Observation List
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{ "count": 10256, "next": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=9000", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=8800", "results": [ { "ob_id": 39504, "uuid": "abb030f60cf848278fe519379a2aaac9", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.25 (v20221215)", "abstract": "Data for Figure 6.25 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\n\r\nFigure 6.25 shows the effect of dedicated air pollution or climate policy on population-weighted PM2.5 (Fine particulate matter) concentrations (µg m-3) and share of population (%) exposed to different PM2.5 levels across 10 world regions.\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 Szopa, 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 30 panels with data provided for all panels in one single file.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains PM2.5 (Fine particulate matter) concentrations (µg m–3) and share of population (%) exposed to different PM2.5 levels across selected world regions.\r\n \r\n - Percentage of population exposed to PM2.5 Exposure threshold <= 10 microgram per m^3, including dust + sea salt.\r\n - Percentage of population exposed to PM2.5 Exposure threshold > 10 microgram per m^3 and<= 35 microgram per m^3, including dust + sea salt.\r\n - Percentage of population exposed to PM2.5 Exposure threshold > 35 microgram per m^3, including dust + sea salt.\r\n - Population weighted mean PM2.5 concentration [mean microgram per m^3]\r\n Regions: North America, Europe, Southern Asia, Eastern Asia, South-East Asia and developing Pacific, Asia-Pacific developed, Africa, Middle East, Latin America and Caribbean, Eurasia\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n All panels:\r\n \r\n - Data file: Fig_6.25_plot_data.csv\r\n - rows 4 to 54: left panels\r\n - rows 55 to 105: central panels\r\n - rows 106 to 156: right panels\r\n - column 2: white\r\n - column 3: light grey\r\n - column 4: dark grey\r\n - column 5: orange line\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", "creationDate": "2022-12-15T11:16:49.650454", "lastUpdatedDate": "2022-12-15T11:16:49", "latestDataUpdateTime": "2022-12-15T11:31:17", "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 files converted to CSV format by CEDA staff.\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "PM2.5, air pollution, climate policies, IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-03-01T17:00:29", "doiPublishedTime": "2023-03-22T11:05:45.505865", "removedDataTime": null, "geographicExtent": { "ob_id": 3731, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 223, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39505, "dataPath": "/badc/ar6_wg1/data/ch_06/ch6_fig25/v20221215", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7457, "numberOfFiles": 4, "fileFormat": "Data are CSV formatted" }, "timePeriod": { "ob_id": 10953, "startTime": "2015-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4149, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-12-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39506, "uuid": "98b47ef7b7de48d8bc769c96a75bf424", "short_code": "comp", "title": "Caption for Figure 6.25 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Effect of dedicated air pollution or climate policy on population-weighted PM2.5 concentrations (µg m–3) and share of population (%) exposed to different PM2.5 levels across selected world regions. Thresholds of 10 µg m–3 and 35 µg m–3 represent the WHO air quality guideline and the WHO interim target 1, respectively; WHO (2017). Results are compared for SSP3-7.0 (no major improvement of current legislation is assumed), SSP3-lowSLCF (strong air pollution controls are assumed), and a climate change mitigation scenario SSP3-3.4; details of scenario assumptions are discussed in Riahi et al. (2017) and Rao et al. (2017). Analysis performed with the TM5-FASST model (Van Dingenen et al., 2018) using emission projections from the Shared economic Pathway (SSP) database (Riahi et al., 2017; Rogelj et al., 2018a; Gidden et al., 2019). 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": [ 12430 ], "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": [ 192669, 192670, 192671, 192672, 192673, 192674, 192675, 192668, 192676, 192677 ], "onlineresource_set": [ 82640, 82641, 82642 ] }, { "ob_id": 39510, "uuid": "bf31afbbbafc49d39546aa78a2268f44", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.26 (v20221215)", "abstract": "Data for Figure 6.26 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\n\r\nFigure 6.26 shows the effect of dedicated air pollution or climate policy on population-weighted ozone concentrations (SOMO0; ppb) and share of population (%) exposed to different ozone levels across 10 world regions.\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 Szopa, 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 30 panels with data provided for all panels in one single file.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains population-weighted ozone concentrations (SOMO0; ppb) and share of population (%) exposed to chosen ozone levels across across 10 world regions.\r\n \r\n - Percentage of population exposed to Ozone level exposure threshold <= 35 ppb, annual average daily maximum 8-hourly ozone concentration.\r\n - Percentage of population exposed to Ozone level exposure threshold > 35 ppb and<= 60 ppb, annual average daily maximum 8-hourly ozone concentration\r\n - Percentage of population exposed to Ozone level exposure threshold > 60 ppb, annual average daily maximum 8-hourly ozone concentration\r\n - Population weighted mean Ozone level concentration [mean ppb], annual average daily maximum 8-hourly ozone concentration.\r\n\r\n Regions: North America, Europe, Southern Asia, Eastern Asia, South-East Asia and developing Pacific, Asia-Pacific developed, Africa, Middle East, Latin America and Caribbean, Eurasia\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n All panels:\r\n \r\n - Data file: Fig_6.26_plot_data.csv\r\n - rows 4 to 54: left panels\r\n - rows 55 to 105: central panels\r\n - rows 106 to 156: right panels\r\n - column 2: white\r\n - column 3: light grey\r\n - column 4: dark grey\r\n - column 5: orange line\r\n\r\nppb stands for parts per billion.\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", "creationDate": "2022-12-15T19:45:35.312441", "lastUpdatedDate": "2022-12-15T19:45:35", "latestDataUpdateTime": "2022-12-15T19:47:37", "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": "ozone, air pollution, climate policies, IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-03-02T09:38:55", "doiPublishedTime": "2023-03-22T11:13:23.772398", "removedDataTime": null, "geographicExtent": { "ob_id": 3732, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 224, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39511, "dataPath": "/badc/ar6_wg1/data/ch_06/ch6_fig26/v20221215", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7577, "numberOfFiles": 4, "fileFormat": "Data are CSV formatted" }, "timePeriod": { "ob_id": 10954, "startTime": "2015-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4150, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-12-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39512, "uuid": "27b781b39d224ce08f00e45c4ba35179", "short_code": "comp", "title": "Caption for Figure 6.26 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Effect of dedicated air pollution or climate policy on population-weighted ozone concentrations (SOMO0; ppb) and share of population (%) exposed to chosen ozone levels across across 10 world regions. Results are compared for SSP3-7.0 (no major improvement of current legislation is assumed), SSP3-low NTCF (strong air pollution controls are assumed), and a climate change mitigation scenario (SSP3-3.4); details of scenario assumptions are discussed in Riahi et al. (2017) and Rao et al. (2017). Analysis performed with the TM5-FASST model (Van Dingenen et al., 2018) using emission projections from the Socio-economic Pathway (SSP) database (https://tntcat.iiasa.ac.at/SspDb/dsd (Riahi et al., 2017; Rogelj et al., 2018a; Gidden et al., 2019). 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": [ 12431 ], "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": [ 192685, 192686, 192687, 192688, 192689, 192690, 192691, 192692, 192693, 192694 ], "onlineresource_set": [ 82648, 82649, 82650 ] }, { "ob_id": 39513, "uuid": "fe6074fee8a64a738cf89f0294bd9fb9", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Box TS.7, Figure 1 (v20221216)", "abstract": "Data for Box TS.7, 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).\r\n\r\n\r\nBox TS.7 figure 1 shows the effects of short-lived climate forcers (SLCFs) on global surface temperature and air pollution 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\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 three panels, data provided for the right part of panels b and c in one single file. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains global changes in air pollutant concentrations (ozone and PM2.5), based on multimodel CMIP6 simulations:\r\n \r\n - changes in 5-year mean surface continental concentrations for 2040, relative to 2019.\r\n - changes in 5-year mean surface continental concentrations for 2098, relative to 2019.\r\n\r\n Data are for the SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP3-7.0 lowSLCF highCH4, SSP3-7.0 lowSLCF lowCH4 and SSP5-8.5 scenarios.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file Box_TS7_figure1.csv relates to the right part of panels b and c.\r\n \r\n - Column 5 is used for the green bars\r\n - Columns 4 and 6 are used for the standard deviation plotted over the green bars.\r\n - Column 8 is used for the purple bars.\r\n - Columns 7 and 9 are used for the standard deviation plotted over the purple bars.\r\n\r\nPM2.5 refers to particulate matter that are 2.5 micrometers or less in diameter.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP1-2.6 is based on Shared Socioeconomic Pathway SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP2-4.5 is based on Shared Socioeconomic Pathway SSP2 with medium challenges to climate change mitigation and adaptation and RCP4.5, a future pathway with a radiative forcing of 4.5 W/m2 in the year 2100.\r\nSSP5-8.5 is based on Shares Socioeconomic Pathway SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\nSLCF stands for short-lived climate forcers.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n - Data Sources for effect on GSAT are the same as in Figure 6.24 (see Chapter 6 Supplementary Material Table 6.SM.3). \r\n\r\n- Data Sources for effect on surface PM2.5 and O3 are the same as in Figures 6.20 and 6.21 (see Chapter 6 Supplementary Material Table 6.SM.3)\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 - Links to the report components of the underlying chapter figures from which part of this figure was generated (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", "creationDate": "2022-12-16T12:39:08.247008", "lastUpdatedDate": "2022-12-16T12:39:08", "latestDataUpdateTime": "2022-12-16T13:12:40", "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": "SLCFs, air pollution, ozone, PM2.5, SSP, mitigation, GSAT, IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-06T14:44:10", "doiPublishedTime": "2023-09-26T13:52:59.194966", "removedDataTime": null, "geographicExtent": { "ob_id": 3733, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 225, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39514, "dataPath": "/badc/ar6_wg1/data/TS/BOX_ts7_fig1/v20221216", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8818, "numberOfFiles": 4, "fileFormat": "Data are CSV formatted" }, "timePeriod": { "ob_id": 10955, "startTime": "2040-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4151, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-12-16" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39515, "uuid": "685156be91b14dcfa41c4116f6c3fbf8", "short_code": "comp", "title": "Caption for Box TS.7, 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": "Effects of short-lived climate forcers (SLCFs) on global surface temperature and air pollution across the WGI core set of Shared Socio-economic Pathways (SSPs). The intent of this figure is to show the climate and air quality (surface ozone and particulate matter smaller than 2.5 microns in diameter, or PM2.5) response to SLCFs in the SSP scenarios for the near and long-term. Effects of net aerosols, tropospheric ozone, hydrofluorocarbons (HFCs; with lifetimes less than 50 years), and methane (CH4) are compared with those of total anthropogenic forcing for 2040 and 2100 relative to year 2019. The global surface temperature changes are based on historical and future evolution of effective radiative forcing (ERF) as assessed in Chapter 7 of this Report. The temperature responses to the ERFs are calculated with a common impulse response function (RT) for the climate response, consistent with the metric calculations in Chapter 7 (Box 7.1). The RT has an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 concentration (feedback parameter of –1.31 W m–2°C–1). The scenario total (grey bar) includes all anthropogenic forcings (long- and short-lived climate forcers, and land-use changes). Uncertainties are 5–95% ranges. The global changes in air pollutant concentrations (ozone and PM2.5) are based on multimodel Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations and represent changes in five-year mean surface continental concentrations for 2040 and 2098 relative to 2019. Uncertainty bars represent inter-model ±1 standard deviation. {6.7.2, 6.7.3, Figure 6.24}" }, "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": [ 12690 ], "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": [ 192699, 192700, 192701, 192702, 192703, 192704, 192705, 192706, 192707, 192708, 192709 ], "onlineresource_set": [ 82655, 82656, 82661, 82660 ] }, { "ob_id": 39521, "uuid": "60eeb3cce51a457cb5ee1c577a0c8674", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.4 (v20221219)", "abstract": "Data for Figure 2.4 from Chapter 2 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 2.4 shows Atmospheric well-mixed greenhouse gases concentration from ice cores. \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 Gulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. 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. 287–422, doi:10.1017/9781009157896.004.\r\n\r\nPlease also include citations of the related publications provided at the end of this abstract.\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 in the main directory.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Atmospheric WMGHG concentration records during the last 800 kyr with the LGM to Holocene transition as inset.\r\n - Multiple high-resolution records over the CE\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nSubdirectory 'fig2_4_data_Feb24_2021' contains figure data with listed publications from which the datasets originate. Datasets are also provided in BADC-CSV formats as described below:\r\n\r\n Panel a: \r\n - Data file: fig2_4a_main figure_data.csv; column 2: red line (main figure); column 4: green line (main figure); column six: purple line (main figure).\r\n - Data file: fig2_4a_inset_data.csv: column 3: red line (inset); column 6: orange line (inset)\r\n\r\n Panel b:\r\n - Data file: fig2_4b_data_v2.csv: column 2: pink dot (top panel); column 5: brown dot (top panel); column 8: orange dot (top panel); column 11: red line (top panel); column 13: sky blue line (middle panel); column 15: green line (middle panel); column 18: purple square (bottom panel); column 21: blue circle N2O (bottom panel); column 24: brown dot (bottom panel); column 26: green circle (bottom panel); column 29: red circle (bottom panel); column 32: orange square (bottom panel); column 35: blue line (bottom panel)\r\n\r\nWMGHG stands for well-mixed green-house gases.\r\nLGM stands for Last Glacial Maximum.\r\n\r\n\r\n---------------------------------------------------\r\nTemporal range of data\r\n---------------------------------------------------\r\nThis dataset covers a timespan from 800kyr ago to 2000 CE.\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 from the IPCC AR6 website \r\n - Link to the report component containing the figure (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 related publications listed below\r\n\r\n\r\n---------------------------------------------------\r\nRelated publications for figure datasets\r\n---------------------------------------------------\r\nPlease include the following citations of related publications from which the figure datasets originate. Relations to individual datasets are listed at the top of each dataset. Links are provided in the Related Documents section of this catalogue record. \r\n\r\nBereiter, B., Eggleston, S., Schmitt, J., Nehrbass-Ahles, C., Stocker, T. F., Fischer, H., Kipfstuhl, S., & Chappellaz, J. (2015). Revision of the EPICA Dome C CO2 record from 800 to 600-kyr before present. Geophysical Research Letters. https://doi.org/10.1002/2014GL061957\r\n\r\nLoulergue, L. et al. Orbital and millennial-scale features of atmospheric CH4 over the past 800,000 years. Nature 453, 383–386 (2008)\r\n\r\nSchilt, A., Baumgartner, M., Blunier, T., Schwander, J., Spahni, R., Fischer, H., and Stocker, T. F.: Glacial-interglacial and millennialscale variations in the atmospheric nitrous oxide concentration uring the last 800 000 years, Quaternary Science Reviews, 29, 182–192, doi:10.1016/j.quascirev.2009.03.011 (2010)\r\n\r\nKöhler, P., Nehrbass-Ahles, C., Schmitt, J., Stocker, T. F., & Fischer, H. (2017). A 156 kyr smoothed history of the atmospheric greenhouse gases CO2, CH4, and N2O and their radiative forcing. Earth System Science Data. https://doi.org/10.5194/essd-9-363-2017\r\n\r\nLüthi, D., M. Le Floch, B. Bereiter, T. Blunier, J.-M. Barnola, U. Siegenthaler, D. Raynaud, J. Jouzel, H. Fischer, K. Kawamura, and T.F. Stocker. 2008. High-resolution carbon dioxide concentration record 650,000-800,000 years before present. Nature, Vol. 453, pp. 379-382, 15 May 2008.\r\n\r\nMarcott, S. A., Bauska, T. K., Buizert, C., Steig, E. J., Rosen, J. L., Cuffey, K. M., Fudge, T. J., Severinghaus, J. P., Ahn, J., Kalk, M. L., McConnell, J. R., Sowers, T., Taylor, K. C., White, J. W. C., & Brook, E. J. (2014). Centennial-scale changes in the global carbon cycle during the last deglaciation. Nature, 514(7524), 616–619.\r\n\r\nBereiter, B., Eggleston, S., Schmitt, J., Nehrbass-Ahles, C., Stocker, T. F., Fischer, H., Kipfstuhl, S., & Chappellaz, J. (2015). Revision of the EPICA Dome C CO2 record from 800 to 600-kyr before present. Geophysical Research Letters. https://doi.org/10.1002/2014GL061957\r\n\r\nMonnin E, Indermühle A, Dällenbach A, Flückiger J, Stauffer B, Stocker TF, Raynaud D, Barnola JM. Atmospheric CO2 concentrations over the last glacial termination. Science. 2001 Jan 5;291(5501):112-4. doi: 10.1126/science.291.5501.112. PMID: 11141559.\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": "2022-12-19T13:04:48.924301", "lastUpdatedDate": "2022-12-19T13:04:48", "latestDataUpdateTime": "2023-02-14T14:52:08", "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": "Atmospheric WMGHG concentrations, ice cores, Well-Mixed Greenhouse Gases, IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-11T14:19:10", "doiPublishedTime": "2023-07-03T17:25:46.644098", "removedDataTime": null, "geographicExtent": { "ob_id": 3734, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 228, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39522, "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig04/v20221219", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 503221, "numberOfFiles": 11, "fileFormat": "Data are CSV formatted" }, "timePeriod": { "ob_id": 10956, "startTime": "0001-01-01T00:00:00", "endTime": "0001-01-01T00:00:00" }, "resultQuality": { "ob_id": 4154, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-12-19" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39523, "uuid": "42c34fdcaa514bea986b06a47f06e7a3", "short_code": "comp", "title": "Caption for Figure 2.4 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Atmospheric well-mixed greenhouse gas (WMGHG) concentrations from ice cores. (a) Records during the last 800 kyr with the Last Glacial Maximum (LGM) to Holocene transition as inset. (b) Multiple high-resolution records over the CE. The horizontal black bars in panel (a) inset indicate LGM and Last Deglacial Termination (LDT) respectively. The red and blue lines in (b) are 100-year running averages for CO2 and N2O concentrations, respectively. The numbers with vertical arrows in (b) are instrumentally measured concentrations in 2019. Further details on data sources and processing are available in the chapter data table (Table 2.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": [ 12610 ], "observationcollection_set": [ { "ob_id": 32717, "uuid": "3da412ad9912427d9bb808b57faa21a7", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\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 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1" }, { "ob_id": 43269, "uuid": "9177e927f0664da9bf1e25f57561d0d8", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Synthesis Report (SYR)", "abstract": "This dataset collection contains datasets relating to the figures found in the Synthesis Report (SYR) of the IPCC Sixth Assessment Report (AR6).\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 2.4\r\n- data for Figure 2.5\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6" } ], "responsiblepartyinfo_set": [ 192724, 192725, 192726, 192727, 192728, 192729, 192730, 192731 ], "onlineresource_set": [ 82921, 82900, 82901, 82902, 82903, 82904, 82905, 82906, 82907, 82908, 82909, 82910, 82911, 82912, 82913, 82914, 82915, 82916, 82919, 82920 ] }, { "ob_id": 39530, "uuid": "83f0d8ce1af040c09636c41366238217", "title": "Particulate Distribution Data from Palas FIDAS 200 Instrument at Manchester Air Quality Site 2019 onwards", "abstract": "Particulate Matter Distribution 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": "2023-10-05T15:31:37", "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 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": "2023-01-17T12:27:36", "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": 39531, "dataPath": "/badc/osca/data/manchester/OSCA_Manc_FIDAS_Distribution", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1618291817, "numberOfFiles": 109, "fileFormat": "Netcdf and CSV" }, "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": 37933, "uuid": "a96249df0f1148279c8d35d2ab9a6421", "short_code": "acq", "title": "Acquisition for: Particle Distribution Data from Palas FIDAS 200 Instrument at Manchester Air Quality Site 2019-22", "abstract": "Acquisition for: Particle Distribution Data from Palas FIDAS 200 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, 65567, 65568, 65569, 65570 ], "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": [ 192746, 192747, 192748, 192749, 192750, 192751, 192752, 192753 ], "onlineresource_set": [ 82747 ] }, { "ob_id": 39532, "uuid": "a7e811fe11d34df5abac6f18c920bbeb", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): All-weather MicroWave Land Surface Temperature (MW-LST) global data record (1996-2020), v2.33", "abstract": "MW-LST is a data record of land surface temperature (LST) derived from the microwave instruments Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager / Sounder (SSMIS). Observations available at frequencies close to 18, 22, 26, and 85 GHz are used as an input to a retrieval algorithm that produces LST over all continental surfaces, twice per day (6 am/pm), at a spatial resolution of ~25 km, and over 25 years (1996-2020). \r\n\r\nThe data record has been produced by the company Estellus working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci). Compared with the remaining infrared LST data records of the LST_cci, the spatial resolution of the MW-LST is coarser, and the associated retrieval errors are larger. However, it offers LST estimates for clear-sky and cloudy conditions, therefore complementing the IR LST data records, which can only provide LST for clear skies. The data record is temporally and spatially complete, although in rare occasions some data can be missing due to missing observations, e.g., due to satellite maintenance operations or anomalous behavior. The data record is provided on a regular grid of 0.25x0.25 degrees, saved as daily, monthly, and yearly netcdf files. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.\r\n\r\nThis version of the data is v2.33. It fixes an issue that was found with the variable 'lst_unc_time_correction' in the previous v2.23, but is otherwise identical.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-01-19T11:40:17", "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the company Estellus working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). ). The source of satellite Level 1 observations is the Fundamental Climate Data Record of Microwave Imager Radiances (https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V003) kindly provided by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF).\r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "land surface temperature, CCI", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-01-26T16:13:34", "doiPublishedTime": "2023-01-26T16:17:09", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 39533, "dataPath": "/neodc/esacci/land_surface_temperature/data/SSMI_SSMIS/L3C/v2.33/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 66212097204, "numberOfFiles": 18913, "fileFormat": "Data are in NetCDF format." }, "timePeriod": { "ob_id": 9163, "startTime": "1996-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33373, "uuid": "96cc894239c341d182941c9205e62ca6", "short_code": "cmppr", "title": "Composite process for ESA Land Surface Temperature Climate Change Initiative (LST_cci): All-weather daily MicroWave Land Surface Temperature (MW-LST) global data record (1996-2020)", "abstract": "The land surface temperature (LST) data has been derived from the microwave instruments Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager / Sounder (SSMIS). Observations available at frequencies close to 18, 22, 26, and 85 GHz are used as an input to a retrieval algorithm that produces LST over all continental surfaces, twice per day (6 am/pm), at a spatial resolution of ~25 km, and over 25 years (1996-2020)." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2558, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 30, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_lst_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 33361, "uuid": "555149fdc3ef4e23a1de8ece93c29f5d", "short_code": "proj", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci)", "abstract": "The land surface temperature (LST) CCI project, which is funded by the European Space Agency (ESA) as part of the Agency’s Climate Change Initiative (CCI) Programme, aims to deliver a significant improvement on the capability of current satellite LST data records to meet the challenging Global Climate Observing System (GCOS) requirements for climate applications to realise the full potential of long-term LST data for climate science.\r\n\r\nAccurate knowledge of LST plays a key role in describing the physics of land-surface processes at regional and global scales as they combine information on both the surface-atmosphere interactions and energy fluxes within the Earth Climate System. LST provides a metric of surface state when combined with vegetation parameters and soil moisture and is one of the drivers of vegetation phenology. Furthermore, LST is an independent temperature data set for quantifying climate change complementary to the near-surface air temperature ECV based on in situ measurements and reanalyses.\r\n\r\nThe team uses data from a variety of satellites to provide an accurate view of temperatures across land surfaces globally over the past +20 years. This involves developing innovative techniques to merge data from different satellites into combined long-term satellite records for climate. These will all be evaluated by scientists working at leading climate centres." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62779, 62780, 62781, 62790, 62791, 62792, 66302, 66303, 66304, 66305, 66306, 66307, 66308, 66309, 66310, 66311, 66312, 66313, 66314 ], "vocabularyKeywords": [], "identifier_set": [ 12338 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 192754, 192755, 192756, 192757, 192758, 192759, 192760, 192761, 192762 ], "onlineresource_set": [ 82749, 82748, 82750, 82751, 82752 ] }, { "ob_id": 39537, "uuid": "8d8613a3d67a4e76adfe94c94ede6a4f", "title": "Mass Concentration of the Non-Refractory Component of Ambient Aerosol Particles in Air Data from Aerosol Chemical Species Monitor (ACSM) at Manchester Air Quality Site, 2019-present", "abstract": "Mass Concentration of the Nitrate, Chloride, Sulphate, Ammonium and Organic Component of Ambient Aerosol Particles in Air measured at 7 metres above ground level by an Aerosol Chemical Species Monitor (ACSM) at the Manchester Air Quality Site (MAQS), from 2019 onwards, for the Integrated Research Observation System for Clean Air (OSCA) project.", "creationDate": "2022-08-23T15:54:29.635295", "lastUpdatedDate": "2022-08-23T15:54:29", "latestDataUpdateTime": "2023-11-20T16:32:05", "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 using the Calibration data within the ACSM data. It is then divided into monthly files and data is flagged where data is suspect. \r\nData 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.", "removedDataReason": "", "keywords": "OSCA, maqs, air quality, OSCA-AQ", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-17T12:19:13", "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": 39538, "dataPath": "/badc/osca/data/manchester/OSCA_MAQS_ACSM_non-refractory", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 31535171, "numberOfFiles": 210, "fileFormat": "NetCDF and CSV" }, "timePeriod": { "ob_id": 10957, "startTime": "2019-09-13T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4156, "explanation": "Data is calibrated for nitrate mass ionisation efficiencies (mIE) and the relative ionisation efficiencies (RIE) for ammonium and sulphate measurements. For full report on ACSM processing method see github file: https://github.com/naiwatson/OSCA/blob/main/Report%20on%20ACSM%20data.pdf .", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-01-09" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 39536, "uuid": "756c7752d70f41fba65fd81398288b39", "short_code": "acq", "title": "Acquisition for MAQS Mass Concentration of the Non-Refractory Component of Ambient Aerosol Particles in Air Data from Aerosol Chemical Species Monitor (ACSM) at Manchester Air Quality Site", "abstract": "Data are collected by ACSM instrument and processed using IGOR 7.0" }, "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, 65559, 65560, 65561, 65562, 65563, 65564, 65565, 65566 ], "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": [ 192776, 192769, 192770, 192771, 192772, 192773, 192774, 192775, 192777, 192778, 192779 ], "onlineresource_set": [] }, { "ob_id": 39539, "uuid": "b109bd69e1af425aa0f661b01c40dc51", "title": "EuroCORDEX-UK: Regional climate projections for the UK domain at 12 km Resolution for 1980-2080", "abstract": "Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. 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The state of the Greenland Ice Sheet is of global importance, and has consequently been included in the ESA CCI Programme as a monitored Essential Climate Variable (ECV).\r\n\r\nThe project is producing data products of the following five parameters, which are important in characterizing the Greenland Ice Sheet as an Essential Climate Variable: Surface Elevation Change (SEC) gridded data from radar altimetry; Ice Velocity (IV) gridded data from synthetic aperture radar interferometry and feature tracking; Calving Front Location (CFL) time series of marine-terminating glaciers; Grounding Line Location (GLL) time series of marine-terminating glaciers; Gravimetry Mass Balance (GMB) maps and time series." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12918 ], "observationcollection_set": [ { "ob_id": 14316, "uuid": "394464f9c39445d3b6445d8e305841d7", "short_code": "coll", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland Ice Sheet CCI) Dataset Collection", "abstract": "The Greenland Ice Sheet CCI project aims to maximize the impact of ESA satellite data on climate research, by analysing data from ESA Earth Observation missions such as ERS, Envisat, CryoSat, GRACE and the new Sentinel series of satellites. Over the last decade, the Greenland Ice Sheet has shown rapid change, characterized by rapid thinning along the margins, accelerating outlet glaciers, and overall increasing mass loss. The state of the Greenland Ice Sheet is of global importance, and has consequently been included in the ESA CCI Programme as a monitored Essential Climate Variable (ECV).\r\n\r\nThe project is producing data products of the following five parameters, which are important in characterizing the Greenland Ice Sheet as an Essential Climate Variable: Surface Elevation Change (SEC) gridded data from radar altimetry; Ice Velocity (IV) gridded data from synthetic aperture radar interferometry and feature tracking; Calving Front Location (CFL) time series of marine-terminating glaciers; Grounding Line Location (GLL) time series of marine-terminating glaciers; Gravimetry Mass Balance (GMB) maps and time series." } ], "responsiblepartyinfo_set": [ 192841, 192837, 192844, 192835, 192836, 192838, 192839, 192840, 192843, 192842 ], "onlineresource_set": [ 82768, 82769, 82770, 86162, 86310, 82767 ] }, { "ob_id": 39550, "uuid": "dde78ab3388a4452b43ffe3e69e91fce", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Mass flow rate ice discharge (MFID) for Greenland from CCI IV, CCI SEC, and BedMachine v1.0", "abstract": "Mass flow rate ice discharge (MFID) for Greenland ice sheet sectors. This data set is part of the ESA Greenland Ice sheet CCI project. \r\n\r\nIt provides the following CSV files: \r\n- Mass flow rate ice discharge. Units are Gt yr^{-1}.\r\n- Mass flow rate ice discharge uncertainty. Units are Gt yr^{-1}.\r\n- Coverage for each sector at each timestamp. Unitless [0 to 1].\r\n\r\nIce discharge is calculated from the CCI Ice Velocity (IV) product, the CCI Surface Elevation Change (SEC) product (where it overlaps with the ice discharge gates), and ice thickness from BedMachine. Ice discharge gates are placed 10 km upstream from all marine terminating glacier termini that have baseline velocities of more than 150 m/yr. Results are summed by Zwally et al. (2012) sectors.\r\n\r\nThe methods, including description of \"coverage\", are described in Mankoff et al. 2020.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-06-26T16:56:47", "updateFrequency": "notPlanned", "dataLineage": "Ice discharge is calculated from the CCI Ice Velocity (IV) product, the CCI Surface Elevation Change (SEC) product (where it overlaps with the ice discharge gates), and ice thickness from BedMachine. Ice discharge gates are placed 10 km upstream from all marine terminating glacier termini that have baseline velocities of more than 150 m/yr. Results are summed by Zwally et al. (2012) sectors. The methods, including description of \"coverage\", are described in Mankoff et al. (2020; DOI: 10.5194/essd-12-1367-2020)", "removedDataReason": "", "keywords": "Greenland, Ice sheet, CCI, ESA", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2024-07-08T15:38:25", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1650, "bboxName": "Greenland", "eastBoundLongitude": -10.0, "westBoundLongitude": -80.0, "southBoundLatitude": 60.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 43051, "dataPath": "/neodc/esacci/ice_sheets_greenland/data/greenland_mass_flow_rate_ice_discharge/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 384604, "numberOfFiles": 8, "fileFormat": "csv, txt, png" }, "timePeriod": { "ob_id": 7282, "startTime": "2014-10-01T00:00:00", "endTime": "2020-12-31T22:59:59" }, "resultQuality": { "ob_id": 3240, "explanation": "As provided by the CCI Greenland Ice Sheets team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2019-02-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 43079, "uuid": "b2bea2f51f024e3cbe83c1a4dbf355a5", "short_code": "comp", "title": "Mass flow rate ice discharge (MFID) derived by the ESA Greenland Ice Sheets Climate Change Initiative project, v1.0", "abstract": "Ice discharge is calculated from the CCI Ice Velocity (IV) product, the CCI Surface Elevation Change (SEC) product (where it overlaps with the ice discharge gates), and ice thickness from BedMachine. Ice discharge gates are placed 10 km upstream from all marine terminating glacier termini that have baseline velocities of more than 150 m/yr. Results are summed by Zwally et al. (2012) sectors. The methods, including description of \"coverage\", are described in Mankoff et al. 2020. \r\n\r\nFor further details see the documentation." }, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2553, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 25, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_icesheets_greenland_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 14317, "uuid": "362f66a7e09a4a59be2a40af6b41d0a6", "short_code": "proj", "title": "ESA Greenland Ice Sheet Climate Change Initiative Project", "abstract": "The Greenland Ice Sheet CCI project aims to maximize the impact of ESA satellite data on climate research, by analysing data from ESA Earth Observation missions such as ERS, Envisat, CryoSat, GRACE and the new Sentinel series of satellites. Over the last decade, the Greenland Ice Sheet has shown rapid change, characterized by rapid thinning along the margins, accelerating outlet glaciers, and overall increasing mass loss. The state of the Greenland Ice Sheet is of global importance, and has consequently been included in the ESA CCI Programme as a monitored Essential Climate Variable (ECV).