Observation List
Get a list of Observation objects.
GET /api/v3/observations/?format=api&offset=7700
{ "count": 10256, "next": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=7800", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=7600", "results": [ { "ob_id": 34606, "uuid": "fc3310aaa2fe4a26a29a63642e1164f2", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour daily gridded data over land at 0.05 degree resolution, version 3.2", "abstract": "This dataset consists of daily total column water vapour (TCWV) over land, at a 0.05 degree resolution, observed by various satellite instruments. It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1 (TCWV-land (CDR-1).\r\n\r\nThis version of the data is v3.2. This is an updated dataset, which fixes an issue with the filtering of the v3.1 data.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2022-02-16T20:47:49", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "water vapour, CCI, TCWV", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.05 degrees", "status": "completed", "dataPublishedTime": "2022-02-21T16:42:58", "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": 34869, "dataPath": "/neodc/esacci/water_vapour/data/TCWV-land/L3/v3.2/0.05deg/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 403492397164, "numberOfFiles": 5664, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 8796, "startTime": "2002-07-01T00:00:00", "endTime": "2017-12-31T23:59:59" }, "resultQuality": { "ob_id": 3785, "explanation": "For inormation on the data quality see the documentation at https://climate.esa.int/projects/water-vapour/", "passesTest": true, "resultTitle": "water vapour cci", "date": "2021-11-01" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33286, "uuid": "1d6b6c09b11040359ada94fe2a03595c", "short_code": "cmppr", "title": "ESA Water Vapour Climate Change Initiative: Total Column Water Vapour over land, v3.1", "abstract": "The ESA Water Vapour Climate Change Initiative Total Column Water Vapour dataset has been derived from the following satellite instruments: MERIS on ENVISAT, MODIS on TERRA and OLCI on Sentinel-3. For more details, see the documenation at https://climate.esa.int/projects/water-vapour." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2529, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 9, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_watervapour_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32934, "uuid": "24186d13405b4000aef0ba577b9031aa", "short_code": "proj", "title": "ESA Water Vapour Climate Change Initiative Project", "abstract": "The Water Vapour Climate Change Initiatve Project (Water_Vapour_cci) is part of the European Space Agency's Climate Change Initiative Programme. The project aims to generate new global high-quality climate data records of both total column and vertically resolved water vapour." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1633, 6814, 6816, 9797, 43317, 43318, 43319, 43320, 43321, 43324, 43325, 43326, 46671, 46672 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 34602, "uuid": "4a85c0ef880e4f668cd4ec8e846855ef", "short_code": "coll", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour over land (CDR1), v3.2", "abstract": "This collection of data comprises an updated version of the European Space Agency (ESA) Water Vapour Climate Change Initiative (Water Vapour_cci) Total Column Water Vapour over land, Climate Data Record 1 (TCWV-land (CDR1)). It comprises four datasets providing daily and monthly averages at 0.5 and 0.05 degree resolution respectively.\r\n\r\nThis is an updated version of the previous v3.1 version of the data which corrects an issue with the filtering of the data." } ], "responsiblepartyinfo_set": [ 164228, 164229, 164230, 164231, 164232, 164233, 164227, 164226, 164234, 164235, 164236, 164237, 164238, 164239 ], "onlineresource_set": [ 47558, 47559, 47557 ] }, { "ob_id": 34607, "uuid": "41d0dfd6d2064b158f244c0123c83972", "title": "FIDUCEO: Fundamental Climate Data Record of brightness temperatures for High-resolution Infrared Radiation Sounder (HIRS) 1978 - 2016, V2.0", "abstract": "The FIDelity and Uncertainty in Climate data records from Earth Observations (FIDUCEO) project Fundamental Climate Data Record of brightness temperatures for the High-resolution Infrared Radiation Sounder (HIRS) contains brightness temperatures for HIRS for all editions of HIRS/2, HIRS/2I, HIRS/3, and HIRS/4 satellite instruments. It contains HIRS l1b data as available from the NOAA Comprehensive Large Array-Data Stewardship System (CLASS). Geolocation and calibration information is available in the files. For more information and relevant product guides please see the documentation section. This data set was used as input to the Fundamental Climate Data Record of recalibrated brightness temperatures for the High-resolution Infrared Radiation Sounder (HIRS) with uncertainties, 1978 - 2016, v1.0 dataset.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-01-22T14:26:16", "updateFrequency": "", "dataLineage": "Data was originally downloaded by the University of Hamburg from the NOAA class archive. It was transferred to the JASMIN FIDUCEO group work space and restructured for project purposes. It was then supplied to CEDA for archival by Jon Mittaz of the University of Reading.", "removedDataReason": "", "keywords": "FIDUCEO, HIRS, Brightness Temperature", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-08-10T17:50:15", "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": 37607, "dataPath": "/neodc/fiduceo/data/fcdr/hirs/v2.00/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1786452362336, "numberOfFiles": 728665, "fileFormat": "HIRS- NOAA Native Binary format: Data formats for HIRS/2 (NOAA-14 and before) are documented in the NOAA POD User's Guide. available online in PDF linked to in the documentation section" }, "timePeriod": { "ob_id": 7557, "startTime": "1978-10-13T00:00:00", "endTime": "2016-12-31T00:00:00" }, "resultQuality": { "ob_id": 3305, "explanation": "Data was dowloaded from the NOAA class archive and contains quality metricsdescribed in the documentation. It was then provide to CEDA byby fiduceo project team", "passesTest": true, "resultTitle": "FIDUCEO project Data Quality Statement", "date": "2019-07-12" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37848, "uuid": "8001c7a2ee3944e7a83bec2f937418ba", "short_code": "acq", "title": "FIDUCEO HIRS V2.0 data acquisition", "abstract": "FIDUCEO HIRS V1.0 data aquisition; Core input data are L1B data files obtained from the NOAA CLASS archive. Spectral response functions are obtained from NOAA NESDIS STAR PRT coefficients are obtained from CPIDS and also provided by NOAA" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2591, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 54, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/NOAA%20Open%20Access%20Data.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 5002, "uuid": "60e718d3f2957f742c89b2b4fc159718", "short_code": "proj", "title": "National Centre for Earth Observation (NCEO)", "abstract": "The National Centre for Earth Observation is a partnership of scientists and institutions, from a range of disciplines, who are using data from Earth observation satellites to monitor global and regional changes in the environment and to improve understanding of the Earth system so that we can predict future environmental conditions.\r\n\r\nNCEO's Vision is to unlock the full potential of Earth observation to monitor, diagnose and predict climate and environmental changes, ensuring that these scientific advances are delivered to the wider community embedded in world class science." }, { "ob_id": 8448, "uuid": "f6c2c29e5d29eb559fee85837e6ad27e", "short_code": "proj", "title": "NOAA Polar Orbiting Satellite Series", "abstract": null }, { "ob_id": 27632, "uuid": "a6f76ccab0844a278b41315868460a88", "short_code": "proj", "title": "Fidelity and Uncertainty in Climate data records from Earth Observations (FIDUCEO) H2020 Project", "abstract": "FIDUCEO has created new climate datasets from Earth Observations with rigorous treatment of uncertainty informed by the discipline of metrology. This responds 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. The project also built new FCDRs and CDRs and have included complete and traceable estimates of stability and uncertainty. New tools for metrologically rigorous analysis will be created, including tools for stability analysis and ensemble creation. The project co-ordinated by the University of Reading ran from March 2015 - August 2019.\r\n \r\nThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 638822." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 164250, 164247, 164246, 164240, 164243, 164245, 164242, 164241, 180645 ], "onlineresource_set": [ 52656, 52657, 52655, 52717 ] }, { "ob_id": 34608, "uuid": "9f83afcc47ca49feb1d5702de9fa8869", "title": "Chapter 10 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 10.21 (v20220622)", "abstract": "Data for Figure 10.21 from Chapter 10 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 10.21 shows projected Mediterranean summer warming.\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\nDoblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate 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. 1363–1512, doi:10.1017/9781009157896.012.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 4 subpanels. Data for all subpanels is provided.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The data is annual summer (JJA) surface air temperature means for:\r\n \r\n - Modelled anomalies 2015-2100 with respect to 1995-2014 average for the Mediterranean mean (lon: 10°W-40°E, lat: 25°N-50°N)\r\n - Modelled change until 2081‒2100 with respect to 1995-2014 averages over the Mediterranean (lon: 10°W-40°E, lat: 25°N-50°N)\r\n - Trends 2015-2050 for the Mediterranean mean (lon: 10°W-40°E, lat: 25°N-50°N)\r\n - Modelled trends over 2015-2050\r\n - Modelled Mediterranean summer vs global warming\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel (a):\r\n - Data files: \r\nFig_10_21_panel-a_timeseries.csv, \r\nFig_10_21_panel-a_boxplot.csv; \r\nModelled JJA surface air temperature anomalies 2015-2100 (baseline 1995-2014) for the Mediterranean mean (lon: 10°W-40°E, lat: 25°N-50°N, CMIP5 (blue), CMIP6 (dark red), HighResMIP (orange), CORDEX EUR-44 (light blue), CORDEX EUR-11 (green)) and change until 2081‒2100 in respect to 1995-2014 averages (SSP1-2.6 dark blue, SSP2-4.5 yellow, SSP3-7.0 red, SSP5-8.5 dark red)\r\n \r\n Panel (b):\r\n - Data file: Fig_10_21_panel-b_trends.csv; \r\nModelled JJA OLS linear trends in surface air temperature 2015-2050 for the Mediterranean mean (lon: 10°W-40°E, lat: 25°N-50°N) CMIP5 (blue circles), CMIP6 (dark red circles), HighResMIP (orange circles), CORDEX EUR-44 (light blue circles), CORDEX EUR-11 (green circles)) and box-and-whisker plots for the SMILEs: MIROC6, CSIRO-Mk3-6-0, MPI-ESM (grey shading)\r\n \r\n Panel (c):\r\n - Data files: \r\nFig_10_21_panel-c_mapplot_tas_cmip5_mean_trend_future_tas_cmip5_maps_trend_MultiModelMean_trend.nc, \r\nFig_10_21_panel-c_mapplot_tas_cmip6_mean_trend_future_tas_cmip6_maps_trend_MultiModelMean_trend.nc, \r\nFig_10_21_panel-c_mapplot_tas_cordex_11_mean_trend_future_tas_cordex_11_maps_trend_native_MultiModelMean_trend.nc, \r\nFig_10_21_panel-c_mapplot_tas_cordex_44_mean_trend_future_tas_cordex_44_maps_trend_native_MultiModelMean_trend.nc, \r\nFig_10_21_panel-c_mapplot_tas_hrmip_mean_trend_future_tas_hrmip_maps_trend_05_MultiModelMean_trend.nc; \r\nModelled OLS linear surface air temperature trends over 2015-2050 of CMIP5, CMIP6, HighResMIP, CORDEX EUR-44, and CORDEX EUR-11 ensemble means\r\n \r\n Panel (d):\r\n - Data file: Fig_10_21_panel-d_GWLRWL.csv; \r\nModelled Mediterranean summer (JJA) vs global warming under CMIP5 (RCP2.6 dark blue dashed line, RCP4.5 light blue dashed line, RCP6.0 orange dashed line and RCP8.5 red dashed line) and CMIP6 (SSP1-2.6 dark blue line, SSP2-4.5 yellow line, SSP3-7.0 red line, SSP5-8.5 dark red line) scenarios.\r\n\r\n\r\nAcronyms: \r\nCMIP - Coupled Model Intercomparison Project, \r\nCordex – Coordinated Regional Climate Downscaling Experiment, \r\nHighResMIP - High Resolution Model Intercomparison Project, \r\nSSP- Shared Socioeconomic Pathways, \r\nSMILEs -single model initial-condition large ensembles, \r\nMIROC - Model for Interdisciplinary Research on Climate, \r\nCSIRO - Commonwealth Scientific and Industrial Research Organisation, \r\nMPI - Max-Planck-Institut für Meteorologie, ESM - Earth System Model, \r\nRCP - Representative Concentration Pathway, \r\nOLS - ordinary least squares regression. \r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The code for ESMValTool is provided.\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 10)\r\n - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:51:33", "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 I, Physical Science Basis, Chapter 10, teleconnections, drivers, feedbacks, Linking global to regional, Regional scale, internal variability, forced change, model improvements, Figure 10.21, surface air temperature, surface air temperature trend, Mediterranean, Mediterranean summer warming, future period", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-29T09:49:04", "doiPublishedTime": "2023-05-17T13:50:52.644917", "removedDataTime": null, "geographicExtent": { "ob_id": 2926, "bboxName": "", "eastBoundLongitude": 39.0, "westBoundLongitude": -10.0, "southBoundLatitude": 25.0, "northBoundLatitude": 50.0 }, "verticalExtent": { "ob_id": 98, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37646, "dataPath": "/badc/ar6_wg1/data/ch_10/ch10_fig21/v20220622", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9192977, "numberOfFiles": 12, "fileFormat": "txt, netCDF, csv" }, "timePeriod": { "ob_id": 9591, "startTime": "2015-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 3825, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 34610, "uuid": "46305c06411e47e5a538d48db18d3143", "short_code": "comp", "title": "Caption for Figure 10.21 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected Mediterranean summer warming. (a) Time series of area averaged Mediterranean (25°N‒50°N, 10°W‒40°E) land point summer surface air temperature anomalies (°C, baseline period is 1995–2014). Orange, light blue and green lines show low-pass filtered ensemble means of HighResMIP (highres-future, four members), CORDEX EUR-44 (RCP8.5, 20 members) and CORDEX EUR-11 (RCP8.5, 37 members). Blue and dark red lines and shadings show low-pass filtered ensemble means and standard deviations of CMIP5 (RCP8.5, 41 members) and CMIP6 (SSP5-8.5, 36 members). The filter is the same as the one used in Figure 10.10. The box-and-whisker plots show long-term (until 2081–2100) temperature changes of different CMIP6 scenarios with respect to the baseline period (SSP1-2.6 in dark blue, SSP2-4.5 in yellow, SSP3-7.0 in red, SSP5-8.5 in dark red). (b) Distribution of 2015‒2050 Mediterranean summer temperature linear trends (°C per decade) for CORDEX EUR-11 (RCP8.5, green circles), CORDEX EUR-44 (RCP8.5, light blue circles), HighResMIP (highres-future, orange circles), CMIP6 (SSP5-8.5, dark red circles), CMIP5 (RCP8.5, blue circles) and selected SMILEs (grey box-and-whisker plots, MIROC6, CSIRO-Mk3-6-0 and MPI-ESM). Ensemble means are also shown. CMIP6 models showing a very high ECS (Box 4.1) have been marked with a black cross. All trends are estimated using ordinary least-squares and box-and-whisker plots follow the methodology used in Figure 10.6. (c) Projections of ensemble mean 2015‒2050 linear trends (°C per decade) of CMIP5 (RCP8.5), CORDEX EUR-44 (RCP8.5), CORDEX EUR-11 (RCP8.5), CMIP6 (SSP5-8.5) and HighResMIP (highres-future). All trends are estimated using ordinary least-squares. (d) Projected Mediterranean summer warming in comparison to global annual mean warming of CMIP5 (dashed lines, RCP2.6 in dark blue, RCP4.5 in light blue, RCP6.0 in orange and RCP8.5 in red) and CMIP6 (solid lines, SSP1-2.6 in dark blue, SSP2-4.5 in yellow, SSP3-7.0 in red and SSP5-8.5 in dark red) ensemble means. