, 62670, 62671, 62672, 62673, 62674, 62675, 62676, 62677, 62678, 62680, 62681, 62682 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181761, 181762, 181763, 181764, 181765, 181766, 181767, 181768, 181769 ], "onlineresource_set": [ 52887, 52888 ] }, { "ob_id": 38161, "uuid": "2981d0a585b7407abcb15444151014b8", "title": "FAAM C303 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C303 took place on 25th July 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:57:09.998590", "lastUpdatedDate": "2022-09-20T15:57:09", "latestDataUpdateTime": "2025-05-08T17:11:51", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:47", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3631, "bboxName": "", "eastBoundLongitude": -106.57572937011719, "westBoundLongitude": -107.61967468261719, "southBoundLatitude": 33.54457473754883, "northBoundLatitude": 35.04540252685547 }, "verticalExtent": null, "result_field": { "ob_id": 38160, "dataPath": "/badc/faam/data/2022/c303-jul-25", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8436666817, "numberOfFiles": 61, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10517, "startTime": "2022-07-25T13:29:52", "endTime": "2022-07-25T20:08:33" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38162, "uuid": "74f36d41a3714cf0804e7faec66ad0b8", "short_code": "acq", "title": "FAAM Flight C303 Acquisition", "abstract": "FAAM Flight C303 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 50968, 50969, 50972, 50973, 50980, 50981, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51124, 51129, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60200, 60201, 62667, 62668, 62669, 62670, 62671, 62672, 62673, 62674, 62675, 62676, 62677, 62678, 62680, 62681, 62682 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181777, 181778, 181779, 181780, 181781, 181782, 181783, 181784, 181785 ], "onlineresource_set": [ 52890, 52891 ] }, { "ob_id": 38165, "uuid": "2523de0b012241f5b70b2a94888d7c65", "title": "FAAM C304 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C304 took place on 26th July 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:57:15.623823", "lastUpdatedDate": "2022-09-20T15:57:15", "latestDataUpdateTime": "2025-05-08T17:12:03", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:48", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3632, "bboxName": "", "eastBoundLongitude": -106.51121520996094, "westBoundLongitude": -107.36605072021484, "southBoundLatitude": 33.47703552246094, "northBoundLatitude": 35.0444450378418 }, "verticalExtent": null, "result_field": { "ob_id": 38164, "dataPath": "/badc/faam/data/2022/c304-jul-26", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8100205553, "numberOfFiles": 62, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10518, "startTime": "2022-07-26T13:00:40", "endTime": "2022-07-26T19:43:11" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38166, "uuid": "dc80875b86c84661b46cd755f23e5d96", "short_code": "acq", "title": "FAAM Flight C304 Acquisition", "abstract": "FAAM Flight C304 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 50842, 50851, 50853, 50856, 50857, 50865, 50880, 50888, 50889, 50905, 50907, 50911, 50932, 50933, 50934, 50936, 50937, 50942, 50947, 50948, 50949, 50950, 50952, 50953, 50965, 50967, 50969, 50970, 50971, 50973, 50978, 50979, 50982, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51044, 51045, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51098, 51124, 51129, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 54967, 54971, 54975, 54976, 59964, 59965, 59966, 59967, 59968, 59969, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60119, 60172, 60173, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60201, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60214, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62658, 62659, 62660, 62661, 62662, 62663, 62664, 62665, 62666, 62669, 62671, 62678, 62679, 62680, 62681, 62682, 65806, 71784, 72012, 74055, 74056, 74057, 74058, 74059, 74060, 74061, 74062, 74063, 74064, 74065, 74066, 74067, 74068, 74069, 74070, 74071, 74072, 74073, 74074, 74075, 74076, 74077, 74078, 74079, 74080, 74081, 74082, 74083, 74084, 74085, 74086, 74087, 74088, 74089, 74090, 74092, 74093, 74096, 74097, 74098, 74099, 74100, 74101, 74102, 74103, 74104, 74105, 74160, 74161, 74506, 75052, 75061, 76041, 76042, 76043, 76044, 76045, 76046, 76047, 76048, 76049, 76050, 76051, 76052, 76053, 76054, 82188, 82190, 82192, 82194, 82195, 82198, 82200, 82202, 82203, 82204, 82208, 82209, 82210, 82212, 82217, 82218, 82219, 82220, 82223, 82233, 82235, 82237, 82238, 82241, 82242, 82243, 82248, 89695, 91924, 91925 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181793, 181794, 181795, 181796, 181797, 181798, 181799, 181800, 181801 ], "onlineresource_set": [ 52893, 52894 ] }, { "ob_id": 38169, "uuid": "4f0668da8939492f9af508a06db51551", "title": "FAAM C305 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C305 took place on 27th July 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:57:20.738509", "lastUpdatedDate": "2022-09-20T15:57:20", "latestDataUpdateTime": "2025-05-08T17:12:17", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:49", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3633, "bboxName": "", "eastBoundLongitude": -106.57572937011719, "westBoundLongitude": -107.4879150390625, "southBoundLatitude": 33.470176696777344, "northBoundLatitude": 35.044857025146484 }, "verticalExtent": null, "result_field": { "ob_id": 38168, "dataPath": "/badc/faam/data/2022/c305-jul-27", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8462610690, "numberOfFiles": 61, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10519, "startTime": "2022-07-27T13:36:17", "endTime": "2022-07-27T20:16:41" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38170, "uuid": "388d9d8bf2a74666a2c834e43a183a97", "short_code": "acq", "title": "FAAM Flight C305 Acquisition", "abstract": "FAAM Flight C305 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 50842, 50851, 50853, 50856, 50857, 50865, 50880, 50888, 50889, 50905, 50907, 50911, 50932, 50933, 50934, 50936, 50937, 50942, 50947, 50948, 50949, 50950, 50952, 50953, 50965, 50967, 50969, 50970, 50971, 50973, 50978, 50979, 50982, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51044, 51045, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51098, 51124, 51129, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 54967, 54971, 54975, 54976, 59964, 59965, 59966, 59967, 59968, 59969, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60119, 60172, 60173, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60200, 60201, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60214, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62658, 62659, 62660, 62661, 62662, 62663, 62664, 62665, 62666, 62669, 62671, 62678, 62679, 62680, 62681, 62682, 65806, 71784, 72012, 73930, 73931, 73932, 73933, 73934, 73935, 73936, 73937, 73938, 73939, 73940, 73941, 73942, 73943, 73944, 73945, 73946, 73947, 73948, 73949, 73950, 73951, 73953, 73954, 73955, 73956, 73957, 73958, 73960, 73961, 73962, 73963, 73965, 73966, 74055, 74056, 74057, 74058, 74059, 74060, 74061, 74063, 74064, 74065, 74066, 74067, 74068, 74069, 74070, 74071, 74072, 74073, 74074, 74075, 74076, 74077, 74078, 74079, 74080, 74081, 74082, 74083, 74084, 74085, 74086, 74087, 74088, 74089, 74090, 74092, 74093, 74097, 74099, 74100, 74101, 74102, 74103, 74104, 74105, 74131, 74155, 74157, 74160, 74161, 74504, 74505, 74506, 74507, 74508, 74509, 74510, 74511, 74512, 74513, 74514, 74515, 74516, 75052, 75061, 76041, 76042, 76043, 76044, 76045, 76046, 76047, 76048, 76049, 76050, 76051, 76052, 76053, 76054, 82188, 82190, 82192, 82194, 82198, 82200, 82202, 82203, 82204, 82208, 82209, 82210, 82212, 82217, 82219, 82220, 82233, 82235, 82237, 82238, 82241, 82242, 82243, 82248, 88564, 88565 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181809, 181810, 181811, 181812, 181813, 181814, 181815, 181816, 181817 ], "onlineresource_set": [ 52896, 52897 ] }, { "ob_id": 38173, "uuid": "2dd2f730cdf647b69dbd6b8b8e89ede4", "title": "FAAM C306 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C306 took place on 29th July 