\r\n\r\nThe project is producing data products of the following five parameters, which are important in characterizing the Greenland Ice Sheet as an Essential Climate Variable: Surface Elevation Change (SEC) gridded data from radar altimetry; Ice Velocity (IV) gridded data from synthetic aperture radar interferometry and feature tracking; Calving Front Location (CFL) time series of marine-terminating glaciers; Grounding Line Location (GLL) time series of marine-terminating glaciers; Gravimetry Mass Balance (GMB) maps and time series." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 14316, "uuid": "394464f9c39445d3b6445d8e305841d7", "short_code": "coll", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland Ice Sheet CCI) Dataset Collection", "abstract": "The Greenland Ice Sheet CCI project aims to maximize the impact of ESA satellite data on climate research, by analysing data from ESA Earth Observation missions such as ERS, Envisat, CryoSat, GRACE and the new Sentinel series of satellites. Over the last decade, the Greenland Ice Sheet has shown rapid change, characterized by rapid thinning along the margins, accelerating outlet glaciers, and overall increasing mass loss. The state of the Greenland Ice Sheet is of global importance, and has consequently been included in the ESA CCI Programme as a monitored Essential Climate Variable (ECV).\r\n\r\nThe project is producing data products of the following five parameters, which are important in characterizing the Greenland Ice Sheet as an Essential Climate Variable: Surface Elevation Change (SEC) gridded data from radar altimetry; Ice Velocity (IV) gridded data from synthetic aperture radar interferometry and feature tracking; Calving Front Location (CFL) time series of marine-terminating glaciers; Grounding Line Location (GLL) time series of marine-terminating glaciers; Gravimetry Mass Balance (GMB) maps and time series." } ], "responsiblepartyinfo_set": [ 192845, 192846, 192847, 192848, 192850, 192851, 192852, 192849, 192853, 192854 ], "onlineresource_set": [ 82774, 82771, 82772, 82773, 86305, 86304 ] }, { "ob_id": 39552, "uuid": "338774c1a9f34ce287f4cef9eb7bcdee", "title": "GloCAEM: Atmospheric electricity measurements at Universidad Nacional San Luis Gonzaga, Ica, Peru", "abstract": "Global Coordination of Atmospheric Electricity Measurements (GloCAEM) project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1.\r\n\r\nThis dataset contains measurements of atmospheric electricity and electric potential gradient made using a Boltek field meter at Universidad Nacional San Luis Gonzaga, Ica, Peru.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "unknown", "dataLineage": "Data collected by project team and sent to CEDA", "removedDataReason": "", "keywords": "GloCAEM, GEC, electric potential, electric field, GCR", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3740, "bboxName": "Universidad Nacional San Luis Gonzaga, P", "eastBoundLongitude": -75.736, "westBoundLongitude": -75.736, "southBoundLatitude": -14.087, "northBoundLatitude": -14.087 }, "verticalExtent": null, "result_field": { "ob_id": 39576, "dataPath": "/badc/glocaem/data/ica/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 921, "numberOfFiles": 1, "fileFormat": "BADC-CSV" }, "timePeriod": null, "resultQuality": { "ob_id": 3068, "explanation": "Data provided by project group", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2016-10-18" }, "validTimePeriod": { "ob_id": 7148, "startTime": "2016-11-25T00:00:00", "endTime": null }, "procedureAcquisition": { "ob_id": 39556, "uuid": "82d01d9bca4c42a7982b09866c7f95ab", "short_code": "acq", "title": "GLOCaeM potential gradient - Universidad Nacional San Luis Gonzaga, Ica, Peru", "abstract": "Measurements of atmospheric electric potential gradient made at Universidad Nacional San Luis Gonzaga, Ica, Peru" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2546, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 20068, "uuid": "6ee6e0a3f57c4ca79e8cbc0daaafe76f", "short_code": "proj", "title": "Global Coordination of Atmospheric Electricity Measurements (GloCAEM)", "abstract": "It is well established that Earth has a \"Global atmospheric Electric Circuit\" (GEC), through which charge separation in thunderstorms sustains large scale current flow around the planet. The GEC generates an atmospheric electric field which is present globally, and is typically 100V/m near the surface in fair weather conditions. Measurements of electric field have been shown to include information about global thunderstorm activity, local aerosol concentrations and cloud cover, as well as changes in the space weather environment. Recent work has also suggested that atmospheric electrical changes may be effective as earthquake precursors, as well as being sensitive to release of radioactivity, as evidenced by the Fukushima disaster in 2011. \r\n\r\nThe global nature of the GEC means that in order that truly global signals are considered in understanding the processes within the circuit, many validating measurements must be made at different locations around the world. To date, no genuinely global network of FW atmospheric electricity measurements has ever existed, therefore, given the growing number of groups now involved in atmospheric electricity monitoring, such a proposal is timely. \r\n\r\nThis project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 24981, "uuid": "bffd0262439a4ecb8fadf0134c4a4a41", "short_code": "coll", "title": "GloCAEM: Atmospheric electric potential gradient measurements", "abstract": "Global Coordination of Atmospheric Electricity Measurements (GloCAEM) project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1.\r\n\r\nThis dataset collection contains measurements of atmospheric electricity and electric potential gradient made using a Cambell Scientific CS110 electric-field mill." } ], "responsiblepartyinfo_set": [ 192865, 192867, 192868, 192869, 192860, 192861, 192862, 192863, 192864 ], "onlineresource_set": [ 82776 ] }, { "ob_id": 39557, "uuid": "60c28523d8c54c58831b2608164cf35e", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2022, v3.3.0.2022f", "abstract": "This is version v3.3.0.2022f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data.\r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20230101_v3.3.1.2022f.nc. The station codes can be found under the docs tab. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note.\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-02-13T14:44:40", "updateFrequency": "notPlanned", "dataLineage": "HadISD the global sub-daily station dataset was produced by the Met Office Hadley Centre. It was derived from the Integrated Surface Dataset (ISD) from NOAA's National Climatic Data Center (NCDC). HadISD has been passed to the Centre for Environmental Data Analysis (CEDA) for archiving and distribution.", "removedDataReason": "", "keywords": "HadISD, temperature, dewpoint, pressure, wind, speed, direction, cloud, precipitation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2023-02-13T16:55:45", "doiPublishedTime": "2023-02-14T15:23:32", "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": 39558, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadISD/subdaily/HadISDTable/r1/v3-3-0-2022f", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 53899151101, "numberOfFiles": 9569, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 11515, "startTime": "1931-01-01T00:00:00", "endTime": "2022-12-31T23:00:00" }, "resultQuality": { "ob_id": 3104, "explanation": "CF-Compliant NetCDF, see documentation for quality control processes used to produce these data. These data are quality controlled by the data provider, the Met Office Hadley Centre (MOHC) and not the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-02-19" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 20043, "uuid": "32eff53af32442d1a347da2cc45bb9db", "short_code": "comp", "title": "HadISD station data processing performed at the Met Office Hadley Centre", "abstract": "The HadISD station data were produced by the Met Office Hadley Centre. Individual station data within the ISD were selected selected on the basis of their length of record and reporting frequency. A merging algorithm using their location, elevation and station name identified candidates suitable to combine together. All stations were passed through a suite of automated quality control tests designed to remove bad data whilst keeping the extremes. None of the ISD flags were used in this process. The QC tests focussed on the temperature, dewpoint temperature and sea-level pressure variables, although some were applied to the wind speed and direction and cloud data. The data files also contain other variables which were pulled through from the raw ISD record, but have had no QC applied (e.g. cloud base and precipitation depth). \r\n\r\nNotes:\r\n1. These data have not yet been homogenised and so trend fitting should be undertaken with caution. The homogeneity has been assessed and results are available from the Met Office Hadley Centre HadISD website: http://www.metoffice.gov.uk/hadobs/hadisd/. \r\n2. A long-standing bug (affecting versions v2.0.2_2017p through to v3.3.0.2022f), was discovered in autumn 2023 whereby the neighbour checks (and associated [un]flagging for some other tests) were not being implemented. This was corrected for the later version v3.4.0.2023f to HadISD. For more details see the posts on the HadISD blog: https://hadisd.blogspot.com/2023/10/bug-in-buddy-checks.html & https://hadisd.blogspot.com/2024/01/hadisd-v3402023f-future-look.html(v2.0.2_2017p through to v3.3.0.2022f), and as noted this has been fixed for v3.4.02023f.\r\n\r\n\r\nFor further details see: \r\nDunn, R. J. H., et al., (2016), Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geoscientific Instrumentation, Methods and Data Systems, and Dunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations\r\nfrom 1973-2011, Climate of the Past.\r\nDunn, R. J. H., et al. (2014), Pairwise Homogeneity Assessment of HadISD, Climate of the Past, 10, 1501-1522 (see Docs for links to publications)." }, "procedureCompositeProcess": null, "imageDetails": [ 157 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2561, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 32, "licenceURL": "http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/", "licenceClassifications": [ { "ob_id": 6, "classification": "personal" }, { "ob_id": 4, "classification": "academic" }, { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 54776, 54777, 54778, 54780, 54781, 54782, 54783, 54784, 54785, 54786, 54787, 54788, 54789, 54790, 54791, 54792, 54793, 54794, 54795, 62629, 62630, 62631, 62632, 62633, 62634, 62635, 62636, 62637, 62638 ], "vocabularyKeywords": [], "identifier_set": [ 12404 ], "observationcollection_set": [ { "ob_id": 13521, "uuid": "f579035b3c954475922e4b13705a7669", "short_code": "coll", "title": "HadISD: global sub-daily station data for climate extremes", "abstract": "HadISD is a station based dataset comprising 6103 stations covering 1973-present. These stations are a subset of the stations available in the Integrated Surface Database (ISD), and are ones selected to be those most useful for climate studies (long records and high reporting frequency). Individual stations within the ISD were composited when it was appropriate to do so to improve the coverage.\r\n \r\nHadISD is a multi-variate dataset, where the following fields are available: temperature, dewpoint temperature, sea-level pressure, wind speed, wind direction and cloud data (total, low, mid and high levels). These variables are all quality controlled using an automatic suite of tests, the code for which is available on request. The QC tests were designed to remove bad data whilst keeping true extremes. A number of other variables are also carried through to the final NetCDF files, but have not been quality controlled (e.g. precipitation period, precipitation depth, sunshine duration)." } ], "responsiblepartyinfo_set": [ 192885, 192886, 192887, 192888, 192889, 192890, 192891, 192892, 192884, 192893, 192894, 192895 ], "onlineresource_set": [ 82781, 82786, 82780, 82784, 82783, 82785, 82788, 82787, 82782, 89651, 89652, 89653, 89654, 89655, 89656, 89657 ] }, { "ob_id": 39560, "uuid": "21a10e3de91d4d2da9a60489ba45df10", "title": "RisesAM-NEA-ERA: An hourly 1/12° regional simulation of the North East Atlantic Ocean sea surface waves (historic forcing based on 1979-2015 ERA interim)", "abstract": "This dataset covers a regional model of the North East Atlantic run for a historic reanalysis period (1979-2015) forced by ECMWF Re-Analysis winds (ERA-Interim, https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim) The data are produced with a spectral wave model; WaveWatchIII (Tolman, 1997) on a regular lat/lon grid with resolution around 1/12th degree. The netcdf data include high frequency (hourly) wave data of bulk parameters representing significant wave height, mean wave direction, and energy period. Partitioned wave data are also included for significant wave height, peak period, and direction. These data are presented in 3 partitions, with a fraction of wind sea specified for each. The data set is produced to benchmark the model before investigating future wave conditions of Europe under climate change. The simulations were run using funding from the European Union’s Seventh Programme for Research, Technological Development and Demonstration under Grant Agreement No: FP7-ENV-2013-Two-Stage-603396-RISES-AM-. 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Partitioned wave data are also included for significant wave height, peak period, and direction. These data are presented in 3 partitions, with a fraction of wind sea specified for each. There are 2 different climate futures explored; RCP8.5 and RCP4.5 (regional concentration pathways). The two scenarios are driven by different Representative Concentration Pathways (RCPs). The RCPs, RCP4.5 and RCP8.5, are labelled after a possible range of radiative forcing values in the year 2100 relative to pre-industrial values (+4.5, and +8.5 W/m2, respectively). The data set is produced to benchmark the model before investigating future wave conditions under climate change. The simulations were run using funding from the European Union’s Seventh Programme for Research, Technological Development and Demonstration under Grant Agreement No: FP7-ENV-2013-Two-Stage-603396-RISES-AM-. 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No quality control procedures were applied by BODC.", "removedDataReason": "", "keywords": "surface, model, wave, ERA, interim, reanalysis, UK, climate, projections, UKCP18", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-04-04T14:10:14", "doiPublishedTime": "2023-04-06T08:12:05", "removedDataTime": null, "geographicExtent": { "ob_id": 3746, "bboxName": "", "eastBoundLongitude": 179.65, "westBoundLongitude": -179.35, "southBoundLatitude": -78.05, "northBoundLatitude": 82.73 }, "verticalExtent": null, "result_field": { "ob_id": 39571, "dataPath": "/bodc/POL230027/RisesAM-OceanWaves/RisesAM-Global-clim", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3083830878847, "numberOfFiles": 2701, "fileFormat": "Data are CF-Compliant NetCDF formatted data files" }, "timePeriod": { "ob_id": 10969, "startTime": "1970-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 3732, "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": "2021-07-02" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39563, "uuid": "6d7a2e491ec94cbc958b89c72b3e61fb", "short_code": "comp", "title": "WaveWatch III", "abstract": "WAVEWATCH III® (Tolman 1997, 1999a, 2009) is a third generation wave model developed at NOAA/NCEP in the spirit of the WAM model (WAMDIG 1988, Komen et al. 1994). It is a further development of the model WAVEWATCH, as developed at Delft University of Technology (Tolman 1989, 1991a) and WAVEWATCH II, developed at NASA, Goddard Space Flight Center (e.g., Tolman 1992). WAVEWATCH III®, however, differs from its predecessors in many important points such as the governing equations, the model structure, the numerical methods and the physical parameterizations. Furthermore, with model version 3.14, WAVEWATCH III® is evolving from a wave model into a wave modeling framework, which allows for easy development of additional physical and numerical approaches to wave modeling. WAVEWATCH III® solves the random phase spectral action density balance equation for wavenumber-direction spectra. The implicit assumption of this equation is that properties of medium (water depth and current) as well as the wave field itself vary on time and space scales that are much larger than the variation scales of a single wave. With version 3.14 some source term options for extremely shallow water (surf zone) have been included, as well as wetting and drying of grid points. Whereas the surf-zone physics implemented so far are still fairly rudimentary, it does imply that the wave model can now be applied to arbitrary shallow water." }, "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": 39562, "uuid": "a1b718f148f24b8bb5d2eea9e3d25a3d", "short_code": "proj", "title": "RisesAM-OceanWaves", "abstract": "While there has been a strong focus on mitigating climate change to below 2°C, the lack of international action and the continued growth in greenhouse gas concentrations make it important to analyse the implications of higher-end scenarios (global average warming > 2°C with respect to pre-industrial level) for vulnerable areas. This will allow a better quantification of impacts and vulnerabilities associated with such climate change, showing that adaptation is possible at an affordable cost (compared to risk). It will furthermore inform policy and decision makers, contributing to raise awareness on the multiple dimensions of possible adaptation pathways. Coastal zones are amongst the most vulnerable regions of the world in the face of climatic change (threatened by sea-level rise, run-off into coastal lowlands and coastal storms). The obtained results are based on a set of models that consider global projections especially prepared for coastal systems to allow evaluating climate change impacts for present adaptation levels. The RISES-AM project aimed to investigate a range of future scenarios, considering the physical and the socio-economic components, in order to introduce a risk approach into climatic analysis to achieve a higher level of objectivity in the assessments and to account for uncertainties in drivers and responses. The results of RISES-AM have been derived from conceptual and numerical models that deal with impact projection for coastal systems and allow evaluating climate impacts for present adaptation levels. The results of the assessments show the importance of planning ahead in all categories to reduce climate change impacts and show the feedbacks between the physical and socio-economic sub-systems in the coastal zone." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 54763, 54764, 62612, 62613, 62614, 62615, 62616, 62617, 62618, 62619, 62620, 62621, 62622, 62623, 62624, 62625, 62626, 62627 ], "vocabularyKeywords": [], "identifier_set": [ 12458 ], "observationcollection_set": [ { "ob_id": 39572, "uuid": "b21d053ef801467298cf87811467a7eb", "short_code": "coll", "title": "RisesAM-OceanWaves: An hourly simulation of sea surface waves for historic and future climate", "abstract": "This data collection comprises four simulations; covering two different domains and two different periods. RisesAM-NEA is a regional model of the North East Atlantic, and RisesAM-Global covers the world ocean. Each simulation is run for a historic reanalysis period (1979-2015) forced by ECMWF Re-Analysis winds (ERA-Interim, https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim) The global models are also forced with sea-ice cover from the same data source. The climate projections (1970-2100) are forced by EC-Earth (https://ec-earth.org/) Regional models are driven by the same climate models, downscaled to 12km resolution, under EuroCordex programme (https://euro-cordex.net/) The data are produced with a spectral wave model; WaveWatchIII (Tolman, 1997). The data on a regular lat/long grid and the global wave model resolution is around 0.8 degrees, and the regional model is 1/12th degree. The netcdf data include high frequency (hourly) wave data of bulk parameters representing significant wave height, mean wave direction, and energy period. Partitioned wave data are also included for significant wave height, peak period, and direction. These data are presented in 3 partitions, with a fraction of wind sea specified for each. The data set is produced to investigate the future wave conditions of Europe under climate change. There are 2 different climate futures explored; RCP8.5 and RCP4.5 (regional concentration pathways). The simulations were run using funding from the European Union’s Seventh Programme for Research, Technological Development and Demonstration under Grant Agreement No: FP7-ENV-2013-Two-Stage-603396-RISES-AM-. This work used the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk)" } ], "responsiblepartyinfo_set": [ 192963, 192966, 192967, 192960, 192962, 192961, 192964, 192965, 192968 ], "onlineresource_set": [ 82797, 82798, 82805, 83219 ] }, { "ob_id": 39573, "uuid": "8b4c2455c27a4f0581e753be50ed94c7", "title": "UKCP projected future extreme sea levels at approximately 2 km spacing around the UK coastline for 2020-2100, using 21st century time-mean sea level projections", "abstract": "The data are projected extreme sea levels at approximately 2 km spacing around the UK coastline, including England, Wales, Scotland, Northern Ireland, and Isle of Man. The data were produced by the Met Office, using estimates of present-day extreme sea levels at all open-coast Coastal Flood Boundary point locations, provided by the Environment Agency and projections of 21st Century time-mean sea level change prepared at the Met Office. \r\n\r\nProjected extreme sea level values are described for 16 different annual probabilities of exceedance (return levels), ranging from 1:1 to 1:10,000, identified in the shapefile as ‘t1’ to ‘t10000’. Confidence levels relating to the 5% and 95% lower and upper bounds of confidence are included, identified in the shapefile with the prefix ‘c1_’ and ‘c3_’ respectively, as well as the 70% confidence level identified in the shapefile with the prefix ‘c2_’. \r\n\r\nThe data were produced to put the projected future mean sea level change in the context of the present-day extremes. The data were produced by combining the best estimates of present-day extreme sea levels with projections of mean sea level change. The data covers the period from 2020 to 2100 and is available for each decade (i.e., 2020, 2030... 2100).\r\n\r\nFurther information on this dataset and UKCP18 can be found in the documentation section.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-02T15:43:21", "latestDataUpdateTime": "2023-03-14T09:45:28", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office", "removedDataReason": "", "keywords": "UKCP, extreme sea level, UK", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-03-14T10:12:47", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2303, "bboxName": "", "eastBoundLongitude": 1.75, "westBoundLongitude": -10.58, "southBoundLatitude": 49.17, "northBoundLatitude": 60.83 }, "verticalExtent": null, "result_field": { "ob_id": 39774, "dataPath": "/badc/ukcp18/data/marine-sim/ext-sea-lev-shp", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 16441733, "numberOfFiles": 28, "fileFormat": "The data are provided as zipped shapefiles." }, "timePeriod": { "ob_id": 10970, "startTime": "2020-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 212 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": 26015, "uuid": "3500c35912e34566adb9948920d1216d", "short_code": "proj", "title": "UKCP18: The Marine Component of the UK Climate Projections 2018", "abstract": "The marine component of the UKCP18 project is comprised of:\r\nA multi-model ensemble of sea level projections for UK waters which extend to 2100 and include contributions from glaciers, ice sheets, freshwater reservoirs, groundwater and thermal expansion. \r\nA multi-model ensemble of historical sea level simulations for UK waters from 1970 to 2006.\r\nCase studies of three short events.\r\nSimulated impacts of mean sea level change on tidal characteristics around the UK." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 192979, 192986, 192985, 192983, 192982, 192981, 192980, 192988, 192987 ], "onlineresource_set": [ 95101, 82807, 82808 ] }, { "ob_id": 39606, "uuid": "9c41e3aa67024bbdad796290a861e968", "title": "Large ensemble of global mean temperatures: 6-hourly HadAM4 model run data using the Climateprediction.net platform", "abstract": "Large ensembles of global temperature are provided for three climate scenarios: historical (2006-16), 1.5 C and 2.0 C above pre-industrial levels. Each scenario has 700 members (70 runs per year for 10-year periods) of 6-hourly mean temperatures at a resolution of 0.833 degrees x 0.556 degrees (longitude x latitude). The data was generated using the climateprediction.net (CPDN) climate simulation environment, to run the Met Office HadAM4 Atmosphere-only General Circulation Model (AGCM) from the UK Met Office Hadley Centre. Biases in simulated temperature were identified and corrected using quantile mapping with reference temperature data from ERA5 reanalysis. \r\n\r\nData were generated using the Met Office HadAM4 model at 6-hourly temporal resolution and 0.833 degrees x 0.556 degrees (longitude x latitude) over global domain. The data from each scenario is divided into 4 batches. Historic scenario (2006-2016): December-March data in Batch 889, April-May data in Batch 920, June-September data in Batch 901, October-November data in Batch 923. 1.5C scenario: December-March data in Batch 891, April-May data in Batch 921, June-September data in Batch 902, October-November data in Batch 924. 2.0C scenario: December-March data in Batch 895, April-May data in Batch 922, June-September data in Batch 903, October-November data in Batch 925.", "creationDate": "2023-01-26T13:09:21.875235", "lastUpdatedDate": "2023-01-26T12:39:54", "latestDataUpdateTime": "2023-06-23T20:16:35", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "HadAM4, Climateprediction.net", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "0.833 degree x 0.556 degree", "status": "completed", "dataPublishedTime": "2023-06-26T14:36:29", "doiPublishedTime": "2023-06-28T13:55:34.179546", "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": 39607, "dataPath": "/badc/deposited2023/CPDN_HadAM4", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1698725532892, "numberOfFiles": 8401, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 10973, "startTime": "2006-12-01T00:00:00", "endTime": "2101-11-30T21:00:00" }, "resultQuality": { "ob_id": 4162, "explanation": "Data are bias corrected by the data provider and technical validation applied.", "passesTest": true, "resultTitle": "Bias corrected and technical validation applied", "date": "2023-01-26" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40180, "uuid": "fbdb0f9a175b4fb5be47cb4e25a5430c", "short_code": "comp", "title": "Computation for Large ensemble of global mean temperatures: 6-hourly HadAM4 model run data using the Climateprediction.net platform", "abstract": "The global HadAM4 atmosphere and land surface model includes prognostic cloud, convection and gravity-wave drag parameterisation schemes, a radiation scheme that treats water vapour and ice crystals separately, and a land surface scheme, like its predecessor, HadAM3. The updates in HadAM4 include a mixed-phase precipitation scheme, parameterisation of ice cloud particle size and the radiative effects of non-spherical ice particles, and a revised boundary layer scheme. The version used here incorporates an upgrade to the spatial resolution, which matches the horizontal resolution of the HadGEM3-GC3.05 simulations analysed here. HadAM4 has 38 vertical levels." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2521, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 39604, "uuid": "bb6199da25834a028e84df57edf788bf", "short_code": "proj", "title": "Future of Cooling Programme - Oxford Martin School", "abstract": "Cooling is necessary for the quality of life of billions of people living in developing countries and, increasingly, for those developed countries traditionally unprepared for ever more frequent heatwaves due to climate change.\r\n\r\nThe energy needed for air conditioning is likely to triple by 2050, with ten new air conditioning units projected to be sold every second for the next 30 years. This huge demand has the potential to drive up greenhouse gas emissions and therefore further exacerbate the very problem it is designed to alleviate.\r\n\r\nShaping future cooling demand patterns is potentially the most significant opportunity we have to moderate the trajectory of energy demand. However, we do not yet understand where the greatest social, technical and economic innovations could be made, and therefore lack an evidence base for interventions. Similarly, the benefits of cooling for reducing rates of heat-related illness have not yet been fully researched.\r\n\r\nThis study investigates the future of cooling as a dynamic system, and examines its interlinkages across Sustainable Development Goals (SDGs) in the developing and developed world. Our aim is to steer the system towards sustainable cooling for all, and to establish cooling as a global priority for the successful implementation of the SDGs.\r\n\r\nWe are concentrating on space cooling (from air conditioning, fans, and other, non-energy-dependent passive cooling techniques), which is the largest energy consumer amongst the cooling sectors, and are examining three critical, inter-related aspects of future cooling.\r\n\r\nThese include:\r\n\r\ntransitioning technologies and cooling cultures and behaviours that determine energy demand\r\nthe implications of severe heat for morbidity and the potential to mitigate negative health effects\r\nmapping unaccounted-for impacts of the global cooling production network, including refrigerant gases\r\nOur methods include surveys and trend analysis, modelling, experiments, qualitative fieldwork and case studies in fast-growing developing countries (India and South Africa) and developed countries at risk of increased heatwaves (France and the UK).\r\n\r\nOur research focuses explicitly on creating solutions, including policy influence on cooling energy demand and social aspects of climate mitigation, the design and implementation of alternative supply chains, enhancing the uptake of the best-in-class refrigerant gases as well as evidence-based guidelines to better handle the healthcare burden of severe heat.\r\n\r\nIn addition to its focus on space cooling, the programme is critically assessing agricultural and industrial cold-chain systems. It is developing a typology of different types of cold chains and evaluating them in terms of their environmental, economic and social sustainability. Crucially, in addition to suggesting their optimised design, it aims to derive context-specific policy recommendations how governments can best incentivise, govern and regulate low-carbon and effective cold chains." }, { "ob_id": 39605, "uuid": "cca965d6235d429fad6ee5c4143da932", "short_code": "proj", "title": "Marie Curie Fellowship", "abstract": "European Union’s Horizon 2020 Research and Innovation Programme Marie Sklodowska-Curie Grant. Grant number: 101023241" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62607, 62608, 62609, 62610, 62611 ], "vocabularyKeywords": [], "identifier_set": [ 12536 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 193044, 193045, 193046, 193047, 193048, 193049, 193050, 193057, 193051, 193052, 193053, 193054, 193055, 193056 ], "onlineresource_set": [] }, { "ob_id": 39630, "uuid": "2b0261d03ae34e4abc8e32cb1a805887", "title": "UKCP projected future extreme sea levels at approximately 2 km spacing around the UK coastline for 2020-2300, using exploratory extended time-mean sea level projections", "abstract": "The data are projected extreme sea levels at approximately 2 km spacing around the UK coastline, including England, Wales, Scotland, Northern Ireland, and Isle of Man. The data were produced by the Met Office, using estimates of present-day extreme sea levels at all open-coast Coastal Flood Boundary point locations, provided by the Environment Agency and projections of exploratory extended time-mean sea level change prepared at the Met Office. \r\n\r\nProjected extreme sea level values are described for 16 different annual probabilities of exceedance (return levels), ranging from 1:1 to 1:10,000, identified in the shapefile as ‘t1’ to ‘t10000’. Confidence levels relating to the 5% and 95% lower and upper bounds of confidence are included, identified in the shapefile with the prefix ‘c1_’ and ‘c3_’ respectively, as well as the 70% confidence level identified in the shapefile with the prefix ‘c2_’. \r\n\r\nThe data were produced to put the projected future mean sea level change in the context of the present-day extremes. The data were produced by combining the best estimates of present-day extreme sea levels with projections of mean sea level change. The data covers the period from 2020 to 2300 and is available for each decade (i.e., 2020, 2030... 2300).\r\n\r\nFurther information on this dataset and UKCP18 can be found in the documentation section.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-02T15:43:38", "latestDataUpdateTime": "2023-03-14T09:53:40", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office", "removedDataReason": "", "keywords": "UKCP, extreme sea level, UK", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-03-14T10:13:31", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2303, "bboxName": "", "eastBoundLongitude": 1.75, "westBoundLongitude": -10.58, "southBoundLatitude": 49.17, "northBoundLatitude": 60.83 }, "verticalExtent": null, "result_field": { "ob_id": 39773, "dataPath": "/badc/ukcp18/data/marine-sim/ext-sea-lev-expl-shp", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 53336444, "numberOfFiles": 88, "fileFormat": "The data are provided as zipped shapefiles." }, "timePeriod": { "ob_id": 11004, "startTime": "2020-01-01T00:00:00", "endTime": "2300-12-31T23:59:59" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 212 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": 26015, "uuid": "3500c35912e34566adb9948920d1216d", "short_code": "proj", "title": "UKCP18: The Marine Component of the UK Climate Projections 2018", "abstract": "The marine component of the UKCP18 project is comprised of:\r\nA multi-model ensemble of sea level projections for UK waters which extend to 2100 and include contributions from glaciers, ice sheets, freshwater reservoirs, groundwater and thermal expansion. \r\nA multi-model ensemble of historical sea level simulations for UK waters from 1970 to 2006.\r\nCase studies of three short events.\r\nSimulated impacts of mean sea level change on tidal characteristics around the UK." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 193113, 193114, 193115, 193116, 193117, 193118, 193119, 193120, 193121 ], "onlineresource_set": [ 82950, 82951, 95100 ] }, { "ob_id": 39633, "uuid": "f9525557d49b4363adccd8e68c0893de", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from University of L'Aquila's vaisala-cl51 instrument deployed at Aquila, Italy", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from University of L'Aquila's vaisala-cl51 deployed at Aquila, Italy.\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: 0-20008-0-CEO.\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": "2023-02-02T16:55:49.531298", "lastUpdatedDate": "2023-02-02T16:55:49.531310", "latestDataUpdateTime": "2025-07-18T08:15:22", "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": "2023-01-31T20:08:43", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3747, "bboxName": "Aquila", "eastBoundLongitude": 13.314399719238281, "westBoundLongitude": 13.314399719238281, "southBoundLatitude": 42.3828010559082, "northBoundLatitude": 42.3828010559082 }, "verticalExtent": null, "result_field": { "ob_id": 39632, "dataPath": "/badc/eprofile/data/daily_files/italy/aquila/aquila-university-vaisala-cl51_A", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2814669751, "numberOfFiles": 1084, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 10975, "startTime": "2023-01-20T00:00:00", "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": 39634, "uuid": "86ed96135bcc4956b6d72682a792b122", "short_code": "acq", "title": "University of L'Aquila: vaisala-cl51 instrument deployed at Aquila", "abstract": "vaisala-cl51 instrument instrument deployed at Aquila operated by University of L'Aquila providing cloud base height and aerosol profile data." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 220 ], "discoveryKeywords": [], "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": [ 193133, 193134, 193135, 193136, 193137, 193138, 193139, 193140, 193141 ], "onlineresource_set": [ 82954, 82953 ] }, { "ob_id": 39638, "uuid": "f06e1510b56049559e87aa2f0db5fdcc", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's lufft-chm15k instrument deployed at Borssele Alfa, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s lufft-chm15k deployed at Borssele Alfa, Netherlands.