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": [ 6021, 6022, 6023, 11573, 19043, 49990, 50510, 50559, 50561, 53130, 53131, 54061, 54062, 58113, 60438 ], "vocabularyKeywords": [], "identifier_set": [ 12521 ], "observationcollection_set": [ { "ob_id": 32726, "uuid": "6fbf9d885b7444aba681868c4006c86a", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate 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 10.6\r\n- data for Figure 10.10\r\n- data for Figure 10.11\r\n- data for Figure 10.12\r\n- data for Figure 10.13\r\n- data for Figure 10.18\r\n- data for Figure 10.19\r\n- data for Figure 10.20\r\n- data for Figure 10.21\r\n- data for CCB 10.4, Figure 1" } ], "responsiblepartyinfo_set": [ 164253, 164254, 164255, 164256, 164257, 164258, 164259, 164260, 164261, 168279, 179515, 164262, 164263, 164264 ], "onlineresource_set": [ 52416, 47592, 82673, 47593, 88552 ] }, { "ob_id": 34611, "uuid": "19ec340e6f2d47479ddb483961b0c1bb", "title": "Chapter 10 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 10.20 (v20220113)", "abstract": "Data for Figure 10.20 from Chapter 10 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 10.20 shows aspects of Mediterranean summer warming.\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\nDoblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate 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. 1363–1512, doi:10.1017/9781009157896.012.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 7 subpanels. Data for subpanels d, e, f and g is provided.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The data is annual summer (JJA) means for:\r\n \r\n - Observed trends over 1960-2014\r\n - Anomalies 1960-2014 with respect to 1995-2014 average for the Mediterranean mean (lon: 10°W-40°E, lat: 25°N-50°N)\r\n - Trends 1960-2014 for the Mediterranean mean (lon: 10°W-40°E, lat: 25°N-50°N)\r\n - Modelled trend differences to the observed over 1960-2014\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel (d):\r\n - Data file: Fig_10_20_panel-d_mapplot_tas_obs_trend_single_single_trend.nc; \r\nJJA Berkeley Earth surface air temperature OLS linear trends over 1960-2014 over the Mediterranean (lon: 10°W-40°E, lat: 25°N-50°N)\r\n \r\n Panel (e):\r\n - Data file: Fig_10_20_panel-e_timeseries.csv; \r\nObserved and modelled JJA surface air temperature anomalies 1960-2014 (baseline 1995-2014) for the Mediterranean mean (lon: 10°W-40°E, lat: 25°N-50°N): CMIP5 (blue), CMIP6 (red), HighResMIP (orange), CORDEX EUR-44 (light blue), CORDEX EUR-11 (green), Berkeley Earth (dark blue), CRU TS (brown), HadCRUT5 (cyan)\r\n \r\n Panel (f):\r\n - Data file: Fig_10_20_panel-f_trends.csv; \r\nJJA OLS linear trends in surface air temperature 1960-2014 for the Mediterranean mean (lon: 10°W-40°E, lat: 25°N-50°N) of observations (Berkeley Earth, CRU TS, HadCRUT5: black crosses) and models (CMIP5 (blue circles), CMIP6 (red circles), HighResMIP (orange circles), CORDEX EUR-44 (light blue circles), CORDEX EUR-11 (green circles)) and box-and-whisker plots for the SMILEs: MIROC6, CSIRO-Mk3-6-0, MPI-ESM, d4PDF (grey shading)\r\n \r\n Panel (g):\r\n - Data files: \r\nFig_10_20_panel-g_mapplot_tas_cmip5_mean_trend_bias_tas_cmip5_maps_trend_MultiModelMean_trend-bias.nc, \r\nFig_10_20_panel-g_mapplot_tas_cmip6_mean_trend_bias_tas_cmip6_maps_trend_MultiModelMean_trend-bias.nc, \r\nFig_10_20_panel-g_mapplot_tas_cordex_11_mean_trend_bias_tas_cordex_11_maps_trend_MultiModelMean_trend-bias.nc, \r\nFig_10_20_panel-g_mapplot_tas_cordex_44_mean_trend_bias_tas_cordex_44_maps_trend_MultiModelMean_trend-bias.nc, \r\nFig_10_20_panel-g_mapplot_tas_hrmip_mean_trend_bias_tas_hrmip_maps_trend_MultiModelMean_trend-bias.nc; \r\nModelled OLS linear surface air temperature trend differences to the observed trend (Berkeley Earth) over 1960-2014 of CMIP5, CMIP6, HighResMIP, CORDEX EUR-44, and CORDEX EUR-11 ensemble means\r\n\r\n\r\nAcronyms: \r\nCMIP - Coupled Model Intercomparison Project, \r\nCordex – Coordinated Regional Climate Downscaling Experiment, \r\nCRU TS- Climatic Research Unit Time Series, \r\nCSIRO - Commonwealth Scientific and Industrial Research Organisation, \r\nMIROC - Model for Interdisciplinary Research on Climate, \r\nSMILEs - single model initial-condition large ensembles, \r\nd4PDF - Database for Policy Decision-Making for Future Climate Change, \r\nOLS - ordinary least squares regression. \r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The code for ESMValTool is provided.\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 10)\r\n - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-06-22T17:05:30", "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 I, Physical Science Basis, Chapter 10, teleconnections, drivers, feedbacks, Linking global to regional, Regional scale, internal variability, forced change, model improvements, Figure 10.20, surface air temperature, surface air temperature trend, Mediterranean, Mediterranean summer warming, historical period", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-29T09:46:40", "doiPublishedTime": "2023-05-17T13:43:27.668805", "removedDataTime": null, "geographicExtent": { "ob_id": 2927, "bboxName": "", "eastBoundLongitude": 40.0, "westBoundLongitude": -10.0, "southBoundLatitude": 25.0, "northBoundLatitude": 49.0 }, "verticalExtent": { "ob_id": 99, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37645, "dataPath": "/badc/ar6_wg1/data/ch_10/ch10_fig20/v20220622", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 270651, "numberOfFiles": 11, "fileFormat": "txt, netCDF, csv" }, "timePeriod": { "ob_id": 9592, "startTime": "1960-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3826, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 34613, "uuid": "b80ce638036f4676b6cb6c9fe66812bf", "short_code": "comp", "title": "Caption for Figure 10.20 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Aspects of Mediterranean summer warming. (a) Mechanisms and feedbacks involved in enhanced Mediterranean summer warming. (b) Locations of observing stations in E-OBS and Donat et al. (2014). (c) Differences in temperature observational datasets (NOAA Global Temp, Berkeley Earth, CRUTEM4 and GISTEMP) with respect to E-OBS for the land points between the Mediterranean Sea and 46°N and west of 30°E. (d) Observed summer (June to August) surface air temperature linear trends (°C decade–1) over the 1960‒2014 period from Berkeley Earth. (e) Time series of area averaged Mediterranean (25°N‒50°N, 10°W‒40°E) land point summer temperature anomalies (°C, baseline 1995–2014). Dark blue, brown and turquoise lines show low-pass filtered temperature of Berkeley Earth, CRU TS and HadCRUT5, respectively. Orange, light blue and green lines show low-pass filtered ensemble means of HighResMIP (4 members), CORDEX EUR-44 (20 members) and CORDEX EUR-11 (37 members). Blue and red lines and shadings show low-pass filtered ensemble means and standard deviations of CMIP5 (41 members) and CMIP6 (36 members). The filter is the same as the one used in Figure 10.10. (f) Distribution of 1960‒2014 Mediterranean summer temperature linear trends (°C decade–1) for observations (black crosses), CORDEX EUR-11 (green circles), CORDEX EUR-44 (light blue circles), HighResMIP (orange circles), CMIP6 (red circles), CMIP5 (blue circles) and selected SMILEs (grey box-and-whisker plots, MIROC6, CSIRO-Mk3-6-0, MPI-ESM and d4PDF). Ensemble means are also shown. CMIP6 models showing a very high ECS (Box. 4.1) have been marked with a black cross. All trends are estimated using ordinary least-squares and box-and-whisker plots follow the methodology used in Figure 10.6. (g) Ensemble mean differences with respect to the Berkeley Earth linear trend for 1960‒2014 (°C decade–1) of CMIP5, CMIP6, HighResMIP, CORDEX EUR-44 and CORDEX EUR-11. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": [ 6021, 6022, 6023, 19043, 49990, 50498, 50510, 50516, 53130, 53131, 54037, 58113, 60438, 62354, 63164, 64247, 64414, 79133, 79134, 79135, 79136, 79137, 79138, 79139, 79140, 79141, 79142, 79143, 79144, 79145, 79146, 79147, 79148, 79149, 79150, 79151, 79152, 79153, 79154, 79155, 79156, 79157, 79158, 79159, 79160, 79161, 79162, 79163, 79164, 79165 ], "vocabularyKeywords": [], "identifier_set": [ 12520 ], "observationcollection_set": [ { "ob_id": 32726, "uuid": "6fbf9d885b7444aba681868c4006c86a", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate 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 10.6\r\n- data for Figure 10.10\r\n- data for Figure 10.11\r\n- data for Figure 10.12\r\n- data for Figure 10.13\r\n- data for Figure 10.18\r\n- data for Figure 10.19\r\n- data for Figure 10.20\r\n- data for Figure 10.21\r\n- data for CCB 10.4, Figure 1" } ], "responsiblepartyinfo_set": [ 164267, 164268, 164269, 164270, 164271, 164272, 164273, 164274, 164275, 168246, 179514, 164276, 164277, 164278 ], "onlineresource_set": [ 47590, 52415, 82672, 47591, 88551 ] }, { "ob_id": 34614, "uuid": "e79aab21bf644e61bf5dacd02199daa3", "title": "Chapter 10 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 10.19 (v20220622)", "abstract": "Data for Figure 10.19 from Chapter 10 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 10.19 shows changes in the Indian summer monsoon in the historical and future periods.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nDoblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate 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. 1363–1512, doi:10.1017/9781009157896.012.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 6 subpanels. Data for all subpanels is provided.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains:\r\n APHRODITE station density for June-September (JJAS) 1956\r\n Precipitation June-September (JJAS):\r\n \r\n - Model mean bias 1985-2010\r\n - Observed and modelled trends: CRU TS 1950-2000, CMIP6 hist-GHG & hist-aer 1950-2000, and CMIP6 SSP5-8.5 2015-2100 trends\r\n - Observed and model relative anomalies over 1950-2100 with respect to 1995-2014 averages over central India (lon: 76°E-87°E, lat: 20°N-28°N)\r\n - Modelled change until 2081‒2100 with respect to 1995-2014 averages over central India (lon: 76°E-87°E, lat: 20°N-28°N)\r\n - Trends in relative precipitation anomalies (baseline 1995-2014) over past (1950-2000) and future (2015-2100) period over central India (lon: 76°E-87°E, lat: 20°N-28°N).\r\n - Trend difference between the 3 MPI-ESM runs with the lowest and the 3 MPI-ESM runs with the highest trend\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel (a):\r\nAPHRODITE station density for JJAS 1956:\r\n - Data file: \r\nFig_10_19_panel-a_mapplot_APHRODITE_stationdensity_single_mean.nc\r\n \r\n Panel (b):\r\nCMIP6 mean precipitation bias June-September mean 1985-2010 mean with respect to CRU TS:\r\n - Data file: \r\nFig_10_19_panel-b_mapplot_pr_cmip6_bias_pr_cmip6_maps_past_bias_MultiModelMean_bias.nc\r\n \r\n Panel (c):\r\nOLS linear precipitation for June-September mean trend of CRU TS 1950-2000 (top left), CMIP6 hist-GHG (bottom left) & hist-aer (bottom right) 1950-2000, and CMIP6 SSP5-8.5 2015-2100 (top right):\r\n - Data files: \r\nFig_10_19_panel-c_mapplot_pr_cmip6_mean_trend_future_pr_cmip6_maps_trend_future_MultiModelMean_trend.nc,\r\nFig_10_19_panel-c_mapplot_pr_histaer_mean_trend_past_pr_aer_maps_trend_past_MultiModelMean_trend.nc, \r\nFig_10_19_panel-c_mapplot_pr_histghg_mean_trend_past_pr_ghg_maps_trend_past_MultiModelMean_trend.nc, \r\nFig_10_19_panel-c_mapplot_pr_obs_mean_trend_past_CRU_single_trend.nc;\r\n \r\n Panel (d):\r\nObserved and model relative precipitation June-September mean anomalies over 1950-2100 in respect to 1995-2014 averages over central India (lon: 76°E-87°E, lat: 20°N-28°N) (CRU TS (brown), GPCC (dark blue), REGEN (green), APHRO-MA (light brown), IITM all-India rainfall (light blue), CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink), CMIP6 hist-aer (grey), hist-GHG (light blue) CMIP6 historical/SSP5-8.5 (dark red) and CMIP5 historical/RCP8.5 (dark blue) and Modelled change until 2081‒2100 in respect to 1995-2014 averages over central India (CMIP6 SSP5-8.5 (dark red) and CMIP5 historical/RCP8.5 (dark blue)):\r\n - Data files: \r\nFig_10_19_panel-d_timeseries.csv, \r\nFig_10_19_panel-d_boxplot.csv\r\n \r\n Panel (e):\r\nOLS linear trends in relative precipitation June-September mean anomalies (baseline 1995-2014) over past (1950-2000) and future (2015-2100) period over central India (lon: 76°E-87°E, lat: 20°N-28°N) of observations (GPCC, CRU TS, REGEN and APRHO-MA: black crosses) and models (individual members of CMIP5 historical-RCP8.5 (blue), CMIP6 historical-SSP5-8.5 (dark red), CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink circles), CMIP6 hist-GHG (blue triangles), CMIP6 hist-aer (grey triangles)), and box-and-whisker plots for the SMILEs: MIROC6, CSIRO-Mk3-6-0, MPI-ESM, d4PDF (grey shading):\r\n - Data file: \r\nFig_10_19_panel-e_trends.csv\r\n \r\n Panel (f):\r\nJune-September mean 2016-2045 OLS linear trend difference in precipitation between the 3 MPI-ESM runs with the lowest and the 3 MPI-ESM runs with the highest trend:\r\n - Data file: \r\nFig_10_19_panel-f_mapplot_pr_mpige_mean_trend_future_spread_single_trend-difference-min3-max3.nc\r\n\r\nAcronyms: \r\nCMIP - Coupled Model Intercomparison Project, \r\nAPHRODITE - ASIAN PRECIPITATION - HIGHLY-RESOLVED OBSERVATIONAL DATA INTEGRATION TOWARDS EVALUATION OF WATER RESOURCES, \r\nCRU TS- Climatic Research Unit Time Series, \r\nGHG - Greenhouse gas, \r\nIITM - Indian Institute of Technology Madras, \r\nRCP - Representative Concentration Pathway, \r\nDAIMP - Detection and Attribution Model Intercomparison Project, \r\nSSP - Shared Socioeconomic Pathways, \r\nGPCC - GLOBAL PRECIPITATION CLIMATOLOGY CENTRE, \r\nREGEN - Rainfall Estimates on a Gridded Network, S\r\nMILEs -single model initial-condition large ensembles, \r\nd4PDF - Database for Policy Decision-Making for Future Climate Change, \r\nMIROC - Model for Interdisciplinary Research on Climate, \r\nMPI - Max-Planck-Institut für Meteorologie, \r\nESM - Earth System Model, \r\nCordex – Coordinated Regional Climate Downscaling Experiment, \r\nOLS - ordinary least squares regression. \r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The code for ESMValTool is provided.\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 10)\r\n - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:09:53", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data files converted to BADC-CSV format by CEDA staff.