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:57:25.421879", "lastUpdatedDate": "2022-09-20T15:57:25", "latestDataUpdateTime": "2025-05-08T17:12:30", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:49", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3634, "bboxName": "", "eastBoundLongitude": -106.51165771484375, "westBoundLongitude": -107.41815948486328, "southBoundLatitude": 33.383121490478516, "northBoundLatitude": 35.044960021972656 }, "verticalExtent": null, "result_field": { "ob_id": 38172, "dataPath": "/badc/faam/data/2022/c306-jul-29", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8239499002, "numberOfFiles": 62, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10520, "startTime": "2022-07-29T13:27:22", "endTime": "2022-07-29T20:08:36" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38174, "uuid": "ee724ce524c449599a856cc1a620da12", "short_code": "acq", "title": "FAAM Flight C306 Acquisition", "abstract": "FAAM Flight C306 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 50851, 50853, 50856, 50857, 50934, 50936, 50937, 50942, 50949, 50950, 50952, 50953, 50965, 50968, 50969, 50972, 50973, 50980, 50981, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51124, 51129, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 59964, 59965, 59966, 59967, 59968, 59969, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60076, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60172, 60173, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60200, 60201, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60214, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 60307, 60308, 60309, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62658, 62659, 62660, 62661, 62662, 62663, 62664, 62665, 62666, 62667, 62668, 62669, 62670, 62671, 62672, 62673, 62674, 62675, 62676, 62677, 62678, 62679, 62680, 62681, 62682 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181825, 181826, 181827, 181828, 181829, 181830, 181831, 181832, 181833 ], "onlineresource_set": [ 52899, 52900 ] }, { "ob_id": 38177, "uuid": "baaf1b5b374e45e0a367c9cfb993cf0f", "title": "FAAM C307 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C307 took place on 30th July 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:57:30.264041", "lastUpdatedDate": "2022-09-20T15:57:30", "latestDataUpdateTime": "2025-05-08T17:12:44", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:50", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3635, "bboxName": "", "eastBoundLongitude": -106.50957489013672, "westBoundLongitude": -107.49838256835938, "southBoundLatitude": 33.197166442871094, "northBoundLatitude": 35.04811096191406 }, "verticalExtent": null, "result_field": { "ob_id": 38176, "dataPath": "/badc/faam/data/2022/c307-jul-30", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8608594446, "numberOfFiles": 62, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10521, "startTime": "2022-07-30T13:24:31", "endTime": "2022-07-30T20:03:59" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38178, "uuid": "db9eccbd1db04d949c95be5fd2633b8d", "short_code": "acq", "title": "FAAM Flight C307 Acquisition", "abstract": "FAAM Flight C307 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 50851, 50853, 50856, 50857, 50934, 50936, 50937, 50942, 50949, 50950, 50952, 50953, 50965, 50968, 50969, 50972, 50973, 50980, 50981, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51124, 51129, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 59964, 59965, 59966, 59967, 59968, 59969, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60076, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60172, 60173, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60200, 60201, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60214, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62658, 62659, 62660, 62661, 62662, 62663, 62664, 62665, 62666, 62667, 62668, 62669, 62670, 62671, 62672, 62673, 62674, 62675, 62676, 62677, 62678, 62680, 62681, 62682 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181841, 181842, 181843, 181844, 181845, 181846, 181847, 181848, 181849 ], "onlineresource_set": [ 52902, 52903 ] }, { "ob_id": 38181, "uuid": "d1ab16c4aa144de3b7f015ef90bdaf89", "title": "FAAM C308 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C308 took place on 31st July 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:57:35.984053", "lastUpdatedDate": "2022-09-20T15:57:35", "latestDataUpdateTime": "2025-05-08T17:12:56", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:50", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3636, "bboxName": "", "eastBoundLongitude": -106.57572937011719, "westBoundLongitude": -107.40567779541016, "southBoundLatitude": 33.417659759521484, "northBoundLatitude": 35.06218338012695 }, "verticalExtent": null, "result_field": { "ob_id": 38180, "dataPath": "/badc/faam/data/2022/c308-jul-31", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8130162900, "numberOfFiles": 60, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10522, "startTime": "2022-07-31T13:29:47", "endTime": "2022-07-31T20:15:49" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38182, "uuid": "e79e3a23edcc4b62bb426e25bc78125a", "short_code": "acq", "title": "FAAM Flight C308 Acquisition", "abstract": "FAAM Flight C308 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 50842, 50851, 50853, 50856, 50857, 50865, 50880, 50888, 50889, 50905, 50907, 50911, 50932, 50933, 50934, 50936, 50937, 50942, 50947, 50948, 50949, 50950, 50952, 50953, 50965, 50967, 50969, 50970, 50971, 50973, 50978, 50979, 50982, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51044, 51045, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51098, 51124, 51129, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 54967, 54971, 54975, 54976, 59964, 59965, 59966, 59967, 59968, 59969, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60119, 60172, 60173, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60200, 60201, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60214, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 60308, 60309, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62658, 62659, 62660, 62661, 62662, 62663, 62664, 62665, 62666, 62669, 62671, 62678, 62679, 62680, 62681, 62682, 65806, 71784, 72012, 73930, 73931, 73932, 73933, 73934, 73935, 73936, 73937, 73938, 73939, 73940, 73941, 73942, 73943, 73944, 73945, 73946, 73947, 73948, 73949, 73950, 73951, 73952, 73953, 73954, 73955, 73956, 73957, 73958, 73959, 73960, 73961, 73962, 73963, 73964, 73965, 73966, 73967, 74055, 74056, 74057, 74058, 74059, 74060, 74061, 74063, 74064, 74065, 74066, 74067, 74068, 74069, 74070, 74071, 74072, 74073, 74074, 74075, 74076, 74077, 74078, 74079, 74080, 74081, 74082, 74083, 74084, 74085, 74086, 74087, 74088, 74089, 74090, 74092, 74093, 74097, 74099, 74100, 74101, 74102, 74103, 74104, 74105, 74155, 74157, 74505, 74506, 74507, 74508, 74509, 74510, 74511, 74512, 74513, 74514, 74515, 74516, 75052, 75061, 76041, 76042, 76043, 76044, 76045, 76046, 76047, 76048, 76049, 76050, 76051, 76052, 76053, 76054, 82188, 82192, 82198, 82202, 82203, 82210, 82212, 82219, 82233, 82237, 82238, 82241, 82243, 83059, 87101, 89186, 89187 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181857, 181858, 181859, 181860, 181861, 181862, 181863, 181864, 181865 ], "onlineresource_set": [ 52905, 52906 ] }, { "ob_id": 38185, "uuid": "be8987cb2da44a5db1a486e7b4df1736", "title": "FAAM C309 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C309 took place on 1st August 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:57:41.846067", "lastUpdatedDate": "2022-09-20T15:57:41", "latestDataUpdateTime": "2025-05-08T17:13:07", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:51", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3637, "bboxName": "", "eastBoundLongitude": -106.57571411132812, "westBoundLongitude": -107.44636535644531, "southBoundLatitude": 33.4690055847168, "northBoundLatitude": 35.0452880859375 }, "verticalExtent": null, "result_field": { "ob_id": 38184, "dataPath": "/badc/faam/data/2022/c309-aug-01", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8378522290, "numberOfFiles": 62, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10523, "startTime": "2022-08-01T13:42:46", "endTime": "2022-08-01T20:21:17" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38186, "uuid": "1e8b8456478a42bf803a04919fa731f0", "short_code": "acq", "title": "FAAM Flight C309 Acquisition", "abstract": "FAAM