\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: 0-528-0-06317.\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": "2023-02-02T16:58:01.487971", "lastUpdatedDate": "2023-02-02T16:58:01.487984", "latestDataUpdateTime": "2024-12-01T00:42:11", "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": "2023-01-08T12:09:03", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3748, "bboxName": "Borssele-Alfa", "eastBoundLongitude": 3.0566599369049072, "westBoundLongitude": 3.0566599369049072, "southBoundLatitude": 51.69977951049805, "northBoundLatitude": 51.69977951049805 }, "verticalExtent": null, "result_field": { "ob_id": 39637, "dataPath": "/badc/eprofile/data/daily_files/netherlands/borssele-alfa/knmi-lufft-chm15k_A", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2934794363, "numberOfFiles": 1101, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 10976, "startTime": "2023-01-06T00:00:00", "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": 39639, "uuid": "9ea09007d0f240b8b1ee42733f08bd49", "short_code": "acq", "title": "KNMI: lufft-chm15k instrument deployed at Borssele Alfa", "abstract": "lufft-chm15k instrument instrument deployed at Borssele Alfa operated by Royal Netherlands Meteorological Institute (KNMI) providing cloud base height and aerosol profile data." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 220 ], "discoveryKeywords": [], "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, 62346, 62347, 62526 ], "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": [ 193150, 193151, 193152, 193153, 193154, 193155, 193156, 193157, 193158 ], "onlineresource_set": [ 82956, 82957 ] }, { "ob_id": 39642, "uuid": "0078d944259049a4b1bc5947623f6e97", "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.12 (v20230203)", "abstract": "Data for Figure 4.12 from Chapter 4 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 4.12 shows the projected near-term change of seasonal mean surface temperature.\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 Lee, J.-Y., J. Marotzke, G. Bala, L. Cao, S. Corti, J.P. Dunne, F. Engelbrecht, E. Fischer, J.C. Fyfe, C. Jones, A. Maycock, J. Mutemi, O. Ndiaye, S. Panickal, and T. Zhou, 2021: Future Global Climate: Scenario-Based Projections and Near-Term Information. 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. 553–672, doi:10.1017/9781009157896.006.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for all panels in one NetCDF file.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n CMIP6 multi-model mean projected change in surface air temperature (2021–2040) from SSP1‑2.6 and SSP3‑7.0 relative to 1995–2014.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n The variable tas includes the map information as a function of latitude and longitude and has a dimension named panel, which includes the data for all panels a-d.\r\n\r\n\r\n\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP1-2.6 is based on Shared Socioeconomic Pathway SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP3-7.0 is based on Shared Socioeconomic Pathway SSP3 which is characterized by high challenges to both mitigation and adaptation and RCP7.0, a future pathway with a radiative forcing of 7.0 W/m2 in the year 2100.\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 4)\r\n - Link to the Supplementary Material for Chapter 4, which contains details on the input data used in Table 4.SM.1", "creationDate": "2023-02-03T11:57:20.316316", "lastUpdatedDate": "2023-02-03T11:57:20", "latestDataUpdateTime": "2024-03-09T01:48:56", "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, Chapter 4, Sixth Assessment Report, Working Group 1, Physical Science Basis, surface temperature", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-07T11:03:17", "doiPublishedTime": "2023-07-03T09:30:50.930259", "removedDataTime": null, "geographicExtent": { "ob_id": 3749, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 229, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39643, "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig12/v20230203", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5727575, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10977, "startTime": "2021-01-01T12:00:00", "endTime": "2040-12-31T12:00:00" }, "resultQuality": { "ob_id": 4167, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-03" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39644, "uuid": "9004f18f721a4ee3b6ebd216a3478fd4", "short_code": "comp", "title": "Caption for Figure 4.12 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Near-term change of seasonal mean surface temperature. Displayed are projected spatial patterns of CMIP6 multi-model mean change (°C) in (top) December–January–February (DJF) and (bottom) June–July–August (JJA) near-surface air temperature for 2021–2040 from SSP1-2.6 and SSP3-7.0 relative to 1995–2014. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.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": [ 46706, 46708, 46709, 52664, 52665, 62359, 62360 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 32719, "uuid": "5b30b3c2146048388ac97e4278cb5128", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information.\r\n\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 4.12\r\n- data for Figure 4.13\r\n- data for Figure 4.19\r\n- data for Figure 4.22\r\n- data for Figure 4.23\r\n- data for Figure 4.24\r\n- data for Figure 4.25\r\n- data for Figure 4.26\r\n- data for Figure 4.31\r\n- data for Figure 4.32\r\n- data for Figure 4.41\r\n- data for Figure 4.42" } ], "responsiblepartyinfo_set": [ 193166, 193167, 193168, 193169, 193170, 193171, 193172, 193173 ], "onlineresource_set": [ 82958, 82960, 82959, 88601 ] }, { "ob_id": 39645, "uuid": "11d45679506d44fda224d65326edcdb4", "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.13 (v20230203)", "abstract": "Data for Figure 4.13 from Chapter 4 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 4.13 shows the projected near-term change of seasonal mean precipitation.\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 Lee, J.-Y., J. Marotzke, G. Bala, L. Cao, S. Corti, J.P. Dunne, F. Engelbrecht, E. Fischer, J.C. Fyfe, C. Jones, A. Maycock, J. Mutemi, O. Ndiaye, S. Panickal, and T. Zhou, 2021: Future Global Climate: Scenario-Based Projections and Near-Term Information. 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. 553–672, doi:10.1017/9781009157896.006.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for all panels in one NetCDF file.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n CMIP6 multi-model mean projected change in precipitation (2021–2040) from SSP1‑2.6 and SSP3‑7.0 relative to 1995–2014.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n The variable pr includes the map information as a function of latitude and longitude and has a dimension named panel, which includes the data for all panels a-d.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP1-2.6 is based on Shared Socioeconomic Pathway SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP3-7.0 is based on Shared Socioeconomic Pathway SSP3 which is characterized by high challenges to both mitigation and adaptation and RCP7.0, a future pathway with a radiative forcing of 7.0 W/m2 in the year 2100.\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 4)\r\n - Link to the Supplementary Material for Chapter 4, which contains details on the input data used in Table 4.SM.1", "creationDate": "2023-02-03T12:33:04.701489", "lastUpdatedDate": "2023-02-03T12:33:04", "latestDataUpdateTime": "2024-03-09T03:01:43", "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, Chapter 4, Sixth Assessment Report, Working Group 1, Physical Science Basis, precipitation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-07T11:06:50", "doiPublishedTime": "2023-07-03T09:39:41.870467", "removedDataTime": null, "geographicExtent": { "ob_id": 3750, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 230, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39646, "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig13/v20230203", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5723143, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10978, "startTime": "2021-01-01T12:00:00", "endTime": "2040-12-31T12:00:00" }, "resultQuality": { "ob_id": 4168, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-03" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39647, "uuid": "1b9151cce8ed46268790734353b6f3db", "short_code": "comp", "title": "Caption for Figure 4.13 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Near-term change of seasonal mean precipitation. Displayed are projected spatial patterns of CMIP6 multi-model mean change (%) in (top) December–January–February (DJF) and (bottom) June–July–August (JJA) precipitation from SSP1-2.6 and SSP3-7.0 in 2021–2040 relative to 1995–2014. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.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": [ 30956, 46706, 46708, 52664, 52665, 62359, 62360 ], "vocabularyKeywords": [], "identifier_set": [ 12550 ], "observationcollection_set": [ { "ob_id": 32719, "uuid": "5b30b3c2146048388ac97e4278cb5128", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information.\r\n\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 4.12\r\n- data for Figure 4.13\r\n- data for Figure 4.19\r\n- data for Figure 4.22\r\n- data for Figure 4.23\r\n- data for Figure 4.24\r\n- data for Figure 4.25\r\n- data for Figure 4.26\r\n- data for Figure 4.31\r\n- data for Figure 4.32\r\n- data for Figure 4.41\r\n- data for Figure 4.42" } ], "responsiblepartyinfo_set": [ 193179, 193180, 193181, 193182, 193183, 193184, 193185, 193186 ], "onlineresource_set": [ 82961, 82962, 82963, 88602 ] }, { "ob_id": 39648, "uuid": "dce10ff4596241e190aaea9291cc4249", "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.19 (v20230203)", "abstract": "Data for Figure 4.19 from Chapter 4 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 4.19 shows the projected mid- and long-term change of annual mean surface temperature.\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 Lee, J.-Y., J. Marotzke, G. Bala, L. Cao, S. Corti, J.P. Dunne, F. Engelbrecht, E. Fischer, J.C. Fyfe, C. Jones, A. Maycock, J. Mutemi, O. Ndiaye, S. Panickal, and T. Zhou, 2021: Future Global Climate: Scenario-Based Projections and Near-Term Information. 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. 553–672, doi:10.1017/9781009157896.006.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for all panels in one NetCDF file. \r\na) Global projected spatial patterns of multi-model mean change in annual mean near-surface air temperature (°C) in 2041-2060 relative to 1995-2014 in SSP1-2.6 \r\nb) Global projected spatial patterns of multi-model mean change in annual mean near-surface air temperature (°C) in 2081-2100 relative to 1995-2014 in SSP1-2.6 \r\nc) Global projected spatial patterns of multi-model mean change in annual mean near-surface air temperature (°C) in 2041-2060 relative to 1995-2014 in SSP3-7.0 \r\nd) Global projected spatial patterns of multi-model mean change in annual mean near-surface air temperature (°C) in 2081-2100 relative to 1995-2014 in SSP3-7.0\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n CMIP6 multi-model mean projected change in surface air temperature (2041–2060 and 2081-2100) from SSP1‑2.6 and SSP3‑7.0 relative to 1995–2014.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n The variable tas includes the map information as a function of latitude and longitude and has a dimension named panel, which includes the data for all panels a-d.\r\n\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP1-2.6 is based on Shared Socioeconomic Pathway SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP3-7.0 is based on Shared Socioeconomic Pathway SSP3 which is characterized by high challenges to both mitigation and adaptation and RCP7.0, a future pathway with a radiative forcing of 7.0 W/m2 in the year 2100.\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 4)\r\n - Link to the Supplementary Material for Chapter 4, which contains details on the input data used in Table 4.SM.1", "creationDate": "2023-02-03T12:46:24.652062", "lastUpdatedDate": "2023-02-03T12:46:24", "latestDataUpdateTime": "2023-02-15T16:36:03", "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, Chapter 4, Sixth Assessment Report, Working Group 1, Physical Science Basis, surface temperature", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-07T13:52:55", "doiPublishedTime": "2023-07-03T09:47:01.536248", "removedDataTime": null, "geographicExtent": { "ob_id": 3751, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 231, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39649, "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig19/v20230203", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7092435, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10979, "startTime": "2041-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4169, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-03" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39650, "uuid": "c43ee0357d1744a4bc64198ec9a29e02", "short_code": "comp", "title": "Caption for Figure 4.19 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Mid- and long-term change of annual mean surface temperature. Displayed are projected spatial patterns of multi-model mean change in annual mean near-surface air temperature (°C) in 2041–2060 and 2081–2100 relative to 1995–2014 for (top) SSP1-2.6 and (bottom) SSP3-7.0. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal-variability threshold (see Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.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": [ 46706, 46708, 46709, 52664, 52665, 62359, 62360 ], "vocabularyKeywords": [], "identifier_set": [ 12551 ], "observationcollection_set": [ { "ob_id": 32719, "uuid": "5b30b3c2146048388ac97e4278cb5128", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information.\r\n\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 4.12\r\n- data for Figure 4.13\r\n- data for Figure 4.19\r\n- data for Figure 4.22\r\n- data for Figure 4.23\r\n- data for Figure 4.24\r\n- data for Figure 4.25\r\n- data for Figure 4.26\r\n- data for Figure 4.31\r\n- data for Figure 4.32\r\n- data for Figure 4.41\r\n- data for Figure 4.42" } ], "responsiblepartyinfo_set": [ 193189, 193190, 193191, 193192, 193193, 193194, 193195, 193196 ], "onlineresource_set": [ 82966, 82964, 82965, 88603 ] }, { "ob_id": 39651, "uuid": "9527d9be07c243599f00af5ab945c7ed", "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.22 (v20230203)", "abstract": "Data for Figure 4.22 from Chapter 4 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 4.22 shows the projected long-term change of annual and zonal mean atmospheric temperature.\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 Lee, J.-Y., J. Marotzke, G. Bala, L. Cao, S. Corti, J.P. Dunne, F. Engelbrecht, E. Fischer, J.C. Fyfe, C. Jones, A. Maycock, J. Mutemi, O. Ndiaye, S. Panickal, and T. Zhou, 2021: Future Global Climate: Scenario-Based Projections and Near-Term Information. 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. 553–672, doi:10.1017/9781009157896.006.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for panel a in the file \r\n named Data_shown_in_figure_panel_a.nc and for panel b in the file named Data_shown_in_figure_panel_b.nc.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n CMIP6 multi-model mean projected change in air temperature (2081–2100) from SSP1‑2.6 and SSP3‑7.0 relative to 1995–2014.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file fig4_22a_ta.nc (panel a) includes the multi-model mean zonal mean temperature change as a function of latitude and pressure level for SSP1-2.6\r\n Data file fig4_22b_ta.nc (panel a) includes the multi-model mean zonal mean temperature change as a function of latitude and pressure level for SSP3-7.0\r\n\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP1-2.6 is based on Shared Socioeconomic Pathway SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP3-7.0 is based on Shared Socioeconomic Pathway SSP3 which is characterized by high challenges to both mitigation and adaptation and RCP7.0, a future pathway with a radiative forcing of 7.0 W/m2 in the year 2100.\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 4)\r\n - Link to the Supplementary Material for Chapter 4, which contains details on the input data used in Table 4.SM.1", "creationDate": "2023-02-03T13:27:39.948633", "lastUpdatedDate": "2023-02-03T13:27:39", "latestDataUpdateTime": "2023-02-15T16:37:17", "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, Chapter 4, Sixth Assessment Report, Working Group 1, Physical Science Basis, atmospheric temperature", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-07T11:19:20", "doiPublishedTime": "2023-07-03T09:51:36.240241", "removedDataTime": null, "geographicExtent": { "ob_id": 3752, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 232, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39652, "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig22/v20230203", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4197888, "numberOfFiles": 5, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10980, "startTime": "2081-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4170, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-03" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39653, "uuid": "16e53ff760d8460b9e4852e9dd829206", "short_code": "comp", "title": "Caption for Figure 4.22 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Long-term change of annual and zonal mean atmospheric temperature. Displayed are multi-model mean change in annual and zonal mean atmospheric temperature (°C) in 2081–2100 relative to 1995–2014 for (left) SSP1-2.6 and (right) SSP5-8.5. The number of models used is indicated in the top right of the maps. Diagonal lines indicate regions where less than 80% of the models agree on the sign of the change and no overlay where 80% or more of the models agree on the sign of the change. Further details on data sources and processing are available in the chapter data table (Table 4.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": [ 28766, 46706, 46708, 50418, 52664, 62360 ], "vocabularyKeywords": [], "identifier_set": [ 12552 ], "observationcollection_set": [ { "ob_id": 32719, "uuid": "5b30b3c2146048388ac97e4278cb5128", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information.\r\n\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 4.12\r\n- data for Figure 4.13\r\n- data for Figure 4.19\r\n- data for Figure 4.22\r\n- data for Figure 4.23\r\n- data for Figure 4.24\r\n- data for Figure 4.25\r\n- data for Figure 4.26\r\n- data for Figure 4.31\r\n- data for Figure 4.32\r\n- data for Figure 4.41\r\n- data for Figure 4.42" } ], "responsiblepartyinfo_set": [ 193199, 193200, 193201, 193202, 193203, 193204, 193205, 193206 ], "onlineresource_set": [ 82967, 82968, 82969, 88604, 94635 ] }, { "ob_id": 39654, "uuid": "34810c5e2d2047b487ade01830cac1f4", "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.23 (v20230203)", "abstract": "Data for Figure 4.23 from Chapter 4 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 4.23 shows the projected long-term changes in seasonal mean relative humidity.\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 Lee, J.-Y., J. Marotzke, G. Bala, L. Cao, S. Corti, J.P. Dunne, F. Engelbrecht, E. Fischer, J.C. Fyfe, C. Jones, A. Maycock, J. Mutemi, O. Ndiaye, S. Panickal, and T. Zhou, 2021: Future Global Climate: Scenario-Based Projections and Near-Term Information. 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. 553–672, doi:10.1017/9781009157896.006.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for all panels in one NetCDF file. \r\n\r\na) Global projected spatial patterns of multi-model mean change in DJF seasonal mean relative humidity in 2081-2100 relative to 1995-2014 in SSP1-2.6\r\n b) Global projected spatial patterns of multi-model mean change in DJF seasonal mean relative humidity in 2081-2100 relative to 1995-2014 in SSP3-7.0\r\n c) Global projected spatial patterns of multi-model mean change in JJA seasonal mean relative humidity in 2081-2100 relative to 1995-2014 in SSP1-2.6\r\n d) Global projected spatial patterns of multi-model mean change in JJA seasonal mean relative humidity in 2081-2100 relative to 1995-2014 in SSP3-7.0\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n CMIP6 multi-model mean projected change in DJF and JJA seasonal mean relative humidity (2081-2100) from SSP1‑2.6 and SSP3‑7.0 relative to 1995-2014.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n The variable hurs includes the map information as a function of latitude and longitude and has a dimension named panel, which includes the data for all panels a-d.\r\n\r\n\r\nDJF stands for December, January, February.\r\nJJA stands for June, July, August.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP1-2.6 is based on Shared Socioeconomic Pathway SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP3-7.0 is based on Shared Socioeconomic Pathway SSP3 which is characterized by high challenges to both mitigation and adaptation and RCP7.0, a future pathway with a radiative forcing of 7.0 W/m2 in the year 2100.\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 4)\r\n - Link to the Supplementary Material for Chapter 4, which contains details on the input data used in Table 4.SM.1", "creationDate": "2023-02-03T14:31:00.679055", "lastUpdatedDate": "2023-02-03T14:31:00", "latestDataUpdateTime": "2024-03-09T03:20:39", "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\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Chapter 4, Sixth Assessment Report, Working Group 1, Physical Science Basis, relative humidity", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-07T11:25:25", "doiPublishedTime": "2023-07-03T09:55:29.933183", "removedDataTime": null, "geographicExtent": { "ob_id": 3753, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 233, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39655, "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig23/v20230203", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5126107, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10981, "startTime": "2081-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4171, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-03" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39656, "uuid": "9ebdbf6975474b48a8e7c119d800ecf7", "short_code": "comp", "title": "Caption for Figure 4.23 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Long-term changes in seasonal mean relative humidity. Displayed are projected spatial patterns of multi-model mean change (%) in seasonal (top) December–January–February (DJF) and (bottom) June–July–August (JJA) mean near-surface relative humidity in 2081–2100 relative to 1995–2014, for (left) SSP1-2.6 and (right) SSP3-7.0. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.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": [ 46706, 46708, 52664, 52665, 62359, 62360, 63526 ], "vocabularyKeywords": [], "identifier_set": [ 12553 ], "observationcollection_set": [ { "ob_id": 32719, "uuid": "5b30b3c2146048388ac97e4278cb5128", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information.\r\n\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 4.12\r\n- data for Figure 4.13\r\n- data for Figure 4.19\r\n- data for Figure 4.22\r\n- data for Figure 4.23\r\n- data for Figure 4.24\r\n- data for Figure 4.25\r\n- data for Figure 4.26\r\n- data for Figure 4.31\r\n- data for Figure 4.32\r\n- data for Figure 4.41\r\n- data for Figure 4.42" } ], "responsiblepartyinfo_set": [ 193209, 193210, 193211, 193212, 193213, 193214, 193215, 193216 ], "onlineresource_set": [ 82970, 82971, 82972, 88605, 94636 ] }, { "ob_id": 39661, "uuid": "ebc32b4b3e3d4e1788bbdd66b6abb5de", "title": "Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA): Met Office Unified Model data and associated Cloudnet outputs (UM_CASIM-100_Cloudnet)", "abstract": "Met Office Unified Model single-site (Oden) output during MOCCHA. These model and observation data are used in McCusker et al. : Evaluating Arctic clouds modelled with the Unified Model and Integrated Forecasting System, Atmospheric Chemistry and Physics, 2023. \r\n\r\n Model data from the Met Office Unified Model are in directory 'um_model_data/'. Data are hourly data taken from grid box closest to ship location. Where the ship covers more than one grid box within an hour period, data are averaged from all grid boxes crossed. All data files are in a netCDF format, with one file per day. Rose suite ID: u-cc278. Model options include: \r\n Unified Model version - 11.3, \r\n CASIM microphysics + cloud scheme (i_cld_vn = 1). \r\n Double-moment cloud microphysics - droplet activation = Abdul-Razzak and Ghan (2000); ice nucleation = Cooper (1986). \r\n 3 modes of soluble aerosol, no insoluble aerosol. \r\n Accumulation mode soluble aerosol - num = 1.00e8 /m3, mass = 1.50e-9 kg/kg. \r\n Aitken and coarse modes = 0. \r\n No aerosol processing. \r\n Updated RHcrit profile used in Unified Model vn11.4. \r\n Uses sea ice options from the global model (alpham = 0.72, dtice = 2.0). \r\n U and V wind components interpolated on to common vertical grid. \r\n\r\n Model data from directory um_model_data/ are subsequently passed through the Cloudnet algorithm to produce calibrated model data that may be used for direct comparisons with observations. Cloudnet combines cloud radar, ceilometer, microwave radiometer, and radiosonde profiles averaged to a common grid at the cloud radar resolution to derive a set of retrieved cloud properties. The Cloudnet products are designed to be used for evaluation of weather forecast models as well as fundamental process studies of cloud. From a modelling perspective, Cloudnet converts liquid and ice mass mixing ratios to the respective cloud water contents for direct comparison with observations, as well as filtering ice water contents for values that would be unobservable by radar. Note that the latitude/longitude relevant for each date in question can be found in these Cloudnet files. \r\n\r\nIn directories:\r\n iwc-Z-T-metum-grid/ - data include ice water content and total ice water path for observations and model. \r\n lwc-scaled-metum-grid/ - data include cloud liquid water content and liquid water path for observations and model. \r\n cloud-fraction-metum-grid/ - data include cloud fractions by volume for observations and model.", "creationDate": "2023-02-06T11:11:45.966694", "lastUpdatedDate": "2023-02-06T11:11:45", "latestDataUpdateTime": "2023-02-09T11:30:21", "updateFrequency": "notPlanned", "dataLineage": "Model data were generated using the Met Office Unified Model (UM) at 1.5km horizontal resolution over a 500x500 domain centred on 86.625N, 30E. Data has been converted to netCDF by Gillian Young McCusker (G.Y.McCusker@leeds.ac.uk) using Python (Iris/netCDF4), then passed on to the CEDA for long-term archiving. Cloudnet data were generated by Jutta Vuellers (j.vuellers@leeds.ac.uk)", "removedDataReason": "", "keywords": "MOCCHA, Met Office, Unified Model, Cloudnet, Oden, cloud microphysics, aerosol", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-03-07T11:37:49", "doiPublishedTime": "2023-03-07T12:01:16.569499", "removedDataTime": null, "geographicExtent": { "ob_id": 3756, "bboxName": "", "eastBoundLongitude": 73.76, "westBoundLongitude": 3.98, "southBoundLatitude": 56.01, "northBoundLatitude": 89.9 }, "verticalExtent": null, "result_field": { "ob_id": 39662, "dataPath": "/badc/deposited2023/moccha_UM_CASIM-100_Cloudnet", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 23729452, "numberOfFiles": 126, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10984, "startTime": "2018-08-14T00:00:00", "endTime": "2018-09-14T00:00:00" }, "resultQuality": { "ob_id": 4174, "explanation": "Data quality were checked by the MOCCHA project data authors before supplying to CEDA. 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The expedition was centered around a 4-week long intensive observation period, where icebreaker Oden was moored to an ice flow and drifted passively with the ice. \r\n\r\nMOCCHA is a joint project of the Universities of Stockholm (Sweden), Michigan (US), Leeds (UK), Leipzig/TROPOS (Germany), Oldenburg (Germany), Dalhousie (Canada), Lund (Sweden), Gothenburg (Sweden), California, Irvine (US), Paul Scherrer Institute (Switzerland), College of Charleston (USA), Virginia Institute of Marine Science (USA), Environment and Climate Change Canada, Alfred Wegener Institute (Germany), Bigelow Laboratory for Ocean Sciences (USA) and University College London (UK)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62562, 62563, 62564, 62565, 62566, 62567, 62568, 62569, 62570, 62571, 62572, 62573, 62574, 62575, 62576, 62577, 62578, 62579, 62580 ], "vocabularyKeywords": [], "identifier_set": [ 12413 ], "observationcollection_set": [ { "ob_id": 29977, "uuid": "7f35130cb8c947bf9013594aec6b3043", "short_code": "coll", "title": "Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA)", "abstract": "This dataset collection contains data collected from the Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA) project. 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The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- Evaluation of CMIP6 DECK experiments with CNRM-CM6-1: Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P., Nabat, P., Roehrig, R., y Mélia, D. 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Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 47592, 50415, 50417, 50419, 50426, 50427, 50428, 50429, 50431, 50445, 50475, 50481, 50496, 50498, 50549, 50566, 50579, 50582, 50587, 50588, 50590, 50597, 50605, 50609, 50615, 52755, 53111, 54228, 54350, 62368, 62559, 62560, 62561, 64078, 64079, 64080, 66075, 66076, 66077, 66082, 71614, 71619, 71634, 74366, 74367, 82407 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 39704, "uuid": "201dac63fd1046e39299801dbf47dd3f", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the CNRM-CM 6.1 model at Météo France", "abstract": "The CNRM-CM 6.1 model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by the modelling team at Météo France.\r\n\r\nData produced using the Centre National de Recherches Météorologiques fully coupled atmosphere‐ocean general circulation model CNRM-CM 6.1. 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S., Séférian, R., Valcke, S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes, J., Douville, H., Ethé, C., Franchistéguy, L., Geoffroy, O., Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez-Gomez, E., Terray, L., and Waldman, R., J. Adv. Model Earth Sy., 11, 2177–2213,\r\nhttps://doi.org/10.1029/2019MS001683, 2019" } ], "responsiblepartyinfo_set": [ 193309, 193310, 193311, 193312, 193313, 193314, 193308, 193307 ], "onlineresource_set": [ 82981, 82982, 83082 ] }, { "ob_id": 39697, "uuid": "bbe6dfaa0c9a4dfb8a0e7f131cc4d0b4", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): free data produced by the CNRM-CM 6.1 model at Météo France", "abstract": "This dataset contains model data for SNAPSI experiment 'free' produced by the seasonal prediction research team at Météo-France. It is generated with the coupled climate model CNRM-CM 6.1. \r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe free experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- Evaluation of CMIP6 DECK experiments with CNRM-CM6-1: Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P., Nabat, P., Roehrig, R., y Mélia, D. S., Séférian, R., Valcke, S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes, J., Douville, H., Ethé, C., Franchistéguy, L., Geoffroy, O., Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez-Gomez, E., Terray, L., and Waldman, R., J. Adv. Model Earth Sy., 11, 2177–2213, https://doi.org/10.1029/2019MS001683, 2019", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2023-06-05T12:03:07", "updateFrequency": "", "dataLineage": "Data were generated using the Centre National de Recherches Météorologiques fully coupled atmosphere‐ocean general circulation model CNRM-CM 6.1 at TL359 horizontal resolution. 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Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 47592, 50415, 50417, 50419, 50496, 50498, 50566, 62368 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 39704, "uuid": "201dac63fd1046e39299801dbf47dd3f", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the CNRM-CM 6.1 model at Météo France", "abstract": "The CNRM-CM 6.1 model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by the modelling team at Météo France.\r\n\r\nData produced using the Centre National de Recherches Météorologiques fully coupled atmosphere‐ocean general circulation model CNRM-CM 6.1. 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Data was output as NetCDF files by the XIOS tool, then passed on to CEDA for archiving.", "removedDataReason": "", "keywords": "nudged, CNRM-CM 6.1, Météo France, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1x1 degree", "status": "ongoing", "dataPublishedTime": "2024-09-25T13:25:40", "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": 39701, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/Meteo-France/CNRM-CM61/nudged", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3198164952608, "numberOfFiles": 11401, "fileFormat": "Data are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39696, "uuid": "2a45b4e5a3674115899120f56747eb0c", "short_code": "comp", "title": "CNRM-CM 6.1 atmosphere-ocean general circulation model deployed on Météo-France computing facilities.", "abstract": "CNRM-CM 6.1 atmosphere-ocean general circulation model deployed on Météo-France computing facilities. The model consists of the ARPEGE-Climat 6.3 atmospheric model, the NEMO 3.6 ocean model, the GELATO 6 sea ice model and the ISBA-CTRIP land surface model." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 47592, 50415, 50417, 50419, 50426, 50427, 50428, 50429, 50431, 50445, 50475, 50481, 50496, 50498, 50549, 50566, 50579, 50582, 50587, 50588, 50590, 50597, 50605, 50609, 50615, 52755, 53111, 54228, 54350, 62368, 62559, 62560, 62561, 64078, 64079, 64080, 66075, 66076, 66077, 66082, 71614, 71619, 71634, 74366, 74367, 82407 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 39704, "uuid": "201dac63fd1046e39299801dbf47dd3f", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the CNRM-CM 6.1 model at Météo France", "abstract": "The CNRM-CM 6.1 model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by the modelling team at Météo France.\r\n\r\nData produced using the Centre National de Recherches Météorologiques fully coupled atmosphere‐ocean general circulation model CNRM-CM 6.1. The model consists of the ARPEGE-Climat 6.3 atmospheric model, the NEMO 3.6 ocean model, the GELATO 6 sea ice model and the ISBA-CTRIP land surface model.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- Evaluation of CMIP6 DECK experiments with CNRM-CM6-1: Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P., Nabat, P., Roehrig, R., y Mélia, D. S., Séférian, R., Valcke, S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes, J., Douville, H., Ethé, C., Franchistéguy, L., Geoffroy, O., Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez-Gomez, E., Terray, L., and Waldman, R., J. Adv. Model Earth Sy., 11, 2177–2213,\r\nhttps://doi.org/10.1029/2019MS001683, 2019" } ], "responsiblepartyinfo_set": [ 193328, 193329, 193330, 193324, 193325, 193326, 193327, 193331 ], "onlineresource_set": [ 82986, 82985, 83083 ] }, { "ob_id": 39702, "uuid": "8b1801552b7e40b5acb3bd12d0b4203a", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): nudged-full data produced by the CNRM-CM 6.1 model at Météo France", "abstract": "This dataset contains model data for SNAPSI experiment 'nudged-full' produced by the seasonal prediction research team at Météo-France. It is generated with the coupled climate model CNRM-CM 6.1. \r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe nudged-full experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, stratospheric temperatures and horizontal winds are nudged towards the observed time-evolving state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- Evaluation of CMIP6 DECK experiments with CNRM-CM6-1: Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P., Nabat, P., Roehrig, R., y Mélia, D. S., Séférian, R., Valcke, S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes, J., Douville, H., Ethé, C., Franchistéguy, L., Geoffroy, O., Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez-Gomez, E., Terray, L., and Waldman, R., J. Adv. Model Earth Sy., 11, 2177–2213, https://doi.org/10.1029/2019MS001683, 2019", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2023-06-05T12:25:14", "updateFrequency": "", "dataLineage": "Data were generated using the Centre National de Recherches Météorologiques fully coupled atmosphere‐ocean general circulation model CNRM-CM 6.1 at TL359 horizontal resolution. Data was output as NetCDF files by the XIOS tool, then passed on to CEDA for archiving.", "removedDataReason": "", "keywords": "nudged-full, CNRM-CM 6.1, Météo France, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1x1 degree", "status": "ongoing", "dataPublishedTime": "2024-09-25T13:28:30", "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": 39703, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/Meteo-France/CNRM-CM61/nudged-full", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1599082500781, "numberOfFiles": 5701, "fileFormat": "Data are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39696, "uuid": "2a45b4e5a3674115899120f56747eb0c", "short_code": "comp", "title": "CNRM-CM 6.1 atmosphere-ocean general circulation model deployed on Météo-France computing facilities.", "abstract": "CNRM-CM 6.1 atmosphere-ocean general circulation model deployed on Météo-France computing facilities. The model consists of the ARPEGE-Climat 6.3 atmospheric model, the NEMO 3.6 ocean model, the GELATO 6 sea ice model and the ISBA-CTRIP land surface model." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 47592, 50415, 50417, 50419, 50426, 50427, 50428, 50429, 50431, 50445, 50475, 50481, 50496, 50498, 50549, 50566, 50579, 50582, 50587, 50588, 50590, 50597, 50605, 50609, 50615, 52755, 53111, 54228, 54350, 62368, 62559, 62560, 62561, 64078, 64079, 64080, 66075, 66076, 66077, 66082, 71614, 71619, 71634, 74366, 74367, 82407 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 39704, "uuid": "201dac63fd1046e39299801dbf47dd3f", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the CNRM-CM 6.1 model at Météo France", "abstract": "The CNRM-CM 6.1 model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by the modelling team at Météo France.\r\n\r\nData produced using the Centre National de Recherches Météorologiques fully coupled atmosphere‐ocean general circulation model CNRM-CM 6.1. The model consists of the ARPEGE-Climat 6.3 atmospheric model, the NEMO 3.6 ocean model, the GELATO 6 sea ice model and the ISBA-CTRIP land surface model.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- Evaluation of CMIP6 DECK experiments with CNRM-CM6-1: Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P., Nabat, P., Roehrig, R., y Mélia, D. S., Séférian, R., Valcke, S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes, J., Douville, H., Ethé, C., Franchistéguy, L., Geoffroy, O., Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez-Gomez, E., Terray, L., and Waldman, R., J. Adv. Model Earth Sy., 11, 2177–2213,\r\nhttps://doi.org/10.1029/2019MS001683, 2019" } ], "responsiblepartyinfo_set": [ 193332, 193333, 193334, 193335, 193336, 193337, 193338, 193339 ], "onlineresource_set": [ 82987, 82988, 83085 ] }, { "ob_id": 39710, "uuid": "f154fc6376da4d188a5f460816f064d0", "title": "EQUIPT4Risk: GEOS-Chem Chemical Transport Model Output", "abstract": "EQUIPT4Risk GEOS-Chem model simulations as part of EQUIPT4Risk WP1 (Model simulations of the concentration of pollutants over the UK with and without additional emissions associated with the new hydrocarbon sources). A nested-grid version of the GEOS-Chem chemistry transport model (v12.6.1) at 0.5°x0.625° horizontal resolution over the domain 15W-5E, 45N-65N, with additional emissions at two Shale extraction sites, was used to evaluate the impact of additional emissions of nitrogen oxides (NOx) on surface air quality over the UK for 2019. \r\n\r\nThis dataset includes 6 files corresponding to hourly GEOS-Chem model output of surface concentrations for either gaseous (“SpeciesConc”,) or aerosol (“AerosolMass”) species for the period 2019-01-01T00:00 to 2020-01-01T00:00.\r\n\r\nOutput from three model runs are provided:\r\n (1) “baseline”: standard model output, replacing default CEDS anthropogenic emissions over Europe with EMEP 2017 emissions \r\n(2) “PNR_fracking”: the same emissions as (1), with additional NOx (as NO) emissions of 29 tonnes per year at the Preston New Road (PNR, (53.787282,-2.951474)) fracking site. \r\n(3) “BGR_fracking”: the same emissions as (1), with additional NOx (as NO) emissions of 29 tonnes per year at the British Geological Survey Kirby Misperton (BGS, (54.204185,0.892848)) fracking site.", "creationDate": "2023-02-09T16:38:34.065333", "lastUpdatedDate": "2023-02-09T16:38:34", "latestDataUpdateTime": "2023-02-09T16:38:34", "updateFrequency": "notPlanned", "dataLineage": "Data were generated using the GEOS-Chem Chemical Transport Model v12.6.1 (CTM) at 0.5°x0.625° horizontal resolution over the domain 15W-5E, 45N-65N. Model output provided by L. Fakes in February 2023.", "removedDataReason": "", "keywords": "EQUIPT4Risk GEOS-Chem, EQUIPT4Risk, WP1, pollutants", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3760, "bboxName": "", "eastBoundLongitude": 5.0, "westBoundLongitude": 45.0, "southBoundLatitude": -15.0, "northBoundLatitude": 65.0 }, "verticalExtent": null, "result_field": { "ob_id": 39711, "dataPath": "/badc/deposited2023/EQUIPT4RISK", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 19686626200, "numberOfFiles": 7, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10991, "startTime": "2019-01-01T00:00:00", "endTime": "2020-01-01T00:00:00" }, "resultQuality": { "ob_id": 4178, "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": "2023-02-09" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39713, "uuid": "7e7faf2001424594816ba1df878b712a", "short_code": "comp", "title": "GEOS-Chem chemical transport model (CTM) deployed on University of York Viking research computing cluster\n", "abstract": "GEOS-Chem chemical transport model (CTM) deployed on University of York Viking research computing cluster\n" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2521, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 39712, "uuid": "b958c9b400a447058b2cb119fae8adb9", "short_code": "proj", "title": "EQUIPT4RISK - Evaluation, Quantification and Identification of Pathways and Targets for the assessment of Shale Gas RISK", "abstract": "The project will identify, characterise and parameterise the multiple direct and indirect pathways within the shallow subsurface and the atmosphere (and across interfaces), which link sources of contamination and hazards associated with shale gas (SG) operations to human and sensitive environmental receptors, and surface infrastructure.\r\nFor each component of the domain (Water, Air, Solid Earth), Source-Pathway-Receptor combinations will be examined and then integrated in a probabilistic risk assessment (PRA) framework for quantification of the risks to humans, infrastructure and the environment. A key aspect of the study will be to understand how the risk profile evolves over the life cycle of shale gas operations - from single site to multiple operations across an area.\r\nThe focus will be on investigating the processes that affect and influence the near-surface (i.e. <400 m bgl) Source-Pathway-Receptor combinations (and their interactions). Processes that will be investigated include; hydro-geochemical controls on contaminant behaviour and transport, climatological and chemical controls on air quality, and attenuation of ground motion e.g. from seismic events. The on-going environmental monitoring at the shale gas sites in North Yorkshire and Lancashire, along with their detailed conceptual and geological models will provide rich and continuous quality-assured high precision datasets and information. The sites represent different types of shale gas operation in different geo-environmental settings. Information from these sites, along with other non-UK sites where project partners have worked, will be a starting point, with additional data from UKGEO supplementing the evidence base. Analysis of these data will then support the design of experiments at UKGEO and other sites to improve confidence in process understanding and test different aspects of the risk model under controlled conditions, quantifying properties and better characterising/quantifying uncertainty through evaluation of the sensitivity of environmental and human receptors. \r\nThe experiments will also consider non-shale gas-related activities such as analogues and crowd-sourcing of information on ground movement. Attention will be given to identifying the key indicator parameters and techniques required to detect environmental changes arising from shale gas activity in both the short-term, providing early warning, and the long-term. This will include new technology tested as part of the experiments and case studies that will allow differentiation of stimulated reservoir source fluids and other contaminants from extraneous natural and anthropogenic sources in measured groundwater, soil gas or atmospheric samples. Improved measurement, monitoring and quantification will be critical to effectively evaluating and managing the risks arising from shale gas development and supporting the integrated risk model developed as an outcome of this project." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 52945, 52946, 57351, 57352, 63361, 63362, 63363, 63364, 63365, 63366, 63367, 63368, 63369, 63370, 63371, 63372, 63373, 63374, 63375, 63376, 63377, 63378, 63379, 63380, 63381, 63382, 63383, 63384, 63385, 63386, 63387, 63388, 63389, 63390, 63391, 63392, 63393, 63394, 63395, 63396, 63397, 63398, 63399, 63400, 63401, 63402, 63403, 63404, 63405, 63406, 63407, 63408, 63409, 63410, 63411, 63412, 63413, 63414, 63415, 63416, 63417, 63418, 63419, 63420, 63421, 63422, 63423, 63424, 63425, 63426, 63427, 63428, 63429, 63430, 63431, 63432, 63433, 63434, 63435, 63436, 63437, 63438, 63439, 63440, 63441, 63442, 63443, 63444, 63445, 63446, 63447, 63448, 63449, 63450, 63451, 63452, 63453, 63454, 63455, 63456, 63457, 63458, 63459, 63460, 63461, 63462, 63463, 63464, 63465, 63466, 63467, 63468, 63469, 63470, 63471, 63472, 63473, 63474, 63475, 63476, 63477, 63478, 63479, 63480, 63481, 63482, 63483, 63484, 63485, 63486, 63487, 63488, 63489, 63490, 63491, 63492, 63493, 63494, 63495, 63496, 63497, 63498, 63499, 63500, 63501, 63502, 63503, 63504, 63505, 63506, 63507, 63508, 63509, 63510, 63511, 63512, 63513, 63514, 63515, 63516, 63517, 63518, 63519, 63520, 63521, 63522, 63523, 63524, 63525 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 39714, "uuid": "0c05980e9c2e4809887ff2c2dcf99851", "short_code": "coll", "title": "EQUIPT4RISK - Evaluation, Quantification and Identification of Pathways and Targets for the assessment of Shale Gas RISK", "abstract": "This dataset collection contains data from the EQUIPT4RISK project which aims to identify, characterise and parameterise the multiple direct and indirect pathways within the shallow subsurface and the atmosphere (and across interfaces), which link sources of contamination and hazards associated with shale gas (SG) operations to human and sensitive environmental receptors, and surface infrastructure.\r\n\r\nThis collection contains model simulations of the concentration of pollutants over the UK with and without additional emissions associated with the new hydrocarbon sources." } ], "responsiblepartyinfo_set": [ 193417, 193418, 193419, 193420, 193421, 193422, 193427, 193428, 193429 ], "onlineresource_set": [ 83003 ] }, { "ob_id": 39715, "uuid": "c4031dd3227c4bceb6eae480d2a47a0c", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 6.7 (v20230210)", "abstract": "Input Data for Figure 6.7 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\n\r\nFigure 6.7 shows distribution of PM2.5 composition mass concentration (in μg m-3) for the major PM2.5 aerosol components.\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 Szopa, 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 9 panels with input data provided for all panels in subdirectories named Asia, Europe, North_America, SPARTAN and stations.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains monthly averaged major PM2.5 aerosol component (sulphate, nitrate, ammonium, sodium, chloride, organic carbon and elemental carbon) measurements for the following regions:\r\n \r\n - Latin American and Caribbean\r\n - North America (rural)\r\n - North America (urban)\r\n - Europe\r\n - Eurasia\r\n - Eastern Asia\r\n - Southern Asia\r\n - South-East Asia and Developing Pacific\r\n - Asia-Pacific Developed\r\n\r\n\r\nAdditionally, input file for separating the world in individual regions following the IPCC Sixth Assessment Report Working Group III (AR6 WGIII) is provided as AR6WG3_Atlas_IntermedRegions_1.0x1.0.nc\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Instructions in how to relate the input data with the figure in the 'Notes on reproducing the figure from the provided data' field.\r\n\r\nPM2.5 stands for particulate matter in the air that are 2.5 micrometers or less in diameter.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The data is used as input of the code (violin_regions.py and worldmap_AR6_nocoasts.py). \r\n\r\nviolin_regions.py: Python code that creates the regional violin subplots. The user needs to run the code for each subregion separately by activating the lines 6-17 accordingly.\r\n\r\nworldmap_AR6_nocoasts.py: Python code that creates the world map in the center of the figure, including the location of the observational sites used.\r\n\r\nEach of these scripts work independently. The nine individual plots produced by violin_regions.py can be collated with the map created by worldmap_AR6_nocoasts.py to produce the figure 6.7. A link to the code archived on Zenodo are provided in the Related Documents section of this catalogue record.\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 code archived on Zenodo", "creationDate": "2023-02-10T12:29:58.249734", "lastUpdatedDate": "2023-02-10T12:29:58", "latestDataUpdateTime": "2024-03-09T02:28:51", "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, PM2.5, aerosol components, Chapter 6, Figure 6.7, short-lived climate forcers", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-16T15:16:53", "doiPublishedTime": "2023-07-03T14:19:39.858119", "removedDataTime": null, "geographicExtent": { "ob_id": 3761, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 234, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39716, "dataPath": "/badc/ar6_wg1/data/ch_06/inputdata_ch6_fig07/v20230210", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 42605878, "numberOfFiles": 42, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10992, "startTime": "2000-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 4179, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-10" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39717, "uuid": "8fa6daf7618b48dcb7015f6209c507e6", "short_code": "comp", "title": "Caption for Figure 6.7 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Distribution of PM2.5 composition mass concentration (in μg m–3) for the major PM2.5 aerosol components. Those aerosol components are sulphate, nitrate, ammonium, sodium, chloride, organic carbon and elemental carbon. The central world map depicts the intermediate-level regional breakdown of observations (10 regions) following the IPCC Sixth Assessment Report Working Group III (AR6 WGIII). Monthly averaged PM2.5 aerosol component measurements are from: (i) the Environmental Protection Agency (EPA) network which include 211 monitor sites primarily in urban areas of North America during 2000–2018 (Solomon et al., 2014), (ii) the Interagency Monitoring of Protected Visual Environments (IMPROVE) network during 2000–2018 over 198 monitoring sites representative of the regional haze conditions over North America, (iii) the European Monitoring and Evaluation Programme (EMEP) network over 70 monitoring in Europe and (eastern) Eurasia during 2000–2018, (iv) the Acid Deposition Monitoring Network in Eastern Asia (EANET) network with 39 (18 remote, 10 rural, 11 urban) sites in Eurasia, Eastern Asia, South East Asia and Developing Pacific, and Asia-Pacific Developed during 2001–2017, (v) the global Surface Particulate Matter Network (SPARTAN) during 2013–2019 with sites primarily in highly populated regions around the world (i.e., North America, Latin America and Caribbean, Africa, Middle East, Southern Asia, Eastern Asia, South East Asia and Developing Pacific; Snider et al., 2015, 2016), and (vii) individual observational field campaign averages over Latin America and Caribbean, Africa, Europe, Eastern Asia, and Asia-Pacific Developed (Celis et al., 2004; Feng et al., 2006; Bourotte et al., 2007; Fuzzi et al., 2007; Mariani and de Mello, 2007; Molina et al., 2007, 2010; Favez et al., 2008; Mkoma, 2008; Aggarwal and Kawamura, 2009; Mkoma et al., 2009; de Souza et al., 2010; Li et al., 2010; Martin et al., 2010; Radhi et al., 2010; Weinstein et al., 2010; Batmunkh et al., 2011; Gioda et al., 2011; Pathak et al., 2011; F. Zhang et al., 2012; Cho and Park, 2013; Zhao et al., 2013; Wang et al., 2019; Kuzu et al., 2020). Further details on data sources and processing are available in the chapter data table (Table 6.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": [ 62550, 62551, 62552, 62553, 62554, 62555, 62556, 62557, 62558 ], "vocabularyKeywords": [], "identifier_set": [ 12568 ], "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": [ 193438, 193439, 193440, 193441, 193442, 193443, 193444, 193445 ], "onlineresource_set": [ 83004, 83005, 83006, 83312 ] }, { "ob_id": 39718, "uuid": "dc11fff6fda04b1d82e317132b93a3bf", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 6.8 (v20230213)", "abstract": "Input Data for Figure 6.8 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\n\r\nFigure 6.8 depicts the time evolution of changes in global mean aerosol optical depth (AOD) at 550 nm\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 Szopa, 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 List of data provided\r\n ---------------------------------------------------\r\n This dataset contains netcdf files of 3-dimensional (time,lat,lon) aerosol optical depth (od550aer) time-series from 1850 to 2014 from the historical simulations performed by 16 CMIP6 models. Multiple ensemble members are included for some models.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Instructions in how to relate the input data with the figure in the 'Notes on reproducing the figure from the provided data' field.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n This data file should be used in conjunction with the code Fig6.8_trend_plot_AOD_rolling_10change_volcanic_mask.py \r\nThis code creates the AOD timeseries (1850-2014) using the ensemble mean of individual CMIP6 models output in AOD_trend.png and is linked in the Related Documents section of this catalogue record.\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 code for the figure, archived on Zenodo.", "creationDate": "2023-02-13T09:34:34.632729", "lastUpdatedDate": "2023-02-13T09:34:34", "latestDataUpdateTime": "2024-03-09T02:29:08", "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\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, aerosol optical depth, AOD, historical simulations, Chapter 6, Figure 6.8, short-lived climate forcers", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-16T15:26:17", "doiPublishedTime": "2023-07-03T14:25:59.989912", "removedDataTime": null, "geographicExtent": { "ob_id": 3762, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 235, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39719, "dataPath": "/badc/ar6_wg1/data/ch_06/inputdata_ch6_fig08/v20230213", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1581196, "numberOfFiles": 172, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10993, "startTime": "1850-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 4180, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39720, "uuid": "31584495b6b44402a6a95449bc324ab3", "short_code": "comp", "title": "Caption for Figure 6.8 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 changes in global mean aerosol optical depth (AOD) at 550 nm. The year of reference is 1850. Data shown from individual Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulations. Each time series corresponds to the ensemble mean of realizations done by each model. Simulation results from years including major volcanic eruptions (e.g., Novarupta, 1912; Pinatubo, 1991), are excluded from the analysis for models encompassing the contribution of stratospheric volcanic aerosols to total AOD. Further details on data sources and processing are available in the chapter data table (Table 6.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, 50559, 50561, 62501, 62548, 71632 ], "vocabularyKeywords": [], "identifier_set": [ 12569 ], "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": [ 193449, 193450, 193451, 193452, 193453, 193454, 193455, 193456 ], "onlineresource_set": [ 83009, 83316, 83007, 83008 ] }, { "ob_id": 39721, "uuid": "5ed073b87dbc45d6a66d7c704caef01d", "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.24 (v20230213)", "abstract": "Data for Figure 4.24 from Chapter 4 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 4.24 shows the projected long-term changes in seasonal mean precipitation.\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 Lee, J.-Y., J. Marotzke, G. Bala, L. Cao, S. Corti, J.P. Dunne, F. Engelbrecht, E. Fischer, J.C. Fyfe, C. Jones, A. Maycock, J. Mutemi, O. Ndiaye, S. Panickal, and T. Zhou, 2021: Future Global Climate: Scenario-Based Projections and Near-Term Information. 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. 553–672, doi:10.1017/9781009157896.006.\r\n\r\n\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for all panels in one NetCDF file.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n a) Global projected spatial patterns of multi-model mean change in DJF seasonal mean precipitation in 2081-2100 relative to 1995-2014 in SSP1-2.6 b) Global projected spatial patterns of multi-model mean change in DJF seasonal mean precipitation in 2081-2100 relative to 1995-2014 in SSP3-7.0 c) Global projected spatial patterns of multi-model mean change in JJA seasonal mean precipitation in 2081-2100 relative to 1995-2014 in SSP1-2.6 d) Global projected spatial patterns of multi-model mean change in JJA seasonal mean precipitation in 2081-2100 relative to 1995-2014 in SSP3-7.0\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n The variable pr includes the map information as a function of latitude and longitude and has a dimension named panel, which includes the data for all panels a-d.\r\n\r\n\r\n\r\nDJF stands for December, January, February.\r\nJJA stands for June, July, August.\r\nSSP1-2.6 is based on Shared Socioeconomic Pathway SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP3-7.0 is based on Shared Socioeconomic Pathway SSP3 which is characterized by high challenges to both mitigation and adaptation and RCP7.0, a future pathway with a radiative forcing of 7.0 W/m2 in the year 2100.\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 4)\r\n - Link to the Supplementary Material for Chapter 4, which contains details on the input data used in Table 4.SM.1", "creationDate": "2023-02-13T10:10:47.730412", "lastUpdatedDate": "2023-02-13T10:10:47", "latestDataUpdateTime": "2024-03-09T02:28:59", "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, Chapter 4, Sixth Assessment Report, Working Group 1, Physical Science Basis, precipitation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-07T11:30:49", "doiPublishedTime": "2023-07-03T10:00:12.061395", "removedDataTime": null, "geographicExtent": { "ob_id": 3763, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 236, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39722, "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig24/v20230213", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5684527, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10994, "startTime": "2081-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4181, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39723, "uuid": "4aaea88dbbc8476fb970c44caca89908", "short_code": "comp", "title": "Caption for Figure 4.24 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Long-term change of seasonal mean precipitation. Displayed are projected spatial patterns of multi-model mean change (%) in (top) December–January–February (DJF) and (bottom) June–July–August (JJA) mean precipitation in 2081–2100 relative to 1995–2014, for (left) SSP1-2.6 and (right) SSP3-7.0. The number of models used is indicated in the top right of the maps. No map overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.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": [ 46706, 46708, 52664, 52665, 62359, 62360, 62361 ], "vocabularyKeywords": [], "identifier_set": [ 12554 ], "observationcollection_set": [ { "ob_id": 32719, "uuid": "5b30b3c2146048388ac97e4278cb5128", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information.\r\n\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 4.12\r\n- data for Figure 4.13\r\n- data for Figure 4.19\r\n- data for Figure 4.22\r\n- data for Figure 4.23\r\n- data for Figure 4.24\r\n- data for Figure 4.25\r\n- data for Figure 4.26\r\n- data for Figure 4.31\r\n- data for Figure 4.32\r\n- data for Figure 4.41\r\n- data for Figure 4.42" } ], "responsiblepartyinfo_set": [ 193463, 193464, 193465, 193466, 193459, 193460, 193461, 193462 ], "onlineresource_set": [ 83012, 83010, 83011, 88606, 94639 ] }, { "ob_id": 39727, "uuid": "b1d79f8dea6244ea943d49040f0f9f6d", "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.25 (v20230213)", "abstract": "Data for Figure 4.25 from Chapter 4 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 4.25 shows the projected long-term changes in seasonal mean sea level pressure.\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 Lee, J.-Y., J. Marotzke, G. Bala, L. Cao, S. Corti, J.P. Dunne, F. Engelbrecht, E. Fischer, J.C. Fyfe, C. Jones, A. Maycock, J. Mutemi, O. Ndiaye, S. Panickal, and T. Zhou, 2021: Future Global Climate: Scenario-Based Projections and Near-Term Information. 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. 553–672, doi:10.1017/9781009157896.006.\r\n\r\n\r\n ---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for all panels in one NetCDF file.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n a) Global projected spatial patterns of multi-model mean change in DJF seasonal mean sea level pressure in 2081-2100 relative to 1995-2014 in SSP1‑2.6\r\n b) Global projected spatial patterns of multi-model mean change in DJF seasonal mean sea level pressure in 2081-2100 relative to 1995-2014 in SSP3‑7.0\r\n c) Global projected spatial patterns of multi-model mean change in JJA seasonal mean sea level pressure in 2081-2100 relative to 1995-2014 in SSP1‑2.6\r\n d) Global projected spatial patterns of multi-model mean change in JJA seasonal mean sea level pressure in 2081-2100 relative to 1995-2014 in SSP3‑7.0\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n The variable psl includes the map information as a function of latitude and longitude and has a dimension named panel, which includes the data for all panels a-d.\r\n\r\n\r\nDJF stands for December, January, February.\r\nJJA stands for June, July, August.\r\nSSP1-2.6 is based on Shared Socioeconomic Pathway SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP3-7.0 is based on Shared Socioeconomic Pathway SSP3 which is characterized by high challenges to both mitigation and adaptation and RCP7.0, a future pathway with a radiative forcing of 7.0 W/m2 in the year 2100.\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 4)\r\n - Link to the Supplementary Material for Chapter 4, which contains details on the input data used in Table 4.SM.1", "creationDate": "2023-02-13T10:22:35.317320", "lastUpdatedDate": "2023-02-13T10:22:35", "latestDataUpdateTime": "2023-02-15T16:39:07", "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, Chapter 4, Sixth Assessment Report, Working Group 1, Physical Science Basis, sea level pressure", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-07T11:33:26", "doiPublishedTime": "2023-07-03T10:07:40.466918", "removedDataTime": null, "geographicExtent": { "ob_id": 3765, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 238, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39728, "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig25/v20230213", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5607551, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10996, "startTime": "2081-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4183, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39729, "uuid": "2473deaec7c842689b58f851c5ee93de", "short_code": "comp", "title": "Caption for Figure 4.25 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Long-term change of seasonal-mean sea level pressure. Displayed are projected spatial patterns of multi-model mean change in (top) December–January–February (DJF) and (bottom) June–July–August (JJA) mean sea level pressure (hPa) in 2081–2100 relative to 1995–2014, for (left) SSP1-2.6 and (right) SSP3-7.0. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.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": [ 46706, 46708, 52664, 52665, 62359, 62360, 63360 ], "vocabularyKeywords": [], "identifier_set": [ 12555 ], "observationcollection_set": [ { "ob_id": 32719, "uuid": "5b30b3c2146048388ac97e4278cb5128", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information.\r\n\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 4.12\r\n- data for Figure 4.13\r\n- data for Figure 4.19\r\n- data for Figure 4.22\r\n- data for Figure 4.23\r\n- data for Figure 4.24\r\n- data for Figure 4.25\r\n- data for Figure 4.26\r\n- data for Figure 4.31\r\n- data for Figure 4.32\r\n- data for Figure 4.41\r\n- data for Figure 4.42" } ], "responsiblepartyinfo_set": [ 193479, 193480, 193481, 193482, 193483, 193484, 193485, 193486 ], "onlineresource_set": [ 83015, 83013, 83014, 88607 ] }, { "ob_id": 39730, "uuid": "e397fe6f20024295b095e2e3ca1e9f04", "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.41 (v20230213)", "abstract": "Data for Figure 4.41 from Chapter 4 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 4.41 shows high-warming storylines for changes in annual mean temperature.\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 Lee, J.-Y., J. Marotzke, G. Bala, L. Cao, S. Corti, J.P. Dunne, F. Engelbrecht, E. Fischer, J.C. Fyfe, C. Jones, A. Maycock, J. Mutemi, O. Ndiaye, S. Panickal, and T. Zhou, 2021: Future Global Climate: Scenario-Based Projections and Near-Term Information. 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. 553–672, doi:10.1017/9781009157896.006.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has eight panels, with data provided for all panels in two files one including data for panels a, c, e, g and the other for panels b, d, f, h.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains temperature change patterns for the multi model means and different low-likelihood high warming storylines.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file Data_shown_in_figure_panels_aceg.nc includes the temperature change pattern for the four panels as different variables called panelA, panelC, panelE and panel G, respectively.\r\n Data file Data_shown_in_figure_panels_bdfh.nc includes the temperature change pattern for the four panels as different variables called panelB, panelD, panelF and panel H, respectively.\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 4)\r\n - Link to the Supplementary Material for Chapter 4, which contains details on the input data used in Table 4.SM.1", "creationDate": "2023-02-13T10:30:08.112371", "lastUpdatedDate": "2023-02-13T10:30:08", "latestDataUpdateTime": "2024-03-09T02:28:57", "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, Chapter 4, Sixth Assessment Report, Working Group 1, Physical Science Basis, temperature", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-07T12:03:00", "doiPublishedTime": "2023-07-03T10:25:52.871081", "removedDataTime": null, "geographicExtent": { "ob_id": 3766, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 239, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39731, "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig41/v20230213", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2088562, "numberOfFiles": 5, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10997, "startTime": "2081-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4184, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39732, "uuid": "02fc2f12f737468eab597e4164b67f84", "short_code": "comp", "title": "Caption for Figure 4.41 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "High-warming storylines for changes in annual mean temperature. (a, b) Changes in 2081–2100 relative to 1995–2014 consistent with the assessed best global surface air temperature (GSAT) estimate (0.9°C and 3.5°C relative to 1995–2014 for SSP1-2.6 and SSP5-8.5, respectively). The CMIP6 multi-model mean is linearly pattern-scaled to the best GSAT estimate. (c–h) Annual mean warming above the best estimate, relative to panels (a) and (b) respectively; note the different colour bar in a high and very high-warming storyline for 2081–2100. (c, d) Multi-model mean warming pattern scaled to very high GSAT level corresponding to the upper bound of the assessed very likely range (4.8°C for SSP5-8.5 and 1.5°C for SSP1-2.6; see Section 4.3.4). (e, f) Average of five models with high GSAT warming nearest to the upper estimate of the very likely range (CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-1-HR, EC-Earth3 for SSP1-2.6 and ACCESS-CM2, CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-1-HRfor SSP5-8.5); (g, h) Average of four and five models, respectively (ACCESS-CM2, HadGEM3-GC31-LL, HadGEM3-GC31-MM, UKESM1-0-LL for SSP1-2.6 and CanESM5, CanESM5-CanOE, HadGEM3-GC31-LL: HadGEM3-GC31-MM, UKESM1-0-LL for SSP5-8.5) projecting very high GSAT warming exceeding the very likely range. Further details on data sources and processing are available in the chapter data table (Table 4.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": [ 52664, 52665, 62547 ], "vocabularyKeywords": [], "identifier_set": [ 12559 ], "observationcollection_set": [ { "ob_id": 32719, "uuid": "5b30b3c2146048388ac97e4278cb5128", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information.\r\n\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 4.12\r\n- data for Figure 4.13\r\n- data for Figure 4.19\r\n- data for Figure 4.22\r\n- data for Figure 4.23\r\n- data for Figure 4.24\r\n- data for Figure 4.25\r\n- data for Figure 4.26\r\n- data for Figure 4.31\r\n- data for Figure 4.32\r\n- data for Figure 4.41\r\n- data for Figure 4.42" } ], "responsiblepartyinfo_set": [ 193489, 193490, 193491, 193492, 193493, 193494, 193495, 193496 ], "onlineresource_set": [ 83018, 83016, 83017, 88611 ] }, { "ob_id": 39733, "uuid": "e5e7afe5355a439e8d63be47ee7467c8", "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.42 (v20230213)", "abstract": "Data for Figure 4.42 from Chapter 4 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 4.42 illustrates high-warming storylines for changes in annual mean precipitation.\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 Lee, J.-Y., J. Marotzke, G. Bala, L. Cao, S. Corti, J.P. Dunne, F. Engelbrecht, E. Fischer, J.C. Fyfe, C. Jones, A. Maycock, J. Mutemi, O. Ndiaye, S. Panickal, and T. Zhou, 2021: Future Global Climate: Scenario-Based Projections and Near-Term Information. 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. 553–672, doi:10.1017/9781009157896.006.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has seven panels, with data provided for panels a-c and e-f in two separate files.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains precipitation change patterns for the multi model means and different low-likelihood high warming storylines, and a low and high change storyline reflection the uncertainty due to model response and unforced internal variability at the grid point level.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file Data_shown_in_figure_panels_abc.nc includes the precipitation change pattern for the three panels as different variables called panelA, panelB, and panelC, respectively.\r\n Data file Data_shown_in_figure_panels_ef.nc includes the precipitation change pattern for the two panels as different variables called panelE and panelF, respectively.\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 4)\r\n - Link to the Supplementary Material for Chapter 4, which contains details on the input data used in Table 4.SM.1", "creationDate": "2023-02-13T10:35:52.682090", "lastUpdatedDate": "2023-02-13T10:35:52", "latestDataUpdateTime": "2024-03-09T02:29:10", "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, Chapter 4, Sixth Assessment Report, Working Group 1, Physical Science Basis, precipitation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-07T12:04:55", "doiPublishedTime": "2023-07-03T10:29:45.198230", "removedDataTime": null, "geographicExtent": { "ob_id": 3767, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 240, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39734, "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig42/v20230213", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1013786, "numberOfFiles": 5, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10998, "startTime": "2081-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4185, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39735, "uuid": "155ff0aee5fc41f4b14e4dbe9c3f0aac", "short_code": "comp", "title": "Caption for Figure 4.42 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "High-warming storylines for changes in annual mean precipitation. (a) Estimates for annual mean precipitation changes in 2081–2100, relative to 1995–2014, consistent with the best global surface air temperature (GSAT) estimate derived by linearly scaling the CMIP6 multi-model mean changes to a GSAT change of 3.5°C. (b, c) Estimates for annual mean precipitation changes in 2081–2100 relative 1995–2014 in a storyline representing a physically plausible high-global-warming level. (b) Multi-model-mean precipitation scaled to high-global-warming level (corresponding to 4.8°C, the upper bound of the very likely range; see Section 4.3.4). (c) Average of five models with GSAT warming nearest to the high level of warming (ACCESS-CM2, CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-1-HR) (d) Annual mean precipitation changes in four of the five individual model simulations averaged in (c). (e, f) Local upper estimate (95% quantile across models) and lower estimate (5% quantile across models) at each grid point. Information at individual grid points comes from different model simulations and illustrates local uncertainty range but should not be interpreted as a pattern. (g) Area fraction of changes in annual mean precipitation 2081–2100 relative to 1995–2014 for (i) all CMIP6 model simulations (thin black lines), (ii) models shown in (c) (red lines), and (iii) models showing very high warming above the models shown in (c) (dark red lines). The grey range illustrates the 5–95% range across CMIP6 models and the solid black line the area fraction of the multi-model mean pattern shown in (a). Further details on data sources and processing are available in the chapter data table (Table 4.