\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 I, Physical Science Basis, Chapter 10, teleconnections, drivers, feedbacks, Linking global to regional, Regional scale, internal variability, forced change, model improvements, Figure 10.19, precipitation change, precipitation trends, Central India, Indian summer monsoon, climate projections", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-29T09:42:59", "doiPublishedTime": "2023-05-17T13:36:40", "removedDataTime": null, "geographicExtent": { "ob_id": 2928, "bboxName": "", "eastBoundLongitude": 100.0, "westBoundLongitude": 60.0, "southBoundLatitude": -5.0, "northBoundLatitude": 35.0 }, "verticalExtent": { "ob_id": 100, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37644, "dataPath": "/badc/ar6_wg1/data/ch_10/ch10_fig19/v20220622", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 535900, "numberOfFiles": 12, "fileFormat": "txt, netCDF, csv" }, "timePeriod": { "ob_id": 9593, "startTime": "1950-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 3827, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 34616, "uuid": "16e4aae3e57949aca9d68523651bc73c", "short_code": "comp", "title": "Caption for Figure 10.19 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Changes in the Indian summer monsoon in the historical and future periods. (a) Observational uncertainty demonstrated by a snapshot of rain-gauge density (% of 0.05° subgrid boxes containing at least one gauge) in the APHRO-MA 0.5° daily precipitation dataset for June to September 1956. (b) Multi-model ensemble (MME) mean bias of 34 CMIP6 models for June to September precipitation (mm day–1) compared to CRU TS observations for the 1985‒2010 period. (c) Maps of rainfall trends (mm day–1 per decade) in CRU TS observations (1950‒2000), the CMIP6 MME-mean of SSP5-8.5 future projections for 2015‒2100 (34 models), the CMIP6 hist-GHG and hist-aer runs, both measured over 1950 to 2000. (d) Low-pass filtered time series of June to September precipitation anomalies (%, relative to 1995‒2014 baseline) averaged over the central India box shown in panel (b). The averaging region (20°N‒28°N, 76°E‒87°E) follows other works (Bollasina et al., 2011; Jin and Wang, 2017; Huang et al., 2020b). Time series are shown for CRU TS (brown), GPCC (dark blue), REGEN (green), APHRO-MA (light brown) observational estimates and the IITM all-India rainfall product (light blue) in comparison with the CMIP6 mean of 13 models for the all-forcings historical (pink) the aerosol-only (hist-aer, grey) and greenhouse gas-only (hist-GHG, blue). Dark red and blue lines show low-pass filtered MME-mean change in the CMIP6 historical/SSP5-8.5 (34 models) and CMIP5 historical/RCP8.5 (41 models) experiments for future projections to 2100. The filter is the same as that used in Figure 10.11 (d). To the right, box-and-whisker plots show the 2081‒2100 change averaged over the CMIP5 (blue) and CMIP6 (dark red) ensembles. Note that some models exceed the plotting range (CMIP5: GISS-E2-R-CC, GISS-E2-R, IPSL-CM5B-LRl and CMIP6: CanESM5-CanOE, CanESM5 and GISS-E2-1-G). (e) Precipitation linear trend (% per decade) over Central India for historical 1950‒2000 (left) and future 2015‒2100 (right) periods in Indian Monsoon rainfall in observed estimates (black crosses), the CMIP5 historical-RCP8.5 simulations (blue), the CMIP6 ensemble (dark red) for historical all-forcings experiment and SSP5-8.5 future projection, the CMIP6 hist-GHG (light blue triangles), hist-aer (grey triangles) and historical all-forcings (same sample as for hist-aer and hist-GHG, pink circles). Ensemble means are also shown. Box-and-whisker plots show the trend distribution of the three coupled and the d4PDF atmosphere-only (for past period only) SMILEs used throughout Chapter 10 and follow the methodology used in Figure 10.6. (f) Example spread of trends (mm day–1 per decade) for the period 2016‒2045 in RCP8.5 SMILE experiments of the MPI-ESM model, showing the difference between the three driest and three wettest trends among ensemble members over central India. All trends are estimated using ordinary least-squares regression. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": [ 6021, 6022, 6023, 9042, 9043, 19043, 49990, 50510, 50516, 50559, 50561, 53130, 53131, 53939, 53940, 54037, 54989, 62501, 63166, 64247, 64414, 89242, 89289, 90401, 90402, 90403, 90404, 90405, 90406, 90407, 90408, 90409, 90410, 90411, 90412, 90413, 90414, 90415, 90416, 90417, 90418, 90419, 90420, 90421, 90422, 90423, 90424, 90425, 90426, 90427, 90428, 90429, 90430, 90431, 90432, 90433, 90434, 90435, 90436, 90437, 90438, 90439, 90440, 90441, 90442, 90443, 90444, 90445 ], "vocabularyKeywords": [], "identifier_set": [ 12519 ], "observationcollection_set": [ { "ob_id": 32726, "uuid": "6fbf9d885b7444aba681868c4006c86a", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate 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 10.6\r\n- data for Figure 10.10\r\n- data for Figure 10.11\r\n- data for Figure 10.12\r\n- data for Figure 10.13\r\n- data for Figure 10.18\r\n- data for Figure 10.19\r\n- data for Figure 10.20\r\n- data for Figure 10.21\r\n- data for CCB 10.4, Figure 1" } ], "responsiblepartyinfo_set": [ 164281, 164282, 164283, 164284, 164285, 164286, 164287, 164288, 164289, 168244, 179513 ], "onlineresource_set": [ 47588, 52414, 47589, 82671, 88550, 94604 ] }, { "ob_id": 34617, "uuid": "5d64c2103c534f83b8ec11a2a4cab10d", "title": "Chapter 10 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 10.13 (v20220622)", "abstract": "Data for Figure 10.13 from Chapter 10 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 10.13 shows attribution of the southwestern North America precipitation decline during the 1983-2014 period.\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\nDoblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate 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. 1363–1512, doi:10.1017/9781009157896.012.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 3 subpanels. Data for all subpanels is provided.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The data is annual October-September (water year) precipitation means for:\r\n \r\n - Observed and modelled trends over 1983-2014\r\n - Observed and modelled relative anomalies with respect to 1971-2000 averages over southwestern North America (lon: 240°E-255°E, lat: 28°N-40°N)\r\n - Trends in relative precipitation anomalies between 1983-2014 (baseline 1983-2014) over southwestern North America (lon: 240°E-255°E, lat: 28°N-40°N)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel (a):\r\nObserved and Model (MPI-ESM and d4PDF runs with min and max trends as well as mean trends) OLS linear trends in precipitation between 1983 and 2014 over North America:\r\n - Data files: \r\nFig_10_13_panel-a_mapplot_pr_trend_CRU_single_trend.nc, \r\nFig_10_13_panel-a_mapplot_pr_trend_REGEN_single_trend.nc, \r\nFig_10_13_panel-a_mapplot_pr_trend_GPCC_single_trend.nc, \r\nFig_10_13_panel-a_mapplot_pr_trend_GPCP_single_trend.nc, \r\nFig_10_13_panel-a_mapplot_pr_trend_d4pdf_d4PDF_max_single-MultiModelMean_trend-min-mean-max.nc, \r\nFig_10_13_panel-a_mapplot_pr_trend_d4pdf_d4PDF_min_single-MultiModelMean_trend-min-mean-max.nc, \r\nFig_10_13_panel-a_mapplot_pr_trend_d4pdf_d4PDF_MultiModelMean_single-MultiModelMean_trend-min-mean-max.nc, \r\nFig_10_13_panel-a_mapplot_pr_trend_mpige_MPI-GE_max_single-MultiModelMean_trend-min-mean-max.nc, \r\nFig_10_13_panel-a_mapplot_pr_trend_mpige_MPI-GE_min_single-MultiModelMean_trend-min-mean-max.nc, F\r\nig_10_13_panel-a_mapplot_pr_trend_mpige_MPI-GE_MultiModelMean_single-MultiModelMean_trend-min-mean-max.nc\r\n \r\n Panel (b):\r\nObserved (CRU TS, black) and Model (d4PDF runs with min (brown) and max (green) trends) timeseries relative precipitation anomalies in respect to 1971-2000 averages over southwestern North America (lon: 240°E-255°E, lat: 28°N-40°N):\r\n - Data file: \r\nFig_10_13_panel-b_timeseries.csv\r\n \r\n Panel (c):\r\nOLS linear trends in relative precipitation anomalies between 1983-2014 (baseline 1983-2014) over southwestern North America (lon: 240°E-255°E, lat: 28°N-40°N): observed data (CRU TS, REGEN, GPCC and GPCP, black crosses), individual members of CMIP6 historical (red circles), and box-and-whisker plots for the SMILEs: MIROC6, CSIRO-Mk3-6-0, MPI-ESM, d4PDF (grey shading):\r\n - Data file: \r\nFig_10_13_panel-c_trends.csv\r\n\r\n\r\nAcronyms: \r\nCMIP - Coupled Model Intercomparison Project, \r\nHighResMIP - High Resolution Model Intercomparison Project, \r\nCordex – Coordinated Regional Climate Downscaling Experiment, \r\nCRU TS- Climatic Research Unit Time Series, \r\nGPCC - GLOBAL PRECIPITATION CLIMATOLOGY CENTRE, \r\nGPCP - Global Precipitation Climatology Project, \r\nd4PDF - Database for Policy Decision-Making for Future Climate Change, \r\nMPI GE - Max-Planck-Institut für Meteorologie Grand Ensemble, \r\nESM - Earth System Model, \r\nSMILEs -single model initial-condition large ensembles, \r\nMIROC - Model for Interdisciplinary Research on Climate, \r\nCSIRO - Commonwealth Scientific and Industrial Research Organisation, \r\nREGEN -Rainfall Estimates on a Gridded Network, \r\nOLS - ordinary least squares regression. \r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The code for ESMValTool is provided.\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 10)\r\n - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:14", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 10, teleconnections, drivers, feedbacks, Linking global to regional, Regional scale, internal variability, forced change, model improvements, Figure 10.13, precipitation change, precipitation decline, southwestern North America, historical period", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-29T09:16:50", "doiPublishedTime": "2023-05-17T12:52:46.729927", "removedDataTime": null, "geographicExtent": { "ob_id": 2929, "bboxName": "", "eastBoundLongitude": -60.0, "westBoundLongitude": -130.0, "southBoundLatitude": 20.0, "northBoundLatitude": 50.0 }, "verticalExtent": { "ob_id": 101, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37643, "dataPath": "/badc/ar6_wg1/data/ch_10/ch10_fig13/v20220622", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 693122, "numberOfFiles": 15, "fileFormat": "txt, netCDF, csv" }, "timePeriod": { "ob_id": 9594, "startTime": "1971-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3828, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 34619, "uuid": "8f397320a4174eaf8042b13edd586082", "short_code": "comp", "title": "Caption for Figure 10.13 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Attribution of the south-western North America precipitation decline during the 1983–2014 period. (a) Water year (October to September) precipitation spatial linear trend (in percent per decade) over North America from 1983 to 2014. Trends are estimated using ordinary least squares. Top row: observed trends from CRU TS, REGEN, GPCC, and the Global Precipitation Climatology Project (GPCP). Middle row: driest, mean and wettest trends (relative to the region enclosed in the black quadrilateral, bottom row) from the 100 members of the MPI-ESM coupled SMILE. Bottom row: driest, mean and wettest trends relative to the above region from the 100 members of the d4PDF atmosphere-only SMILE. (b) Time series of water year precipitation anomalies (%, baseline 1971–2000) over the above south-western North America region for CRU TS (grey bar charts). Black, brown and green lines show low-pass filtered time series for CRU TS, driest and wettest members of the d4PDF SMILE, respectively. The filter is the same as the one used in Figure 10.10. (c) Distribution of south-western region-averaged water-year precipitation 1983‒2014 trends (in percent per decade) for observations (CRU TS, REGEN, GPCC and GPCP, black crosses), CMIP6 all-forcing historical simulations (red circles), the MIROC6, CSIRO-Mk3-6-0, MPI-ESM and d4PDF SMILEs (grey box-and-whisker plots). Grey squares refer to ensemble mean trends of their respective SMILE and the red circle refers to the CMIP6 multi-model mean. Box-and-whisker plots follow the methodology used in Figure 10.6. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": [ 6021, 6022, 6023, 49990, 50510, 50516, 50559, 50561, 54037, 60438, 62354, 63165 ], "vocabularyKeywords": [], "identifier_set": [ 12517 ], "observationcollection_set": [ { "ob_id": 32726, "uuid": "6fbf9d885b7444aba681868c4006c86a", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate 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 10.6\r\n- data for Figure 10.10\r\n- data for Figure 10.11\r\n- data for Figure 10.12\r\n- data for Figure 10.13\r\n- data for Figure 10.18\r\n- data for Figure 10.19\r\n- data for Figure 10.20\r\n- data for Figure 10.21\r\n- data for CCB 10.4, Figure 1" } ], "responsiblepartyinfo_set": [ 164292, 164293, 164294, 164295, 164296, 164297, 164298, 164299, 164300, 168242, 179512 ], "onlineresource_set": [ 52412, 47586, 82669, 47587, 88548 ] }, { "ob_id": 34620, "uuid": "b981b3f983df4aa48a16ddbe3d8bf38d", "title": "Chapter 10 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 10.12 (v20220622)", "abstract": "Data for Figure 10.12 from Chapter 10 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 10.12 shows Southeastern South America positive mean precipitation trend and its drivers during 1951-2014.\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\nDoblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate 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. 1363–1512, doi:10.1017/9781009157896.012.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 4 subpanels. Data for 3 subpanels (b-d) is provided. Subpanel (a) is a schematic.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The data is annual December-Jannuary (DJF) precipitation means for:\r\n \r\n - Observed and model relative anomalies over 1951-2014 with respect to 1995-2014 average over south-eastern South America (26.25°S–38.75°S, 56.25°W–66.25°W)\r\n - Observed precipitation trends 1951-2014 South America\r\n - Trends in precipitation over 1951-2014 over south-eastern South America (26.25°S–38.75°S, 56.25°W–66.25°W)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel (b):\r\nObserved (CRU TS, black line, and CRU TS no-running mean (bars)) and Model (MPI-ESM runs with min (brown) and max (green) trends) precipitation rate relative anomalies over 1951-2014 with respect to 1995-2014 average over south-eastern South America (26.25°S–38.75°S, 56.25°W–66.25°W):\r\n - Data file: \r\nFig_10_12_panel-b_timeseries.csv\r\n \r\n Panel (c):\r\nObserved precipitation OLS linear trends 1951-2014 over South America:\r\n - Data files: \r\nFig_10_12_panel-c_mapplot_pr_trend_CRU_single_trend.nc, \r\nFig_10_12_panel-c_mapplot_pr_trend_GPCC_single_trend.nc \r\n \r\n Panel (d):\r\nOLS linear trends in precipitation over 1951-2014 over south-eastern South America (26.25°S–38.75°S, 56.25°W–66.25°W): observed data (GPCC, CRU TS: black crosses), individual members of CMIP6 historical (red circles), and box-and-whisker plots for the SMILEs: MIROC6, CSIRO-Mk3-6-0, MPI-ESM, d4PDF (grey shading):\r\n - Data file: \r\nFig_10_12_panel-d_trends.csv\r\n\r\n\r\nAcronyms: \r\nCRU TS- Climatic Research Unit Time Series, \r\nCMIP - Coupled Model Intercomparison Project, \r\nSMILEs -single model initial-condition large ensembles, \r\nMIROC - Model for Interdisciplinary Research on Climate, \r\nCSIRO - Commonwealth Scientific and Industrial Research Organisation, \r\nMPI - Max-Planck-Institut für Meteorologie, \r\nESM - Earth System Model, \r\nd4PDF - Database for Policy Decision-Making for Future Climate Change, OLS - ordinary least squares regression. \r\n\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The code for ESMValTool is provided.