Flight C309 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 51124, 51129, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60200, 60201, 60307, 60308, 60309, 62679, 62680, 62681, 62682 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181873, 181874, 181875, 181876, 181877, 181878, 181879, 181880, 181881 ], "onlineresource_set": [ 52908, 52909 ] }, { "ob_id": 38189, "uuid": "4d98b78a85fa41bfa43a5775aee3c00f", "title": "FAAM C310 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C310 took place on 2nd August 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:57:49.405878", "lastUpdatedDate": "2022-09-20T15:57:49", "latestDataUpdateTime": "2025-05-08T17:13:19", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:51", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3638, "bboxName": "", "eastBoundLongitude": -106.50570678710938, "westBoundLongitude": -107.49400329589844, "southBoundLatitude": 33.54821014404297, "northBoundLatitude": 35.044368743896484 }, "verticalExtent": null, "result_field": { "ob_id": 38188, "dataPath": "/badc/faam/data/2022/c310-aug-02", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8594062088, "numberOfFiles": 62, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10524, "startTime": "2022-08-02T13:26:01", "endTime": "2022-08-02T20:11:13" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38190, "uuid": "5b2259ee7f6243a198b2e5bf616303af", "short_code": "acq", "title": "FAAM Flight C310 Acquisition", "abstract": "FAAM Flight C310 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 50842, 50851, 50853, 50856, 50857, 50865, 50880, 50888, 50889, 50905, 50907, 50911, 50932, 50933, 50934, 50936, 50937, 50942, 50947, 50948, 50949, 50950, 50952, 50953, 50965, 50967, 50969, 50970, 50971, 50973, 50978, 50979, 50982, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51044, 51045, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51098, 51124, 51129, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 54967, 54971, 54975, 54976, 59964, 59965, 59966, 59967, 59968, 59969, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60119, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60200, 60201, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60214, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62664, 62665, 62666, 62669, 62671, 62678, 62679, 62680, 62681, 62682, 65806, 71784, 72012, 74055, 74056, 74057, 74058, 74059, 74060, 74061, 74063, 74064, 74065, 74066, 74067, 74068, 74069, 74070, 74071, 74072, 74073, 74074, 74075, 74076, 74077, 74078, 74079, 74080, 74081, 74082, 74083, 74084, 74085, 74086, 74087, 74088, 74089, 74090, 74092, 74093, 74097, 74099, 74100, 74101, 74102, 74103, 74104, 74105, 74160, 74161, 74504, 74505, 74506, 74507, 74508, 74509, 74510, 74511, 74512, 74513, 74514, 74515, 74516, 75052, 75061, 76041, 76042, 76043, 76044, 76045, 76046, 76047, 76048, 76049, 76050, 76051, 76052, 76053, 76054, 82188, 82190, 82192, 82194, 82198, 82200, 82202, 82203, 82204, 82208, 82209, 82210, 82212, 82217, 82219, 82220, 82233, 82235, 82237, 82238, 82241, 82242, 82243, 82248, 88552, 88553 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181889, 181890, 181891, 181892, 181893, 181894, 181895, 181896, 181897 ], "onlineresource_set": [ 52911, 52912 ] }, { "ob_id": 38193, "uuid": "d3e3ef7a3b8e425caec7fb89b194eeb3", "title": "FAAM C311 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C311 took place on 3rd August 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:58:42.537012", "lastUpdatedDate": "2022-09-20T15:58:42", "latestDataUpdateTime": "2025-05-08T17:13:29", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:52", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3639, "bboxName": "", "eastBoundLongitude": -106.5089340209961, "westBoundLongitude": -107.5634765625, "southBoundLatitude": 33.56700897216797, "northBoundLatitude": 35.0445671081543 }, "verticalExtent": null, "result_field": { "ob_id": 38192, "dataPath": "/badc/faam/data/2022/c311-aug-03", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5042587712, "numberOfFiles": 52, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10525, "startTime": "2022-08-03T13:26:25", "endTime": "2022-08-03T18:26:08" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38194, "uuid": "2b031976f1db42879be710d59c0a85e4", "short_code": "acq", "title": "FAAM Flight C311 Acquisition", "abstract": "FAAM Flight C311 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 50842, 50851, 50853, 50856, 50857, 50865, 50880, 50888, 50889, 50905, 50907, 50911, 50932, 50933, 50934, 50936, 50937, 50942, 50947, 50948, 50949, 50950, 50952, 50953, 50965, 50967, 50969, 50970, 50971, 50973, 50978, 50979, 50982, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51044, 51045, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51098, 51124, 51129, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 54967, 54971, 54975, 54976, 59964, 59965, 59966, 59967, 59968, 59969, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60119, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60200, 60201, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60214, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 60308, 60309, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62664, 62665, 62666, 62669, 62671, 62678, 62679, 62680, 62681, 62682, 65806, 71784, 72012, 73930, 73931, 73932, 73933, 73934, 73935, 73936, 73937, 73938, 73939, 73940, 73941, 73942, 73943, 73944, 73945, 73946, 73947, 73948, 73949, 73950, 73951, 73952, 73953, 73954, 73955, 73956, 73957, 73958, 73959, 73960, 73961, 73962, 73963, 73964, 73965, 73966, 73967, 74055, 74056, 74057, 74058, 74059, 74060, 74061, 74063, 74064, 74065, 74066, 74067, 74068, 74069, 74070, 74071, 74072, 74073, 74074, 74075, 74076, 74077, 74078, 74079, 74080, 74081, 74082, 74083, 74084, 74085, 74086, 74087, 74088, 74089, 74090, 74092, 74093, 74097, 74099, 74100, 74101, 74102, 74103, 74104, 74105, 74155, 74157, 74504, 74505, 74506, 74507, 74508, 74509, 74510, 74511, 74512, 74513, 74514, 74515, 74516, 75052, 75061, 76041, 76042, 76043, 76044, 76045, 76046, 76047, 76048, 76049, 76050, 76051, 76052, 76053, 76054, 82188, 82190, 82192, 82194, 82198, 82200, 82202, 82203, 82204, 82208, 82209, 82210, 82212, 82217, 82219, 82220, 82233, 82235, 82237, 82238, 82241, 82242, 82243, 82248, 92107, 92108 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181905, 181906, 181907, 181908, 181909, 181910, 181911, 181912, 181913 ], "onlineresource_set": [ 52914, 52915 ] }, { "ob_id": 38197, "uuid": "44e63af3fc9e4b88948e1325c73132a2", "title": "FAAM C312 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C312 took place on 4th August 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:58:46.938343", "lastUpdatedDate": "2022-09-20T15:58:46", "latestDataUpdateTime": "2025-05-10T01:56:22", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:52", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3640, "bboxName": "", "eastBoundLongitude": -106.57572937011719, "westBoundLongitude": -107.3808822631836, "southBoundLatitude": 33.483924865722656, "northBoundLatitude": 35.044769287109375 }, "verticalExtent": null, "result_field": { "ob_id": 38196, "dataPath": "/badc/faam/data/2022/c312-aug-04", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8167182744, "numberOfFiles": 60, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10526, "startTime": "2022-08-04T14:04:45", "endTime": "2022-08-04T20:51:49" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38198, "uuid": "8bdb825108a14b839b87f155c34b8936", "short_code": "acq", "title": "FAAM Flight C312 Acquisition", "abstract": "FAAM Flight C312 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 50851, 50853, 50856, 50857, 50934, 50936, 50937, 50942, 50949, 50950, 50952, 50953, 50965, 50968, 50969, 50972, 50973, 50980, 50981, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51124, 51129, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 59964, 59965, 59966, 59967, 59968, 59969, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60076, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60172, 60173, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60200, 60201, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60214, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 60307, 60308, 