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": [ 11306, 11307, 52664, 52665, 63359 ], "vocabularyKeywords": [], "identifier_set": [ 12560 ], "observationcollection_set": [ { "ob_id": 32719, "uuid": "5b30b3c2146048388ac97e4278cb5128", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information.\r\n\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 4.12\r\n- data for Figure 4.13\r\n- data for Figure 4.19\r\n- data for Figure 4.22\r\n- data for Figure 4.23\r\n- data for Figure 4.24\r\n- data for Figure 4.25\r\n- data for Figure 4.26\r\n- data for Figure 4.31\r\n- data for Figure 4.32\r\n- data for Figure 4.41\r\n- data for Figure 4.42" } ], "responsiblepartyinfo_set": [ 193499, 193500, 193501, 193502, 193503, 193504, 193505, 193506 ], "onlineresource_set": [ 83019, 83021, 83020, 88612 ] }, { "ob_id": 39745, "uuid": "27273a9683be4c6991911c66535334ca", "title": "UK and Europe Methane (CH4) surface flux inverse modelling estimates for 2015 - 2019", "abstract": "Monthly CH4 flux estimates for the UK and western Europe, derived from inverse modelling work by the Atmospheric Chemistry Research Group (ACRG) at the University of Bristol.\r\nPosterior flux estimates were derived from a Markov Chain Monte Carlo (MCMC) inversion process, using 4-hourly atmospheric mole fraction observations of CH4 from UK observation sites Mace Head (MHD), Tacolneston (TAC), Ridge Hill (RGL), Bilsdale (BSD) and Heathfield (HFD) and a priori estimate of CH4 emissions from the UK Greenhouse Gas (UKGHG) and Emissions Database for Global Atmospheric Research (EDGAR) models. Total CH4 flux estimates at a resolution of 0.23 degrees latitude x 0.35 degrees longitude over 10.7 to 79.3 degrees north and -97.9 to 39.7 degrees east are provided for 2015 - 2019.", "creationDate": "2023-02-14T14:30:26.090753", "lastUpdatedDate": "2023-02-14T14:30:26", "latestDataUpdateTime": "2024-03-09T03:20:15", "updateFrequency": "notPlanned", "dataLineage": "CH4 flux estimates produced during 2021 and reprocessed in Nov 2022 by Alice Ramsden before uploading to CEDA.", "removedDataReason": "", "keywords": "Methane", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-16T09:23:11", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3769, "bboxName": "", "eastBoundLongitude": 39.7, "westBoundLongitude": -97.9, "southBoundLatitude": 10.7, "northBoundLatitude": 79.3 }, "verticalExtent": null, "result_field": { "ob_id": 39746, "dataPath": "/badc/deposited2023/ACRG_monthly_methane_inversion/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 110001889, "numberOfFiles": 3, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 11000, "startTime": "2015-01-01T00:00:00", "endTime": "2020-01-01T00:00:00" }, "resultQuality": { "ob_id": 4188, "explanation": "Posterior CH4 flux estimates were checked against those made by the ACRG's alternative inverse modelling method, to ensure repeatability.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39748, "uuid": "fcc644708ec94e5f9eeb26504b87fb3c", "short_code": "comp", "title": "Tracer inverse model", "abstract": "The tracer inverse model, an inverse model with optional use of secondary trace gases for source attribution was developed by Alice Ramsden and the Atmospheric Chemistry Research Group at the University of Bristol. The inverse model produces most-likely posterior estimates of CH4 surface fluxes by considering the likelihood of emissions in relation to amtospheric mole fraction observations of methane and the probability of emissions in relation to an a priori flux estimate." }, "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": 39747, "uuid": "1f3f0aa1eb364300a7882a0d9213c003", "short_code": "proj", "title": "Inverse estimation of sector-level methane emissions using observations of secondary trace gases - PhD project at the University of Bristol", "abstract": "This work was carried out as park of a NERC-funded PhD project, by Alice Ramsden at the University of Bristol. This project aimed to improve sector-level top-down estimates of methane fluxes and reduce posterior flux uncertainty, by using high-frequency observations of secondary trace gases during the inversion process." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 66297, 66298, 66299, 66300, 66301 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 193562, 193563, 193564, 193565, 193566, 193567, 193572, 193573, 193574, 193575, 193576, 193577, 193578, 193579, 193580, 193581, 193582, 193583, 193584, 193585, 193586, 193587, 193588 ], "onlineresource_set": [ 83032, 83033 ] }, { "ob_id": 39750, "uuid": "bb759548ee444168b4554569741c8e61", "title": "UKCP18 projected future extreme sea levels at selected tide gauge locations for 2020-2300, using exploratory extended time-mean sea level projections", "abstract": "The data are projected extreme sea levels at 46 UK tide gauge locations. The data were produced by the Met Office, using estimates of present-day extreme sea levels provided by the Environment Agency and projections of exploratory extended time-mean sea level change prepared at the Met Office.\r\n\r\nProjected extreme sea level values are described for 16 different annual probabilities of exceedance (return levels), ranging from 1:1 to 1:10,000. Confidence levels relating to the 5% and 95% lower and upper bounds of confidence are included.\r\n\r\nThe data were produced to put the projected future mean sea level change in the context of the present-day extremes. The data were produced by combining the best estimates of present-day extreme sea levels with projections of mean sea level change. The data covers the period from 2020 to 2300 and is available for each decade (i.e., 2020, 2030... 2300).\r\n\r\nFurther information on this dataset and UKCP18 can be found in the documentation section.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-03-14T09:33:43", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office", "removedDataReason": "", "keywords": "UKCP18, extreme sea level, UK", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-03-14T10:11:53", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2303, "bboxName": "", "eastBoundLongitude": 1.75, "westBoundLongitude": -10.58, "southBoundLatitude": 49.17, "northBoundLatitude": 60.83 }, "verticalExtent": null, "result_field": { "ob_id": 39772, "dataPath": "/badc/ukcp18/data/marine-sim/ext-sea-lev-expl", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 338311780, "numberOfFiles": 4, "fileFormat": "Data are in NetCDF format." }, "timePeriod": { "ob_id": 11003, "startTime": "2020-01-01T00:00:00", "endTime": "2300-12-31T23:59:59" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 212 ], "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": 26015, "uuid": "3500c35912e34566adb9948920d1216d", "short_code": "proj", "title": "UKCP18: The Marine Component of the UK Climate Projections 2018", "abstract": "The marine component of the UKCP18 project is comprised of:\r\nA multi-model ensemble of sea level projections for UK waters which extend to 2100 and include contributions from glaciers, ice sheets, freshwater reservoirs, groundwater and thermal expansion. \r\nA multi-model ensemble of historical sea level simulations for UK waters from 1970 to 2006.\r\nCase studies of three short events.\r\nSimulated impacts of mean sea level change on tidal characteristics around the UK." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 51186, 51187, 58992, 58993, 62545, 62546 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 193597, 193604, 193603, 193602, 193601, 193600, 193599, 193598, 193605 ], "onlineresource_set": [ 83050, 83051, 95130 ] }, { "ob_id": 39752, "uuid": "62b675f929974746bbf72fdc773cf0ec", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Cross-Section Box TS.1, Figure 1 (v20230203)", "abstract": "Data for Figure CSB TS.1, 1 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\nCross-Section Box TS.1 Figure 1, shows Earth's surface temperature history and projections to 2100\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 five panels with data provided for panels a, b and c from the underlying chapter figures (2.11, 4.19). Data for panel e is from Figure 4.11 and a link is provided to data and code in the Related Documents section.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Global surface temperature changer over the Holocene divided into three time scales: (i) 12,000 to 1000 years ago (10,000 BCE to 1000 CE) in 100-year time steps, (ii) 1000 to 1900 CE, 10-year smooth, and (iii) 1900 to 2020 CE. All temperatures are relative to 1850–1900.\r\n - Spatially resolved temperature trends (°C per decade) for HadCRUTv5 over 1981–2020\r\n - Multi-model temperature mean projected changes from 1995–2014 to 2081–2100 in the SST3-7.0 scenario.\r\n - Temperature from instrumental data for 1850–2020.\r\n - Assessed projected temperature change in 20-year running mean global surface temperature for five scenarios (central estimate, very likely range for SSP1-2.6 and SSP3-7.0), relative to 1995–2014 and 1850–1900.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - Panels (a), (c) and the top part of (b) are from Figure 2.11.\r\n - The bottom part of (b) is from Figure 4.19.\r\n - Panel (e) is from Figure 4.11.\r\n\r\nPanel a (from Figure 2.11 panel a):\r\n- Figure_2_11a-PAGES_2k_Consortium.csv (center) \r\n- Figure_2_11_panel_a.csv (right)\r\n\r\n\r\n Panel b (upper part from Figure 2.11 input data)\r\n- IndermediateData_Figure-2_11-HadCRUT_significance_overlay_1981-2020.txt\r\n- IntermediateData_Figure-2_11-HadCRUT_significance_overlay_1900-1980.txt\r\n- IntermediateData_Figure-2_11-HadCRUT_trends_1900-1980.txt\r\n- IntermediateData_Figure-2_11-HadCRUT_trends_1981-2020.txt\r\n- Figure_2_11-notes_on_HadCRUT_trend_files.pdf\r\n\r\n Panel b (lower part from Figure 4.19)\r\n- ‘Data_shown_in_figure.nc’\r\n\r\n\r\n Panel c (from Figure 2.11 panel c input data)\r\n- Figure_2_11c-lower_panel.csv (lower)\r\n- Figure_2_11c-lower_panel.xlsx : same as Figure_2_11c-lower_panel.csv but with the format used by the code that generates the figure.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Panel d:\r\n\r\nThe data lines are single ensemble members of SSP2-4.5 from BCC-CSM2-MR, FGOALS-f3-L, MPI-ESM1-2-HR, MPI-ESM1-2-LR, selected on the criterium that they have a diagnosed ECS less than 0.1K away from 3.0.\r\n\r\nThe CMIP6 repository is identical to the one used for Box 4.1 figure 1. The same 39 models with historical simulations are plotted. HadCRUT5 is the same reference as for Box 4.1 figure 1. Assessed GSAT is the same as produced by the figure 4.11 code.\r\n\r\nNotes on HadCRUT trend maps\r\nFor each time period, there are two plain text files: one showing the trend values (in °C per decade), the other an indicator of significant (1 = significant, 0 = non-significant, -99 = missing data). Gridpoints with insufficient data for trend calcula)on are shown with a trend of -999.0. The values are on a 5-degree grid, with rows from longitude -177.5 to 177.5 and columns from latitudes -87.5 to 87.5.\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 - Links to the report components of the underlying chapter figures from which part of this figure was generated (Chapter 2 and Chapter 4)\r\n - Link to the Supplementary Material for Chapter 4, which contains details on the input data used in Table 4.SM.1\r\n- Link to figure 4.11 data stored on WDC-Climate\r\n - Link to the code for figure 4.11, archived on Zenodo.", "creationDate": "2023-02-17T16:29:44.176303", "lastUpdatedDate": "2023-02-17T16:29:44", "latestDataUpdateTime": "2023-06-07T16:53:13", "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": "Global surface temperature, paleo temperature, global temperature projection, IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-09T09:23:35", "doiPublishedTime": "2023-09-26T14:06:49.935648", "removedDataTime": null, "geographicExtent": { "ob_id": 3770, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 241, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39753, "dataPath": "/badc/ar6_wg1/data/TS/CSB_TS1_fig1/v20230203", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7306036, "numberOfFiles": 15, "fileFormat": "Data are netCDF, csv and txt formatted" }, "timePeriod": { "ob_id": 11002, "startTime": "0001-01-01T00:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4214, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39754, "uuid": "0fbb2006d84446d5acd495c08167a7fd", "short_code": "comp", "title": "Caption for Cross- Section Box TS.1, 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": "Earth’s surface temperature history and future with key findings annotated within each panel. The intent of this figure is to show global surface temperature observed changes from the Holocene to now, and projected changes. (a) Global surface temperature over the Holocene divided into three time scales: (i) 12,000 to 1000 years ago (10,000 BCE to 1000 CE) in 100-year time steps, (ii) 1000 to 1900 CE, 10-year smooth, and (iii) 1900 to 2020 CE (mean of four datasets in panel c). Bold lines show the median of the multi-method reconstruction, with 5% and 95% percentiles of the ensemble members (thin lines). Vertical bars are 5–95th percentile ranges of estimated global surface temperature for the Last Interglacial and mid-Holocene (medium confidence) (Section 2.3.1.1). All temperatures are relative to 1850–1900. (b) Spatially resolved trends (°C per decade) for (upper map) HadCRUTv5 over 1981–2020, and (lower map, total change) multi-model mean projected changes from 1995–2014 to 2081–2100 in the SST3-7.0 scenario. Observed trends have been calculated where data are present in both the first and last decade and for at least 70% of all years within the period using ordinary least squares. Significance is assessed with autoregressive AR(1) model correction and denoted by stippling. Hatched areas in the lower map show areas of conflicting model evidence on significance of changes. (c) Temperature from instrumental data for 1850–2020, including annually resolved averages for the four global surface temperature datasets assessed in Section 2.3.1.1.3 (see text for references). The grey shading shows the uncertainty associated with the HadCRUTv5 estimate. All temperatures are relative to the 1850–1900 reference period. (d) Recent past and 2015–2050 evolution of annual mean global surface temperature change relative to 1850–1900, from HadCRUTv5 (black), Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulations (up to 2014, in grey, ensemble mean solid, 5% and 95% percentiles dashed, individual models thin), and CMIP6 projections under scenario SSP2-4.5, from four models that have an equilibrium climate sensitivity near the assessed central value (thick yellow). Solid thin coloured lines show the assessed central estimate of 20-year change in global surface temperature for 2015–2050 under three scenarios, and dashed thin coloured lines the corresponding 5% and 95% quantiles. (e) Assessed projected change in 20-year running mean global surface temperature for five scenarios (central estimate solid, very likely range shaded for SSP1-2.6 and SSP3-7.0), relative to 1995–2014 (left y-axis) and 1850–1900 (right y-axis). The y-axis on the right-hand side is shifted upward by 0.85°C, the central estimate of the observed warming for 1995–2014, relative to 1850–1900. The right y-axis in (e) is the same as the y-axis in (d). {2.3, 4.3, 4.4}" }, "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": [ 46706, 46708, 46709, 52664, 52665, 62359, 62360 ], "vocabularyKeywords": [], "identifier_set": [ 12692 ], "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": [ 193634, 193635, 193636, 193637, 193638, 193639, 193640, 195822, 195823, 195824, 195825, 195826, 195827, 195828, 195829 ], "onlineresource_set": [ 83054, 83055, 83056, 83057, 83065, 83067, 83066 ] }, { "ob_id": 39759, "uuid": "7b3d379fc1f040978df4806c6775a0df", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 7.5 (v20230221)", "abstract": "Input Data for Figure 7.5 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 7.5 shows net aerosol effective radiative forcing (ERF) from 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 1 panel, with input data provided for this panel.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\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\nThe headline AR6 assessment of –1.3 [–2.0 to –0.6] W m–2 is highlighted in purple for 1750–2014 and compared to the AR5 assessment of –0.9 [–1.9 to –0.1] W m–2 for 1750–2011. The evidence comprising the AR6 assessment is shown below this (shown in brackets in the list of data provided). \r\n\r\nEstimates from individual CMIP5 (Zelinka et al., 2014) and CMIP6 (Smith et al., 2020b and Table 7.6) models are depicted by blue and red crosses respectively. \r\nFor each line of evidence the assessed best-estimate contributions from ERFari and ERFaci are shown with darker and paler shading respectively. \r\nThe observational assessment for ERFari is taken from the IRFari. \r\nUncertainty ranges are represented by black bars for the total aerosol ERF and depict very likely 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 7.5\r\n \r\n - Data file: table7.6.csv\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\nERFari stands for Effective Radiative Forcing of aerosol-radiation interaction.\r\nERFaci stands for Effective Radiative Forcing of aerosol-cloud interaction.\r\nIRFari stands for Instantaneous 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 of the Chapter 7 GitHub repository. \r\nThe notebook to produce this figure uses Table 7.6 from the report chapter and data from Zelinka et al., 2014 written into the code.\r\nTo reproduce the figure from the input data provided here ('table7.6.csv'), you will need to edit the path in box 5 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 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": "2023-05-31T15:33:32", "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, forcing, aerosols, observations, CMIP6, CMIP5, effective radiative forcing", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-31T14:37:23", "doiPublishedTime": "2023-07-06T09:54:46.709147", "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": 39760, "dataPath": "/badc/ar6_wg1/data/ch_07/inputdata_ch7_fig05/v20230221", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5384, "numberOfFiles": 4, "fileFormat": "Data are CSV formatted" }, "timePeriod": null, "resultQuality": { "ob_id": 4289, "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": 39583, "uuid": "2740dacb1116498ba481751b4298e660", "short_code": "comp", "title": "Caption for Figure 7.5 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Net aerosol effective radiative forcing (ERF) from different lines of evidence. The headline AR6 assessment of –1.3 [–2.0 to –0.6] W m–2 is highlighted in purple for 1750–2014 and compared to the AR5 assessment of –0.9 [–1.9 to –0.1] W m–2 for 1750–2011. The evidence comprising the AR6 assessment is shown below this: energy balance constraints [–2 to 0 W m–2 with no best estimate]; observational evidence from satellite retrievals of –1.4 [–2.2 to –0.6] W m–2; and climate model-based evidence of –1.25 [–2.1 to –0.4] W m–2. Estimates from individual CMIP5 (Zelinka et al., 2014) and CMIP6 (Smith et al., 2020b and Table 7.6) models are depicted by blue and red crosses respectively. For each line of evidence the assessed best-estimate contributions from ERFari and ERFaci are shown with darker and paler shading respectively. The observational assessment for ERFari is taken from the IRFari. Uncertainty ranges are represented by black bars for the total aerosol ERF and depictvery likely 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": [ 12633 ], "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": [ 193663, 193664, 193665, 193666, 193667, 193668, 193669, 193670, 193671 ], "onlineresource_set": [ 83077, 83079, 83078, 83080, 83587 ] }, { "ob_id": 39765, "uuid": "df93e92a3adc46b9a5c4bd3a547cd242", "title": "3D Displacements and Strain from the 2023 February Turkey Earthquakes, version 1", "abstract": "This data set contains strain, motion magnitude, 3D displacements, and surface slip distributions from the 2023 February Türkiye Earthquakes. COMET presents 0.001 degree (~100 m) resolution 3D displacement fields, with associated uncertainties, jointly inverted from 4 tracks of Sentinel-1 range and azimuth offsets and a set of north and east displacements from Sentinel-2 pixel tracking. From the 3D displacement fields, we calculate 0.001-degree resolution horizontal motion magnitudes, from which we extract surface slip distribution along the two faults ruptured during the Mw7.8 and Mw7.5 earthquakes. Further calculation of a 0.01-degree resolution strain field from the east and north displacement fields highlights the surface ruptures caused by the two earthquakes.", "creationDate": "2023-02-28T15:45:41.938719", "lastUpdatedDate": "2023-02-28T15:35:14", "latestDataUpdateTime": "2023-03-10T16:46:58", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\nData quality: Quantitative offset and Cartesian motion uncertainties are provided with the data. The northern half of the scene is noisier than the south due to snow cover. The Cartesian motions have mode uncertainties of 0.03 m for the east and up components and 0.06 m for the north component. The median and mean uncertainties of the 3 components are ~0.3 m and ~1 m, respectively.", "removedDataReason": "", "keywords": "COMET, Sentinel 1, Sentinel 2", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-03-03T15:35:59", "doiPublishedTime": "2023-03-14T15:47:11.077377", "removedDataTime": null, "geographicExtent": { "ob_id": 3771, "bboxName": "Turkey 2023 earthquake", "eastBoundLongitude": 41.0, "westBoundLongitude": 34.0, "southBoundLatitude": 34.0, "northBoundLatitude": 40.0 }, "verticalExtent": null, "result_field": { "ob_id": 39766, "dataPath": "/neodc/comet/publications_data/Turkey_earthquake/v1.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4982772539, "numberOfFiles": 74, "fileFormat": "These data are provided in GeoTIFF (.tif) or png format." }, "timePeriod": { "ob_id": 11005, "startTime": "2023-01-25T00:00:00", "endTime": "2023-02-17T23:59:59" }, "resultQuality": { "ob_id": 4222, "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": "2023-02-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 39767, "uuid": "22921105d0354d2aadc2b7785e404fc4", "short_code": "cmppr", "title": "Composite process for the 3D displacements and strain maps from the 2023 February Turkey Earthquakes", "abstract": "Processing from COMET to create these 3D displacement and strain maps for the February 2023 Turkey earthquakes." }, "imageDetails": [ 222 ], "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": 11687, "uuid": "b46fbc668f6547fda79f2899046c29a9", "short_code": "proj", "title": "Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics", "abstract": "The Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET+) represents the Dynamic Earth and Geohazards research group within the National Centre for Earth Observation (NCEO)'s Theme 6 during NCEO phase 1. NCEO phase 1 was is funded by the Natural Environment Research Council (NERC). NCEO phase 2 no longer has the theme 6 within its remit, though COMT+ continues within NERC.\r\n\r\nCOMET+ involves scientists from the University of Oxford, University of Cambridge, University of Leeds, University of Bristol, University oSf Glasgow, University of Reading, and University College London. We aim to combine satellite observations of Earth's surface movements, topography and gas release with terrestrial observations and modelling to advance understanding of the earthquake cycle, continental deformation and volcanic eruptions, and to quantify seismic and volcanic hazards." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12421 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 193686, 193687, 193688, 193689, 193690, 193691, 193692, 193699, 193693, 193696, 193694, 193695 ], "onlineresource_set": [ 83113, 83114, 83148, 83115, 83116, 83117, 83146, 83147, 87721, 87935, 87950 ] }, { "ob_id": 39769, "uuid": "e046adc115b04395937e793c9f3dbcb1", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure TS.12 (v20230301)", "abstract": "Input data for Figure TS.12 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.12 shows land-related changes relative to the 1850-1900 as a function of global warming levels. \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 six panels, with input data provided for all panels in subdirectories named Extremes, ModelSnow/sncbin and WaterCyclePanels.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n - Relative frequency and intensity changes of 1-in-10- and 1-in-50-year extreme daily heat (TXx) in CMIP6 models (ScenarioMIP) with respect to 1850-1900. Medians and 5, 17, 83, 95 percentiles.\r\n - Relative frequency and intensity changes of 1-in-10- and 1-in-50-year extreme daily precipitation rates (Rx1day) in CMIP6 models (ScenarioMIP) with respect to 1850-1900. Medians and 5, 17, 83, 95 percentiles.\r\n - Relative frequency and intensity changes of 1-in-10-year drought events in CMIP6 models (ScenarioMIP) with respect to 1850-1900. Medians and 5, 17, 83, 95 percentiles.\r\n - Monthly NH snow cover extent changes (in %), dependent on the GWL (with respect to 1850-1900), for CMIP6 models (historical + ScenarioMIP), with respect to snow cover extent at 0°C GWL (1850-1900)\r\n - Average precipitable water (annual mean), precipitation rate (annual mean + interannual variability), and runoff (annual mean + interannual variability) over tropical land (|latitude|<30°), in the CMIP6 models that reach +5°C GWL in SSP5-8.5.\r\n - Average precipitable water (annual mean), precipitation rate (annual mean + interannual variability), and runoff (annual mean + interannual variability) over tropical land (|latitude|>30°), in the CMIP6 models that reach +5°C GWL in SSP5-8.5.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - Data file: Extremes/TXx_freq_change_10_year_event.csv; relates to the orange clock symbols and small orange 3-pronged symbols above and below.\r\n - Data file: Extremes/TXx_intens_change_10_year_event.csv; relates to the orange thermometer symbols and small orange 3-pronged symbols above and below.\r\n - Data file: Extremes/TXx_freq_change_50_year_event.csv; relates to the brown clock symbols and small orange 3-pronged symbols above and below.\r\n - Data file: Extremes/TXx_intens_change_50_year_event.csv; relates to the brown thermometer symbols and small orange 3-pronged symbols above and below.\r\n \r\n Panel b:\r\n - Data file: Extremes/Rx1day _freq_change_10_year_event.csv; relates to the orange clock symbols and small orange 3-pronged symbols above and below.\r\n - Data file: Extremes/Rx1day _intens_change_10_year_event.csv; relates to the orange rain cloud symbols and small orange 3-pronged symbols above and below.\r\n - Data file: Extremes/Rx1day _freq_change_50_year_event.csv; relates to the brown clock symbols and small orange 3-pronged symbols above and below.\r\n - Data file: Extremes/Rx1day _intens_change_50_year_event.csv; relates to the brown rain cloud symbols and small orange 3-pronged symbols above and below.\r\n \r\n Panel c:\r\n - Data file: Extremes/drought _freq_change_10_year_event.csv; relates to the orange clock symbols and small orange 3-pronged symbols above and below.\r\n - Data file: Extremes/drought _intens_change_10_year_event.csv; relates to the orange drop symbols and small orange 3-pronged symbols above and below.\r\n \r\n Panel d:\r\n Data files: ModelSnow/sncbin/sncbin_{model}_historical_ssp{xyy}.nc. For each model and scenario, these files contain a table that gives the snow cover extent changes for each month of the year and for 0.2°C-wide temperature bins. The colours represent the 5 scenarios (see legend, standard IPCC scenario colour code). Each dot represents one GWL (0.2°C bins) for one model and one scenario. The linear multi-model regression lines are coloured dependent on the scenario they represent.\r\n\r\n The filenames have been changed from the GitHub for archival with '_ssp' in the filename replacing the original '+ssp'.For example, file 'sncbin_BCC-CSM2-MR_historical_ssp126.nc' is named 'sncbin_BCC-CSM2-MR_historical+ssp126.nc' on GitHub. This needs to be changed back when plotting with the code or amended in the code. \r\n\r\n Panel e:\r\n - Data files: WaterCyclePanels/Hydro_vars_change_20210201_derived_tropic.json. Multi-model mean precipitable water, precipitation, and runoff (annual mean and interannual variability (standard deviation)).\r\n Left part of the panel:\r\n * Black full line: Precipitable water, annual mean, multi-model mean\r\n * Brown full line: Runoff, annual mean, multi-model mean\r\n * Brown dashed line: Runoff, interannual variability, multi-model mean\r\n * Blue full line: Precipitation, annual mean, multi-model mean\r\n * Blue dashed line: Precipitation, interannual variability, multi-model mean\r\n Right part of the panel: 17-83% inter-model ranges for the +5°C GWL.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThis figure is plotted using python scripts which are archived on Zenodo at the link provided in the 'Related Documents' section of this catalogue record. A link to the GitHub repository for this figure is also provided. Please note that the filenames of the net-cdf files in ModelSnow/ have been changed from '+ssp' to '_ssp' which also needs to be amended in the code when running with these input files.\r\n\r\n Panel a:\r\n The Python script Extremes/TS2-Land-Extremes-202110.py reads the csv data sheets and plots the panel.\r\n\r\nPanel b:\r\n The Python script Extremes/TS2-Land-Extremes-202110.py reads the csv data sheets and plots the panel.\r\n\r\nPanel c:\r\n The Python script Extremes/TS2-Land-Extremes-202110.py reads the csv data sheets and plots the panel.\r\n\r\nPanel d:\r\n The Python script ModelSnow/AR6TS2-Snow.py reads the net-cdf data and plots the panel.\r\n\r\nPanel e:\r\n The Python script WaterCyclePanels/TS12-WaterCycle.py reads the json data sheets and plots the panel.\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 TS_Fig12 GitHub repository", "creationDate": "2023-03-01T10:46:25.068757", "lastUpdatedDate": "2023-03-01T10:46:25", "latestDataUpdateTime": "2024-03-09T02:29:09", "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": "Extremes, land, hot (TXx), GWL, frequency, intensity, CMIP6, ScenarioMIP, daily precipitation (Rx1day), drought, normalized change of annual mean soil moisture, Northern hemisphere snow cover extent, spring (March-May), Hydrological changes on tropical land, precipitable water, precipitation, runoff, SSP5-8.5, Hydrological changes on extratropical land, IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-05T13:30:53", "doiPublishedTime": "2023-07-27T13:38:34.367401", "removedDataTime": null, "geographicExtent": { "ob_id": 3772, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 242, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39770, "dataPath": "/badc/ar6_wg1/data/TS/inputdata_TS_12/v20230301", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1134270, "numberOfFiles": 112, "fileFormat": "Data are netCDF, csv and json formatted" }, "timePeriod": { "ob_id": 11006, "startTime": "1850-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4223, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-03-01" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39771, "uuid": "9b8646a130504b09bf2efeded8a1e2a2", "short_code": "comp", "title": "Caption for Figure TS.12 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Land-related changes relative to the 1850-1900 as a function of global warming levels. The intent of this figure is to show that extremes and mean land variables change consistently with warming levels and to show the changes with global warming levels of water cycle indicators (i.e., precipitation and runoff) over tropical and extratropical land in terms of mean and interannual variability (interannual variability increases at a faster rate than the mean). (a) Changes in the frequency (left scale) and intensity (in °C, right scale) of daily hot extremes occurring every 10 and 50 years. (b) as (a), but for daily heavy precipitation extremes, with intensity change in %. (c) Changes in 10-year droughts aggregated over drought-prone regions (WNA, CNA, NCA, SCA, NSA, NES, SAM, SWS, SSA, WCE, MED, WSAF, ESAF, MDG, SAU, and EAU; for definitions of these regions, see Figure Atlas.2), with drought intensity (right scale) represented by the change of annual mean soil moisture, normalized with respect to interannual variability. Limits of the 5%−95% confidence interval are shown in panels (a–c). (d) Changes in Northern Hemisphere spring (March–April–May) snow cover extent relative to 1850–1900; (e,f) Relative change (%) in annual mean of total precipitable water (grey line), precipitation (red solid lines), runoff (blue solid lines) and in standard deviation (i.e., variability) of precipitation (red dashed lines) and runoff (blue dashed lines) averaged over (e) tropical and (f) extratropical land as function of global warming levels. Coupled Model Intercomparison Project Phase 6 (CMIP6) models that reached a 5°C warming level above the 1850–1900 average in the 21st century in SSP5-8.5 have been used. Precipitation and runoff variability are estimated by respective standard deviation after removing linear trends. Error bars show the 17–83% confidence interval for the warmest +5°C global warming level. {Figures 8.16, 9.24, 11.6, 11.7, 11.12, 11.15, 11.18 and Atlas.2}" }, "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": [ 62542, 62543, 62544 ], "vocabularyKeywords": [], "identifier_set": [ 12655 ], "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": [ 193703, 193704, 193705, 193706, 193707, 193708, 193709, 193710, 193711, 193712, 193713, 193714, 193715, 193716, 193717 ], "onlineresource_set": [ 83118, 83119, 83120, 83605 ] }, { "ob_id": 39775, "uuid": "d54f2a1e4d2f42e68c10e2b11668dcd6", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for CCB 9.1, Figure 1 (v20230310)", "abstract": "Input Data for CCB 9.1, Figure 1 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nCross Chapter Box 9.1, Figure 1 shows observed and simulated regional probability ratio of marine heatwaves (MHWs) for the 1985-2014 period and for the end of the 21st century under two different greenhouse gas emissions 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\n Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. 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. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels with input data provided for all panels in the main directory. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Pre-processed annual timeseries of global ocean heat content change (ZJ) and global thermal expansion (mm) for the period 1870 to 2020. \r\n\r\n Timeseries are global integrals over the following vertical layers: 0-300 m; 0-700 m; 0-2000 m; 700-2000 m; > 2000 m; Full-depth.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a: global ocean heat content change (ZJ) for the layers 0-700 m, 700-2000 m and > 2000 m are represented by the blue shaded regions as indicated in the figure legend.\r\n Panel b: global thermal expansion (mm) for the layers 0-700 m, 700-2000 m and > 2000 m are represented by the blue shaded regions as indicated in the figure legend.\r\n\r\nFor files 'AR6_OHC_timeseries_MDP_2021-01-20_more_than_2000m.csv' and 'AR6_OHC_timeseries_MDP_2021-01-20_more_than_2000m_error.csv', 'more_than' has replaced '>' from the original filenames for archival. \r\n\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n These files contain the data used as input to the code used to generate the ensemble assessment time series of ocean heat content (OHC) change and global thermal expansion (ThSL) that were developed for the IPCC AR6 WG1 report.\r\n\r\n\r\nThe Python script used is called: compute_OHC_ThSL_ensemble_FGD_python3.py. The link to the code, archived on Zenodo, is provided in the Related Documents section of this catalogue record.\r\n\r\n\r\nThe actual file used as input data to the code is in a non proprietary format (AR6_GOHC_GThSL_timeseries_MDP_2021-01-20.mat) and is archived on Zenodo together with the code. The files archived here contain the same information but in a csv format.\r\n\r\n\r\nTo run the code, you will need to edit the paths for plotdir, savedir and datadir based on your local directory structure. \r\n\r\n\r\nOn running the code, the script creates two *.pickle files and corresponding *.csv files that contain the ensemble estimates of OHC and ThSL. It also generates four figure files that show the original input timeseries and the ensemble estimate, following the approach described by Palmer et al [2021].