\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 10)\r\n - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:10:13", "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\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 I, Physical Science Basis, Chapter 10, teleconnections, drivers, feedbacks, Linking global to regional, Regional scale, internal variability, forced change, model improvements, Figure 10.12, precipitation trend, south-eastern South America, historical period", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-06-29T09:13:48", "doiPublishedTime": "2023-05-17T12:44:19", "removedDataTime": null, "geographicExtent": { "ob_id": 2930, "bboxName": "", "eastBoundLongitude": -46.25, "westBoundLongitude": -76.25, "southBoundLatitude": -48.75, "northBoundLatitude": -16.25 }, "verticalExtent": { "ob_id": 102, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37642, "dataPath": "/badc/ar6_wg1/data/ch_10/ch10_fig12/v20220622", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 124224, "numberOfFiles": 7, "fileFormat": "txt, netCDF, csv" }, "timePeriod": { "ob_id": 9595, "startTime": "1951-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3829, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 34622, "uuid": "408a6971525e4e2ab5d92d03b9fee782", "short_code": "comp", "title": "Caption for Figure 10.12 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "South-Eastern South America positive mean precipitation trend and its drivers during 1951–2014. (a) Mechanisms that have been suggested to contribute to South-Eastern South America summer wetting. (b) Time series of austral summer (December to February) precipitation anomalies (%, baseline 1995–2014) over the South-Eastern South American region (26.25°S–38.75°S, 56.25°W–66.25°W), black quadrilateral in the first map of panel (c). Black, brown and green lines show low-pass filtered time series for CRU TS), and the members with driest and wettest trends of the MPI-ESM single-model initial-condition large ensemble (SMILE; between 1951–2014), respectively. The filter is the same as the one used in Figure 10.10. (c) Mean austral summer precipitation spatial linear 1951–2014 trends (mm per month and decade) from CRU TS and GPCC. Trends are estimated using ordinary least squares regression. (d) Distribution of precipitation 1951–2014 trends over South-Eastern South America from GPCC and CRU TS (black crosses), CMIP6 all-forcing historical (red circles) and MIROC6, CSIRO-Mk3-6-0, MPI-ESM and d4PDF SMILEs (grey box-and-whisker plots). Grey squares refer to ensemble mean trends of their respective SMILE and the red circle refers to the CMIP6 multi-model mean. Box-and-whisker plots follow the methodology used in Figure 10.6. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": [ 6021, 6022, 6023, 49990, 50510, 50516, 53130, 53131, 54037, 62354, 62501, 63715 ], "vocabularyKeywords": [], "identifier_set": [ 12516 ], "observationcollection_set": [ { "ob_id": 32726, "uuid": "6fbf9d885b7444aba681868c4006c86a", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate 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 10.6\r\n- data for Figure 10.10\r\n- data for Figure 10.11\r\n- data for Figure 10.12\r\n- data for Figure 10.13\r\n- data for Figure 10.18\r\n- data for Figure 10.19\r\n- data for Figure 10.20\r\n- data for Figure 10.21\r\n- data for CCB 10.4, Figure 1" } ], "responsiblepartyinfo_set": [ 164303, 164304, 164305, 164306, 164307, 164308, 164309, 164310, 164311, 168240, 179511 ], "onlineresource_set": [ 52411, 47584, 82668, 47585, 88512 ] }, { "ob_id": 34623, "uuid": "970847e5690c4f9e8c4ad455641bd558", "title": "Chapter 10 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 10.11 (v20220622)", "abstract": "Data for Figure 10.11 from Chapter 10 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. 10.11 shows attribution of historic precipitation change in the Sahelian West African monsoon during June to September.\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\nDoblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate 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. 1363–1512, doi:10.1017/9781009157896.012.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 5 subpanels. Data for all subpanels is provided.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The data is annual June-September (JJAS) precipitation means for:\r\n \r\n - Observed anomalies over 1920-2018 respect to 1955-1984 average over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N)\r\n - Model anomalies over 1920-2018 respect to 1955-1984 average over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N)\r\n - Observed precipitation difference 1980-1990 mean - 1950-1960 mean\r\n - Model differences between 1.5x and 0.2x aerosol scalings over 1955-1984\r\n - Trends in relative precipitation anomalies (baseline 1955-1984) over decline (1955-1984) and recovery (1985-2014) period over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel (a):\r\nObserved (CRU TS) timeseries anomalies over 1920-2018 in respect to 1955-1984 average over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N):\r\n - Data file: \r\nFig_10_11_panel-a_timeseries_obs.csv\r\n \r\n Panel (b):\r\nObserved (CRU TS) precipitation difference 1980-1990 mean - 1950-1960 mean:\r\n - Data file: \r\nFig_10_11_panel-b_mapplot_pr_change_CRU_single_mean.nc\r\n \r\n Panel (c):\r\nModel differences between 1.5x and 0.2x aerosol scalings over 1955-1984:\r\n - Data file: \r\nFig_10_11_panel-c_mapplot_pr_diff_SMURPHS_single_mean.nc\r\n \r\n Panel (d):\r\nModel timeseries anomalies over 1920-2018 respect to 1955-1984 average over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N) for CMIP6 hist all-forcings (red), CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink), CMIP6 hist-aer (grey) and CMIP6 hist-GHG (pale blue):\r\n - Data file: \r\nFig_10_11_panel-d_timeseries_cmip6.csv\r\n \r\n Panel (e):\r\nObserved and modelled OLS linear trends in relative precipitation anomalies (baseline 1955-1984) over decline (1955-1984) and recovery (1985-2014) period over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N): observed data (GPCC, CRU TS: black crosses), 34 CMIP5 models (dark blue circles), individual members of CMIP6 hist all-forcings (red circles), CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink circles), CMIP6 hist-GHG (blue triangles), CMIP6 hist-aer (grey triangles), and box-and-whisker plots for the SMILEs: MIROC6, CSIRO-Mk3-6-0, MPI-ESM, d4PDF (grey shading):\r\n - Data file: \r\nFig_10_11_panel-e_trends.csv;\r\n\r\nAcronyms: \r\nCMIP - Coupled Model Intercomparison Project, \r\nCRU TS- Climatic Research Unit Time Series, \r\nSMURPHS - Securing Multidisciplinary UndeRstanding and Prediction of Hiatus and Surge events, \r\nDAMIP - Detection and Attribution Model Intercomparison Project, \r\nGHG - Greenhouse Gases, \r\nGPCC - GLOBAL PRECIPITATION CLIMATOLOGY CENTRE, \r\nSMILEs -single model initial-condition large ensembles, \r\nCSIRO - Commonwealth Scientific and Industrial Research Organisation, \r\nMIROC - Model for Interdisciplinary Research on Climate, \r\nMPI - Max-Planck-Institut für Meteorologie, \r\nd4PDF - Database for Policy Decision-Making for Future Climate Change, \r\nOLS - ordinary least squares regression. \r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The code for ESMValTool is provided.\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 10)\r\n - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:52:05", "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 I, Physical Science Basis, Chapter 10, teleconnections, drivers, feedbacks, Linking global to regional, Regional scale, internal variability, forced change, model improvements, Figure 10.11, precipitation rate, S.E. South America, historical and future period, MPI-ESM, internal variability", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-29T10:11:36", "doiPublishedTime": "2023-05-17T11:38:42", "removedDataTime": null, "geographicExtent": { "ob_id": 2931, "bboxName": "", "eastBoundLongitude": 60.0, "westBoundLongitude": -30.0, "southBoundLatitude": -10.0, "northBoundLatitude": 40.0 }, "verticalExtent": { "ob_id": 103, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37641, "dataPath": "/badc/ar6_wg1/data/ch_10/ch10_fig11/v20220622", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 298024, "numberOfFiles": 8, "fileFormat": "txt, netCDF, csv" }, "timePeriod": { "ob_id": 9596, "startTime": "1920-01-01T12:00:00", "endTime": "2018-12-31T12:00:00" }, "resultQuality": { "ob_id": 3830, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 34625, "uuid": "cc9c599a2bee4cb394404b801665e2fc", "short_code": "comp", "title": "Caption for Figure 10.11 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Attribution of historic precipitation change in the Sahelian West African monsoon during June to September. (a) Time series of CRU TS precipitation anomalies (mm day–1, baseline 1955–1984) in the Sahel box (10°N–20°N, 20°W–30°E) indicated in panel (b) applying the same low-pass filter as that used in Figure 10.10. The two periods used for difference diagnostics are shown in grey columns. (b) Precipitation change (mm day–1) in CRU TS data for 1980–1990 minus 1950–1960 periods. (c) Precipitation difference (mm day–1) between 1.5× and 0.2× historical aerosol emissions scaling factors averaged over 1955–1984 and five ensemble members of HadGEM3 experiments after Shonk et al. (2020). (d) Sahel precipitation anomaly time series (mm day–1, baseline 1955–1984) in Coupled Model Intercomparison Project Phase 6 (CMIP6) for 49 historical simulations with all forcings (red), and thirteen for each of greenhouse gas-only forcing (light blue) and aerosol-only forcing (grey), with a thirteen-point weighted running mean applied (a variant on the binomial filter with weights [1-6-19-42-71-96-106-96-71-42-19-6-1]). The CMIP6 subsample of all forcings matching the individual forcing simulations is also shown (pink). (e) Precipitation linear trend (% per decade) for (left) decline (1955–1984) and (right) recovery periods (1985–2014) for ensemble means and individual CMIP6 historical experiments (including single-forcing) as in panel (d) plus 34 CMIP5 models (dark blue). Box-and-whisker plots show the trend distribution of the three coupled and the d4PDF atmosphere-only single-model initial-condition large ensembles (SMILEs) used throughout Chapter 10 and follow the methodology used in Figure 10.6. The two black crosses represent observational estimates from GPCC and CRU TS. Trends are estimated using ordinary least-squares regression. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": [ 6021, 6022, 6023, 19043, 50516, 50559, 50561, 53130, 53131, 54037, 60438, 62354, 63166, 63714 ], "vocabularyKeywords": [], "identifier_set": [ 12515 ], "observationcollection_set": [ { "ob_id": 32726, "uuid": "6fbf9d885b7444aba681868c4006c86a", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate 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 10.6\r\n- data for Figure 10.10\r\n- data for Figure 10.11\r\n- data for Figure 10.12\r\n- data for Figure 10.13\r\n- data for Figure 10.18\r\n- data for Figure 10.19\r\n- data for Figure 10.20\r\n- data for Figure 10.21\r\n- data for CCB 10.4, Figure 1" } ], "responsiblepartyinfo_set": [ 164314, 164315, 164316, 164317, 164318, 164319, 164320, 164321, 164322, 168238, 164323, 179510 ], "onlineresource_set": [ 52410, 47582, 82667, 47583, 88511, 94603 ] }, { "ob_id": 34626, "uuid": "d4eccbbd51db4ab7a8ad05a6f2f6a98a", "title": "Chapter 10 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 10.10 (v20220622)", "abstract": "Data for Figure 10.10 from Chapter 10 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 10.10 shows observed and projected changes in austral summer (December to February) mean precipitation in Global Precipitation Climatology Centre (GPCC), Climatic Research Unit Time-Series (CRU TS) and 100 members of the Max-Planck-Institut für Meteorologie Earth-System Model (MPI-ESM).\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\nDoblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate 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. 1363–1512, doi:10.1017/9781009157896.012.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, with data provided for both panels. Panel (a) consists of two maps, panel (b) shows multiple timeseries and boxplots.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains data of relative precipitation anomalies over 1950-2100 with respect to 1995-2014 average for global, S.E.South-America, Sao Paulo and Buenos Aires for:\r\n \r\n - Observational data (GPCC and CRU TS)\r\n - Model data (100 runs of MPI-ESM)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel (a):\r\n - Data files: \r\nModelled precipitation rate OLS linear trends between 2015-2070 with respect to 1995-2014 average over S.E. South America region, from left to right (MPI-ESM member with min (driest) and max (wettest) trends):\r\nFig_10_10_panel-a_mapplot_trend_SES_DJF_MPI-GE_min_single-MultiModelMean_trend-min-median-max.nc, \r\nFig_10_10_panel-a_mapplot_trend_SES_DJF_MPI-GE_max_single-MultiModelMean_trend-min-median-max.nc\r\n \r\n Panel (b):\r\n - Data files: \r\nPrecipitation rate anomalies 1950-2100 with respect to 1995-2014 average for the global mean, S.E.South-America mean, Sao Paulo mean and Buenos Aires mean of GPCC (dark blue), CRU (dark brown), members of the MPI-ESM (grey), the MPI-ESM member with the driest (brown) and wettest (green) trend:\r\nFig_10_10_panel-b_timeseries_global.csv, \r\nFig_10_10_panel-b_timeseries_SES.csv, \r\nFig_10_10_panel-b_timeseries_SaoPaulo.csv, \r\nFig_10_10_panel-b_timeseries_BuenosAires.csv\r\n\r\n - Data files: \r\nUnderlying data points of the boxplot showing MPI-ESM modelled precipitation rate OLS linear trends over all members between 2015-2070 with respect to 1995-2014 average for the global mean, S.E.South-America mean, Sao Paulo mean and Buenos Aires mean:\r\nFig_10_10_panel-b_boxplot_BuenosAires.csv, \r\nFig_10_10_panel-b_boxplot_global.csv, \r\nFig_10_10_panel-b_boxplot_SaoPaulo.csv, \r\nFig_10_10_panel-b_boxplot_SES.csv; \r\n\r\nOLS - ordinary least squares regression.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The code for ESMValTool is provided.\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 10)\r\n - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:10:11", "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 I, Physical Science Basis, Chapter 10, teleconnections, drivers, feedbacks, Linking global to regional, Regional scale, internal variability, forced change, model improvements, Figure 10.10", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-29T09:06:51", "doiPublishedTime": "2023-05-17T11:30:42.414570", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 104, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37640, "dataPath": "/badc/ar6_wg1/data/ch_10/ch10_fig10/v20220622", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 652574, "numberOfFiles": 13, "fileFormat": "txt, netCDF, csv" }, "timePeriod": { "ob_id": 9597, "startTime": "1950-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 3831, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 34628, "uuid": "4e16f1e6c55c430b8fd805c48340de73", "short_code": "comp", "title": "Caption for Figure 10.10 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Observed and projected changes in austral summer (December to February) mean precipitation in Global Precipitation Climatoloy Centre (GPCC), Climatic Research Unit Time Series (CRU TS) and 100 members of the Max Planck Institute for Meteorology Earth System Model (MPI-ESM. (a) 55-year trends (2015‒2070) from the ensemble members with the lowest (left) and highest (right) trend (% per decade, baseline 1995–2014). (b) Time series (%, baseline 1995–2014) for different spatial scales (from top to bottom: global averages; South-Eastern South America; grid boxes close to São Paulo and Buenos Aires) with a five-point weighted running mean applied (a variant on the binomial filter with weights [1-3-4-3-1]). The brown (green) lines correspond to the ensemble member with weakest (strongest) 55-year trend and the grey lines to all remaining ensemble members. Box-and-whisker plots show the distribution of 55-year linear trends across all ensemble members, and follow the methodology used in Figure 10.6. Trends are estimated using ordinary least squares. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": [ 6021, 6022, 6023, 49990, 50510, 50516, 50559, 50561, 54037, 60438, 62354, 63165, 64247, 64414, 89229, 89230, 89231, 89232, 89233, 89234, 89235, 89236, 89237, 89238, 89239, 89240, 89241, 89242, 89243, 89244, 89245, 89246, 89247, 89248, 89249, 89250, 89251, 89252, 89253, 89254, 89255, 89256, 89257, 89258, 89259, 89260, 89261, 89262, 89263, 89264, 89265, 89266, 89267, 89268, 89269, 89270, 89271, 89272, 89273, 89274, 89275, 89276, 89277, 89278, 89279, 89280, 89281, 89282, 89283, 89284, 89285, 89286, 89287, 89288, 89289, 89290, 89291, 89292, 89293, 89294, 89295, 89296, 89297, 89298, 89299, 89300, 89301, 89302, 89303, 89304, 89305, 89306, 89307, 89308, 89309, 89310, 89311, 89312, 89313, 89314, 89315, 89316, 89317, 89318, 89319, 89320, 89321, 89322, 89323, 89324, 89325, 89326, 89327, 89328, 89329, 89330, 89331, 89332, 89333 ], "vocabularyKeywords": [], "identifier_set": [ 12514 ], "observationcollection_set": [ { "ob_id": 32726, "uuid": "6fbf9d885b7444aba681868c4006c86a", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate 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 10.6\r\n- data for Figure 10.10\r\n- data for Figure 10.11\r\n- data for Figure 10.12\r\n- data for Figure 10.13\r\n- data for Figure 10.18\r\n- data for Figure 10.19\r\n- data for Figure 10.20\r\n- data for Figure 10.21\r\n- data for CCB 10.4, Figure 1" } ], "responsiblepartyinfo_set": [ 164326, 164327, 164328, 164329, 164330, 164331, 164332, 164333, 164334, 168236, 179509 ], "onlineresource_set": [ 52409, 47580, 82666, 47581 ] }, { "ob_id": 34629, "uuid": "567ca2ab6d6043479a1eaec678bfe91a", "title": "Chapter 10 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 10.18 (v20220622)", "abstract": "Data for Figure 10.18 from Chapter 10 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 10.18 shows historical and projected rainfall and Southern Annular Mode (SAM) over the Cape Town region.\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\nDoblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate 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. 1363–1512, doi:10.1017/9781009157896.012.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 4 subpanels. Data for all subpanels is provided.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The data is for the Cape Town region:\r\n \r\n - Observed yearly accumulation of rainfall, 1933-2014 mean, 2015, 2016, 2017\r\n - Observed annual precipitation cycle, 1933-2014 mean, 2015-2017 mean, 2015, 2016, 2017\r\n - Rainfall anomalies 1930-2100 with respect to 1980–2010 means\r\n - SAM index (calculated from sea-level pressure) 1930-2100\r\n - Precipitation trends 1933-2017, 1979-2017 and 2018-2100\r\n - SAM index trends 1933-2017, 1979-2017 and 2018-2100\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel (a):\r\nStation data of yearly accumulation of rainfall over the Cape Town region, 1933-2014 (grey lines), 2015 (orange line), 2016 (red line), 2017 (purple line):\r\n - Data file: \r\nFig_10_18_panel-a.csv\r\n \r\n Panel (b):\r\nStation data of annual precipitation cycle over the Cape Town region, 1933-2014 mean (black line), 2015-2017 mean (grey line), 2015 (orange bars), 2016 (red bars), 2017 (purple bars):\r\n - Data file: \r\nFig_10_18_panel-b.csv\r\n \r\n Panel (c):\r\nSAM index (top part) and rainfall (bottom part) anomalies over the Cape Town region between 1930 and 2100 of observed and reanalysis data (SAM: station based (black line), NCEP/NCAR (light blue line), ERA20C (dark red line) and 20CR (yellow line); rainfall: station based (black line), GPCC (green line) and CRU TS (olive line)) and model data (SAM and rainfall: CMIP5 (blue shading), CMIP6 (red shading), MIROC6 (orange shading); Rainfall: CORDEX (cyan shading), 6 CCAM (purple shading)):\r\n - Data files: \r\nFig_10_18_panel-c_timeseries_precipitation.csv, \r\nFig_10_18_panel-c_timeseries_SAM_index.csv\r\n \r\n Panel (d):\r\nSAM index (top part) and rainfall (bottom part) Theil-Sen trends between 1933-2017, 1979-2017 and 2018-2100 over the Cape Town region (31°S‒35°S, 18°W‒20.5°W) of observed and reanalysis data (SAM: station based (black), NCEP/NCAR (light blue), ERA20C (dark red) and 20CR (yellow); rainfall: station based (black), GPCC (green) and CRU TS (olive)) and model data (CMIP5 (blue), CMIP6 (red), MIROC6 (orange)):\r\n - Data files: \r\nFig_10_18_panel-d_trends_precipitation_1933-2017.csv, \r\nFig_10_18_panel-d_trends_precipitation_2018-2100.csv, \r\nFig_10_18_panel-d_trends_SAM_index_1979-2017.csv, \r\nFig_10_18_panel-d_trends_precipitation_1979-2017.csv, \r\nFig_10_18_panel-d_trends_SAM_index_1933-2017.csv, \r\nFig_10_18_panel-d_trends_SAM_index_2018-2100.csv\r\n\r\n\r\nAcronyms: \r\nCMIP - Coupled Model Intercomparison Project, \r\nNCEP - National Centers for Environmental Prediction, \r\nNCAR - National Center for Atmospheric Research, \r\nCRU TS - Climatic Research Unit (CRU) Time-series (TS), \r\nCORDEX - Coordinated Regional Climate Downscaling Experiment, \r\nCCAM - Conformal Cubic Atmospheric Model, \r\nMIROC6 - Model for Interdisciplinary Research on Climate, \r\nGPCC- Global Precipitation Climatology Centre, \r\nOLS - ordinary least squares regression. \r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The code for ESMValTool is provided.\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 10)\r\n - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:51:58", "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 I, Physical Science Basis, Chapter 10, teleconnections, drivers, feedbacks, Linking global to regional, Regional scale, internal variability, forced change, model improvements, Figure 10.18, Southern Annular Mode (SAM), precipitation trends, Cape Town region, historical and future period, climate projection, drought", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-07-12T07:55:04", "doiPublishedTime": "2023-05-17T13:01:55.884248", "removedDataTime": null, "geographicExtent": { "ob_id": 3553, "bboxName": "", "eastBoundLongitude": 20.5, "westBoundLongitude": 18.0, "southBoundLatitude": -35.0, "northBoundLatitude": -31.0 }, "verticalExtent": { "ob_id": 105, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 40059, "dataPath": "/badc/ar6_wg1/data/ch_10/ch10_fig18/v20220622", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 92078, "numberOfFiles": 12, "fileFormat": "csv" }, "timePeriod": { "ob_id": 9598, "startTime": "1930-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 3832, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 34631, "uuid": "7790d1c20843498cabd2177559728f42", "short_code": "comp", "title": "Caption for Figure 10.18 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Historical and projected rainfall and Southern Annular Mode (SAM) over the Cape Town region. (a) Yearly accumulation of rainfall (in mm) obtained by summing monthly totals between January and December, with the drought years 2015 (orange), 2016 (red), and 2017 (purple) highlighted in colour. (b) Monthly rainfall for the drought years (in colour) compared with the 1981‒2014 climatology (grey line). Rainfall in (a) and (b) is the average of 20 quality controlled and gap-filled series from stations within the Cape Town region (31ºS‒35ºS, 18ºW‒20.5ºW). (c) Time series of the SAM index and of historical and projected rainfall anomalies (%, baseline 1980–2010) over the Cape Town region. Observed data presented as 30-year running means of relative total annual rainfall over the Cape Town region for station-based data (black line, average of 20 stations as in (a) and (b), and gridded data (average of all gridcells falling within 31ºS‒35ºS, 18ºW‒20.5ºW), GPCC (green line) and CRU TS (olive line). Model ensemble results presented as the 90th-percentile range of relative 30-year running means of rainfall and the SAM index from 35 CMIP5 (blue shading) and 35 CMIP6 (red shading) simulations, 6 CORDEX simulations driven by 1 to 10 GCMs (cyan shading), 6 CCAM (purple shading) simulations from individual ensemble members, and 50 members from the MIROC6 SMILE simulations (orange shading). The light blue, dark red and yellow lines correspond to NCEP/NCAR, ERA20C and 20CR, respectively. The SAM index is calculated from sea level pressure reanalysis and GCM data as per Gong and Wang (1999) and averaged over the aforementioned bounding box. CMIP5, CORDEX and CCAM projections use RCP8.5, and CMIP6 and MIROC6 SMILE projections use SSP5-8.5. (d) Historical and projected trends in rainfall over the Cape Town region and in the SAM index. Observations and gridded data processed as in (c). Trends calculated as Theil-Sen trend with block-bootstrap confidence interval estimate. Markers show median trend, bars 95% confidence interval. Global models in each CMIP group were ordered according to the magnitude of trend in rainfall, and the same order is maintained in panels showing trends in the SAM. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11)." }, "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": [ 64414, 64415, 64416, 64417, 64418, 64419, 64420, 64421, 64422, 64423, 64424, 64425, 64426, 64427, 64428, 64429, 64430, 64431, 64432, 64433, 64434, 64435, 64436, 64437, 64438, 64439, 64440, 64441, 64442, 64443, 64444, 64445, 64446, 64447, 64448, 64449, 64450, 64451 ], "vocabularyKeywords": [], "identifier_set": [ 12518 ], "observationcollection_set": [ { "ob_id": 32726, "uuid": "6fbf9d885b7444aba681868c4006c86a", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate 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 10.6\r\n- data for Figure 10.10\r\n- data for Figure 10.11\r\n- data for Figure 10.12\r\n- data for Figure 10.13\r\n- data for Figure 10.18\r\n- data for Figure 10.19\r\n- data for Figure 10.20\r\n- data for Figure 10.21\r\n- data for CCB 10.4, Figure 1" } ], "responsiblepartyinfo_set": [ 164337, 164338, 164339, 164340, 164341, 164342, 164343, 164344, 164345, 168234, 179508, 164346 ], "onlineresource_set": [ 47579, 52413, 82670, 47578, 88549 ] }, { "ob_id": 34632, "uuid": "2dc808195d984efe8de7b52942796924", "title": "Chapter 10 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 10.6 (v20220113)", "abstract": "Data for Figure 10.6 from Chapter 10 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 10.6 is an illustration of some model biases in simulations performed with dynamical models.\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\nDoblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate 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. 1363–1512, doi:10.1017/9781009157896.012.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels ((a) and (b)), which are further divided into 6 maps and 1 boxplot. Data is provided for all subpanels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Boxplot data point is annual summer mean (JJA) surface air temperature (panel (a)) and precipitation (panel (b)) for western Mediterranean mean (lon: 10°W-10°E, lat: 33°N-45°N) between 1986 and 2005 for:\r\n \r\n - Observational datasets\r\n - Each model of CMIP5, CMIP6, HighResMIP, EURO-CORDEX 11 and EURO-CORDEX 44\r\n \r\nMapplot data is mean (1986-2005) annual summer mean (JJA) surface air temperature (panel (a)) and precipitation (panel (b)) for the western Mediterranean (lon: 15°W-15°E, lat: 28°N-50°N) regrided on a 1°x1° regular grid for:\r\n - Absolute values for reference observational dataset (BerkeleyEarth (a), CRU TS (b))\r\n - Ensemble biases of CMIP5, CMIP6, HighResMIP, EURO-CORDEX 11 and EURO-CORDEX 44\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel (a):\r\n - Data file: \r\nObserved (Berkeley Earth) mean (1986-2005) annual summer mean (JJA) surface air temperature over the western Mediterranean (top left):\r\nFig_10_6_panel-a_mapplot_tas_obs_single_single_mean.nc\r\n\r\n - Data files: \r\nEnsemble mean (1986-2005) annual summer mean (JJA) surface air temperature bias over the western Mediterranean (top right):\r\nFig_10_6_panel-a_mapplot_tas_bias_cmip5_tas_cmip5_map_MultiModelMean_bias.nc, \r\nFig_10_6_panel-a_mapplot_tas_bias_cmip6_tas_cmip6_map_MultiModelMean_bias.nc, \r\nFig_10_6_panel-a_mapplot_tas_bias_hrmip_tas_hrmip_map_MultiModelMean_bias.nc, \r\nFig_10_6_panel-a_mapplot_tas_bias_cdx44_tas_cdx44_map_MultiModelMean_bias.nc, \r\nFig_10_6_panel-a_mapplot_tas_bias_cdx11_tas_cdx11_map_MultiModelMean_bias.nc\r\n\r\n - Data file: \r\nObserved (black boxplots), reanalysis (black boxplots) and modelled (CMIP5: blue boxplots, CMIP6: red boxplots, HighResMIP: orange boxplots, CORDEX EUR-44: light blue boxplots, CORDEX EUR-11: green boxplots) annual summer mean (JJA) surface air temperature values (i.e. underlying data points of the boxplot) over the western Mediterranean (bottom part):\r\nFig_10_6_panel-a_boxplot.csv \r\n\r\n\r\n Panel (b):\r\n - Data file: \r\nObserved (CRU TS) mean (1986-2005) annual summer mean (JJA) precipitation rate over the western Mediterranean (top left):\r\nFig_10_6_panel-b_mapplot_pr_obs_single_masked_cru_single_mean.nc\r\n\r\n - Data files: \r\nEnsemble mean (1986-2005) annual summer mean (JJA) precipitation rate bias over the western Mediterranean (top right):\r\nFig_10_6_panel-b_mapplot_pr_bias_cmip5_pr_cmip5_map_MultiModelMean_bias.nc, \r\nFig_10_6_panel-b_mapplot_pr_bias_cmip6_pr_cmip6_map_MultiModelMean_bias.nc, \r\nFig_10_6_panel-b_mapplot_pr_bias_hrmip_pr_hrmip_map_MultiModelMean_bias.nc, \r\nFig_10_6_panel-b_mapplot_pr_bias_cdx44_pr_cdx44_map_MultiModelMean_bias.nc, \r\nFig_10_6_panel-b_mapplot_pr_bias_cdx11_pr_cdx11_map_MultiModelMean_bias.nc\r\n\r\n - Data file: \r\nobserved (black boxplots), reanalysis (black boxplots) and modelled (CMIP5: blue boxplots, CMIP6: red boxplots, HighResMIP: orange boxplots, CORDEX EUR-44: light blue boxplots, CORDEX EUR-11: green boxplots) annual summer mean (JJA) precipitation rate values (i.e. underlying data points of the boxplot) over the western Mediterranean (bottom part):\r\nFig_10_6_panel-b_boxplot.csv\r\n\r\n\r\nAcronyms: \r\nCMIP - Coupled Model Intercomparison Project, \r\nHighResMIP - High Resolution Model Intercomparison Project, \r\nCordex – Coordinated Regional Climate Downscaling Experiment, \r\nOLS - ordinary least squares regression.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The code for ESMValTool is provided.