60309, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62658, 62659, 62660, 62661, 62662, 62663, 62664, 62665, 62666, 62667, 62668, 62669, 62670, 62671, 62672, 62673, 62674, 62675, 62676, 62677, 62678, 62679, 62680, 62681, 62682 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181921, 181922, 181923, 181924, 181925, 181926, 181927, 181928, 181929, 181930 ], "onlineresource_set": [ 52917, 52918 ] }, { "ob_id": 38201, "uuid": "eefc250e1dfb4142aa04e323aa449846", "title": "FAAM C313 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C313 took place on 6th August 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:58:53.684490", "lastUpdatedDate": "2022-09-20T15:58:53", "latestDataUpdateTime": "2025-05-08T17:13:51", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:53", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3641, "bboxName": "", "eastBoundLongitude": -106.5119857788086, "westBoundLongitude": -107.66194915771484, "southBoundLatitude": 33.478050231933594, "northBoundLatitude": 35.045204162597656 }, "verticalExtent": null, "result_field": { "ob_id": 38200, "dataPath": "/badc/faam/data/2022/c313-aug-06", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7562363045, "numberOfFiles": 58, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10527, "startTime": "2022-08-06T13:26:05", "endTime": "2022-08-06T19:46:28" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38202, "uuid": "85c8a61ed08742bdac8feb5559f45ed7", "short_code": "acq", "title": "FAAM Flight C313 Acquisition", "abstract": "FAAM Flight C313 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 51124, 51129, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60200, 60201, 60307, 60308, 60309, 62679, 62680, 62681, 62682 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181937, 181938, 181939, 181940, 181941, 181942, 181943, 181944, 181945 ], "onlineresource_set": [ 52920, 52921 ] }, { "ob_id": 38205, "uuid": "2ae1acd867b24d98af5e8fbf3ee539f2", "title": "FAAM C314 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C314 took place on 7th August 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:58:58.186945", "lastUpdatedDate": "2022-09-20T15:58:58", "latestDataUpdateTime": "2025-05-08T17:14:02", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:53", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3642, "bboxName": "", "eastBoundLongitude": -106.50916290283203, "westBoundLongitude": -107.4983901977539, "southBoundLatitude": 33.341304779052734, "northBoundLatitude": 35.04436492919922 }, "verticalExtent": null, "result_field": { "ob_id": 38204, "dataPath": "/badc/faam/data/2022/c314-aug-07", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6867709886, "numberOfFiles": 56, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10528, "startTime": "2022-08-07T13:57:06", "endTime": "2022-08-07T20:20:35" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38206, "uuid": "69ca7b37c0fb419db1ce857ce9485c92", "short_code": "acq", "title": "FAAM Flight C314 Acquisition", "abstract": "FAAM Flight C314 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 50842, 50851, 50853, 50856, 50857, 50865, 50880, 50888, 50889, 50905, 50907, 50911, 50932, 50933, 50934, 50936, 50937, 50942, 50947, 50948, 50949, 50950, 50952, 50953, 50965, 50967, 50969, 50970, 50971, 50973, 50978, 50979, 50982, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51044, 51045, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51098, 51124, 51129, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 54967, 54971, 54975, 54976, 59964, 59965, 59966, 59967, 59968, 59969, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60119, 60172, 60173, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60200, 60201, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60214, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 60308, 60309, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62658, 62659, 62660, 62661, 62662, 62663, 62664, 62665, 62666, 62669, 62671, 62678, 62679, 62680, 62681, 62682, 65806, 71784, 72012, 73930, 73931, 73932, 73933, 73934, 73935, 73936, 73937, 73938, 73939, 73940, 73941, 73942, 73943, 73944, 73945, 73946, 73947, 73948, 73949, 73950, 73951, 73952, 73953, 73954, 73955, 73956, 73957, 73958, 73959, 73960, 73961, 73962, 73963, 73964, 73965, 73966, 73967, 74055, 74056, 74057, 74058, 74059, 74060, 74061, 74063, 74064, 74065, 74066, 74067, 74068, 74069, 74070, 74071, 74072, 74073, 74074, 74075, 74076, 74077, 74078, 74079, 74080, 74081, 74082, 74083, 74084, 74085, 74086, 74087, 74088, 74089, 74090, 74092, 74093, 74097, 74099, 74100, 74101, 74102, 74103, 74104, 74105, 74155, 74157, 74504, 74505, 74506, 74507, 74508, 74509, 74510, 74511, 74512, 74513, 74514, 74515, 74516, 75052, 75061, 76041, 76042, 76043, 76044, 76045, 76046, 76047, 76048, 76049, 76050, 76051, 76052, 76053, 76054, 82188, 82190, 82192, 82194, 82198, 82200, 82202, 82203, 82204, 82208, 82209, 82210, 82212, 82217, 82219, 82220, 82233, 82235, 82237, 82238, 82241, 82242, 82243, 82248, 82249, 82250 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181953, 181954, 181955, 181956, 181957, 181958, 181959, 181960, 181961 ], "onlineresource_set": [ 52923, 52924 ] }, { "ob_id": 38209, "uuid": "168284dabc3d46e4a7f1e83c57da2240", "title": "FAAM C315 DCMEX flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft.", "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for the Deep Convective Microphysics Experiment (DCMEX) project. Flight C315 took place on 8th August 2022 over New Mexico, USA.", "creationDate": "2022-09-20T15:59:04.253943", "lastUpdatedDate": "2022-09-20T15:59:04", "latestDataUpdateTime": "2025-05-08T17:14:13", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T14:34:54", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3643, "bboxName": "", "eastBoundLongitude": -106.50605773925781, "westBoundLongitude": -107.46476745605469, "southBoundLatitude": 33.63880157470703, "northBoundLatitude": 35.04477310180664 }, "verticalExtent": null, "result_field": { "ob_id": 38208, "dataPath": "/badc/faam/data/2022/c315-aug-08", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6465416493, "numberOfFiles": 54, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 10529, "startTime": "2022-08-08T13:57:14", "endTime": "2022-08-08T19:26:03" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38210, "uuid": "a11205ba59b1462cb08901b7730e6478", "short_code": "acq", "title": "FAAM Flight C315 Acquisition", "abstract": "FAAM Flight C315 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 50851, 50853, 50856, 50857, 50934, 50936, 50937, 50942, 50949, 50950, 50952, 50953, 50965, 50968, 50969, 50972, 50973, 50980, 50981, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51124, 51129, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 59964, 59965, 59966, 59967, 59968, 59969, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60076, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60172, 60173, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60200, 60201, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60214, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62658, 62659, 62660, 62661, 62662, 62663, 62664, 62665, 62666, 62667, 62668, 62669, 62670, 62671, 62672, 62673, 62674, 62675, 62676, 62677, 62678, 62680, 62681, 62682 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 181969, 181970, 181971, 181972, 181973, 181974, 181975, 181976, 181977 ], "onlineresource_set": [ 52926, 52927 ] }, { "ob_id": 38212, "uuid": "5c6cab17105147ba84a294be46c83153", "title": "FAAM C296 DCMEX Test flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from instruments on board the FAAM BAE-146 aircraft collected for DCMEX FAAM Aircraft Project project. Flight C296 took place on 22nd June 2022 over the UK.", "creationDate": "2022-09-20T15:54:26.448607", "lastUpdatedDate": "2022-09-20T15:54:26", "latestDataUpdateTime": "2024-01-17T16:45:03", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DCMEX, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-20T16:11:36", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3645, "bboxName": "Bounding box for FAAM flight c296", "eastBoundLongitude": 3.16, "westBoundLongitude": -1.59, "southBoundLatitude": 51.96, "northBoundLatitude": 56.11 }, "verticalExtent": null, "result_field": { "ob_id": 