\r\n\r\n\r\nPre-processed individual ensemble member timeseries are available in *.csv format in the Supplementary Materials of Kuhlbrodt et al [in press]. Full citation: T. Kuhlbrodt, A. Voldoire, M.D. Palmer, O. Geoffroy and R.E. Killick “Historical ocean heat uptake in two pairs of CMIP6 models: global and regional perspectives”, Journal of Climate, in press.\r\n\r\nPlease note that for the files 'AR6_OHC_timeseries_MDP_2021-01-20_more_than_2000m.csv' and 'AR6_OHC_timeseries_MDP_2021-01-20_more_than_2000m_error.csv' CEDA staff were required to change the filenames in order to align with archive naming conventions. The original file names were 'AR6_OHC_timeseries_MDP_2021-01-20_>2000m.csv' and 'AR6_OHC_timeseries_MDP_2021-01-20_>2000m_error.csv'. \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 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2023-03-10T12:46:04.013522", "lastUpdatedDate": "2023-03-10T12:46:04", "latestDataUpdateTime": "2024-03-09T02:28:57", "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 converted from MATLAB to csv format.\r\nCertain files renamed by CEDA in order to align with archive naming conventions. \r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "global ocean heat content, global thermal expansion, ocean observations, in situ observations, IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-07T14:56:06", "doiPublishedTime": "2023-09-26T14:55:13.226492", "removedDataTime": null, "geographicExtent": { "ob_id": 3773, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 243, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39776, "dataPath": "/badc/ar6_wg1/data/ch_09/inputdata_ccb9_fig1/v20230310", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 227677, "numberOfFiles": 15, "fileFormat": "Data are csv formatted" }, "timePeriod": { "ob_id": 11007, "startTime": "1870-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 4224, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-03-10" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39777, "uuid": "b8ebfb65a98746deaeec33a6fc69c00a", "short_code": "comp", "title": "Caption for CCB 9.1, Figure 1 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global Energy Inventory and Sea Level Budget. (a) Observed changes in the global energy inventory for 1971–2018 (shaded time series) with component contributions as indicated in the figure legend. Earth System Heating for the whole period and associated uncertainty is indicated to the right of the plot (red bar = central estimate; shading =very likely range); (b) Observed changes in components of global mean sea level for 1971–2018 (shaded time series) as indicated in the figure legend. Observed global mean sea level change from tide gauge reconstructions (1971–1993) and satellite altimeter measurements (1993–2018) is shown for comparison (dashed line) as a three-year running mean to reduce sampling noise. Closure of the global sea level budget for the whole period is indicated to the right of the plot (red bar = component sum central estimate; red shading =very likely range; black bar = total sea level central estimate; grey shading =very likely range). Full details of the datasets and methods used are available in Annex I. Further details on energy and sea level components are reported in Table 7.1 and Table 9.5." }, "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": [ 12694 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\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 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 193725, 193726, 193727, 193728, 193729, 193730, 193731, 193732, 193733, 193734, 193735, 193736, 193737, 193738, 193739, 193740, 193741, 193742, 193743, 193744, 193745, 193746, 193747, 193748, 193749, 193750, 193751, 193752, 193753, 193754 ], "onlineresource_set": [ 83308, 83309, 83310, 83311 ] }, { "ob_id": 39778, "uuid": "e53831e4dfec4537b7cc93d53ab94f4c", "title": "WCRP CMIP6: Met Office Hadley Centre (MOHC) HadGEM3-GC31-MM model output for the \"dcppA-assim\" experiment", "abstract": "The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the Met Office Hadley Centre (MOHC) HadGEM3-GC31-MM model output for the \"Assimilation run paralleling the historical simulation, which may be used to generate hindcast initial conditions\" (dcppA-assim) experiment. These are available at the following frequency: Omon. The runs included the ensemble members: r10i1p1f2, r1i1p1f2, r2i1p1f2, r3i1p1f2, r4i1p1f2, r5i1p1f2, r6i1p1f2, r7i1p1f2, r8i1p1f2 and r9i1p1f2.\n\nCMIP6 was a global climate model intercomparison project, coordinated by PCMDI (Program For Climate Model Diagnosis and Intercomparison) on behalf of the WCRP and provided input for the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6).\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.", "creationDate": "2023-03-13T11:39:15.158977", "lastUpdatedDate": "2023-03-13T11:39:15.159004", "latestDataUpdateTime": "2023-02-26T00:56:59", "updateFrequency": "asNeeded", "dataLineage": "Data were produced and verified by Met Office Hadley Centre (MOHC) scientists before publication via the Earth Systems Grid Federation (ESGF) and a copy obtained by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CMIP6, WCRP, climate change, MOHC, HadGEM3-GC31-MM, dcppA-assim, Omon", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-03-13T11:39:15.278674", "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": 39779, "dataPath": "/badc/cmip6/data/CMIP6/DCPP/MOHC/HadGEM3-GC31-MM/dcppA-assim", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5905935608506, "numberOfFiles": 7201, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 11008, "startTime": "1959-12-16T12:00:00", "endTime": "2018-11-16T00:00:00" }, "resultQuality": { "ob_id": 3334, "explanation": "The CMIP6 data are checked by CEDA to ensure that they conform to the CMOR metadata standards (https://cmor.llnl.gov) Where any CMIP6 data are identified as having a quality issue this is recorded in the CMIP6 errata service: https://errata.es-doc.org/static/index.html", "passesTest": true, "resultTitle": "CMIP6 Quality Statement - primary data", "date": "2017-02-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39780, "uuid": "e903925c8ea24c8499df3bfb236b84e6", "short_code": "comp", "title": "Met Office Hadley Centre (MOHC) running: experiment dcppA-assim using the HadGEM3-GC31-MM model.", "abstract": "Met Office Hadley Centre (MOHC) running the \"Assimilation run paralleling the historical simulation, which may be used to generate hindcast initial conditions\" (dcppA-assim) experiment using the HadGEM3-GC31-MM model. See linked documentation for available information for each component." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2520, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 1, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/CMIP6_Terms_of_Use.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 28398, "uuid": "04d56adf2c3247fc89af80c8f4bdb41b", "short_code": "proj", "title": "WCRP CMIP6: Met Office Hadley Centre (MOHC) contribution", "abstract": "World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 contribution to the project by the Met Office Hadley Centre (MOHC) team." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 6255, 27828, 27829, 50542, 50543, 50568, 50569, 50570, 50571, 50572, 50609, 50612, 50615, 50616, 50621, 53128, 53129, 54252, 54483, 60438, 71615 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 28763, "uuid": "7fbf7e27ef554981bfff17d6f14f9d8a", "short_code": "coll", "title": "WCRP CMIP6: Met Office Hadley Centre (MOHC) HadGEM3-GC31-MM model output collection", "abstract": "World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6): Collection of simulations from the Met Office Hadley Centre (MOHC) HadGEM3-GC31-MM model.\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record." } ], "responsiblepartyinfo_set": [ 193757, 193758, 193759, 193760, 193761, 193762, 193763, 193765, 193766, 193764 ], "onlineresource_set": [ 83151, 83152, 83154, 83156, 83158, 83160, 83162, 83164, 83166, 83168, 83170, 83172, 83174, 83175, 83176 ] }, { "ob_id": 39784, "uuid": "360cf83cf2354ae88c45ad8e37a2766b", "title": "GloCAEM: Atmospheric electricity measurements at Kakioka Magnetic Observatory, Japan (low sensitivity)", "abstract": "This dataset contains measurements of atmospheric electricity and electric potential gradient made using a Boltek EFM 100 Instrument mounted at 0.6 m height on a metal pole and operated by the University of Shizuoka and Japan Meteorological Agency at Kakioka Magnetic Observatory, Ishioka-shi, Ibaraki-ken, Japan\r\n\r\nThe provided data were calibrated using long-term observation of water dropper data (Nagamachi et al, Geosci. Data J., 2021).The low sensitivity instrument dataset provided through GLOCAEM have a resolution of 65.9 V/m and the measurement range is from -131.8 kV/m to +131.8 kV /m. GPS-synchronized data with higher resolution (6.59 V/m) and with high sampling (10 Hz), all-sky camera images (10 min. sampling), animal camera images (motion detection) and are also available through on-demand request (kamogawa@u-shizuoka-ken.ac.jp).\r\n\r\nGlobal Coordination of Atmospheric Electricity Measurements (GloCAEM) project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "unknown", "dataLineage": "Data were collected by the project team and sent to CEDA", "removedDataReason": "", "keywords": "GloCAEM, GEC, electric potential, electric field", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3774, "bboxName": "Kakioka Magnetic Observatory, Japan", "eastBoundLongitude": 140.186, "westBoundLongitude": 140.186, "southBoundLatitude": 36.232, "northBoundLatitude": 36.232 }, "verticalExtent": null, "result_field": { "ob_id": 39785, "dataPath": "/badc/glocaem/data/kakioka-low/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1624053661, "numberOfFiles": 1097, "fileFormat": "BADC-CSV" }, "timePeriod": { "ob_id": 11009, "startTime": "2021-02-19T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3068, "explanation": "Data provided by project group", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2016-10-18" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 39231, "uuid": "5201abb5dd85431cb701e4740da4e127", "short_code": "acq", "title": "GLOCaeM potential gradient Kakioka Magnetic Observatory, Japan", "abstract": "Measurements of atmospheric electric potential gradient made at Kakioka Magnetic Observatory, Japan" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2546, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 20068, "uuid": "6ee6e0a3f57c4ca79e8cbc0daaafe76f", "short_code": "proj", "title": "Global Coordination of Atmospheric Electricity Measurements (GloCAEM)", "abstract": "It is well established that Earth has a \"Global atmospheric Electric Circuit\" (GEC), through which charge separation in thunderstorms sustains large scale current flow around the planet. The GEC generates an atmospheric electric field which is present globally, and is typically 100V/m near the surface in fair weather conditions. Measurements of electric field have been shown to include information about global thunderstorm activity, local aerosol concentrations and cloud cover, as well as changes in the space weather environment. Recent work has also suggested that atmospheric electrical changes may be effective as earthquake precursors, as well as being sensitive to release of radioactivity, as evidenced by the Fukushima disaster in 2011. \r\n\r\nThe global nature of the GEC means that in order that truly global signals are considered in understanding the processes within the circuit, many validating measurements must be made at different locations around the world. To date, no genuinely global network of FW atmospheric electricity measurements has ever existed, therefore, given the growing number of groups now involved in atmospheric electricity monitoring, such a proposal is timely. \r\n\r\nThis project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64183, 64184, 64185, 64186, 64187, 64188, 64189, 64190, 64191, 64193, 64194, 64195, 64197, 64198, 64199, 64200, 64201, 64202, 66964, 66965, 66966, 66967, 66969, 66970, 86021 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 24981, "uuid": "bffd0262439a4ecb8fadf0134c4a4a41", "short_code": "coll", "title": "GloCAEM: Atmospheric electric potential gradient measurements", "abstract": "Global Coordination of Atmospheric Electricity Measurements (GloCAEM) project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1.\r\n\r\nThis dataset collection contains measurements of atmospheric electricity and electric potential gradient made using a Cambell Scientific CS110 electric-field mill." } ], "responsiblepartyinfo_set": [ 193788, 193790, 193789, 193791, 193792, 193793, 193794, 193795, 193797, 193798 ], "onlineresource_set": [ 83177 ] }, { "ob_id": 39786, "uuid": "5e5b96c9d37344f9850d874378927065", "title": "GloCAEM: Atmospheric electricity measurements at Kakioka Magnetic Observatory, Japan (high sensitivity)", "abstract": "This dataset contains measurements of atmospheric electricity and electric potential gradient made using a Boltek EFM 100 Instrument mounted at 0.6 m height on a metal pole and operated by the University of Shizuoka and Japan Meteorological Agency at Kakioka Magnetic Observatory, Ishioka-shi, Ibaraki-ken, Japan\r\n\r\nThe provided data was calibrated using long-term observation of water dropper data (Nagamachi et al, Geosci. Data J., 2021). The resolution of the high sensitivity data provided through GLOCAEM is 1.9 V/m and the measurement range is from -3.8 kV/m to +3.8 kV/m. GPS-synchronized data with higher resolution (0.19 V/m) and with high sampling (10 Hz), all-sky camera images (10 min. sampling), animal camera images (motion detection) and are also available through on-demand request (kamogawa@u-shizuoka-ken.ac.jp).\r\n\r\nGlobal Coordination of Atmospheric Electricity Measurements (GloCAEM) project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-05-07T15:40:44", "updateFrequency": "unknown", "dataLineage": "Data were collected by the project team and sent to CEDA", "removedDataReason": "", "keywords": "GloCAEM, GEC, electric potential, electric field", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-03-14T17:37:41", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3774, "bboxName": "Kakioka Magnetic Observatory, Japan", "eastBoundLongitude": 140.186, "westBoundLongitude": 140.186, "southBoundLatitude": 36.232, "northBoundLatitude": 36.232 }, "verticalExtent": null, "result_field": { "ob_id": 39787, "dataPath": "/badc/glocaem/data/kakioka-high", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3324508955, "numberOfFiles": 2292, "fileFormat": "BADC-CSV" }, "timePeriod": { "ob_id": 11009, "startTime": "2021-02-19T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3068, "explanation": "Data provided by project group", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2016-10-18" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 39231, "uuid": "5201abb5dd85431cb701e4740da4e127", "short_code": "acq", "title": "GLOCaeM potential gradient Kakioka Magnetic Observatory, Japan", "abstract": "Measurements of atmospheric electric potential gradient made at Kakioka Magnetic Observatory, Japan" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2546, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 20068, "uuid": "6ee6e0a3f57c4ca79e8cbc0daaafe76f", "short_code": "proj", "title": "Global Coordination of Atmospheric Electricity Measurements (GloCAEM)", "abstract": "It is well established that Earth has a \"Global atmospheric Electric Circuit\" (GEC), through which charge separation in thunderstorms sustains large scale current flow around the planet. The GEC generates an atmospheric electric field which is present globally, and is typically 100V/m near the surface in fair weather conditions. Measurements of electric field have been shown to include information about global thunderstorm activity, local aerosol concentrations and cloud cover, as well as changes in the space weather environment. Recent work has also suggested that atmospheric electrical changes may be effective as earthquake precursors, as well as being sensitive to release of radioactivity, as evidenced by the Fukushima disaster in 2011. \r\n\r\nThe global nature of the GEC means that in order that truly global signals are considered in understanding the processes within the circuit, many validating measurements must be made at different locations around the world. To date, no genuinely global network of FW atmospheric electricity measurements has ever existed, therefore, given the growing number of groups now involved in atmospheric electricity monitoring, such a proposal is timely. \r\n\r\nThis project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64174, 64175, 64176, 64177, 64178, 64179, 64180, 64181, 64182, 64183, 64184, 64185, 64186, 64187, 64188, 64189, 64190, 64191, 64192, 64193, 64194, 64195, 64196, 64197, 64198, 64199, 64200, 64201, 64202, 64203, 64204 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 24981, "uuid": "bffd0262439a4ecb8fadf0134c4a4a41", "short_code": "coll", "title": "GloCAEM: Atmospheric electric potential gradient measurements", "abstract": "Global Coordination of Atmospheric Electricity Measurements (GloCAEM) project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1.\r\n\r\nThis dataset collection contains measurements of atmospheric electricity and electric potential gradient made using a Cambell Scientific CS110 electric-field mill." } ], "responsiblepartyinfo_set": [ 193799, 193800, 193801, 193803, 193804, 193805, 193806, 193807, 193808, 193809 ], "onlineresource_set": [ 83178 ] }, { "ob_id": 39793, "uuid": "eade27004395466aaa006135e1b2ad1a", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): High(er) Resolution Sea Ice Concentration Climate Data Record Version 3 (SSM/I and SSMIS)", "abstract": "This climate data record of sea ice concentration (SIC) is obtained using passive microwave satellite data from the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) over the polar regions (Arctic and Antarctic). The processing chain features: 1) dynamic tuning of tie-points and algorithms, 2) correction of atmospheric noise using a Radiative Transfer Model, 3) computation of per-pixel uncertainties, 4) an optimal hybrid sea ice concentration algorithm, and 5) pan-sharpening of the SIC fields using the near-90 GHz imagery channels. This dataset was generated by the ESA Climate Change Initiative (CCI+) Sea Ice Phase 1 project. This dataset is an enhanced-resolution version of the EUMETSAT Ocean and Sea Ice Satellite Application Facility Global Sea Ice Concentration Climate Data Record (OSI SAF OSI-450-a CDR) over the period 1991-2020.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": null, "latestDataUpdateTime": "2023-05-04T16:59:21", "updateFrequency": "notPlanned", "dataLineage": "This dataset is an enhanced-resolution version of the EUMETSAT Ocean and Sea Ice Satellite Application Facility Global Sea Ice Concentration Climate Data Record (OSI SAF OSI-450-a CDR) over the period 1991-2020. It uses satellite data from US satellite missions SSM/I and SSMIS prepared by the EUMETSAT Climate Monitoring SAF and ECMWF/C3S ERA5 data.", "removedDataReason": "", "keywords": "Sea Ice, ESA, CCI, Arctic, Antarctic", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "12.5 km", "status": "completed", "dataPublishedTime": "2023-05-11T15:21:06", "doiPublishedTime": "2023-05-17T10:35:51", "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": 40003, "dataPath": "/neodc/esacci/sea_ice/data/sea_ice_concentration/L4/ssmi_ssmis/12.5km/v3.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 83566854605, "numberOfFiles": 21809, "fileFormat": "The data are in NetCDF format." }, "timePeriod": { "ob_id": 11084, "startTime": "1991-01-07T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 40002, "uuid": "0b1393808b734b339ac887225c5da0e5", "short_code": "cmppr", "title": "CCI+ Phase 1 SIC", "abstract": "CCI+ Phase 1 SIC" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2573, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 40, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_seaice_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 14368, "uuid": "d2112cfb5dab407599a64d9451d960b4", "short_code": "proj", "title": "ESA Sea Ice Climate Change Initiative Project", "abstract": "The ESA CCI Sea Ice project aims to combine and extend ongoing research to develop improved and validated timeseries of ice concentration and ice thickness for use in climate research. Since sea ice is a sensitive climate indicator with large seasonal and regional variability, the climate research community require long-term and regular observations of the key ice parameters in both Arctic and Antarctic. The project includes representatives from the scientific user community and climate research programmes to validate the ice concentration and ice thickness retrievals provided by the EO science team. \r\n\r\nThe ESA CCI Sea Ice project will deliver global data sets on ice concentration for Arctic and Antarctic, and ice thickness data sets for the Arctic, to support climate research and monitoring according to the GCOS requirements for generation of satellite-based data sets and products. This implies provision of data sets with associated metadata, software systems, technical documentation and scientific reports/publications. Ice thickness data from radar altimeters are not available for the Antarctic as a useful data set for climate research. The data sets to be delivered as the sea ice ECV parameters are based on many years of research where the members of the consortium are playing a leading role in development and validation of the EO-based data sets." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 13253, 53130, 53131, 66288, 66289, 66290, 66291, 66292, 66293, 66294, 66295, 66296 ], "vocabularyKeywords": [], "identifier_set": [ 12476, 12512 ], "observationcollection_set": [ { "ob_id": 14367, "uuid": "5e789087d4e847308a39b3fe5b26e281", "short_code": "coll", "title": "ESA Sea Ice Climate Change Initiative (Sea Ice CCI) Dataset Collection", "abstract": "Collection of datasets from the ESA Sea Ice Climate Change Initiative (CCI) project. The Sea Ice CCI is developing improved and validated timeseries of ice concentration for the Arctic and Antarctic and ice thickness datasets for the Arctic to support climate research and monitoring. Since sea ice is a sensitive climate indicator with large seasonal and regional variability, the climate research community require long-term and regular observations of the key ice parameters in both Arctic and Antarctic." } ], "responsiblepartyinfo_set": [ 193827, 193828, 193829, 193822, 193824, 193826, 194552, 193825, 195192, 194553, 194554, 194555, 194556, 194557, 194558, 194559, 194560 ], "onlineresource_set": [ 83187, 83188, 83412, 83413, 83414, 83456, 87486, 87487 ] }, { "ob_id": 39802, "uuid": "98cda325efc54da0aacca1d658e4a54a", "title": "Global Ensemble of Temperatures with Quantified Uncertainties in Observations, Coverage and Spatial modeling (GETQUOCS) from 1850-2018", "abstract": "Instrumental global temperature records are derived from the network of in situ measurements of land and sea surface temperatures. This observational evidence is seen as being fundamental to climate science. Therefore, the accuracy of these measurements is of prime importance for the analysis of temperature variability. There are spatial gaps in the distribution of instrumental temperature measurements across the globe. This lack of spatial coverage introduces coverage error. An approximate Bayesian computation based multi-resolution lattice kriging is developed and used to quantify the coverage errors through the variance of the spatial process at multiple spatial scales. It critically accounts for the uncertainties in the parameters of this advanced spatial statistics model itself, thereby providing, for the first time, a full description of both the spatial coverage uncertainties along with the uncertainties in the modeling of these spatial gaps. These coverage errors are combined with the existing estimates of uncertainties due to observational issues at each station location. It results in an ensemble of 100,000 monthly temperatures fields over the entire globe that samples the combination of coverage, parametric and observational uncertainties from 1850 to 2018 on a 5° by 5° grid. \r\n \r\n The 100,000 equally-plausible ensemble members are stored in a series of separate netcdf files each containing 1000 realisations. \r\n \r\n Additionally, there is 100-realisation subsample that provides an estimate of the uncertainty in the full ensemble. This has been created using conditional Latin hypercube sampling across 25 key regions of the globe. It many cases it would be sufficient to analyse just this 100-member subsample, for example to compute a likely range in a quantity. It is recommended that full 100,000 member ensemble is only investigated in those situations where the precise shape of the uncertainty distribution is required. NetCDF files at both the annual and monthly resolution are provided for this subsample.", "creationDate": "2023-03-20T14:13:59.630514", "lastUpdatedDate": "2023-03-20T13:55:09", "latestDataUpdateTime": "2023-03-29T15:51:05", "updateFrequency": "", "dataLineage": "This spatially complete global temperature dataset is built upon HadCRUT.4.5.0.0, which is itself derived from a merger of CRUTEM.4.5.0.0 and HadSST.3.1.1.0. Data supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "GETQUOCS, temperature, uncertainty, climate reconstruction, sea-surface temperature, approximate bayesian computation, historical climatology network, hypercube, homogenization, reliability", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-04-26T09:03:16", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3775, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 39799, "dataPath": "/badc/deposited2023/GETQUOCS", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 526363658858, "numberOfFiles": 103, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 11010, "startTime": "1850-01-01T00:00:00", "endTime": "2018-12-30T00:00:00" }, "resultQuality": { "ob_id": 4225, "explanation": "Research data", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-03-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39800, "uuid": "b226420753504b3ca1f8149a5d0634d9", "short_code": "comp", "title": "Uncertainty sampling procedure", "abstract": "The starting point for the creation of this dataset was HadCRUT.4.5.0.0, which is a 100-member ensemble of gridded temperature 'observations'. This was then infilled (i.e. expanded to give data everywhere) using multi-resolution lattice krigging with the uncertainty in the statistical model also sample. The R package used for the infilling and sampling was 'fields' (https://doi.org/10.5065/D6W957CT)" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2521, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 39801, "uuid": "c0f2fdfd01494992bd02af3cc425e681", "short_code": "proj", "title": "Global Ensemble of Temperatures with Quantified Uncertainties in Observations, Coverage and Spatial modeling (GETQUOCS)", "abstract": "This project was focussed on exploring the contributions of different sources of uncertainty in global reconstructions of surface temperature, especially those associated with the spatial infilling. The project was undertaken by Maryam Ilyas during her doctoral research at University College London. The title of her PhD thesis was \"Sparse, fitted empirical orthogonal functions for climate reconstruction\". It was supervised by Serge Guillas and Chris Brierley. Funding for the research visit to Doug Nychka, during which the particular innovations present in this dataset were developed, was provided by the ReCOVER project (EP/M008495/1)" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 59154, 59155, 60438, 62541 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 193845, 193846, 193847, 193848, 193849, 193850, 193851, 193855, 193852, 193853, 193854 ], "onlineresource_set": [ 83189 ] }, { "ob_id": 39803, "uuid": "90049a6555d1480bb5ce9637051dede8", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): New network of virtual altimetry stations for measuring sea level along the world coastlines from 2002 to 2019, v2.2", "abstract": "This dataset contains a 17-year-long (January 2002 to December 2019 ), high-resolution (20 Hz), along-track sea level dataset in coastal zones of: Northeast Atlantic, Mediterranean Sea, whole African continent, North Indian Ocean, Southeast Asia, Australia and North and South America. Up to now, satellite altimetry has provided global gridded sea level time series up to 10-15 km from the coast only, preventing the estimation of how sea level changes very close to the coast on interannual to decadal time scales. \r\n\r\nThis dataset has been derived from a new version of the ESA SL_cci+ dataset of coastal sea level anomalies which is based on the reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 satellite missions to derive satellite-sea surface ranges as close as possible to the coast (a process called ‘retracking’) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series.\r\n\r\nThis large amount of coastal sea level estimates has been further analysed to produce the present dataset: a total of 756 altimetry-based virtual coastal stations have been selected and sea level anomalies time series together with associated coastal sea level trends have been computed over the study time span. \r\n\r\nThe main objective of this dataset is to analyze the sea level trends close to the coast and compare them with the sea level trends observed in the open ocean and to determine the causes of the potential differences.\r\n\r\nThe product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). See 'The Climate Change Coastal Sea Level Team (2020). Sea level anomalies and associated trends estimated from altimetry from 2002 to 2018 at selected coastal sites. Scientific Data (Nature), in press'.\r\n\r\nThis dataset is v2.2 of the data and is a copy of the v2.2 data published on the SEANOE (SEA scieNtific Open data Edition) website (https://doi.org/10.17882/74354#98856) \r\n\r\nThe dataset should be cited as: \tCazenave Anny, Gouzenes Yvan, Birol Florence, Legér Fabien, Passaro Marcello, Calafat Francisco M, Shaw Andrew, Niño Fernando, Legeais Jean François, Oelsmann Julius, Benveniste Jérôme (2022). New network of virtual altimetry stations for measuring sea level along the world coastlines. SEANOE. https://doi.org/10.17882/74354\r\n\r\nIn addition,it would be appreciated that the following work(s) be cited too, when using this dataset in a publication :\r\n\r\n - Cazenave Anny, Gouzenes Yvan, Birol Florence, Leger Fabien, Passaro Marcello, Calafat Francisco M., Shaw Andrew, Nino Fernando, Legeais Jean François, Oelsmann Julius, Restano Marco, Benveniste Jérôme (2022). Sea level along the world’s coastlines can be measured by a network of virtual altimetry stations. Communications Earth & Environment, 3 (1). https://doi.org/10.1038/s43247-022-00448-z\r\n\r\n - Benveniste Jérôme, Birol Florence, Calafat Francisco, Cazenave Anny, Dieng Habib, Gouzenes Yvan, Legeais Jean François, Léger Fabien, Niño Fernando, Passaro Marcello, Schwatke Christian, Shaw Andrew (2020). Coastal sea level anomalies and associated trends from Jason satellite altimetry over 2002–2018. Scientific Data, 7 (1). https://doi.org/10.1038/s41597-020-00694-w", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-07-06T18:09:31", "updateFrequency": "", "dataLineage": "The product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative. The data has been archived on https://www.seanoe.org with a doi of https://doi.org/10.17882/74354#98856. A copy has been transferred to CEDA as part of the ESA CCI Open Data Portal project.", "removedDataReason": "", "keywords": "ESA CCI, SLA", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-08-03T16:51:27", "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": 40287, "dataPath": "/neodc/esacci/sea_level/data/XTRACK_ALES_SLA/Trends_SelectedSites/v2.2/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 66376114, "numberOfFiles": 757, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 8719, "startTime": "2002-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 4330, "explanation": "For information on data quality see the associated Sea Level CCI documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-07-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 31954, "uuid": "d54de4d2c9384557aebe9092144d9209", "short_code": "cmppr", "title": "Composite process for the ESA Sea Level CCI Altimeter coastal sea level anomalies datasets based on XTRACK/ALES processing.", "abstract": "The coastal sea level data products are based on a complete reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 missions to derive satellite-sea surface ranges as close as possible to the coast (a process called ‘retracking’) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series at monthly interval, from 20 km offshore to the coast" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2584, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 47, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sealevel_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13331, "uuid": "a0a6fa39470a4a7baf847e3a1751f950", "short_code": "proj", "title": "ESA Sea Level Climate Change Initiative Project", "abstract": "The European Space Agency (ESA) Sea Level Climate Change Initiative (Sea_Level_cci) project is part of the ESA's Climate Change Initiative programme. \r\n\r\nIn the first phases of the CCI programme, the Sea Level project produced and validated global sea level Essential Climate Variable (ECV) products.\r\n\r\nIn the current phase, the objective is to produce a long-term and homogeneous sea level record as close to the coast as possible in order to assess whether the coastal sea level trends experienced at the coast are similar as the observed sea level trends in the open ocean and to determine the causes of the potential discrepancies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50415, 50417, 62546, 66830, 66831, 66832, 66833 ], "vocabularyKeywords": [ { "ob_id": 10976, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/procLev/proc_ind", "resolvedTerm": "Indicator" }, { "ob_id": 10848, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_jason3", "resolvedTerm": "Jason-3" }, { "ob_id": 10723, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/org/org36", "resolvedTerm": "Laboratoire d'Etudes en Geodesie et Oceanographie Spatiales" }, { "ob_id": 10692, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/org/org05", "resolvedTerm": "Centre National de la Recherche Scientific" }, { "ob_id": 10693, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/org/org06", "resolvedTerm": "Centre National d'Etudes Spatiales" }, { "ob_id": 10631, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/dataType/dtype_msltr", "resolvedTerm": "mean sea level trends" }, { "ob_id": 11113, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_poseidon2", "resolvedTerm": "Poseidon-2" }, { "ob_id": 11114, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_poseidon3", "resolvedTerm": "Poseidon-3" }, { "ob_id": 11115, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_poseidon3b", "resolvedTerm": "Poseidon-3B" }, { "ob_id": 11020, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/product/prod_merged", "resolvedTerm": "MERGED" }, { "ob_id": 10702, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/org/org15", "resolvedTerm": "European Space Agency" }, { "ob_id": 10671, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_seaLevel", "resolvedTerm": "sea level" }, { "ob_id": 10683, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/freq/freq_mon", "resolvedTerm": "month" }, { "ob_id": 10846, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_jason1", "resolvedTerm": "Jason-1" }, { "ob_id": 10847, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_jason2", "resolvedTerm": "Jason-2" } ], "identifier_set": [], "observationcollection_set": [ { "ob_id": 12684, "uuid": "56c94cb1410f4f2b8a41729c0e558617", "short_code": "coll", "title": "ESA Sea Level Climate Change Initiative (Sea Level CCI) dataset collection", "abstract": "As part of the European Space Agency's (ESA) Climate Change Initiative (CCI) programme, the Sea Level CCI project has produced a set of gridded multi-satellite merged products relating to the Sea Level Essential Climate Variable (ECV). These consist of a) a time series of monthly gridded Sea Level Anomalies (SLA) and b) Oceanic Indicators describing the evolution of the sea level anomalies.\r\n\r\nSea surface heights are measured above (or below) some reference level by altimeter satellites, surface height being the difference between a satellites position in orbit with respect to an arbitrary reference surface (the Earth's centre or a rough approximation of the Earth's surface: the reference ellipsoid) and the satellite-to-surface range (calculated by measuring the time taken for the signal to make the round trip). Through sending a microwave pulse to the ocean's surface, the satellites measured the surface heights through measuring the time taken for the pulse to return. \r\n\r\nThe current version is v1.1, and covers the period January 1993 - December 2014, and has been derived from the main altimeter missions: ERS-1, ERS-2, Envisat, TOPEX/Poseidon, Jason-1, Jason-2 and Geosat-Follow-On. A detailed description of the SL CCI project and the products can be found in Ablain et al., 2014, and further information is also provided in the Product User Guide. \r\n\r\nThe following DOI can be used to reference the product database (all products in the V1.1 release (as of December 2015)): DOI:10.5270/esa-sea_level_cci-1993_2014-v_1.1-201512. \r\n\r\n When using or referring to the SL_cci products, please mention the associated DOI (see above and the individual datasets) and also use the following citation where a detailed description of the SL_cci project and products can be found:\r\n\r\nAblain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993–2010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.