\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 10)\r\n - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-06-22T13:02:27", "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 I, Physical Science Basis, Chapter 10, teleconnections, drivers, feedbacks, Linking global to regional, Regional scale, internal variability, forced change, model improvements, Figure 10.6, surface air temperature, precipitation rate, western Mediterranean, historical period, model biases", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1°x1° Degrees", "status": "completed", "dataPublishedTime": "2022-06-29T09:04:31", "doiPublishedTime": "2023-05-17T11:21:43.092852", "removedDataTime": null, "geographicExtent": { "ob_id": 2934, "bboxName": "", "eastBoundLongitude": 15.0, "westBoundLongitude": -15.0, "southBoundLatitude": 28.0, "northBoundLatitude": 50.0 }, "verticalExtent": { "ob_id": 106, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "1°x1°" }, "result_field": { "ob_id": 37639, "dataPath": "/badc/ar6_wg1/data/ch_10/ch10_fig06/v20220622", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 633503, "numberOfFiles": 17, "fileFormat": "txt, netCDF, csv" }, "timePeriod": { "ob_id": 9599, "startTime": "1986-01-01T12:00:00", "endTime": "2005-12-31T12:00:00" }, "resultQuality": { "ob_id": 3833, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 34634, "uuid": "47a6cbe7ec024640889e1a379a744a72", "short_code": "comp", "title": "Caption for Figure 10.6 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Illustration of some model biases in simulations performed with dynamical models. (a) Top row: Mean summer (June to August) near-surface air temperature (in °C) over the Mediterranean area in Berkeley Earth and respective mean bias for five multi-model historical experiments with global models (CMIP5, CMIP6 and HighResMIP) and regional climate models (CORDEX EUR-44 and EUR-11) averaged between 1986–2005. Bottom row: Box-and-whisker plot shows spread of the 20 annual mean summer surface air temperature averaged over land areas in the western Mediterranean region (33°N–45°N, 10°W–10°E, black quadrilateral in the first panel of the top row) for a set of references and single model runs of the five multi-model experiments (one simulation per model) between 1986–2005. Additional observation and reanalysis data included in the bottom row are CRU TS, HadCRUT4, HadCRUT5, E-OBS, WFDE5, ERA5, ERA-Interim, CERA-20C, JRA-25, JRA-55, CFSR, MERRA2, MERRA. Berkeley Earth is shown in the first box to the left. (b) As (a) but for precipitation rate (mm day–1) and showing CRU TS in the first panel of the top row. Biases of the five multi-model experiments are shown with respect to CRU TS. Additional observation and reanalysis data included in the bottom row are GPCC, REGEN, E-OBS, GHCN, WFDE5, CFSR, ERA-Interim, ERA5, JRA-55, MERRA2, MERRA. CRU TS is shown in the first box to the left. All box-and-whisker plots show the median (line), and the interquartile range (IQR = Q3–Q1, box), with top whiskers extending to the last data less than Q3 + 1.5 × IQR and analogously for bottom whiskers. Data outside the whiskers range appear as flyers (circles). Further details on data sources and processing are available in the chapter data table (Table 10.SM.11)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": [ 6021, 6022, 6023, 19043, 50516, 50559, 50561, 53130, 53131, 54037, 60438, 62354, 63162, 63163, 63164 ], "vocabularyKeywords": [], "identifier_set": [ 12513 ], "observationcollection_set": [ { "ob_id": 32726, "uuid": "6fbf9d885b7444aba681868c4006c86a", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate 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 10.6\r\n- data for Figure 10.10\r\n- data for Figure 10.11\r\n- data for Figure 10.12\r\n- data for Figure 10.13\r\n- data for Figure 10.18\r\n- data for Figure 10.19\r\n- data for Figure 10.20\r\n- data for Figure 10.21\r\n- data for CCB 10.4, Figure 1" } ], "responsiblepartyinfo_set": [ 164349, 164350, 164351, 164352, 164353, 164354, 164355, 164356, 164357, 168232, 179507 ], "onlineresource_set": [ 47576, 52408, 82665, 47577, 88553, 94608 ] }, { "ob_id": 34635, "uuid": "e4416a7d02ed4eeb9a971a7d3c2f4e42", "title": "Chapter 10 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for CCB 10.4 Figure 1 (v20220622)", "abstract": "Data for CCB 10.4 Figure 1 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nCCB10.4 Figure 1 shows historical annual-mean surface air temperature linear trend (°C per decade) and its attribution over the Hindu Kush Himalaya (HKH) region.\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\nDoblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate 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. 1363–1512, doi:10.1017/9781009157896.012.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four subpanels. Data for all subpanels is provided.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The data is annual means for:\r\n \r\n - Observed and modelled trends over 1961-2014\r\n - Anomalies 1961-2014 with respect to 1961-1980 average for the HKH region mean\r\n - Trends 1961-2014 for the HKH region mean\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel (a):\r\n - Data files: \r\nFig_10_CCB-4_1_panel-a_mapplot_tas_trend_BerkeleyEarth_single_trend.nc, \r\nFig_10_CCB-4_1_panel-a_mapplot_tas_trend_CRU_single_trend.nc, \r\nFig_10_CCB-4_1_panel-a_mapplot_tas_trend_APHRO-MA_single_trend.nc, \r\nFig_10_CCB-4_1_panel-a_mapplot_tas_trend_JRA-55_single_trend.nc; \r\nObserved and reanalysis surface air temperature OLS linear trends over 1961-2014 over the HKH region, from left to right Berkeley Earth, CRU TS, APHRO-MA, JRA-55\r\n \r\n Panel (b):\r\n - Data files: \r\nFig_10_CCB-4_1_panel-b_mapplot_tas_trend_cmip6_CMIP6_min_single-MultiModelMean_trend-min-median-max.nc, \r\nFig_10_CCB-4_1_panel-b_mapplot_tas_trend_cmip6_CMIP6_MultiModelMedian_single-MultiModelMean_trend-min-median-max.nc, \r\nFig_10_CCB-4_1_panel-b_mapplot_tas_trend_cmip6_CMIP6_max_single-MultiModelMean_trend-min-median-max.nc; \r\nModelled surface air temperature OLS linear trends over 1961-2014 over the Hindu Kush Himalaya region, from left to right (CMIP6 models with min (coldest), median and max (warmest) trends)\r\n \r\n Panel (c):\r\n - Data file: Fig_10_CCB-4_1_panel-c_timeseries.csv; \r\nSurface air temperature anomalies 1961-2014 in respect to 1961-1980 average for the Hindu Kush Himalaya (HKH) region mean: means of CMIP6 hist all-forcings (red), and the CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink), for hist-aer (grey) and hist-GHG (pale blue), Berkeley Earth (dark blue), CRU TS (brown), APHRO-MA (light green) and JRA-55 (dark green).\r\n \r\n Panel (d):\r\n - Data file: Fig_10_CCB-4_1_panel-d_trends.csv; \r\nSurface air temperature OLS linear trends 1961-2014 for the Hindu Kush Himalaya (HKH) region mean: observed and reanalysis data (Berkeley Earth, CRU TS, APHRO-MA, JRA-55: black crosses), individual members of CMIP6 hist all-forcings (red circles), CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink circles), CMIP6 hist-GHG (blue triangles), CMIP6 hist-aer (grey triangles), and box-and-whisker plots for the SMILEs: MIROC6, CSIRO-Mk3-6-0, MPI-ESM, d4PDF (grey shading)\r\n\r\n\r\nAcronyms: \r\nCRU TS- Climatic Research Unit Time Series, \r\nCMIP - Coupled Model Intercomparison Project, \r\nJRA - Japanese 55year Reanalysis, \r\nDAMIP - Detection and Attribution Model Intercomparison Project, \r\nGHG - Greenhouse Gas, \r\nSMILEs - Single model initial-condition large ensembles, \r\nMIROC - Model for Interdisciplinary Research on Climate, \r\nCSIRO -Commonwealth Scientific and Industrial Research Organisation, \r\nMPI - Max-Planck-Institut für Meteorologie, \r\nESM - Earth System Model, d4PDF - database for policy decision-making for future climate changes, \r\nOLS - ordinary least squares regression. \r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The code for ESMValTool is provided.\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 10)\r\n - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:10:13", "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\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 I, Physical Science Basis, Chapter 10, teleconnections, drivers, feedbacks, Linking global to regional, Regional scale, internal variability, forced change, model improvements, Figure BBB 10.4.1, surface air temperature, Hindu Kush Himalaya, historical period", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-29T08:57:27", "doiPublishedTime": "2023-05-17T13:58:17.939381", "removedDataTime": null, "geographicExtent": { "ob_id": 2935, "bboxName": "", "eastBoundLongitude": 110.0, "westBoundLongitude": 60.0, "southBoundLatitude": 15.0, "northBoundLatitude": 45.0 }, "verticalExtent": null, "result_field": { "ob_id": 37638, "dataPath": "/badc/ar6_wg1/data/ch_10/ch10_ccb4_fig1/v20220622", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 399724, "numberOfFiles": 12, "fileFormat": "txt, netCDF, csv" }, "timePeriod": { "ob_id": 9600, "startTime": "1961-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 34636, "uuid": "513cc318b4c44c81a575f3f19b70caeb", "short_code": "comp", "title": "Caption for CCB 10.4 Figure 1 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Historical annual-mean surface air temperature linear trend (°C per decade) and its attribution over the Hindu Kush Himalaya (HKH) region. (a) Observed trends from Berkeley Earth (also showing the HKH outline), CRU TS (also showing the AR6 Tibetan Plateau (TIB) outline, for ease of comparison to the Interactive Atlas), APHRO-MA and JRA-55 datasets over 1961–2014. (b) Models showing the coldest, median and warmest HKH temperature linear trends among the CMIP6 historical ensemble over 1961–2014. (c) Low-pass-filtered time series of annual-mean surface air temperature anomalies (°C, baseline 1961–1980) over the HKH region as outlined in panel (a), showing means of CMIP6 hist all-forcings (red), and the CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink), for hist-aer (grey) and hist-GHG (pale blue). Observed datasets are Berkeley Earth (dark blue), CRU (brown), APHRO-MA (light green) and JRA-55 (dark green). The filter is the same as that used in Figure 10.10. (d) Distribution of annual mean surface air temperature trends (°C per decade) over the HKH region from 1961 to 2014 for ensemble means, the aforementioned observed and reanalysis data (black crosses), individual members of CMIP6 hist all-forcings (red circles), CMIP6 hist-GHG (blue triangles), CMIP6 hist-aer (grey triangles), and box-and-whisker plots for the SMILEs used throughout Chapter 10 (grey shading). Ensemble means are also shown. All trends are estimated using ordinary least-squares regression and box-and-whisker plots follow the methodology used in Figure 10.6. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": [ 6021, 6022, 6023, 49990, 50415, 50417, 50498, 50510, 50559, 50561, 53130, 53131, 58113, 60438, 62546, 64247, 64414, 79134, 79135, 79136, 79137, 79142, 79143, 79157, 79165, 80091, 80092, 80093, 80094, 80095, 80096, 80097, 80098, 80099, 80100, 80101, 80102, 80103, 80104, 80105, 80106, 80107, 80108, 80109, 80110 ], "vocabularyKeywords": [], "identifier_set": [ 12522 ], "observationcollection_set": [ { "ob_id": 32726, "uuid": "6fbf9d885b7444aba681868c4006c86a", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 10: Linking global to regional climate 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 10.6\r\n- data for Figure 10.10\r\n- data for Figure 10.11\r\n- data for Figure 10.12\r\n- data for Figure 10.13\r\n- data for Figure 10.18\r\n- data for Figure 10.19\r\n- data for Figure 10.20\r\n- data for Figure 10.21\r\n- data for CCB 10.4, Figure 1" } ], "responsiblepartyinfo_set": [ 164360, 164361, 164362, 164363, 164364, 164365, 164367, 164368, 164369, 168783, 179506, 164370 ], "onlineresource_set": [ 47575, 47574, 52417, 82674, 88648, 94653 ] }, { "ob_id": 34639, "uuid": "8194b416cbee482b89e0dfbe17c5786c", "title": "CHESS-SCAPE: Future projections of meteorological variables at 1 km resolution for the United Kingdom 1980-2080 derived from UK Climate Projections 2018", "abstract": "Gridded daily meteorological variables over the United Kingdom at 1 km resolution for the years 1980-2080. This dataset is an ensemble of four different realisations of future climate for each of four different representative concentration pathway scenarios (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), provided both with and without bias correction. This dataset contains time series of daily mean values of air temperature (K), specific humidity (kg kg-1), relative humidity (%), wind speed (m s-1), downward longwave radiation (W m-2), downward shortwave radiation (W m-2), precipitation (kg m-2 s-2) and surface air pressure (Pa). It also contains time series of daily minimum air temperature (K), daily maximum air temperature (K) and daily temperature range (K). The data are provided in gridded netCDF files at 1 km resolution aligned to the Ordnance Survey / British National Grid. There is one file for each variable for each month of the daily data. Also provided are monthly, seasonal and annual means, for which there is one file for each variable for each time resolution. Additionally twenty year mean-monthly (Jan-Dec) climatologies at ten year intervals are provided, for which there is one file for each variable for each twenty year time slice. The projections use a 360-day calendar, where each month consists of 30 days.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-12-17T04:53:58", "updateFrequency": "notPlanned", "dataLineage": "This dataset was derived from four ensemble members of the United Kingdom Climate Projections 2018 (UKCP18) Regional Projections on a 12km grid over \r\nthe United Kingdom for 1980-2080 (https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604, v20190731), which is a twelve member perturbed parameter ensemble of climate model projections for representative concentration pathway (RCP) scenario RCP8.5. Selected daily UKCP18 RCM variables were downscaled to 1 km using adapted Climate, Hydrology and Ecology research Support System (CHESS) downscaling methodology, taking into account topographic information. Ancillary datasets were obtained from the surface elevation of the climate model (https://data.ceda.ac.uk/badc/ukcp18/data/land-rcm/ancil/orog) the Integrated Hydrological Digital Terrain Model (IHDTM; https://www.ceh.ac.uk/services/integrated-hydrological-digital-terrain-model) the Ordnance Survey of Northern Ireland (OSNI) Open Data 50m Digital Terrain Model (https://www.nidirect.gov.uk/articles/50m-digital-terrain-model-height-data) Standardised Annual Average Rainfall 1961-90 (SAAR 61-90; https://catalogue.ceh.ac.uk/documents/51861941-c38d-46e6-9a31-6e9a293fc3d5) the European Space Agency (ESA) GlobAlbedo project albedo (http://globalbedo.org) and UK Energy Technology Support Unit (ETSU) mean wind speeds. Where applied, bias-correction was calculated seasonally using the Climate Hydrology and Ecology research Support System meteorology dataset for Great Britain (CHESS-met; https://doi.org/10.5285/2ab15bf0-ad08-415c-ba64-831168be7293) observation-based dataset. Variables for RCP2.6, RCP4.5 and RCP6.0 were calculated using time-shifting and pattern-scaling of the RCP8.5 scenarios, using selected members of the 5th Coupled Model Intercomparison Project (CMIP5; https://pcmdi.llnl.gov/mips/cmip5) and UKCP18 Global Climate Model Projections for the entire globe (https://catalogue.ceda.ac.uk/uuid/f1a2fc3c120f400396a92f5de84d596a) to determine future warming trajectories.\r\n\r\n\r\nData were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "1 km", "status": "underDevelopment", "dataPublishedTime": "2022-03-28T16:24:32", "doiPublishedTime": "2022-04-01T08:03:43.246452", "removedDataTime": null, "geographicExtent": { "ob_id": 2936, "bboxName": "", "eastBoundLongitude": 2.50059, "westBoundLongitude": -9.014131, "southBoundLatitude": 49.76619, "northBoundLatitude": 59.39968 }, "verticalExtent": null, "result_field": { "ob_id": 34638, "dataPath": "/badc/deposited2021/chess-scape/data", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 12500921449252, "numberOfFiles": 387841, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 9601, "startTime": "1980-12-01T00:00:00", "endTime": "2080-12-01T00:00:00" }, "resultQuality": { "ob_id": 3834, "explanation": "The data files conform to NetCDF Climate and Forecast (CF) Metadata Conventions 1.8 and Attribute Convention for Data Discovery (ACDD) 1.3. They use the CF Standard Name Table v77 (https://cfconventions.org/standard-names.html) and the NERC Vocabulary Server (https://vocab.nerc.ac.uk/standard_name/)", "passesTest": true, "resultTitle": "NetCDF Climate and Forecast (CF) Metadata Conventions 1.8 and Attribute Convention for Data Discovery (ACDD) 1.3", "date": "2022-01-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 33173, "uuid": "f4efc3cd88914cb0a957f7cffe7f894e", "short_code": "proj", "title": "UK Status, Change and Projections of the Environment (UK-SCAPE)", "abstract": "Significant environmental challenges have been created by pressures on land use, air and soil quality, water and food security, and biodiversity. The UK-SCAPE programme will undertake research and provide national-scale data and models designed to deliver new integrated understanding of the environment to tackle those challenges. It will improve our understanding of the consequences of interventions in the UK landscape and allow researchers to answer high-level questions relating to the environment." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1633, 6019, 6020, 6023, 19461, 27830, 27831, 46766, 46767, 46768, 46769, 46770, 46771, 46772, 46773, 46774, 46775, 46776, 46777, 46778, 46779 ], "vocabularyKeywords": [], "identifier_set": [ 12073 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 164375, 164376, 164377, 164378, 164379, 164380, 164381, 177253, 164382, 164383, 164384 ], "onlineresource_set": [ 95076, 47595, 47596, 47597, 47598, 47599, 47600, 89400, 89401, 89402, 89403, 87629, 87630, 87631, 87632, 87932, 95075 ] }, { "ob_id": 34640, "uuid": "e7c78370837d4f85be6a1f0cbe288a92", "title": "Chapter 11 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 11.16 (v20220117)", "abstract": "Data for Figure 11.16 from Chapter 11 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nProjected changes in annual maximum daily precipitation at 1.5°C, 2°C, and 4°C of global warming compared to the 1850-1900 baseline.\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\nSeneviratne, S.I., X. Zhang, M. Adnan, W. Badi, C. Dereczynski, A. Di Luca, S. Ghosh, I. Iskandar, J. Kossin, S. Lewis, F. Otto, I. Pinto, M. Satoh, S.M. Vicente-Serrano, M. Wehner, and B. Zhou, 2021: Weather and Climate Extreme Events in a Changing Climate. 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. 1513–1766, doi:10.1017/9781009157896.013.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three panels, with data provided for all panels.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Annual maximum daily precipitation change (%) (relative to 1850-1900)\r\n The data is given for global warming levels (GWLs), namely +1.5°C, 2.0°C, and +4.0°C.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - Figure_11_16a_cmip6_Rx1day_change_at_1_5C.nc: simulated annual maximum daily precipitation change (%) at 1.5°C global warming relative to 1850-1900\r\n \r\n Panel b:\r\n - Figure_11_16b_cmip6_Rx1day_change_at_2_0C.nc: simulated annual maximum daily precipitation change (%) at 2.0°C global warming relative to 1850-1900\r\n \r\n Panel c:\r\n - Figure_11_16c_cmip6_Rx1day_change_at_4_0C.nc: simulated annual maximum daily precipitation change (%) at 4.0°C global warming relative to 1850-1900\r\n\r\n\r\nAcronyms: CMIP - Coupled Model Intercomparison Project, SSP - Shared Socioeconomic Pathways\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 11)\r\n - Link to the Supplementary Material for Chapter 11, which contains details on the input data used in Table 11.SM.9\r\n - Link to the Ch11 GitHub repository containing scripts for generating figures\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:51:56", "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\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 I, Physical Science Basis, Chapter 11, Figure 11.16, global warming level, annual maximum daily precipitation, Rx1day, projection, cmip6", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "2.5 Degrees", "status": "completed", "dataPublishedTime": "2022-06-27T11:13:46", "doiPublishedTime": "2023-06-26T16:15:10.492132", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 107, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "2.5" }, "result_field": { "ob_id": 37631, "dataPath": "/badc/ar6_wg1/data/ch_11/ch11_fig16/v20220622", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 596816, "numberOfFiles": 6, "fileFormat": "txt, netCDF" }, "timePeriod": { "ob_id": 9602, "startTime": "1850-01-01T12:00:00", "endTime": "2100-03-11T12:00:00" }, "resultQuality": { "ob_id": 3835, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 34642, "uuid": "bcd546e64c2b4c83941119f1f909cb42", "short_code": "comp", "title": "Caption for Figure 11.16 from Chapter 11 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in annual maximum daily precipitation at (a) 1.5°C, (b) 2°C, and (c) 4°C of global warming compared to the 1850–1900 baseline. Results are based on simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble under the Shared Socio-economic Pathway (SSP), SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The numbers on the top right indicate the number of simulations included. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas 1. For details on the methods see Supplementary Material 11.SM.2. Changes in Rx1day are also displayed in the Interactive Atlas. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": [ 46711, 50559, 50561, 63626 ], "vocabularyKeywords": [], "identifier_set": [ 12533 ], "observationcollection_set": [ { "ob_id": 32727, "uuid": "81fbb6ae0c4e4504bc554fa7f8dcca7d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 11: Weather and climate extreme events in a changing climate", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 11: Weather and climate extreme events in a changing climate.\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\n- data for Figure FAQ 11.1, Figure 1\r\n- data for Figure 11.3\r\n- data for Figure 11.11\r\n- data for Figure 11.16\r\n- data for Figure 11.19\r\n- data for Figure 11.A.1" } ], "responsiblepartyinfo_set": [ 164385, 164386, 164387, 164388, 164389, 164390, 164391, 164392, 179505 ], "onlineresource_set": [ 47602, 47601, 82683, 82998, 83194, 88555 ] }, { "ob_id": 34643, "uuid": "3f415b44b4334725bfcc572c9246aa60", "title": "Chapter 11 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 11.11 (v20220117)", "abstract": "Data for Figure 11.11 from Chapter 11 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nProjected changes in annual maximum temperature (TXx) and annual minimum temperature (TNn) at 1.5°C, 2°C, and 4°C of global warming compared to the 1850-1900 baseline.\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\nSeneviratne, S.I., X. Zhang, M. Adnan, W. Badi, C. Dereczynski, A. Di Luca, S. Ghosh, I. Iskandar, J. Kossin, S. Lewis, F. Otto, I. Pinto, M. Satoh, S.M. Vicente-Serrano, M. Wehner, and B. Zhou, 2021: Weather and Climate Extreme Events in a Changing Climate. 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. 1513–1766, doi:10.1017/9781009157896.013.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three panels, with data provided for all panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Annual maximum temperature (°C) change (relative to 1850-1900)\r\n - Annual minimum temperature (°C) change (relative to 1850-1900)\r\n The data is given for global warming levels (GWLs), namely +1.5°C, 2.0°C, and +4.0°C.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - Figure_11.11a_cmip6_TXx_change_at_1.5C.nc: simulated annual maximum temperature change (°C) at 1.5°C global warming relative to 1850-1900\r\n \r\n Panel b:\r\n - Figure_11.11b_cmip6_TXx_change_at_2.0C.nc: simulated annual maximum temperature change (°C) at 2.0°C global warming relative to 1850-1900\r\n \r\n Panel c:\r\n - Figure_11.11c_cmip6_TXx_change_at_4.0C.nc: simulated annual maximum temperature change (°C) at 4.0°C global warming relative to 1850-1900\r\n Panel d:\r\n - Figure_11.11d_cmip6_TNn_change_at_1.5C.nc: simulated annual minimum temperature change (°C) at 1.5°C global warming relative to 1850-1900\r\n Panel e:\r\n - Figure_11.11e_cmip6_TNn_change_at_2.0C.nc: simulated annual minimum temperature change (°C) at 2.0°C global warming relative to 1850-1900\r\n Panel f:\r\n - Figure_11.11f_cmip6_TNn_change_at_4.0C.nc: simulated annual minimum temperature change (°C) at 4.0°C global warming relative to 1850-1900\r\n\r\nAcronyms: CMIP - Coupled Model Intercomparison Project\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 11)\r\n - Link to the Supplementary Material for Chapter 11, which contains details on the input data used in Table 11.SM.9\r\n - Link to the Ch11 GitHub repository containing scripts for generating figures\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:51:53", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\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 I, Physical Science Basis, Chapter 11, Figure 11.11, global warming level, annual maximum temperature, annual minimum temperature, TXx, TNn, projection, cmip6", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "2.5 Degrees", "status": "completed", "dataPublishedTime": "2022-06-27T11:01:57", "doiPublishedTime": "2023-06-26T16:09:52.817932", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 108, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "2.5" }, "result_field": { "ob_id": 37630, "dataPath": "/badc/ar6_wg1/data/ch_11/ch11_fig11/v20220622", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1161294, "numberOfFiles": 9, "fileFormat": "txt, netCDF" }, "timePeriod": { "ob_id": 9603, "startTime": "1850-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 3836, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 34645, "uuid": "86649a472cb346bd9dfee5e0c6aad196", "short_code": "comp", "title": "Caption for Figure 11.11 from Chapter 11 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in (a–c) annual maximum temperature (TXx) and (d–f) annual minimum temperature (TNn) at 1.5°C, 2°C, and 4°C of global warming compared to the 1850–1900 baseline. Results are based on simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble under the Shared Socio-economic Pathways (SSPs) SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The numbers in the top right indicate the number of simulations included. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas 1. For details on the methods see Supplementary Material 11.SM.2. Changes in TXx and TNn are also displayed in the Interactive Atlas. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": [ 46711, 50559, 50561, 63161 ], "vocabularyKeywords": [], "identifier_set": [ 12532 ], "observationcollection_set": [ { "ob_id": 32727, "uuid": "81fbb6ae0c4e4504bc554fa7f8dcca7d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 11: Weather and climate extreme events in a changing climate", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 11: Weather and climate extreme events in a changing climate.\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\n- data for Figure FAQ 11.1, Figure 1\r\n- data for Figure 11.3\r\n- data for Figure 11.11\r\n- data for Figure 11.16\r\n- data for Figure 11.19\r\n- data for Figure 11.A.1" } ], "responsiblepartyinfo_set": [ 164395, 164396, 164397, 164398, 164399, 164400, 164401, 164402, 179504 ], "onlineresource_set": [ 47603, 47604, 82682, 82997, 83193, 88554 ] }, { "ob_id": 34649, "uuid": "fbf06d6f3c9b4469926d79be38a5cf63", "title": "Anomalies of Biogeochemical Parameters in the Tropical Region (TROPBGC)", "abstract": "This dataset contains biogeochemical parameter anomalies, calculated from Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) (Chlorophyll Concentration), GLOBAL_MULTIYEAR_BGC_001_029 and MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008 datasets. \r\n\r\nData was taken from existing datasets, from National Aeronautics and Space Administration (NASA) the MODIS-Aqua satellite (chlorophyll) and from Copernicus Marine System 2 biogeochemical sets called GLOBAL_MULTIYEAR_BGC_001_029 (https://doi.org/10.48670/moi -00019) and MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008 (https://doi.org/10.48670/moi-00047) from which the anomalies were computed and the files were created using the following Matlab functions:\r\n\r\n1- Humberto L. Varona. (2021). mNC: A tool for Oceanographers and Meteorologists to easily create their NetCDF files using Matlab (1.0). Zenodo. https://doi.org/10.5281/zenodo.5572749\r\n2- Humberto L. Varona. (2021). CalcPlotAnomaly: Matlab function set for the calculation and plotting of anomalies (1.01). Zenodo. https://doi.org/10.5281/zenodo.5576889\r\n\r\nAnomalies were computed for the entire tropic.\r\n\r\nThe parameters are: Chlorophyll concentration, pH, Surface aqueous partial pressure of CO2 , NO3, Surface downward flux of total CO2, PO4, SI, O2, Fe and concentration of phytoplankton.\r\n\r\nAll files are in NetCDF format\r\n\r\nThis dataset was produced for the project Tropical and South Atlantic climate-based marine ecosystem predictions for sustainable management, to Tropical Atlantic Interdisciplinary Laboratory on physical, biogeochemical, ecological and human dynamics (IJL TAPIOCA) and the Brazilian Research Network on Global Climate Change (Rede CLIMA).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2022-01-18T11:46:28", "updateFrequency": "notPlanned", "dataLineage": "Data deposited to the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "TROPBGC, Biogeochemical", "publicationState": "preview", "nonGeographicFlag": true, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": null, "verticalExtent": null, "result_field": { "ob_id": 34650, "dataPath": "/badc/deposited2022/TROPBGC/data/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 65004890065, "numberOfFiles": 10, "fileFormat": "Data are netCDF formatted" }, "timePeriod": null, "resultQuality": { "ob_id": 3839, "explanation": "Data are as given by the data provider with quality control flags defined within each file. No quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 34651, "uuid": "61f80d1ceea540c48fcda2546200b57b", "short_code": "comp", "title": "Anomalies of Biogeochemical Parameters in the Tropical Region (TROPBGC)", "abstract": "Anomalies were computated using these tools:\\n\\nHumberto L. Varona. (2021). mNC: A tool for Oceanographers and Meteorologists to easily create their NetCDF files using Matlab (1.0). Zenodo. https://doi.org/10.5281/zenodo.5572749\\n\\nHumberto L. Varona. (2021). CalcPlotAnomaly: Matlab function set for the calculation and plotting of anomalies (1.01). Zenodo. https://doi.org/10.5281/zenodo.5576889" }, "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": 34652, "uuid": "21b754bf95a848a686f1acefebf00b08", "short_code": "proj", "title": "TRIATLAS: Tropical and South Atlantic climate-based marine ecosystem predictions for sustainable management", "abstract": "Main Objective: To assess the status of the South and Tropical Atlantic marine ecosystem and develop a framework for predicting its future changes, from months to decades, by combining ecosystem observations, climate-based ecosystem prediction and information on future socio-economic and ecosystem service changes, and thus to contribute to the sustainable management of human activities in the Atlantic Ocean as a whole.\r\n\r\nTRIATLAS has the following specific objectives (SO):\r\nSO1 – To enhance knowledge of the present state and seasonal dynamics of the Atlantic marine ecosystem across several trophic levels, through scientifically integrating and extending the physical and biological observing system in key areas of the South and Tropical Atlantic (Core Theme 1; CT1).\r\nSO2 – To quantify the drivers at interannual to decadal time scale in the Atlantic marine ecosystem, and the potential for tipping point behavior and regime shifts, by using observations and numerical (earth system, ocean, and marine ecosystem) model simulations to examine the interactions between different stressors (including climate variability, extremes, and change, as well as fisheries and pollution) and the role of cumulative impacts on ecosystem functioning and associated ecosystem services (CT2).