38213, "dataPath": "/badc/faam/data/2022/c296-jun-22", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6465879602, "numberOfFiles": 49, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 10530, "startTime": "2022-06-22T08:57:33", "endTime": "2022-06-22T15:04:11" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38214, "uuid": "482b7c164bbd48698fd015dbfd10dde9", "short_code": "acq", "title": "FAAM Flight C296 Acquisition", "abstract": "FAAM Flight C296 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49302, 50851, 50853, 50856, 50857, 50934, 50936, 50937, 50942, 50949, 50950, 50952, 50953, 50965, 50968, 50969, 50972, 50973, 50980, 50981, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51124, 51129, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 59964, 59965, 59966, 59967, 59968, 59969, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60076, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60174, 60175, 60176, 60177, 60178, 60179, 60180, 60181, 60182, 60183, 60184, 60186, 60187, 60188, 60189, 60190, 60191, 60194, 60195, 60196, 60197, 60198, 60199, 60200, 60201, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60214, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62664, 62665, 62666, 62667, 62668, 62669, 62670, 62671, 62672, 62673, 62674, 62675, 62676, 62677, 62678, 62680, 62681, 62682 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." } ], "responsiblepartyinfo_set": [ 181985, 181986, 181987, 181988, 181989, 181990, 181991, 181992, 181993 ], "onlineresource_set": [ 52928, 52929 ] }, { "ob_id": 38216, "uuid": "99120ddac5004caa85358f5250e2eece", "title": "CRU CY4.06: Climatic Research Unit year-by-year variation of selected climate variables by country version 4.06 (Jan. 1901 - Dec. 2021)", "abstract": "The Climatic Research Unit (CRU) Country (CY) data version 4.06 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency: including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure, potential evapotranspiration and wet day frequency. This version uses the updated set of country definitions, please see the appropriate Release Notes.\r\n\r\nThis dataset was produced in 2022 by CRU at the University of East Anglia and extends the CRU CY4.06 data to include 2021. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY4.06 is derived directly from the CRU time series (TS) 4.06 dataset. CRU CY version 4.06 spans the period 1901-2021 for 292 countries.\r\n\r\nTo understand the CRU CY4.06 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.06. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.06 release notes listed in the online documentation on this record.\r\n\r\nCRU CY data are available for download to all CEDA users.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:19:37", "updateFrequency": "notPlanned", "dataLineage": "The Climatic Research Unit (CRU) CY data are derived directly from the CRU TS data, and version numbering is matched between the two datasets. The CRU CY data are produced by the CRU unit at the University of East Anglia and passed to the Centre for Environmental Data Analysis (CEDA) for long-term archival and distribution. Previous releases of CRU CY include:\r\nCRU CY 4.06 data were passed to CEDA for archival and distribution by CRU in May 2022.\r\n\r\nCRU CY 4.05 data were passed to CEDA for archival and distribution by CRU in June 2021.\r\n\r\nCRU CY 4.04 data were passed to CEDA for archival and distribution by CRU in October 2020.\r\n\r\nCRU CY 4.03 data were passed to CEDA for archival and distribution by CRU in May 2019.\r\n\r\nCRU CY 4.02 data were passed to CEDA for archival and distribution by CRU in November 2018.\r\n\r\nCRU CY 4.01 data were passed to CEDA for archival and distribution by CRU in September 2017.\r\n\r\nCRU CY 4.00 data were passed to CEDA for archival and distribution by CRU in March 2017.\r\n\r\nCRU CY 3.24.01 data files supplied to CEDA for long term archival by CRU in January 2017.\r\n\r\nThe CRU CY 3.24 data were withdrawn by CRU and CEDA in January 2017 due to known issues with the data.\r\n\r\nCRU CY 3.24 data files supplied to CEDA for long term archival by CRU in October 2016.", "removedDataReason": "", "keywords": "CRU, CRU CY, CY, climate", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "0.5x0.5 degree grid", "status": "ongoing", "dataPublishedTime": "2022-09-30T09:07:25", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 513, "bboxName": "CRU High Resolution Grid", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -60.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38217, "dataPath": "/badc/cru/data/cru_cy/cru_cy_4.06/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 51408645, "numberOfFiles": 2925, "fileFormat": "The CRU CY data are provided as text files with the extension \".per\", most text editors will open these files. See the linked file formats guide for more information." }, "timePeriod": { "ob_id": 10531, "startTime": "1901-01-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 3080, "explanation": "CRU CY data are quality controlled by the Climatic Research Unit (CRU) at the University of East Anglia. Details are given in the paper Harris et al. 2014 and the release notes, links to both can be found in the documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2017-04-07" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 20388, "uuid": "81842aa686174647ae132a4c841d73b6", "short_code": "comp", "title": "UEA Climatic Research Unit (CRU) high resolution gridding software deployed on UEA CRU computer system for v4.00", "abstract": "This computation involved: UEA Climate Research Unit (CRU) High Resolution gridding software deployed on UEA Climate Research Unit (CRU) computer system. For details about the production of CRU TS and CRU CY datasets, please refer to Harris et al. (2020) - see Details/Docs tab, moderated by the Release Notes for v4.00 (which outline the new gridding process)" }, "procedureCompositeProcess": null, "imageDetails": [ 103 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 6672, "uuid": "b6c783922d1ce68c4293d90caede5bb9", "short_code": "proj", "title": "UEA Climatic Research Unit (CRU) Gridded Datasets production project", "abstract": "The Climatic Research Unit at the University of East Anglia is producing various resolution gridded datasets.\r\nSome of those datasets are stored at CEDA-BADC." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 27835, "uuid": "a5fc25a8153148b9872f24ab889f64a9", "short_code": "coll", "title": "Climatic Research Unit (CRU): Year-by-Year Variation of Selected Climate Variables by CountrY (CY) v4", "abstract": "The CRU CY datasets consists of country averages at a monthly, seasonal and annual frequency, for ten climate variables in 289 countries. Spatial averages are calculated using area-weighted means. Variables include cloud cover (cld), diurnal temperature range (dtr), frost day frequency (frs), precipitation (pre), daily mean temperature (tmp), monthly average daily maximum (tmx) and minimum (tmn) temperature, vapour pressure (vap), Potential Evapo-transpiration (pet) and wet day frequency (wet). The CRU CY datasets produced by the Climatic Research Unit (CRU) at the University of East Anglia.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY is derived directly from the CRU TS dataset and version numbering is matched between the two datasets. Thus, the first official version of CRU CY is v3.21, as it is based on CRU TS v3.21 (1901-2012) and the latest version of CRU-CY is v4.03, as it is based on CRU TS v4.03. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nTo understand the CRU-CY dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS. It is therefore recommended that all users read the paper referenced below (Harris et al, 2014)." } ], "responsiblepartyinfo_set": [ 182001, 182002, 182003, 182004, 182005, 182006, 182007, 182008, 182009, 182010, 182011, 182012, 182013, 182015, 182014 ], "onlineresource_set": [ 52933, 52934, 52938, 52932, 52937, 52935, 52936, 52931 ] }, { "ob_id": 38218, "uuid": "38715b12b22043118a208acd61771917", "title": "CRU JRA v2.3: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901 - Dec.2021.", "abstract": "The CRU JRA V2.3 dataset is a 6-hourly, land surface, gridded time series of ten meteorological variables produced by the Climatic Research Unit (CRU) at the University of East Anglia (UEA), and is intended to be used to drive models. The variables are provided on a 0.5 deg latitude x 0.5 deg longitude grid, the grid is near global but excludes Antarctica (this is same as the CRU TS grid, though the set of variables is different). The data are available at a 6 hourly time-step from January 1901 to December 2021.