\r\n\r\nFor further information on the Sea Level CCI products, and to register your interest with the CCI team please email: info-sealevel@esa-sealevel-cci.org" } ], "responsiblepartyinfo_set": [ 193863, 193865, 193867, 193864, 193862, 193861, 193860, 196313, 196314, 196315, 196316, 196317, 196318, 196319, 196320, 196321, 196322, 196323 ], "onlineresource_set": [ 83197, 83198, 83201, 83200, 83199, 83839, 83840 ] }, { "ob_id": 39804, "uuid": "91d1fbc572224b9c86f6c14ba9109479", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Regional coastline profile of Vertical Land Motions in Europe and SE Asia/Oceania, v1", "abstract": "This dataset contains a regional coastline profile of Vertical Land Motions in Europe and SE Asia/Oceania produced as part of the ESA Climate Change Initiative Sea Level project.\r\n\r\nVertical Land Motions have been estimated as the difference between the altimeter coastal sea level v1.1 dataset (available from https://catalogue.ceda.ac.uk/uuid/222cf11f49a94d2da8a6da239df2efc4 ) and tide gauge measurements from the Permanent Service for Mean Sea Level (PMSML) network. Spatial interpolation has allowed the production of a regularly spaced coastline profile of vertical land movements together with their uncertainties.\r\n\r\nThe altimeter input data are from the Jason-1, Jason-2 and Jason-3 missions during the period Jan. 2002 - May 2018.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-07-04T14:18:06", "updateFrequency": "", "dataLineage": "The product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). A copy has been transferred to CEDA as part of the ESA CCI Open Data Portal project.", "removedDataReason": "", "keywords": "ESA CCI, SLA", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-08-03T16:43:59", "doiPublishedTime": "2023-08-04T12:26:25", "removedDataTime": null, "geographicExtent": { "ob_id": 2716, "bboxName": "", "eastBoundLongitude": 160.0, "westBoundLongitude": -30.0, "southBoundLatitude": -45.0, "northBoundLatitude": 60.0 }, "verticalExtent": null, "result_field": { "ob_id": 40269, "dataPath": "/neodc/esacci/sea_level/data/Vertical_Land_Motions_TUM/maps/v1/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 639063, "numberOfFiles": 2, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 11145, "startTime": "2002-01-15T00:00:00", "endTime": "2018-05-30T23:59:59" }, "resultQuality": { "ob_id": 4329, "explanation": "For information on the data quality see the associated Sea Level CCI documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-07-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40270, "uuid": "6f5d9b3d55d941a397c58d811619ac62", "short_code": "comp", "title": "Derivation of the ESA Sea Level Climate Change Initiative (Sea_Level_cci): Regional coastline profile of Vertical Land Motions in Europe and SE Asia/Oceania, v1", "abstract": "This dataset has been estimated as the difference between the altimeter coastal sea level SL_cci+ v1.1 dataset (https://catalogue.ceda.ac.uk/uuid/222cf11f49a94d2da8a6da239df2efc4) and tide gauges measurements from the Permanent Service for Mean Sea Level (PMSML) network (https://psmsl.org/)).\r\n\r\nThe altimeter input data are from the Jason-1, Jason-2 and Jason-3 missions during the period Jan. 2002 - May 2018.\r\n\r\nFor more information see the associated Sea Level CCI documentation (https://climate.esa.int/media/documents/SLCCI_CCN2_D1.1_TUM_v2.pdf)" }, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2584, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 47, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sealevel_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13331, "uuid": "a0a6fa39470a4a7baf847e3a1751f950", "short_code": "proj", "title": "ESA Sea Level Climate Change Initiative Project", "abstract": "The European Space Agency (ESA) Sea Level Climate Change Initiative (Sea_Level_cci) project is part of the ESA's Climate Change Initiative programme. \r\n\r\nIn the first phases of the CCI programme, the Sea Level project produced and validated global sea level Essential Climate Variable (ECV) products.\r\n\r\nIn the current phase, the objective is to produce a long-term and homogeneous sea level record as close to the coast as possible in order to assess whether the coastal sea level trends experienced at the coast are similar as the observed sea level trends in the open ocean and to determine the causes of the potential discrepancies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 66825, 66826, 66827, 66828, 66829 ], "vocabularyKeywords": [ { "ob_id": 10671, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_seaLevel", "resolvedTerm": "sea level" } ], "identifier_set": [ 12667 ], "observationcollection_set": [ { "ob_id": 12684, "uuid": "56c94cb1410f4f2b8a41729c0e558617", "short_code": "coll", "title": "ESA Sea Level Climate Change Initiative (Sea Level CCI) dataset collection", "abstract": "As part of the European Space Agency's (ESA) Climate Change Initiative (CCI) programme, the Sea Level CCI project has produced a set of gridded multi-satellite merged products relating to the Sea Level Essential Climate Variable (ECV). These consist of a) a time series of monthly gridded Sea Level Anomalies (SLA) and b) Oceanic Indicators describing the evolution of the sea level anomalies.\r\n\r\nSea surface heights are measured above (or below) some reference level by altimeter satellites, surface height being the difference between a satellites position in orbit with respect to an arbitrary reference surface (the Earth's centre or a rough approximation of the Earth's surface: the reference ellipsoid) and the satellite-to-surface range (calculated by measuring the time taken for the signal to make the round trip). Through sending a microwave pulse to the ocean's surface, the satellites measured the surface heights through measuring the time taken for the pulse to return. \r\n\r\nThe current version is v1.1, and covers the period January 1993 - December 2014, and has been derived from the main altimeter missions: ERS-1, ERS-2, Envisat, TOPEX/Poseidon, Jason-1, Jason-2 and Geosat-Follow-On. A detailed description of the SL CCI project and the products can be found in Ablain et al., 2014, and further information is also provided in the Product User Guide. \r\n\r\nThe following DOI can be used to reference the product database (all products in the V1.1 release (as of December 2015)): DOI:10.5270/esa-sea_level_cci-1993_2014-v_1.1-201512. \r\n\r\n When using or referring to the SL_cci products, please mention the associated DOI (see above and the individual datasets) and also use the following citation where a detailed description of the SL_cci project and products can be found:\r\n\r\nAblain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993–2010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.\r\n\r\nFor further information on the Sea Level CCI products, and to register your interest with the CCI team please email: info-sealevel@esa-sealevel-cci.org" } ], "responsiblepartyinfo_set": [ 193875, 193868, 193873, 196275, 193872, 193871, 193870, 193869, 196276 ], "onlineresource_set": [ 83204, 83205, 83831, 83832, 83833 ] }, { "ob_id": 39806, "uuid": "f107a4ce186844bb8adf8cd1f2f6d552", "title": "ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover Maps in Africa (Eastern Sahel region) at 10m spatial resolution for 2019 in Geotiff format, v1.2", "abstract": "This dataset contains high resolution (HR) land cover (LC) maps of a subregion of Africa, produced by the ESA High Resolution Land Cover (HRLC) Climate Change Initiative (CCI) project. This consists of the following products:\r\n\r\n1) HRLC10: High Resolution Land Cover Maps at 10m spatial resolution for year 2019 (also referred to as static maps).\r\n2) Associated uncertainty products.\r\n\r\nThey cover the geographic range (0.1°S – 18.1°N; 9.9°E – 43.3°E).\r\n\r\nThe data are provided as both GeoTIFF tiles following the Sentinel-2 MGRS tiling scheme and as a GeoTiff format mosaic. 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For information on the derivation see the project documentation." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2572, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 39, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_high_resolution_land_cover_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 39805, "uuid": "b057708eec1042238fb333ab02ec772e", "short_code": "proj", "title": "ESA High Resolution Land Cover Climate Change Initiative Project", "abstract": "The ESA High Resolution Land Cover Climate Change Initiative Project is part of the European Space Agency's Climate Change Initiative to produce long term datasets of Essential Climate Variables (ECV's) from historic satellite data." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12819 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 193907, 193908, 193909, 193910, 193904, 193905, 193906, 201822, 201823, 201824, 201825, 201826, 201827, 201828, 201829, 201830, 201831, 201832, 201833, 201834, 201835, 201836 ], "onlineresource_set": [ 83214, 83215, 85883, 86081, 86010, 86011 ] }, { "ob_id": 39814, "uuid": "5f7206e5854246a9ae7498305d620590", "title": "UM-UKCA model data for study investigating the QBO response to large tropical eruptions", "abstract": "This dataset contains summary 36-month data of the global mean volcanic stratospheric aerosol optical depth, temperature, sulfate aerosol mass concentration and wind velocities necessary for analysing the quasi-biennial oscillation (QBO). The data are from 8 model simulations of volcanic eruptions that have different sulfur dioxide emissions, eruption initiation date/season and QBO phase at the time of eruption. Three control ensembles were conducted for each initial condition. The simulations are from the Unified Model coupled with the United Kingdom Chemistry and Aerosol Scheme (UM-UKCA) Vn.11.2 and were conducted at a global resolution of 1.875 ° x 1.25°.\r\n\r\nVariables for calculating the QBO momentum budget (w*, flux divergence) are contained in the files ending ‘_momentum.nc’.\r\n\r\nThere are files for each initial condition named '{SEASON}_{SO2_INJECTION_MAGNITUDE}_{QBO_PHASE}.nc'.\r\n\r\nThere are 12 control files to give three ensemble members for each initial condition. These files are named ‘{SEASON}_{QBO_PHASE}_control_{no.}.nc’ for example: ‘July_wQBO_control_2.nc’.", "creationDate": "2023-03-29T12:54:58.808933", "lastUpdatedDate": "2023-03-29T12:54:58", "latestDataUpdateTime": "2023-03-29T15:06:50", "updateFrequency": "notPlanned", "dataLineage": "Data were generated using the Met Office Unified Model coupled with the United Kingdom Chemistry and Aerosol Scheme (UM-UKCA) Vn.11.2.\r\nData was converted to netCDF", "removedDataReason": "", "keywords": "UM-UKCA, QBO, quasi-biennial oscillation, tropical eruptions, volcanic", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-04-05T13:04:12", "doiPublishedTime": "2023-04-06T08:24:41", "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": 39815, "dataPath": "/badc/deposited2023/UKCA_volcanic_eruptions_QBO", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 40538150779, "numberOfFiles": 30, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 11014, "startTime": "1988-09-01T00:00:00", "endTime": "1997-09-01T00:00:00" }, "resultQuality": { "ob_id": 4236, "explanation": "Data as provided by author", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-04-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39816, "uuid": "091b007a16ca45dca0a23f00f349b687", "short_code": "comp", "title": "Met Office Unified Model coupled with the United Kingdom Chemistry and Aerosol Scheme (UM-UKCA) Vn.11.2 deployed on ARCHER", "abstract": "Met Office Unified Model coupled with the United Kingdom Chemistry and Aerosol Scheme (UM-UKCA) Vn.11.2 deployed on ARCHER" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2521, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 39817, "uuid": "95ac58ab5d14408094c825df1a9909b0", "short_code": "proj", "title": "The QBO response to a volcanic eruption", "abstract": "This project investigates simulations of tropical volcanic eruptions using the UM-UKCA aerosol-climate model with an explicit representation of the QBO. Simulations include eruptions emitting 60 Tg of SO2 (i.e., 1815 Mt. Tambora-magnitude) and 15 Tg of SO2 (i.e., 1991 Mt. Pinatubo-magnitude) initiated at the equator during two different QBO states. The dataset includes variables to understand the QBO response to a tropical volcanic eruption and the physical and dynamical drivers of the response." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 18924, 19038, 19039, 19043, 21990, 22005, 54873, 54874, 54875, 54876, 54877, 55648, 55978, 62353, 62528, 62529, 62530, 62531, 62532, 62533, 62534, 62535, 62536, 62537, 62538, 62539, 62540 ], "vocabularyKeywords": [], "identifier_set": [ 12457 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 193923, 193924, 193925, 193926, 193928, 193921, 193922, 193932, 193929, 193930, 193931 ], "onlineresource_set": [] }, { "ob_id": 39819, "uuid": "e0f6a223220d463ea7a5d2a7520000cf", "title": "HyperDrone Flight 20210722 - hyperspectral in situ radiometry and hyperspectral imagery at different altitudes for plastics detection", "abstract": "Airborne remote-sensed hyperspectral in-situ radiometry data and hyperspectral imagery collected by the NERC Field Spectroscopy Facility (FSF) Headwall Co-aligned VNIR and SWIR imager (450-2500 nm) with LiDAR instruments mounted on a drone platform. \r\nThese hyperspectral data collected over a sandy and rocky shore have associated uncertainty estimations that will be used to develop of radiometric proxies for plastics detection and assess future mission requirements. This dataset was collected on 22nd July 2021 from Oban airport's shore using a range of different plastic targets", "creationDate": "2023-03-29T16:41:00.092708", "lastUpdatedDate": "2023-03-29T16:42:48", "latestDataUpdateTime": "2023-03-29T17:28:20", "updateFrequency": "notPlanned", "dataLineage": "The following sensors were loaned from NERC Field Spectroscopy Facility and used in the HyperDrone project:\r\n• The HR-1024i hyperspectral spectrometer from the Spectra Vista Corp. (SVC) covering the range 350-2500 nm.\r\n• The hyperspectral Headwall co-aligned imager (pushbroom) covering the VNIR and SWIR region (450-2500 nm) mounted on a DJI M600 drone platform. This sensor is a instrument able to collect LiDAR data at the same time for direct geo-rectification via the manufacturer software.\r\n\r\nA ReadMe file is included in this project that explains in detail the dataset collected in this project and the processing stages.", "removedDataReason": "", "keywords": "UAS,UAV,drone,hyperspectral,in situ,Plastic,macroplastics,", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-04-28T14:14:00", "doiPublishedTime": "2023-04-28T14:40:26", "removedDataTime": null, "geographicExtent": { "ob_id": 3780, "bboxName": "", "eastBoundLongitude": -5.4029746225, "westBoundLongitude": -5.4046649682, "southBoundLatitude": 56.4614384049, "northBoundLatitude": 56.4626497771 }, "verticalExtent": null, "result_field": { "ob_id": 39850, "dataPath": "/neodc/hyperdrone/data/2021/HyperDrone_20210722", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 275068519154, "numberOfFiles": 103, "fileFormat": "ENVI BIL and BADC-CSV Format" }, "timePeriod": { "ob_id": 11017, "startTime": "2021-07-22T00:00:00", "endTime": "2021-07-22T23:59:59" }, "resultQuality": { "ob_id": 4228, "explanation": "A ReadMe file is included in this project that explains in detail the dataset collected in this project and the processing stages.\r\n\r\nUncertainty estimates are also included in this delivery providing esential information about data quality.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-03-29" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 39825, "uuid": "d0bd2cf6365348caa59e54625805a549", "short_code": "acq", "title": "Acquisition for: HyperDrone _20210722", "abstract": "The Headwall Co-aligned VNIR and SWIR imager (450-2500 nm) with LiDAR sensor mounted on a DJI Matrice 600 drone platform" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": 39820, "uuid": "9b606be6637243eca443dc4a087ad9de", "short_code": "proj", "title": "HyperDrone", "abstract": "HyperDrone was funded by the funded by the Discovery Element of the ESA’s Basic Activities to tackle the problem of marine plastic debris on the shore using remote sensing optical instruments. HyperDrone collected hyperspectral data with uncertainty estimates to develop algorithms for plastic detection of plastics to allowing as well to provide precise mission requirements for theorical new sensors. ESA contract number 4000113163/20/ NL/GLC" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64171, 64172, 64173 ], "vocabularyKeywords": [], "identifier_set": [ 12472 ], "observationcollection_set": [ { "ob_id": 39941, "uuid": "34038fecaaca472b9281184718841acf", "short_code": "coll", "title": "NERC Field Spectroscopy Facility (FSF)", "abstract": "The NERC Field Spectroscopy Facility maintains and provides a pool of state-of-the-art spectroscopy instruments for use by the UK research community. This collection consists of datasets produced by various project teams based on measurements collected by these instruments.\r\nThe FSF is located in the GeoSciences department at the University of Edinburgh and overseen by NERC's National Centre for Earth Observation." } ], "responsiblepartyinfo_set": [ 193936, 193937, 193938, 193939, 193940, 193941, 193942, 194370, 193943 ], "onlineresource_set": [] }, { "ob_id": 39827, "uuid": "2485214239134768820ffb50fb5513bc", "title": "HyperDrone Flight 20200929 - hyperspectral in situ radiometry and hyperspectral imagery at different altitudes for plastics detection", "abstract": "Airborne remote-sensed hyperspectral in-situ radiometry data and hyperspectral imagery collected by the NERC Field Spectroscopy Facility (FSF) Headwall Co-aligned VNIR and SWIR imager (450-2500 nm) with LiDAR instruments mounted on a drone platform. \r\nThese hyperspectral data collected over a sandy and rocky shore have associated uncertainty estimations that will be used to develop of radiometric proxies for plastics detection and assess future mission requirements. \r\nThis dataset was collected on 29th September 2020 at Tyninghame beach, East Lothian, Scotland using a range of different plastic targets.", "creationDate": "2023-03-29T16:48:39.424075", "lastUpdatedDate": "2023-03-29T16:48:39", "latestDataUpdateTime": "2023-03-29T17:15:38", "updateFrequency": "notPlanned", "dataLineage": "The following sensors were loaned from NERC Field Spectroscopy Facility and used in the HyperDrone project:\r\n• The HR-1024i hyperspectral spectrometer from the Spectra Vista Corp. 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This collection consists of datasets produced by various project teams based on measurements collected by these instruments.\r\nThe FSF is located in the GeoSciences department at the University of Edinburgh and overseen by NERC's National Centre for Earth Observation." } ], "responsiblepartyinfo_set": [ 193953, 193954, 193955, 193956, 193957, 193958, 193959, 194371, 193960 ], "onlineresource_set": [] }, { "ob_id": 39829, "uuid": "7a2a80d01dd645b2b14efb5647835c78", "title": "Sensor based ambient air concentration data for nitrogen dioxide and particles in Oxford, measured by the OxAria project 2020 to 2022.", "abstract": "This dataset contains raw and processed levels of nitrogen dioxide (NO2), particles (PM10 & PM2.5) in ambient air in Oxford, UK. These are derived from low cost sensor units located ...... The raw data is at 10-second intervals and the processed data is at 15-minute and 1-hour resolutions. The raw data is available in JSON format 2020 to 2022 and the processed data is available in CSV format Oct 2020 to Oct 2021. These data were collected for the OxAria project.\r\n\r\nThe Oxaria project is a Natural Environmental Research Council funded collaboration between the University of Birmingham and University of Oxford, supported by public and commercial partners. The project has applied advanced technological and environmental health expertise to understand the air and noise impacts of COVID-19 across Oxford City. See also https://oxaria.org.uk/ \r\n\r\nFor Project record: - The application of high-resolution sensing technology in this context offers potential to measure air pollution at an unprecedented scale and scope, providing a more comprehensive picture of air pollution across Oxford than has previously been possible. Data obtained before, during and after relevant COVID-19 restrictions have been used to understand impacts upon road traffic, air and noise pollution\r\nlevels and to assess implications for healthy life expectancy and therefore human health. This information will be used to provide an evidence-base for air quality policy within local authorities, public agencies and national Government.", "creationDate": "2023-03-30T10:25:15.238403", "lastUpdatedDate": "2023-03-30T10:25:15", "latestDataUpdateTime": "2023-03-30T10:25:15", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the National Institute for Health Research (NIHR) and the Natural Environmental Research Council (NERC) grant funded research and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "nitrogen dioxide, NO2, particles, oxaria", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3783, "bboxName": "", "eastBoundLongitude": -1.184354, "westBoundLongitude": -1.277052, "southBoundLatitude": 51.704965, "northBoundLatitude": 51.781494 }, "verticalExtent": null, "result_field": { "ob_id": 39830, "dataPath": "/badc/deposited2023/oxaria_2020_2022/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 32989121339, "numberOfFiles": 11, "fileFormat": "Data are CSV formatted" }, "timePeriod": { "ob_id": 11019, "startTime": "2020-10-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 4231, "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": "2023-03-30" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 39836, "uuid": "0d9f411b9ff149e39bbd4015fb20a58f", "short_code": "acq", "title": "Acquisition for NO2 data for OxAria", "abstract": "Nitrogen dioxide (NO2) and particles (PM10 & PM2.5) in ambient air in Oxford, UK. These are derived from low cost sensor units located ...... The raw data is at 10-second intervals and the processed data is at 15-minute and 1-hour resolutions." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2521, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 39837, "uuid": "5817b366b1464dbeb465868c302b833b", "short_code": "proj", "title": "OxAria: Air quality in Oxford City", "abstract": "The OxAria project is a Natural Environmental Research Council funded collaboration between the University of Birmingham and University of Oxford, supported by public and commercial partners. The project has applied advanced technological and environmental health expertise to understand the air and noise impacts of COVID-19 across Oxford City. See also https://oxaria.org.uk/ \r\n\r\nThe application of high-resolution sensing technology in this context offers potential to measure air pollution at an unprecedented scale and scope, providing a more comprehensive picture of air pollution across Oxford than has previously been possible. Data obtained before, during and after relevant COVID-19 restrictions have been used to understand impacts upon road traffic, air and noise pollution\r\nlevels and to assess implications for healthy life expectancy and therefore human health. This information will be used to provide an evidence-base for air quality policy within local authorities, public agencies and national Government." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 193962, 193963, 193964, 193965, 193966, 193967, 193968, 193969, 193971, 193970, 193972, 193973, 193974, 193975, 193976 ], "onlineresource_set": [] }, { "ob_id": 39844, "uuid": "295ee0ee59ee43439de38f83bb818b4f", "title": "HighResCoralStress: Observed and statistically downscaled CMIP6 projections of daily sea surface temperature (SST) at 0.01°/1 km spatial resolution for the global coral reef area from 1985 to 2100.", "abstract": "The HighResCoralStress dataset comprises high spatial resolution (0.01°/1 km) daily sea surface temperature (SST) for the global coral reef area for past (1985-2019) and future (2020-2100) time periods. There are 12 coral reef regions based on those described by McWilliam et al. (2018). They vary in their functional redundancy and so indicate susceptibility to ecological changes with climate change. Statistically downscaled Coupled Model Intercomparison Project 6 (CMIP6) projections of daily SST are provided for 420,334 1 km coral reef pixels in 12 coral reef regions for four shared socio-economic pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) for the time period (1985-2100). \r\nThere are separate downscaled CMIP6 files for each region and SSP. The 1 km observed dataset used in the statistical downscaling is also provided for the period (1985-2019). CMIP6 projections of SST were statistically downscaled from their native model resolution (25-100 km) to 1 km spatial resolution using asynchronous linear regression. Full details of the statistical downscaling methodology and source datasets used in this project can be found in Dixon et al. (2022). \r\nCoral reefs globally are threatened by rising ocean temperatures and increasing frequency and severity of thermal stress events. High resolution sea surface temperature (SST) projections are a useful tool in climate vulnerability assessments, ecological modelling, spatial planning and conservation decision making for coral reefs around the world. \r\nAll source datasets used in the generation of HighResCoralStress are openly available and details can be found in Dixon et al. (2022). The authors request that you refer to this article before using the datasets, please refer to online resources on this record for links. Thermal stress metrics calculated using HighResCoralStress can be found at the following link: https://highrescoralstress.org/. The coordinates for the regional boundaries can be found in the NetCDF file attributes.\r\nThis work was undertaken by researchers from the University of Leeds, Texas Tech University and James Cook University funded by the Natural Environment Research Council (NERC) project DTP Spheres (NE/L002574/1).", "creationDate": "2023-04-03T08:56:18.904338", "lastUpdatedDate": "2023-04-03T08:56:18", "latestDataUpdateTime": "2024-03-09T01:40:40", "updateFrequency": "notPlanned", "dataLineage": "Dataset was generated for 420,334 1 km coral reef pixels. The four 3-month seasonal periods used in the statistical downscaling were updated to start in December instead of January. For V1.0, dataset first created for 232,828 1 km coral reef pixels in 12 coral reef regions. Details of how the dataset was generated can be found in the following article: https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000004. 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": "CMIP6, coral reefs, sea surface temperature, statistical downscaling", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-06-06T11:52:16", "doiPublishedTime": "2023-06-06T12:22:09", "removedDataTime": null, "geographicExtent": { "ob_id": 3847, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -35.0, "northBoundLatitude": 35.0 }, "verticalExtent": null, "result_field": { "ob_id": 39845, "dataPath": "/bodc/LSB230075", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2735789971244, "numberOfFiles": 698, "fileFormat": "Data are CF-Compliant NetCDF formatted data files" }, "timePeriod": { "ob_id": 11022, "startTime": "1985-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 3732, "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": "2021-07-02" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39847, "uuid": "752cd95d7ebe460db5f86ce90d7de935", "short_code": "comp", "title": "Statistical downscaling of daily SST for the global coral reef area", "abstract": "HighResCoralStress was generated by statistical downscaling of sea surface temperature (SST) projected by CMIP6 models for the global coral reef area. The global coral reef area was determined by extracting the latitude and longitude of 1 km global coral reef pixels from the UNEP World Conservation Monitoring Centre dataset (UNEP-WCMC, WorldFish Centre, WRI, TNC, 2010). The 1 km global coral reef pixels were split into 12 coral reef regions (McWilliam et al., 2018). Observed SST used in the statistical downscaling was a combination of the JPL MUR SST Analysis (Chin et al., 2017; 1 km) and ESA CCI SST Analysis (Merchant et al., 2016; 5 km) resulting in a single 1 km resolution dataset for each global coral reef pixel (01/01/1985 - 31/12/2019) - see S1 Appendix in Dixon et al. (2022) for more information. Data for reef pixels where CCI uses a climatology, likely due to missing data, and MUR does not were replaced with NOAA CRW CoralTemp SST (NOAA Coral Reef Watch, 2018; 5 km) - again see S1 Appendix in Dixon et al. (2022) for more information. CMIP6 model 'tos' output for 14 models and four Shared Socioeconomic Pathways (SSP) was interpolated longitudinally to fill missing data points, converted to 1 km resolution by bilinear interpolation and data extracted for each 1 km global coral reef pixel - see S2 Appendix in Dixon et al. (2022) for more information. For the statistical downscaling, linear regression models were generated for four seasonal periods for each global coral reef pixel using the ranked observed and simulated 1 km SST for a model training period (even years). Different combinations of polynomial trends (1st, 2nd and 3rd order) were removed from the observed and simulated SST datasets prior to downscaling and added back in after to maintain the long-term warming trend simulated by the model - see S3 Appendix in Dixon et al. (2022) for more information. The statistically downscaled SST for the historical period (1985-2019) was evaluated relative to observed SST for a model testing period (odd years) by calculating the root mean square error - see S3 Appendix in Dixon et al. (2022). The 'best' combination of polynomial trends was selected by finding the lowest root mean square error - see S3 Appendix in Dixon et al. (2022). Finally, the seasonal linear regression models were generated using all years in the historical time period (1985-2019) to statistically downscale the simulated SST (1985-2100). The method is described in more detail in Dixon et al. (2022)." }, "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": 39846, "uuid": "f68d0d11c2574efab3893034bf98e287", "short_code": "proj", "title": "Identifying low climate exposure coral reefs for climate-relevant coral reef management", "abstract": "The overarching aim of this project was to develop and test new tools for identifying low exposure coral reefs to better protect coral reef ecosystems from climate change. This aim was explored through the following three research questions: 1. Can climate models project local and regional scale changes in coral reef climate exposure? 2. How will extreme climate exposure on coral reefs change at local and regional scales in the future? 3. Are local-scale climate projections useful in conservation planning? This PhD project was funded by the Natural Environment Research Council (NERC) Spheres Doctoral Training Partnership (grant reference NE/L002574/1) at the University of Leeds." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50415, 50417, 60438, 62527 ], "vocabularyKeywords": [], "identifier_set": [ 12526 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 194030, 194037, 194038, 194027, 194029, 194028, 194031, 194032, 194039, 194040, 194041, 194042 ], "onlineresource_set": [ 83225, 83423, 83424, 83425, 83426, 83427, 83428, 83429, 83430, 83431, 83432, 83433, 83434, 83435, 83436, 83437, 83438, 83439, 83440, 83441, 83442 ] }, { "ob_id": 39853, "uuid": "9ef2331d1a6341f79773440c5ba25fb2", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from DMHZ's lufft-chm15k instrument deployed at Ogulin, Croatia", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Croatian Meteorological and Hydrological Service (DMHZ)'s lufft-chm15k deployed at Ogulin, Croatia.\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: 0-20000-0-14328.\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": "2023-04-05T13:24:34.925550", "lastUpdatedDate": "2023-04-05T13:24:34.925567", "latestDataUpdateTime": "2025-07-18T00:00:07", "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": "2023-03-09T08:43:12", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3786, "bboxName": "Ogulin", "eastBoundLongitude": 15.225000381469727, "westBoundLongitude": 15.225000381469727, "southBoundLatitude": 45.262779235839844, "northBoundLatitude": 45.262779235839844 }, "verticalExtent": null, "result_field": { "ob_id": 39852, "dataPath": "/badc/eprofile/data/daily_files/croatia/ogulin/dhmz-lufft-chm15k_A", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2904352155, "numberOfFiles": 1096, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 11023, "startTime": "2023-03-07T00:00:00", "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": 39854, "uuid": "de2339c3cb1d48299b4a36df35fa8095", "short_code": "acq", "title": "DMHZ: lufft-chm15k instrument deployed at Ogulin", "abstract": "lufft-chm15k instrument instrument deployed at Ogulin operated by Croatian Meteorological and Hydrological Service (DMHZ) providing cloud base height and aerosol profile data." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 220 ], "discoveryKeywords": [], "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, 62346, 62347, 62526 ], "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": [ 194059, 194060, 194061, 194062, 194063, 194064, 194065, 194066, 194067 ], "onlineresource_set": [ 83231, 83232 ] }, { "ob_id": 39857, "uuid": "9ee48040310846bc85666c58a5bd7934", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from DMHZ's lufft-chm15k instrument deployed at Osijek, Croatia", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Croatian Meteorological and Hydrological Service (DMHZ)'s lufft-chm15k deployed at Osijek, Croatia.\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: 0-20000-0-14280.\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": "2023-04-05T13:27:21.337508", "lastUpdatedDate": "2023-04-05T13:27:21.337521", "latestDataUpdateTime": "2025-07-18T00:00:09", "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": "2023-03-09T08:43:12", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3787, "bboxName": "Osijek", "eastBoundLongitude": 18.56166648864746, "westBoundLongitude": 18.56166648864746, "southBoundLatitude": 45.502220153808594, "northBoundLatitude": 45.502220153808594 }, "verticalExtent": null, "result_field": { "ob_id": 39856, "dataPath": "/badc/eprofile/data/daily_files/croatia/osijek/dhmz-lufft-chm15k_A", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2831384744, "numberOfFiles": 1071, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 11024, "startTime": "2023-03-07T00:00:00", "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": 39858, "uuid": "f11f60347f3a44689556af25e76d19ba", "short_code": "acq", "title": "DMHZ: lufft-chm15k instrument deployed at Osijek", "abstract": "lufft-chm15k instrument instrument deployed at Osijek operated by Croatian Meteorological and Hydrological Service (DMHZ) providing cloud base height and aerosol profile data." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 220 ], "discoveryKeywords": [], "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, 62346, 62347, 62526 ], "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": [ 194074, 194075, 194076, 194077, 194078, 194079, 194080, 194073, 194081 ], "onlineresource_set": [ 83234, 83236 ] }, { "ob_id": 39861, "uuid": "bbd5204ace084772b30d1762ce922500", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from DMHZ's lufft-chm15k instrument deployed at Varazdin, Croatia", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Croatian Meteorological and Hydrological Service (DMHZ)'s lufft-chm15k deployed at Varazdin, Croatia.\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. 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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." }, { "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": [ 194177, 194178, 194179, 194180, 194181, 194182, 194183, 194184, 194185 ], "onlineresource_set": [ 83257, 83258 ] }, { "ob_id": 39892, "uuid": "5bdabb4cba5d43e6a577f72ae03ca25b", "title": "OSCA IOP-1 Summer: Roadside site gas data (June-July 2021)", "abstract": "Measurements of a range of atmospheric gases made during the Integrated Research Observation System for Clean Air (OSCA) Intensive Operations Project 1 (IOP) in Summer (June-July) 2021 at the Manchester Air Quality Site (MAQS) and the Upper Brook Street, Manchester, roadside site.\r\n\r\nThis dataset include measurements of Soil NOx using the Lancaster University Dynamic Soil Chambers connected to a Teledyne N500 NOx analyser, hydrogen chloride using the Tunable IR Laser Direct Absorption Spectrometer (TILDAS) at the MAQS site, and carbon dioxide (CO2), Nitrous oxides (NOx) and Volatile Organic Compounds (VOCs) at the roadside site in Manchester", "creationDate": "2022-08-23T15:54:29.635295", "lastUpdatedDate": "2022-08-23T15:54:29", "latestDataUpdateTime": "2025-03-11T16:28:15", "updateFrequency": "asNeeded", "dataLineage": "Data was collected by the instrument operators and uploaded to CEDA.\r\nRoadside data was collected using the WACL Air Sampling Platform by Samuel Cliff at the University of York and converted into BADC format in R.", "removedDataReason": "", "keywords": "OSCA, maqs, air quality, OSCA-AQ", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-04-06T16:18:25", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3794, "bboxName": "", "eastBoundLongitude": -2.214244, "westBoundLongitude": -2.22923, "southBoundLatitude": 53.4566, "northBoundLatitude": 53.4667 }, "verticalExtent": null, "result_field": { "ob_id": 39902, "dataPath": "/badc/osca/data/iop-1-summer/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 333093080, "numberOfFiles": 6, "fileFormat": "BADC-CSV" }, "timePeriod": { "ob_id": 11032, "startTime": "2021-06-15T00:00:00", "endTime": "2021-07-30T23:59:59" }, "resultQuality": { "ob_id": 4237, "explanation": "Data is as provided by the instrument operators", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-04-06" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 39897, "uuid": "d66d2ef43d204f228a88e87de26fc2db", "short_code": "acq", "title": "Acquisition for OSCA IOP Summer 2021", "abstract": "Acquisition for OSCA IOP Summer 2021" }, "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": [ 76067, 76070, 76071, 76074, 76076, 76077, 76078, 76082, 76083, 76086, 76088, 76089, 76091, 76092, 76095, 76097, 76098, 76099, 76103, 76105, 83868, 83869, 83870, 83871, 83872, 83873, 83874, 83875, 83876, 83877, 83878, 83879, 83880, 83881, 83882, 83883, 83884, 83885, 83886, 83887, 83888, 83889, 83890, 83891, 83892, 83893, 83894, 83895, 83896, 83897, 83898, 83899, 83900, 83901, 83902, 83903, 83904 ], "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." } ], "responsiblepartyinfo_set": [ 194199, 194192, 194193, 194194, 194195, 194196, 194197, 194198, 194203, 194204, 194205, 194206 ], "onlineresource_set": [] }, { "ob_id": 39898, "uuid": "5c1742753edb40a78cb7d0c2aa003ab3", "title": "OSCA IOP-2 Winter: Roadside site gas data (January-February 2022)", "abstract": "Measurements of a range of atmospheric gases made during the Integrated Research Observation System for Clean Air (OSCA) Intensive Operations Project 2 (IOP) in Winter (January-February) 2022 at the Manchester Air Quality Site (MAQS) and the Upper Brook Street, Manchester, roadside site.\r\n\r\nThis dataset include measurements of Soil NOx using the Lancaster University Dynamic Soil Chambers connected to a Teledyne N500 NOx analyser at the MAQS site, and carbon dioxide (CO2), Nitrous oxides (NOx) and Volatile Organic Compounds (VOCs) at the roadside site in Manchester", "creationDate": "2022-08-23T15:54:29.635295", "lastUpdatedDate": "2022-08-23T15:54:29", "latestDataUpdateTime": "2023-04-04T16:51:25", "updateFrequency": "asNeeded", "dataLineage": "Data was collected by the instrument operators and uploaded to CEDA.\r\nRoadside data was collected using the WACL Air Sampling Platform by Samuel Cliff at the University of York and converted into BADC format in R.", "removedDataReason": "", "keywords": "OSCA, maqs, air quality, OSCA-AQ", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-04-06T16:17:51", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3794, "bboxName": "", "eastBoundLongitude": -2.214244, "westBoundLongitude": -2.22923, "southBoundLatitude": 53.4566, "northBoundLatitude": 53.4667 }, "verticalExtent": null, "result_field": { "ob_id": 39900, "dataPath": "/badc/osca/data/iop-2-winter/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 202383136, "numberOfFiles": 5, "fileFormat": "BADC-CSV" }, "timePeriod": { "ob_id": 11033, "startTime": "2022-01-21T00:00:00", "endTime": "2022-02-28T23:59:59" }, "resultQuality": { "ob_id": 4237, "explanation": "Data is as provided by the instrument operators", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-04-06" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 39897, "uuid": "d66d2ef43d204f228a88e87de26fc2db", "short_code": "acq", "title": "Acquisition for OSCA IOP Summer 2021", "abstract": "Acquisition for OSCA IOP Summer 2021" }, "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": [ 76067, 76068, 76069, 76070, 76071, 76072, 76073, 76074, 76075, 76076, 76077, 76078, 76079, 76080, 76081, 76082, 76083, 76084, 76085, 76086, 76087, 76088, 76089, 76090, 76091, 76092, 76093, 76094, 76095, 76096, 76097, 76098, 76099, 76100, 76101, 76102, 76103, 76104, 76105, 76106, 76107, 76108, 76109 ], "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." } ], "responsiblepartyinfo_set": [ 194218, 194219, 194220, 194221, 194222, 194223, 194224, 194225, 194226, 194227, 194228, 194229 ], "onlineresource_set": [] }, { "ob_id": 39899, "uuid": "af60720c1e404a9e9d2c145d2b2ead4e", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020, v4", "abstract": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. \r\n\r\nThis release of the data is version 4. Compared to version 3, version 4 consists of an update of the three maps of AGB for the years 2010, 2017 and 2018 and new AGB maps for 2019 and 2020. New AGB change maps have been created for consecutive years (2018-2017, 2019-2018 and 2020-2019) and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.\r\n\r\n\r\n\r\nThe data products consist of two (2) global layers that include estimates of:\r\n1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots\r\n2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)\r\n\r\nIn addition, files describing the AGB change between two consecutive years (i.e., 2018-2017, 2019-2018 and 2020-2010) and over a decade (2020-2010) are provided (labelled as 2018_2017, 2019_2018, 2020_2019 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.\r\n\r\n\r\nData are provided in both netcdf and geotiff format.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-04-20T12:04:56", "updateFrequency": "notPlanned", "dataLineage": "Data was created by the Biomass CCI team.", "removedDataReason": "", "keywords": "ESA, CCI, Biomass", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-04-21T13:38:53", "doiPublishedTime": "2023-04-21T14:21:43", "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": 39940, "dataPath": "/neodc/esacci/biomass/data/agb/maps/v4.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 302039423059, "numberOfFiles": 5182, "fileFormat": "Data are netCDF and geotiff formatted." }, "timePeriod": { "ob_id": 8795, "startTime": "2010-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3355, "explanation": "For information on the quality of the data see the documentation and website for the Biomass CCI", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2019-11-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33268, "uuid": "c57edbf3a959498f9a7d7bcbbb951d59", "short_code": "cmppr", "title": "CCI Biomass v3.0", "abstract": "CCI Biomass" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2542, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 20, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_biomass_terms_and_conditions_v2.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 29954, "uuid": "748552f369494506bc790c8a51854c38", "short_code": "proj", "title": "ESA CCI Biomass Climate Change Initiative (CCI)", "abstract": "The primary science objective ESA’s Climate Change Initiative + Biomass project is to provide global maps of above-ground biomass (Mg ha-1) with these being capable of supporting quantification of biomass change. The mapping aims to achieve 500 m to 1 km spatial resolution with a relative error of less than 20 % where AGB exceeds 50 Mg ha-1. Although this resolution is finer than required for current climate modelling, it will allow more refined information to be inferred (e.g. forest age structure and the disturbance regime) that is relevant for climate and has the potential to be exploited by carbon cycle and climate models as they develop." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 66280, 66281, 66282, 66283, 66284, 66285, 66286, 66287 ], "vocabularyKeywords": [], "identifier_set": [ 12470 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 194230, 194231, 194232, 194233, 194234, 194235, 194236, 194237, 194238, 194239 ], "onlineresource_set": [ 83260, 83259, 83262, 83263, 83510, 84892, 87636, 87637, 87934, 88098, 88112, 88135, 92688, 92706, 95063, 95064 ] }, { "ob_id": 39905, "uuid": "a4323e1c9be9487ea2cabc31878f6655", "title": "UKESM1 fixed concentration simulations at different global warming levels", "abstract": "Test", "creationDate": "2023-04-13T08:53:57.111236", "lastUpdatedDate": "2023-04-13T08:53:57.111244", "latestDataUpdateTime": "2023-04-13T08:53:57.111249", "updateFrequency": "notPlanned", "dataLineage": "test", "removedDataReason": "", "keywords": "", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3795, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 11034, "startTime": "2015-01-01T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4238, "explanation": "test", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-04-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 194246, 194247, 194248, 194249, 194250, 194251 ], "onlineresource_set": [] }, { "ob_id": 39909, "uuid": "899d7de10af547f4a1b19c3e70882e5a", "title": "MOASA flight M326: airborne observations from the Clean Air project over London, UK", "abstract": "In-situ airborne observations collected during flight M326 on 11 April 2022 by the Clean Air instrument suite on board the Met Office Atmospheric Survey Aircraft (MOASA) for the MOASA Clean Air project. \r\n\r\nThis dataset contains in meteorological and situ atmospheric composition measurements (gaseous nitrogen dioxide, ozone, sulphur dioxide and fine mode (PM2.5) aerosol) over London, UK, in a city survey flight pattern as part of the Strategic Priorities Fund (SPF) Clean Air Programme Intense Observational Period. 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During this time 60 flights were made (totalling over 153 hours of atmospheric sampling), across 8 repeatable sorties including two intensive data collection periods, one in summer and one in winter, flying upwind and downwind of the the Natural Environment Research Council (NERC) ‘supersites’ in London, Manchester and Birmingham and circling over them at various altitudes.\r\n\r\nThe aircraft was equipped with with air monitoring instrumentation to observe the three-dimensional distribution of pollutants (gaseous nitrogen dioxide, ozone, sulphur dioxide and fine mode (PM2.5) aerosol).\r\n\r\nThis project was part of the Strategic Priorities Fund (SPF) Clean Air Programme, delivered by UKRI." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 63234, 63235, 63236, 63237, 63238, 63239, 63240, 63241, 63242, 63243, 63244, 63245, 63246, 63247, 63248, 63249, 63250, 63251, 63252, 63253, 63254, 63255, 63256, 63257, 63258, 63259, 63260, 63261, 63262, 63263, 63264, 63265, 63266, 63267, 63268, 63269, 63270, 63271, 63272, 63273, 63274, 63275, 63276, 63277, 63278, 63279, 63280, 63281, 63282, 63283, 63284, 63285, 63286, 63287, 63288, 63289, 63290, 63291, 63292, 63293, 63294, 63295, 63296, 63297, 63298, 63299, 63300, 63301, 63302, 63303, 63304, 63305, 63306, 63307, 63308, 63309, 63310, 63311, 63312, 63313, 63314, 63315, 63316, 63317, 63318, 63319, 63320, 63321, 63322, 63323, 63324, 63325, 63326, 63327, 63328, 63329, 63330, 63331, 63332, 63333, 63334, 63335, 63336, 63337, 63338, 63339, 63340, 63341, 63342, 63343, 63344, 63345, 63346, 63347, 63348, 63349, 63350, 63351, 63352, 63353, 63354, 63355, 63356, 63357, 63358 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 39574, "uuid": "0aa1ec0cf18e4065bdae8ae39260fe7d", "short_code": "coll", "title": "MOASA Clean Air Project: airborne atmospheric measurements collection", "abstract": "A long-term quality assured dataset of the three-dimensional distribution of pollutants (gaseous nitrogen dioxide, ozone, sulphur dioxide and fine mode (PM2.5) aerosol) collected over southern UK, from August 2019 to April 2022, using the instrumented Met Office Atmospheric Survey Aircraft (MOASA). It includes 60 flights (totalling over 153 hours of atmospheric sampling), across 8 repeatable sorties made for the MOASA Clean Air project.\r\n\r\nData are presented as one NetCDF file per flight, containing quality assured, time synchronised, three-dimensionally geo-located time-series of observed pollutants. \r\n\r\nTo ensure optimal traceability and transparency of data, comprehensive metadata is included for each flight, detailing any calibration constants and/or corrections applied to data alongside general information about the data and instruments. \r\n\r\nA full list of NetCDF variables can be found in “Long-term airborne measurements of pollutants over the United Kingdom to support air quality model development and evaluation”, A. Mynard et al, 2023. This paper also provides a technical introduction to the MOASA measurement platform, information on instrumentation, flight strategies and data, as well as example case studies comparing flight observations to the Met Office Air Quality in the Unified Model (AQUM) and the ground based Automated Urban and Rural Network. This paper should be referenced in any research/publications pertaining from this dataset." } ], "responsiblepartyinfo_set": [ 194345, 194346, 194347, 194348, 194349, 194350, 194351, 194352, 194353 ], "onlineresource_set": [ 83306 ] }, { "ob_id": 39935, "uuid": "8795188227b141d4851b43c31f724b57", "title": "MOASA flight M320: airborne observations from the Clean Air project over Manchester, UK", "abstract": "Observations collected during flight M320 on 01 March 2022 by the Clean Air instrument suite on board the Met Office Atmospheric Survey Aircraft (MOASA) for the MOASA Clean Air project. \r\n\r\nThis dataset contains in meteorological and situ atmospheric composition measurements (gaseous nitrogen dioxide, ozone, sulphur dioxide and fine mode (PM2.5) aerosol) over Manchester, UK, in a city survey flight pattern as part of the Strategic Priorities Fund (SPF) Clean Air Programme Intense Observational Period. Quicklook plots are also included.\r\n\r\nFull flight track and other MOASA flights can be seen via the CEDA Flight Finder tool linked to on this record.", "creationDate": "2023-04-17T15:56:30.429343", "lastUpdatedDate": "2023-04-17T15:56:30", "latestDataUpdateTime": "2023-04-14T13:56:46", "updateFrequency": "notPlanned", "dataLineage": "Data were collected and processed by the project team at the Met Office and submitted to the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "MOASA, aircraft, meteorology, Clean Air, Met Office, nitrogen dioxide, NO2, ozone, O3, sulphur dioxide, SO2, PM2.5, aerosol", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-09-19T15:21:04", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3800, "bboxName": "", "eastBoundLongitude": -1.741073, "westBoundLongitude": -2.616894, "southBoundLatitude": 50.778961, "northBoundLatitude": 53.533543 }, "verticalExtent": null, "result_field": { "ob_id": 39936, "dataPath": "/badc/deposited2023/moasa_clean_air_data/M320", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 89656049, "numberOfFiles": 38, "fileFormat": "NetCDF format" }, "timePeriod": { "ob_id": 11042, "startTime": "2022-03-01T11:20:00", "endTime": "2022-03-01T13:59:37" }, "resultQuality": { "ob_id": 4243, "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": "2023-04-17" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 39937, "uuid": "83507cb84dd04f48986feaeb015e634c", "short_code": "acq", "title": "Acquisition for: MOASA flight M320: airborne observations from the Clean Air project", "abstract": "" }, "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": 39906, "uuid": "e1d0be90d6794a398c190d1bd14dd8cf", "short_code": "proj", "title": "MOASA Clean Air Project", "abstract": "The Met Office Atmospheric Survey Aircraft (MOASA) Clean Air project collected air pollution data over the UK between August 2019 to April 2022. During this time 60 flights were made (totalling over 153 hours of atmospheric sampling), across 8 repeatable sorties including two intensive data collection periods, one in summer and one in winter, flying upwind and downwind of the the Natural Environment Research Council (NERC) ‘supersites’ in London, Manchester and Birmingham and circling over them at various altitudes.\r\n\r\nThe aircraft was equipped with with air monitoring instrumentation to observe the three-dimensional distribution of pollutants (gaseous nitrogen dioxide, ozone, sulphur dioxide and fine mode (PM2.5) aerosol).\r\n\r\nThis project was part of the Strategic Priorities Fund (SPF) Clean Air Programme, delivered by UKRI." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 63234, 63235, 63236, 63237, 63238, 63239, 63240, 63241, 63242, 63243, 63244, 63245, 63246, 63247, 63248, 63249, 63250, 63251, 63252, 63253, 63254, 63255, 63256, 63257, 63258, 63259, 63260, 63261, 63262, 63263, 63264, 63265, 63267, 63268, 63269, 63270, 63271, 63272, 63273, 63274, 63275, 63276, 63277, 63278, 63279, 63280, 63281, 63282, 63283, 63284, 63285, 63286, 63287, 63288, 63289, 63290, 63292, 63293, 63294, 63295, 63296, 63297, 63298, 63299, 63300, 63301, 63302, 63303, 63304, 63305, 63306, 63307, 63308, 63309, 63310, 63311, 63312, 63313, 63314, 63315, 63316, 63317, 63318, 63319, 63320, 63321, 63322, 63323, 63324, 63325, 63326, 63327, 63328, 63329, 63330, 63331, 63332, 63333, 63334, 63335, 63336, 63337, 63338, 63339, 63340, 63341, 63342, 63343, 63344, 63345, 63346, 63347, 63348, 63349, 63350, 63351, 63352, 63353, 63354, 63355, 63356, 63357, 63358, 79123, 79124, 79125, 79126, 79127, 79129, 83975 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 39574, "uuid": "0aa1ec0cf18e4065bdae8ae39260fe7d", "short_code": "coll", "title": "MOASA Clean Air Project: airborne atmospheric measurements collection", "abstract": "A long-term quality assured dataset of the three-dimensional distribution of pollutants (gaseous nitrogen dioxide, ozone, sulphur dioxide and fine mode (PM2.5) aerosol) collected over southern UK, from August 2019 to April 2022, using the instrumented Met Office Atmospheric Survey Aircraft (MOASA). It includes 60 flights (totalling over 153 hours of atmospheric sampling), across 8 repeatable sorties made for the MOASA Clean Air project.\r\n\r\nData are presented as one NetCDF file per flight, containing quality assured, time synchronised, three-dimensionally geo-located time-series of observed pollutants. \r\n\r\nTo ensure optimal traceability and transparency of data, comprehensive metadata is included for each flight, detailing any calibration constants and/or corrections applied to data alongside general information about the data and instruments. \r\n\r\nA full list of NetCDF variables can be found in “Long-term airborne measurements of pollutants over the United Kingdom to support air quality model development and evaluation”, A. Mynard et al, 2023. This paper also provides a technical introduction to the MOASA measurement platform, information on instrumentation, flight strategies and data, as well as example case studies comparing flight observations to the Met Office Air Quality in the Unified Model (AQUM) and the ground based Automated Urban and Rural Network. This paper should be referenced in any research/publications pertaining from this dataset." } ], "responsiblepartyinfo_set": [ 194357, 194358, 194359, 194360, 194361, 194362, 194363, 194364, 194365 ], "onlineresource_set": [ 83307 ] }, { "ob_id": 39942, "uuid": "a0782135bcd04d77a1dae4aa71fba47c", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 6.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-09-11T13:11:11", "updateFrequency": "notPlanned", "dataLineage": "These data were produced by the ESA Ocean Colour CCI project and provided to CEDA in the context of the ESA CCI Open Data Portal project.\r\nThis dataset forms part of the v6.0, 4km resolution ocean colour dataset collection that can be cited with the following DOI: 10.5285/5011d22aae5a4671b0cbc7d05c56c4f0", "removedDataReason": "", "keywords": "ESA, CCI, Ocean Colour, Geographic, Reflectance", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "4km", "status": "completed", "dataPublishedTime": "2023-08-01T09:29:22", "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": 39944, "dataPath": "/neodc/esacci/ocean_colour/data/v6.0-release/geographic/netcdf/rrs", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9463744399706, "numberOfFiles": 12575, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 11045, "startTime": "1997-09-04T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3342, "explanation": "Data are as provided by the Ocean Colour CCI team. For details of validation see the linked Product User Guide and documentation on the Ocean Colour CCI website (https://climate.esa.int/en/projects/ocean-colour/)", "passesTest": true, "resultTitle": "Ocean Colour CCI data quality", "date": "2019-10-31" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39950, "uuid": "9aba59ecdfd04a24a67a6ad2a9ef93b8", "short_code": "comp", "title": "Derivation of Ocean Colour v6 data from the ESA Climate Change Initiative Ocean Colour project (Ocean_Colour_cci)", "abstract": "The ocean colour CCI has calculated ocean colour Essential Climate Variable data, using input data from various satellite instruments, as part of the ESA Climate Change Initiative program.\r\n\r\nFor more information see the Ocean Colour v6 Product User Guide." }, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2538, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 18, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_oc_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13365, "uuid": "de8aeb4f1bec4348a1e475691ea651d4", "short_code": "proj", "title": "ESA Ocean Colour Climate Change Initiative Project", "abstract": "The European Space Agency Ocean Colour Climate Change Initiative (Ocean Colour CCI) project aims to produce long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n \r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490 nm. Information on uncertainties is also provided." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 12066, 50559, 50561, 55904, 55905, 55907, 55912, 55913, 55914, 55915, 55916, 55918, 55919, 55920, 55921, 55922, 59892, 59893, 59894, 59895, 59896, 62489, 62490, 62491, 62492, 62493, 62494, 62495, 62496, 62497, 62498, 62499, 62500, 62501, 62522, 62523, 62524, 80424, 80425, 80430, 80432, 87023, 87024, 87025, 87026, 87027, 87028, 87029, 87030, 87031, 87032, 87033, 87034, 87035, 87036, 87037, 87038 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 39943, "uuid": "5011d22aae5a4671b0cbc7d05c56c4f0", "short_code": "coll", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Version 6.0, 4km resolution data", "abstract": "This collection contains version 6.0 datasets at 4km resolution produced by the Ocean Colour project of the ESA Climate Change Inititative (CCI). The Ocean Colour CCI is producing long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490 nm. Information on uncertainties is also provided.\r\n\r\nThis dataset collection refers to the Version 6.0 of the 4km resolution data products held in the CEDA archive covering the period 1997-2022. Links to the individual datasets that make up this collection are given in the record below." }, { "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": [ 194380, 194381, 194382, 194383, 194384, 194385, 194386, 194387, 194388, 194389, 194390, 194391, 194392, 194393, 194394, 194395, 194396, 194397, 194398, 194399, 194400, 194401, 194402, 194403, 194404, 194405, 194406, 194407, 194408, 194409, 194410, 194411, 194412, 194413 ], "onlineresource_set": [ 83328, 83325, 83326, 83327 ] }, { "ob_id": 39945, "uuid": "0875b4675f1e46ebadb526e0b95505c5", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products) at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains all their Version 6.0 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Data are also available as monthly climatologies.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-09-11T11:53:51", "updateFrequency": "notPlanned", "dataLineage": "These data were produced by the ESA Ocean Colour CCI project and provided to CEDA in the context of the ESA CCI Open Data Portal project.\r\nThis dataset forms part of the v6.0, 4km resolution ocean colour dataset collection that can be cited with the following DOI: 10.5285/5011d22aae5a4671b0cbc7d05c56c4f0", "removedDataReason": "", "keywords": "ESA, CCI, Ocean Colour, Geographic", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "4km", "status": "completed", "dataPublishedTime": "2023-08-01T09:28:59", "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": 39946, "dataPath": "/neodc/esacci/ocean_colour/data/v6.0-release/geographic/netcdf/all_products", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 24382256018121, "numberOfFiles": 12575, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 11046, "startTime": "1997-09-04T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3342, "explanation": "Data are as provided by the Ocean Colour CCI team. For details of validation see the linked Product User Guide and documentation on the Ocean Colour CCI website (https://climate.esa.int/en/projects/ocean-colour/)", "passesTest": true, "resultTitle": "Ocean Colour CCI data quality", "date": "2019-10-31" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39950, "uuid": "9aba59ecdfd04a24a67a6ad2a9ef93b8", "short_code": "comp", "title": "Derivation of Ocean Colour v6 data from the ESA Climate Change Initiative Ocean Colour project (Ocean_Colour_cci)", "abstract": "The ocean colour CCI has calculated ocean colour Essential Climate Variable data, using input data from various satellite instruments, as part of the ESA Climate Change Initiative program.\r\n\r\nFor more information see the Ocean Colour v6 Product User Guide." }, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2538, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 18, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_oc_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13365, "uuid": "de8aeb4f1bec4348a1e475691ea651d4", "short_code": "proj", "title": "ESA Ocean Colour Climate Change Initiative Project", "abstract": "The European Space Agency Ocean Colour Climate Change Initiative (Ocean Colour CCI) project aims to produce long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n \r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490 nm. 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The Ocean Colour CCI is producing long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490 nm. Information on uncertainties is also provided.\r\n\r\nThis dataset collection refers to the Version 6.0 of the 4km resolution data products held in the CEDA archive covering the period 1997-2022. Links to the individual datasets that make up this collection are given in the record below." }, { "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": [ 194448, 194449, 194450, 194451, 194452, 194453, 194454, 194455, 194456, 194457, 194458, 194459, 194460, 194461, 194462, 194463, 194464, 194465, 194466, 194467, 194468, 194469, 194470, 194471, 194472, 194473, 194474, 194475, 194476, 194477, 194478, 194479, 194480, 194481 ], "onlineresource_set": [ 83335, 83332, 83333, 83334 ] }, { "ob_id": 39947, "uuid": "474ac06235e54e6cb0ec6eed635e1213", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 6.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the chlorophyll-a data are also included in the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. 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The Ocean Colour CCI is producing long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490 nm. Information on uncertainties is also provided.\r\n\r\nThis dataset collection refers to the Version 6.0 of the 4km resolution data products held in the CEDA archive covering the period 1997-2022. 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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": [ 194487, 194485, 194482, 194484, 194483, 194486, 194488, 194489, 194491, 194490, 194492, 194493, 194494, 194495, 194496, 194497, 194498, 194499, 194500, 194501, 194502, 194503, 194504, 194505, 194506, 194507, 194508, 194509, 194510, 194511, 194512, 194513, 194514, 194515 ], "onlineresource_set": [ 83336, 83339, 83337, 83338 ] }, { "ob_id": 39949, "uuid": "8b9d461f245b4efd8ea9fa080366e3b1", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 6.0 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. 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The Ocean Colour CCI is producing long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490 nm. Information on uncertainties is also provided.\r\n\r\nThis dataset collection refers to the Version 6.0 of the 4km resolution data products held in the CEDA archive covering the period 1997-2022. Links to the individual datasets that make up this collection are given in the record below." }, { "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": [ 194516, 194517, 194518, 194519, 194520, 194521, 194522, 194523, 194524, 194526, 194525, 194527, 194528, 194529, 194530, 194531, 194532, 194533, 194534, 194535, 194536, 194537, 194538, 194539, 194540, 194541, 194542, 194543, 194544, 194545, 194546, 194547, 194548, 194549, 194550 ], "onlineresource_set": [ 83344, 83343, 83341, 83342 ] }, { "ob_id": 39959, "uuid": "024292dcda5d42ceb326850f89f8b40d", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 6.0 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the IOP data are also included in the 'All Products' dataset. \r\n\r\nThe inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. 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The Ocean Colour CCI is producing long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490 nm. Information on uncertainties is also provided.\r\n\r\nThis dataset collection refers to the Version 6.0 of the 4km resolution data products held in the CEDA archive covering the period 1997-2022. 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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. 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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. 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It is computed from the Ocean Colour CCI Version 6.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. 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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. 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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": [ 194644, 194646, 194643, 194645, 194647, 194648, 194649, 194650, 194652, 194651, 194653, 194654, 194655, 194656, 194657, 194658, 194659, 194660, 194661, 194662, 194663, 194664, 194665, 194666, 194667, 194668, 194669, 194670, 194671, 194672, 194673, 194674, 194675, 194676 ], "onlineresource_set": [ 83352, 83353, 83350, 83351 ] }, { "ob_id": 39963, "uuid": "b0ec72a28b6a4829a33ed9adc215d5bc", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 6.0 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day, monthly and yearly composites) covering the period 1997 - 2022. Note, this chlor_a data is also included in the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. 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The Ocean Colour CCI is producing long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490 nm. Information on uncertainties is also provided.\r\n\r\nThis dataset collection refers to the Version 6.0 of the 4km resolution data products held in the CEDA archive covering the period 1997-2022. 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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": [ 194677, 194678, 194679, 194680, 194681, 194682, 194683, 194684, 194686, 194685, 194687, 194688, 194689, 194690, 194691, 194692, 194693, 194694, 194695, 194696, 194697, 194698, 194699, 194700, 194701, 194702, 194703, 194704, 194705, 194706, 194707, 194708, 194709, 194710 ], "onlineresource_set": [ 83355, 83356, 83357, 83354 ] }, { "ob_id": 39965, "uuid": "90682bac7d0e4e418085f30eba43dfba", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 6.0 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the IOP data is also included in the 'All Products' dataset. \r\n\r\nThe inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-06-07T18:32:16", "updateFrequency": "notPlanned", "dataLineage": "These data were produced by the ESA Ocean Colour CCI project and provided to CEDA in the context of the ESA CCI Open Data Portal project.\r\nThis dataset forms part of the v6.0, 4km resolution ocean colour dataset collection that can be cited with the following DOI: 10.5285/5011d22aae5a4671b0cbc7d05c56c4f0", "removedDataReason": "", "keywords": "ESA, CCI, Ocean Colour, Geographic", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "4km", "status": "completed", "dataPublishedTime": "2023-08-01T09:26:09", "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": 39966, "dataPath": "/neodc/esacci/ocean_colour/data/v6.0-release/geographic/netcdf/iop", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 13747999057840, "numberOfFiles": 12577, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 11059, "startTime": "1997-09-04T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3342, "explanation": "Data are as provided by the Ocean Colour CCI team. 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Information on uncertainties is also provided." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 12066, 50559, 50561, 55884, 55885, 55886, 55887, 55891, 55892, 55893, 55894, 55898, 55899, 55900, 55901, 55904, 55905, 55907, 59892, 59897, 59898, 59899, 59900, 59901, 59902, 62501, 62502, 62503, 62504, 62505, 62506, 62507, 62508, 62509, 62510, 62511, 62512, 62513, 62514, 62515, 62516, 62517, 62518, 62519, 62522, 62523, 62524 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 39943, "uuid": "5011d22aae5a4671b0cbc7d05c56c4f0", "short_code": "coll", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Version 6.0, 4km resolution data", "abstract": "This collection contains version 6.0 datasets at 4km resolution produced by the Ocean Colour project of the ESA Climate Change Inititative (CCI). The Ocean Colour CCI is producing long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490 nm. Information on uncertainties is also provided.\r\n\r\nThis dataset collection refers to the Version 6.0 of the 4km resolution data products held in the CEDA archive covering the period 1997-2022. Links to the individual datasets that make up this collection are given in the record below." }, { "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": [ 194711, 194712, 194713, 194714, 194715, 194716, 194717, 194718, 194720, 194719, 194721, 194722, 194723, 194724, 194725, 194726, 194727, 194728, 194729, 194730, 194731, 194732, 194733, 194734, 194735, 194736, 194737, 194738, 194739, 194740, 194741, 194742, 194743, 194744 ], "onlineresource_set": [ 83360, 83361, 83359, 83358 ] }, { "ob_id": 39967, "uuid": "3bdb21a4cd004e5f8cc148fea5f1d4e3", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 6.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. It is computed from the Ocean Colour CCI Version 6.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-09-11T13:10:47", "updateFrequency": "notPlanned", "dataLineage": "These data were produced by the ESA Ocean Colour CCI project and provided to CEDA in the context of the ESA CCI Open Data Portal project.\r\nThis dataset forms part of the v6.0, 4km resolution ocean colour dataset collection that can be cited with the following DOI: 10.5285/5011d22aae5a4671b0cbc7d05c56c4f0", "removedDataReason": "", "keywords": "ESA, CCI, Ocean Colour, Sinusoidal", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "4km", "status": "completed", "dataPublishedTime": "2023-08-01T09:25:28", "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": 39968, "dataPath": "/neodc/esacci/ocean_colour/data/v6.0-release/sinusoidal/netcdf/kd", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1662919915746, "numberOfFiles": 12576, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 11060, "startTime": "1997-09-04T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3342, "explanation": "Data are as provided by the Ocean Colour CCI team. 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The Ocean Colour CCI is producing long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490 nm. Information on uncertainties is also provided.\r\n\r\nThis dataset collection refers to the Version 6.0 of the 4km resolution data products held in the CEDA archive covering the period 1997-2022. 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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": [ 194745, 194746, 194747, 194748, 194749, 194750, 194751, 194752, 194753, 194754, 194755, 194756, 194757, 194758, 194759, 194760, 194761, 194762, 194763, 194764, 194765, 194766, 194767, 194768, 194769, 194770, 194771, 194772, 194773, 194774, 194775, 194776, 194777, 194778 ], "onlineresource_set": [ 83362, 83365, 83363, 83364 ] }, { "ob_id": 39969, "uuid": "86d360431f3b4184b89cdd1cd707bb33", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products) at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains all their Version 6.0 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. \r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. 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The Ocean Colour CCI is producing long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490 nm. Information on uncertainties is also provided.\r\n\r\nThis dataset collection refers to the Version 6.0 of the 4km resolution data products held in the CEDA archive covering the period 1997-2022. 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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. 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