\r\n\r\nThe dataset is constructed by regridding data from the Japanese Reanalysis data (JRA) produced by the Japanese Meteorological Agency (JMA), adjusting where possible to align with the CRU TS 4.06 data (see the Process section and the ReadMe file for full details).\r\n\r\nThe CRU JRA data consists of the following ten meteorological variables: 2-metre temperature, 2-metre maximum and minimum temperature, total precipitation, specific humidity, downward solar radiation flux, downward long wave radiation flux, pressure and the zonal and meridional components of wind speed (see the ReadMe file for further details).\r\n\r\nThe CRU JRA dataset is intended to be a replacement of the CRU NCEP forcing dataset. The CRU JRA dataset follows the style of Nicolas Viovy's original CRU NCEP dataset rather than that which is available from UCAR. A link to the CRU NCEP documentation for comparison is provided in the documentation section. \r\n\r\nIf this dataset is used in addition to citing the dataset as per the data citation string users must also cite the following:\r\n\r\nHarris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS\r\nmonthly high-resolution gridded multivariate climate dataset.\r\nSci Data 7, 109 (2020). https://doi.org/10.1038/s41597-020-0453-3\r\n\r\nHarris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2014), Updated\r\nhigh-resolution grids of monthly climatic observations - the CRU TS3.10\r\nDataset. International Journal of Climatology 34, 623-642.\r\n\r\nKobayashi, S., et. al., The JRA-55 Reanalysis: General Specifications and\r\nBasic Characteristics. J. Met. Soc. Jap., 93(1), 5-48\r\nhttps://dx.doi.org/10.2151/jmsj.2015-001", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-12-15T12:10:51", "updateFrequency": "notPlanned", "dataLineage": "The CRU JRA data are produced by the Climatic Research Unit (CRU) at the University of East Anglia and are passed to the Centre for Environmental Data Analysis (CEDA) for long-term archival and distribution.", "removedDataReason": "", "keywords": "CRU, JRA, CRUJRA, atmosphere, earth science, climate", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "0.5x0.5 degree grid", "status": "completed", "dataPublishedTime": "2022-09-30T09:11:50", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 513, "bboxName": "CRU High Resolution Grid", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -60.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38219, "dataPath": "/badc/cru/data/cru_jra/cru_jra_2.3/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 410346066565, "numberOfFiles": 1212, "fileFormat": "The data are provided as gzipped NetCDF files, with one file per variable, per year." }, "timePeriod": { "ob_id": 10532, "startTime": "1901-01-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 3075, "explanation": "The data are quality controlled by the Climatic Research Unit (CRU) at the University of East Anglia. Details are given in the paper Harries et al. 2014 and the release notes, links to both can be found in the documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2017-02-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 27185, "uuid": "9692143d57aa413eab1277193361de77", "short_code": "comp", "title": "Climatic Research Unit (CRU) procedure to produce the CRU JRA data.", "abstract": "The CRU JRA (Japanese reanalysis) data is a replacement to the CRU NCEP dataset, CRU JRA data follows the style of Nicolas Viovy's original dataset rather than that which is available from UCAR.\r\n\r\nThe CRU JRA dataset is based on the JRA-55 reanalysis dataset and aligned where appropriate with the CRU TS dataset version 3.26 (1901-2017).\r\n\r\nAll JRA variables are regridded from their native TL319 Gaussian grid to the CRU regular 0.5° x 0.5° grid, using the g2fsh spherical harmonics routine from NCL (NCAR Command Language), based on the 'Spherepack' code. The exception is precipitation, which is regridded using ESMF 'nearest neighbour': all other algorithms tried exhibited unwanted artifacts.\r\n\r\nThe JRA-55 reanalysis dataset starts in 1958. The years 1901-1957 are constructed using randomly-selected years between 1958 and 1967. Where alignment with CRU TS occurs, the relevant CRU TS data is used.\r\n\r\nOf the ten variables listed above, the last four do not have analogs in the CRU TS dataset. These are simply regridded, masked for land only, and output as CRUJRA. The other six are aligned with CRU TS as follows:\r\n\r\nTMP is aligned with CRU TS TMP. A monthly mean for the JRA data is\r\ncalculated and compared with the equivalent CRU TS mean. The difference\r\nbetween the means is added to every JRA value.\r\n\r\n---\r\n\r\nTMAX and TMIN are aligned with CRUJRA TMP and CRU TS DTR. Firstly, at\r\neach time step, the TMAX-TMP-TMIN triplets are checked and adjusted so\r\nthat TMAX is always >= TMP, and TMIN is always <= TMP. This triplet\r\nalignment is prioritised above DTR alignment. Secondly, monthly JRA DTR\r\nis calculated by first establishing the daily maxima and minima (max and\r\nmin of the subdaily values in TMAX and TMIN respectively), then monthly\r\nmaxima and minima, (means of the daily DTR values), giving JRA monthly\r\nDTR. This is compared with CRU TS DTR and the fractional difference\r\n(factor) calculated as (CRU TS DTR) / (JRA monthly DTR). This factor is\r\nthen used to adjust the DTR of each pair of subdaily TMAX and TMIN\r\nvalues, though not if the triplet alignment would be broken.\r\n\r\n---\r\n\r\nPRE is aligned with CRU TS PRE and WET (rain day counts). Firstly, the\r\nmonthly total precipitation is calculated for JRA and compared to CRU TS\r\nPRE; an adjustment factor is acquired (crupre/jrapre) and all values\r\nadjusted. Precipitation amounts are now aligned at a monthly level, and\r\nthis alignment is prioritised above WET alignment. Secondly, the number\r\nof rain days is calculated for JRA: a day is declared wet if the total\r\nprecipitation is equal to, or exceeds, 0.1mm (the same threshold as CRU\r\nTS WET). If JRA has more wet days than CRU TS, then the driest of those\r\nare reduced to a random amount below 0.1 (an adjustment factor is\r\ncalculated and applied to each time step, to preserve the subdaily\r\ndistribution). If JRA has fewer wet days than CRU TS, then sufficient\r\ndry days are set to a random amount equal to or closely above 0.1mm,\r\nagain using an adjustment factor to preserve the subdaily distribution. \r\nWhere wet day alignment threatens precipitation alignment, the process\r\nis abandoned and the cell/month reverts to the previously-aligned\r\nprecip version. Exception handling is very complicated and cannot be\r\nsummarised here.\r\n\r\n---\r\n\r\nSPFH is aligned with CRU TS VAP. VAP is converted to SPFH, and JRA mean\r\nmonthly SPFH is calculated. The fractional difference (factor) is\r\ncalculated as (CRU TS SPFH) / (JRA monthly SPFH), this factor is then\r\napplied to the JRA subdaily humidity values.\r\n\r\n---\r\n\r\nDSWRF is aligned with CRU TS CLD. CLD is converted to shortwave\r\nradiation, and JRA mean monthly DSWRF is calculated. The fractional\r\ndifference (factor) is calculated as (CRU TS SWR) / (JRA monthly DSWRF),\r\nthis factor is then applied to the JRA subdaily radiation values.\r\n\r\n---\r\n\r\nWhere appropriate, CRUJRA values are kept within physically-appropriate\r\nconstraints (such as negative precipitation), which could result from\r\nregridding as well as adjustments." }, "procedureCompositeProcess": null, "imageDetails": [ 103 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 6672, "uuid": "b6c783922d1ce68c4293d90caede5bb9", "short_code": "proj", "title": "UEA Climatic Research Unit (CRU) Gridded Datasets production project", "abstract": "The Climatic Research Unit at the University of East Anglia is producing various resolution gridded datasets.\r\nSome of those datasets are stored at CEDA-BADC." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26851, "uuid": "863a47a6d8414b6982e1396c69a9efe8", "short_code": "coll", "title": "CRU JRA: Collection of CRU JRA forcing datasets of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data.", "abstract": "This is a collection of the University of East Anglia Climatic Research Unit (CRU) Japanese Reanalysis (JRA) data. The CRU JRA data are 6-hourly, land surface, gridded time series of ten meteorological variables produced by the Climatic Research Unit (CRU) at the University of East Anglia (UEA), and is intended to be used to drive models.\r\n\r\nThe dataset is constructed by combining data from the Japanese Reanalysis data produced by the Japanese Meteorological Agency (JMA) and adjusted where possible to align with the CRU TS data (these 'ten meteorological variables' are not the same ten available from CRU TS).\r\n\r\nThe CRU JRA dataset is intended to be a replacement of the CRUNCEP forcing dataset. The CRU JRA dataset follows the style of Nicolas Viovy's original CRUNCEP dataset rather than that which is available from UCAR." } ], "responsiblepartyinfo_set": [ 182016, 182017, 182018, 182019, 182020, 182021, 182022, 182023, 182025, 182026, 182024, 182027, 182028 ], "onlineresource_set": [ 52940, 52941, 52939, 52942, 52943, 52944, 52945, 52946 ] }, { "ob_id": 38227, "uuid": "f99ec964a6f345beadb000e295ac2e5b", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.9 v20220922", "abstract": "Data for Figure TS.9 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure TS.9 shows changes in well-mixed greenhouse gas (WMGHG) concentrations and effective radiative forcing (EFR).\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.\r\n\r\nPlease also include citations of the related publications for Figure 2.4b provided at the end of this abstract.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for panels b and c. Links to the code which contains the data for other panels are provided in the Related Documents section of this catalogue record.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n - Panel TS.9a => Figure 2.3 c\r\n - Panel TS.9b => Figure 2.4 b\r\n - Panel TS.9c => Figure 2.5 a,b,c\r\n - Panel TS.9d => Figure 2.10\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - Panel TS.9b => Figure 2.4 b\r\n - Panel TS.9c => Figure 2.5 a,b,c\r\n\r\n\r\n---------------------------------------------------\r\nTemporal Range of Paleoclimate Data\r\n---------------------------------------------------\r\nThis dataset covers a paleoclimate timespan from 450 Ma to 2020. \r\nMa refers to millions of years before present.\r\n\r\n\r\n---------------------------------------------------\r\nNotes on reproducing the figure from the provided data\r\n---------------------------------------------------\r\nData for figures 2.3 c and 2.10 are contained within the code to generate the figures which is linked in the Related Documents section of this catalogue record and data for Figure 2.5 and 2.4 panel b are provided. The corresponding catalogue records for Figure 2.5 and 2.4 are linked in the Related Records section below.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to the report component of the underlying chapter figures from which this figure was generated (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n- Links to catalogue records of relevant figures the data is taken from in the Related Records section of this catalogue record\r\n- Link to code which contains data for figure 2.3 and 2.10\r\n\r\n\r\n---------------------------------------------------\r\nRelated publications for figure 2.4 panel b datasets\r\n---------------------------------------------------\r\nPlease include the following citations of related publications from which the figure 2.4 panel b datasets originate. Relations to individual datasets are listed at the top of each dataset. Links are provided in the Related Documents section of the figure 2.4 catalogue record which is linked to this record.\r\n\r\nAhn, J., Brook, E. J., Mitchell, L., Rosen, J. McConnell, J. R., Taylor, K., Etheridge, D., and Rubino, M. (2012b). Atmospheric CO2 over the last 1000 years: A high-resolution record from the West Antarctic Ice Sheet (WAIS) Divide ice core, Global Biogeochemical Cycles, 26, GB2027 , doi:10.1029/2011GB004247.\r\n\r\nBauska, T. K., Joos, F., Mix, A. C., Roth, R., Ahn, J., & Brook, E. J. (2015). Links between atmospheric carbon dioxide, the land carbon reservoir and climate over the past millennium. Nature Geoscience. https://doi.org/10.1038/ngeo2422\r\n\r\nRubino, M., Etheridge, D. M., Thornton, D. P., Howden, R., Allison, C. E., Francey, R. J., Langenfelds, R. L., Steele, L. P., Trudinger, C. M., Spencer, D. A., Curran, M. A. J., van Ommen, T. D., & Smith, A. M. (2019). Revised records of atmospheric trace gases CO2, CH4, N2O, and d13C-CO2 over the last 2000 years from Law Dome, Antarctica. Earth System Science Data, 11(2), 473–492. https://doi.org/10.5194/essd-11-473-2019\r\n\r\nSIEGENTHALER, U. R. S., MONNIN, E., KAWAMURA, K., SPAHNI, R., SCHWANDER, J., STAUFFER, B., STOCKER, T. F., BARNOLA, J.-M., & FISCHER, H. (2005). Supporting evidence from the EPICA Dronning Maud Land ice core for atmospheric CO2 changes during the past millennium. Tellus B, 57(1), 51–57. https://doi.org/10.1111/j.1600-0889.2005.00131.x\r\n\r\nMitchell, L., Brook, E., Lee, J. E., Buizert, C., & Sowers, T. (2013). Constraints on the late Holocene anthropogenic contribution to the atmospheric methane budget. Science. https://doi.org/10.1126/science.1238920\r\n\r\nFlückiger, J., Dällenbach, A., Blunier, T., Stauffer, B., Stocker, T. F., Raynaud, D., & Barnola, J. M. (1999). Variations in atmospheric N2O concentration during abrupt climatic changes. Science. https://doi.org/10.1126/science.285.5425.227\r\n\r\nMachida, T., Nakazawa, T., Fujii, Y., Aoki, S., & Watanabe, O. (1995). Increase in the atmospheric nitrous oxide concentration during the last 250 years. Geophysical Research Letters, 22(21), 2921–2924. https://doi.org/10.1029/95GL02822\r\n\r\nRyu, Y., Ahn, J., Yang, J.-W., Jang, Y., Brook, E., Timmermann, A., Hong, S., Han, Y., Hur, S., & Kim, S. (2020). Atmospheric nitrous oxide during the past two millennia, Global Biogeochemical Cycles, 34, e2020GB006568. https://doi.org/10.1029/2020GB006568\r\n\r\nSowers, T. (2001). N2O record spanning the penultimate deglaciation from the Vostok ice core. Journal of Geophysical Research: Atmospheres, 106(D23), 31903–31914. https://doi.org/10.1029/2000JD900707", "creationDate": "2022-09-23T09:35:25.281305", "lastUpdatedDate": "2022-09-23T09:35:25", "latestDataUpdateTime": "2024-03-09T02:57:47", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Atmospheric CO2, paleoclimate, Atmospheric WMGHG concentrations, Well-Mixed Greenhouse Gases, global, carbon dioxide, methane, nitrous oxide, Effective Radiative Forcing, ERF, drivers, annual mean, ice cores", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-05T13:17:30", "doiPublishedTime": "2023-07-27T13:35:20.277299", "removedDataTime": null, "geographicExtent": { "ob_id": 3646, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 184, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38228, "dataPath": "/badc/ar6_wg1/data/TS/ts_09/v20220922", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 548361, "numberOfFiles": 10, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10533, "startTime": "0001-01-01T12:00:00", "endTime": "2020-12-31T12:00:00" }, "resultQuality": { "ob_id": 4084, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-09-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 38229, "uuid": "6bf86cbf824a42ffa3ae27d3b714bda4", "short_code": "comp", "title": "Caption for Figure TS.9 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Changes in well-mixed greenhouse gas (WMGHG) concentrations and effective radiative forcing (EFR). The intent of this figure is to show that the changes of the main drivers of climate system over the industrial period are exceptional in a long-term context. (a) Changes in carbon dioxide (CO2) from proxy records over the past 3.5 million years. (b) Changes in all three WMGHGs from ice core records over the Common Era. (c) Directly observed WMGHG changes since the mid-20th century. (d) Evolution of ERF and components since 1750. Further details on data sources and processing are available in the associated FAIR data table. {2.2, Figures 2.3, 2.4 and 2.10}" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12654 ], "observationcollection_set": [ { "ob_id": 39213, "uuid": "7409c7ea8a5045f8ab08e44db9cccb1a", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Technical Summary", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Technical Summary.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure TS.1\r\n- data for Figure TS.9\r\n- input data for Figure TS.12 \r\n- data for Figure TS.13\r\n- data for Figure TS.15\r\n- data for Figure TS.17\r\n- data for Figure TS.19\r\n- data for Figure TS.22\r\n- input data for Figure TS.24\r\n- data for Figure TS.25\r\n- data for Box TS.2, Figure 1\r\n- data for Box TS.2, Figure 2\r\n- data for Box TS.4, Figure 1\r\n- input data for Box Ts.4, Figure 1\r\n- input data for Box TS.5, Figure 1\r\n- data for Box TS.6, Figure 1\r\n- data for Box TS.7, Figure 1\r\n- data for Box TS.13, Figure 1\r\n- data for Cross-Section Box TS.1, Figure 1" } ], "responsiblepartyinfo_set": [ 182048, 182049, 182050, 182051, 182052, 182053, 182054, 193593, 193592, 193594, 193595 ], "onlineresource_set": [ 52947, 52948, 82333, 82334, 82335, 82678, 82679, 82723 ] }, { "ob_id": 38230, "uuid": "d75fd35a7444433c9b5b78ef110495ab", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.22 v20220923", "abstract": "Data for Figure TS.22 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure TS.22 shows a synthesis of the geographical distribution of climatic impact-drivers changes and the number of AR6 WGI reference regions where they are projected to change.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels with data provided for all panels.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - geographical location of regions belonging to one of five groups characterized by a specific combination of changing climatic impact-drivers (CIDs).\r\n - number of AR6 WG1 regions where Climatic Impact Drivers are projected to change if a global warming level of 2°C is reached compared to a climatological reference period included within 1960-2014\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - 'Figure-F-Panels_IDL.xlsx' - Datafile containing data for both figures in excel sheets\r\n\r\nIndividual panel data in csv format:\r\n\r\n - Panel a: 'Figure-F-Panel_a_IDL.csv' - Description of the clustering used to generate panel a\r\n \r\n - Panel b: 'consolidated_data_figure_SPM.9.csv' - Same data used for Figure SPM.9 (count of regions with increasing or decreasing changes in climatic impact-drivers). First row relates to darker purple bars, second row to lighter purple bars, third row refers to lighter brown bars and fourth row to darker brown bars.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Link to the related record SPM.9 identical to panel b is provided in the Related Records section under Datasets.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to the report component of the underlying figures from which this figure was generated (Figure SPM.9)\r\n - Link to the SPM.9 catalogue record at CEDA", "creationDate": "2022-09-23T13:20:22.401470", "lastUpdatedDate": "2022-09-23T13:20:22", "latestDataUpdateTime": "2024-03-09T02:57:43", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, climatic impact-drivers, projected change, AR6 regions", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-06T09:46:02", "doiPublishedTime": "2023-08-02T10:03:27.913754", "removedDataTime": null, "geographicExtent": { "ob_id": 3647, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 185, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38231, "dataPath": "/badc/ar6_wg1/data/TS/ts_22/v20220923", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 88274, "numberOfFiles": 6, "fileFormat": "Data are in excel sheets and csv format" }, "timePeriod": { "ob_id": 10534, "startTime": "2022-05-26T12:00:00", "endTime": "2022-05-26T12:00:00" }, "resultQuality": { "ob_id": 4085, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-09-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 38232, "uuid": "8ccdb8da86294587a14fd709f714d21f", "short_code": "comp", "title": "Caption for Figure TS.22 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Synthesis of the geographical distribution of climatic impact-drivers changes and the number of AR6 WGI reference regions where they are projected to change. Panel (a) shows the geographical location of regions belonging to one of five groups characterized by a specific combination of changing climatic impact-drivers (CIDs). The five groups are represented by the five different colours, and the CID combinations associated with each group are represented in the corresponding ‘fingerprint’ and text below the map. Each fingerprint comprises a set of CIDs projected to change with high confidence in every region in the group and a second set of CIDs, one or more of which are projected to change in each region with high or medium confidence. The CID combinations follow a progression from those becoming hotter and drier (group 1) to those becoming hotter and wetter (group 5). In between (groups 2–4), the CIDs that change include some becoming drier and some wetter and always include a set of CIDs which are getting hotter. Tropical cyclones and severe wind CID changes are represented on the map with black dots in the regions affected. Regions affected by coastal CID changes are described by text on the map. The five groups are chosen to provide a reasonable level of detail for each region while not overwhelming the map with a full summary of all aspects of the assessment, which is available in Table TS.5 and can be visualized in the Regional Synthesis component of the Interactive Atlas. The CID changes summarized in the figure represent high and medium confidence changes for the mid-21st century, considering scenarios SSP2-4.5, RCP4.5, SRES A1B, or above (SSP3-7.0, SSP5-8.5, RCP6.0, RCP8.5, SRES A2), which approximately encompasses global warming levels of 2.0°C to 2.4°C.The bar chart in panel (b) shows the numbers of regions where each CID is increasing or decreasing with medium or high confidence for all land regions and ocean regions listed in Table TS.5. The colours represent the direction of change and the level of confidence in the change: purple indicates an increase while brown indicates a decrease; darker and lighter shades refer to high and medium confidence, respectively. Lighter background colours represent the maximum number of regions for which each CID is broadly relevant. Sub-panel (i) shows the 30 CIDs relevant to the land and coastal regions while sub-panel (ii) shows the 5 CIDs relevant to the open ocean regions. Marine heatwaves and ocean acidity are assessed for coastal ocean regions in panel (i) and for open ocean regions in panel (ii). Changes refer to a 20- to 30-year period centred around 2050 and/or consistent with 2°C global warming compared to a similar period within 1960–2014, except for hydrological drought and agricultural and ecological drought, which is compared to 1850–1900. Definitions of the regions are provided in Atlas.1, the Interactive Atlas (https://interactive-atlas.ipcc.ch/) and Chapter 12. (Table TS.5, Figure TS.24) {11.9, 12.2, 12.4, Atlas.1}" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12660 ], "observationcollection_set": [ { "ob_id": 39213, "uuid": "7409c7ea8a5045f8ab08e44db9cccb1a", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Technical Summary", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Technical Summary.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure TS.1\r\n- data for Figure TS.9\r\n- input data for Figure TS.12 \r\n- data for Figure TS.13\r\n- data for Figure TS.15\r\n- data for Figure TS.17\r\n- data for Figure TS.19\r\n- data for Figure TS.22\r\n- input data for Figure TS.24\r\n- data for Figure TS.25\r\n- data for Box TS.2, Figure 1\r\n- data for Box TS.2, Figure 2\r\n- data for Box TS.4, Figure 1\r\n- input data for Box Ts.4, Figure 1\r\n- input data for Box TS.5, Figure 1\r\n- data for Box TS.6, Figure 1\r\n- data for Box TS.7, Figure 1\r\n- data for Box TS.13, Figure 1\r\n- data for Cross-Section Box TS.1, Figure 1" } ], "responsiblepartyinfo_set": [ 182057, 182058, 182059, 182060, 182061, 182062, 182063, 182064, 182065, 182066, 182067 ], "onlineresource_set": [ 52952, 52953, 82664 ] } ] }