\r\nSO3 – To combine state-of-the-art climate prediction and ecosystem models to improve forecasting capabilities of physical stressors, tipping points, recovery and changes in ecosystem state of the South and Tropical Atlantic from months to decades (CT3)\r\nSO4 – To contribute to improving the sustainable exploitation of Atlantic marine resources by developing scenarios combining climate based ecosystem predictions with Shared Socioeconomic Pathways (SSP), by conducting socioeconomic vulnerability assessments services, with stakeholder engagement and by analysing new value chains (CT4)\r\nSO5 – To enhance capacity in marine ecosystems, oceanography, and climate research in countries bordering the South and Tropical Atlantic Ocean, so as to increase the region’s ability for managing human activities and sustainable development in the Atlantic Ocean (CT4).\r\nSO6 – To ensure that activities are carried out both: 1) in close cooperation and alignment with relevant European Commission services (DG R&I) and the South-South Framework for Scientific and Technical Cooperation in the South and Tropical Atlantic and Southern Ocean; and 2) in coordination with other relevant projects and programmes in the field. This is to ensure coherence with related policy initiatives and to contribute to upscale cooperation along and across the Atlantic Ocean as a whole (CT4)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1050, 1635, 1636, 6645, 6646, 46751, 46752, 46753, 46754, 46755, 46756, 46757, 46758, 46759, 46760, 46761, 46762, 46763, 46764, 46765 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 164410, 164411, 164412, 164413, 164414, 164415, 168200, 168201, 168202, 168807, 168203 ], "onlineresource_set": [] }, { "ob_id": 34653, "uuid": "ab8d21568c81491fbb9a300c36884af7", "title": "ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.0", "abstract": "This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. \r\n\r\nThis is version 2.0 of the dataset. The five thematic climate variables included in this dataset are:\r\n• Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\r\n• Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .\r\n• Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.\r\n• Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\r\n• Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n\r\nData generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop.\r\n\r\nDetailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2022-03-09T15:43:42", "updateFrequency": "notPlanned", "dataLineage": "This dataset was generated in the framework of the Lakes CCI+ project, funded by ESA. Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ESA, CCI, Lakes, ECV", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-03-17T22:52:31", "doiPublishedTime": "2022-03-18T10:43:53", "removedDataTime": null, "geographicExtent": { "ob_id": 2623, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 36809, "dataPath": "/neodc/esacci/lakes/data/lake_products/L3S/v2.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 670280729797, "numberOfFiles": 10325, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 10175, "startTime": "1992-09-26T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3417, "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": "2020-05-22" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 32839, "uuid": "be9fb3c9bda3425286c3fdc9f94bf04c", "short_code": "comp", "title": "Derivation of the ESA Lakes Climate Change Initiative dataset", "abstract": "The data generated by the Lakes_cci project have been derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview.\r\n\r\nFor information on the derivation of the lake products, please see the documentation at https://climate.esa.int/en/projects/lakes/key-documents-lakes/." }, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2551, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 24, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_lakes_terms_and_conditions_v2.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30309, "uuid": "09c9b617a2d6462f9954a3c3a34fcc27", "short_code": "proj", "title": "ESA Lakes Climate Change Initiative Project", "abstract": "The Lakes Climate Change Initiative Project (Lakes_cci) is part of the European Space Agency's Climate Change Initiative Programme to produce long term datasets of Essential Climate Variables (ECV's) derived from global satellite data..\r\n\r\nLakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. In this context, the Lakes_cci develops products for the following five thematic climate variables:\r\n•\tLake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.\r\n•\tLake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.\r\n•\tLake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. \r\n•\tLake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. \r\n•\tLake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n\r\nIn this context, Lakes_cci represents a unique framework to provide consistent and homogenous data to the multiple communities of lake scientists. The project actively engages with this community to assess the utility and future improvement of Lakes_cci products." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1050, 6019, 6020, 8143, 8144, 12066, 28892, 28893, 28894, 28895, 28896, 28897, 28898, 28899, 28900, 28901, 28902, 28903, 28904, 28905, 28906, 28907, 28908, 28909, 28910, 28911, 28912, 28913, 28914, 28915, 28916, 28917, 28918, 28919, 28920, 28921, 28922, 28923, 28924, 28925, 28926, 28927, 28928, 28929, 28930, 28931, 28932, 28933, 28934, 28938, 28940, 28941, 28942, 36220, 36221, 47021, 47022, 47023, 47024, 47025, 47026, 47027, 47028, 47029, 47030, 47031, 47032, 47033, 47034, 47035, 47036, 47037, 47038, 47039, 47040, 47041, 47042, 47043, 47044, 47045, 47046, 47047, 47048, 47049, 47050, 47051, 47052, 47053, 47054, 47055, 47056, 47057, 47058, 47059, 47060, 47061, 47062, 47063, 47064, 47065, 47066, 47067, 47068, 47069, 47070, 47071, 47072, 47073, 47074, 47075, 47076, 47077, 47078, 47079, 47080, 47081, 47082, 47083, 47084, 47085, 47086, 47087, 47088, 47089, 47090, 47091, 47092, 47093, 47094, 47095, 47096, 47097, 47098, 47099, 47100, 47101, 47102, 47103, 47104, 47105, 47106, 47107, 47108, 47109, 47110, 47111, 47112, 47113, 47114, 47115, 47116, 47117 ], "vocabularyKeywords": [ { "ob_id": 11064, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_lakes", "resolvedTerm": "lakes" } ], "identifier_set": [ 12066 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 164422, 164423, 164424, 164425, 164426, 164427, 164428, 164436, 164421, 164437, 164434, 164433, 164432, 164430, 176373, 164429, 176374, 164438, 176375, 164431, 176376, 164439, 176389, 164435 ], "onlineresource_set": [ 47611, 47612, 47613, 47614, 47615, 87752, 90885, 90886, 90887, 90888, 90889, 90890, 90891, 90892, 90893 ] }, { "ob_id": 34654, "uuid": "39786f1e6fe2495291558cdc2a0b0aa1", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.6 (v20220119)", "abstract": "Data for Figure 2.6 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 2.6 shows globally averaged atmospheric mixing ratios of ozone-depleting substances and greenhouse gases.\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\nGulev, 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\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three 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 Each panel contains atmospheric mixing ratios for select greenhouse gases and/or ozone-depleting substances derived from atmospheric observations and historical compilations from 1950-2019, plotted as the annual mean for each year.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n \r\n - Datafile: Data_Figure_2_6.csv, column 20, black line.\r\n - Datafile: Data_Figure_2_6.csv, column 21, blue line.\r\n - Datafile: Data_Figure_2_6.csv, column 26, grey line.\r\n - Datafile: Data_Figure_2_6.csv, column 27, brown line.\r\n - Datafile: Data_Figure_2_6.csv, column 22, turquoise line.\r\n\r\n\r\nPanel b:\r\n \r\n - Datafile: Data_Figure_2_6.csv, column 23, black line.\r\n - Datafile: Data_Figure_2_6.csv, column 2, red line.\r\n - Datafile: Data_Figure_2_6.csv, column 33, turquoise line.\r\n - Datafile: Data_Figure_2_6.csv, column 34, grey line.\r\n - Datafile: Data_Figure_2_6.csv, column 28, violet line.\r\n\r\n\r\nPanel c:\r\n \r\n - Datafile: Data_Figure_2_6.csv, column 24, red line.\r\n - Datafile: Data_Figure_2_6.csv, column 25, cyan line.\r\n - Datafile: Data_Figure_2_6.csv, column 32, black line.\r\n - Datafile: Data_Figure_2_6.csv, column 14, brown line.\r\n - Datafile: Data_Figure_2_6.csv, column 15, orange line.\r\n - Datafile: Data_Figure_2_6.csv, column 13, grey line.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data.\r\n ---------------------------------------------------\r\n Some mixing ratios of multiple species were summed for clarity (see Figure caption for details). The summations are included in the data file. Dichloromethane (CH2Cl2) data were smoothed by applying a 3-yr moving average.\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 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:51:52", "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 I, Physical Science Basis, Chapter 2, Changing state, Multi-millennial context, pre-industrial, Natural forcing, anthropogenic forcing, Radiative forcing, Large-scale indicators, observed changes, Modes of variability, Figure 2.6, global greenhouse gas, chlorofluorocarbon, halon, CFC, HCFC, HFC, CMIP6", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-11T15:04:37", "doiPublishedTime": "2023-07-03T17:40:11", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 109, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39214, "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig06/v20220119", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 19801, "numberOfFiles": 4, "fileFormat": "csv" }, "timePeriod": { "ob_id": 9604, "startTime": "1950-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 3840, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-19" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 34656, "uuid": "8360662a470d427a8f52431d96c01d5f", "short_code": "comp", "title": "Caption for Figure 2.6 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global mean atmospheric mixing ratios of select ozone-depleting substances and other greenhouse gases. Data shown are based on the CMIP6 historical dataset and data from NOAA and AGAGE global networks. PFCs include CF4, C2F6, and C3F8, and c-C4F8; Halons include halon-1211, halon-1301, and halon-2402; other HFCs include HFC-23, HFC-32, HFC-125, HFC-143a, HFC-152a, HFC-227ea, HFC-236fa, HFC-245fa, and HFC-365mfc, and HFC-43-10mee. Note that the y-axis range is different for (a), (b) and (c) and a 25 parts per trillion (ppt) yardstick is given next to each panel to aid interpretation. Further data are in Annex III and 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": [ 12611 ], "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" } ], "responsiblepartyinfo_set": [ 164440, 164441, 164442, 164443, 164444, 164445, 164446, 164447, 196258 ], "onlineresource_set": [ 47616, 47617, 82825 ] }, { "ob_id": 34657, "uuid": "528c3543bc394134916aa792c4a2e700", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 2.25 (v20220119)", "abstract": "Input data for Figure 2.25 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 2.25 shows changes in permafrost temperature for 4 Arctic regions over the period 1974-2019 shown as average departures from the International Polar Year (2007-2009) baseline.\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\nGulev, 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\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Annual mean permafrost temperatures(deg C) for sites in 4 regions at depths indicated based on Romanovsky et al. (2020) in State of the Climate in 2019 BAMS 101(8) p S265-S269 https://doi.org/10.1175/BAMS-D-20-0086.1\r\n Regions based on those in Romanovsky et al. (2017) Ch 4 in Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nTime series for each site that was used to determine the regional anomalies shown in the figure\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 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:51:55", "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 I, Physical Science Basis, Chapter 2, Changing state, Multi-millennial context, pre-industrial, Natural forcing, anthropogenic forcing, Radiative forcing, Large-scale indicators, observed changes, Modes of variability, Input Figure 2.25, permafrost temperature", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "Based on point data Other", "status": "ongoing", "dataPublishedTime": "2023-05-15T14:12:29", "doiPublishedTime": "2023-07-04T15:04:59.215524", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 110, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "Based on point data" }, "result_field": { "ob_id": 37673, "dataPath": "/badc/ar6_wg1/data/ch_02/inputdata_ch2_fig25/v20220119", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 14457, "numberOfFiles": 4, "fileFormat": "txt, csv" }, "timePeriod": { "ob_id": 9605, "startTime": "1974-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 3841, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-19" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 34659, "uuid": "da4655b6164e4cbcbf52b543283a5830", "short_code": "comp", "title": "Caption for Figure 2.25 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Changes in permafrost temperature. Average departures of permafrost temperature (measured in the upper 20–30 m) from a baseline established during International Polar Year (2007–2009) for Arctic regions. 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": [ 12628 ], "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" } ], "responsiblepartyinfo_set": [ 164450, 164451, 164452, 164453, 164454, 164455, 164456, 164457, 164458, 164459, 164460, 164461, 164462, 164463, 164464, 164465, 164466, 164467 ], "onlineresource_set": [ 47618, 47619, 82847 ] }, { "ob_id": 34660, "uuid": "c3450dc769f044898ea5f3be784f354b", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.6 (v20220119)", "abstract": "Data for Figure 3.6 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.6 shows simulated internal variability of global surface air temperature (GSAT) versus observed changes. \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\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on 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. 423–552, doi:10.1017/9781009157896.005.\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three panels. Files are not separated according to the panels. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n obs_gmst.nc contains\r\n - Observed GMST anomalies\r\n - Observed GMST difference between 2010-2019 and 1850-1900\r\n \r\n historical_cmip6_gsat.nc contains\r\n - Simulated GSAT anomalies\r\n - Simulated GSAT difference between 2010-2019 and 1850-1900\r\n of CMIP6 historical-ssp245 simulations\r\n \r\n piControl_cmip6_gsat.nc contains - Simulated GSAT anomalies\r\n - Simulated GSAT difference between the last 10 years and the first 51 years of a 170-year segment\r\n of the first 500 years of CMIP6 piControl simulations\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - 5-year running mean of picontrol_tas_aa in piControl_cmip6_gsat.nc\r\n o BCC-CSM2-MR: E = 3\r\n o CMCC-CM2-SR5: E = 11\r\n o CNRM-CM6-1: E = 12\r\n o CNRM-ESM2-1: E = 13\r\n o EC-Earth3: E = 15\r\n o EC-Earth3-Veg: E = 16\r\n o EC-Earth3-Veg-LR: E = 17\r\n o IPSL-CM6A-LR: E = 29\r\n o KIOST-ESM: E = 30\r\n o MCM-UA-1-0: E = 31\r\n \r\n Panel b:\r\n - obs_tas_aa_trend in obs_gmst.nc: black vertical lines\r\n o HadCRUT5: dataset = 1\r\n o BerkeleyEarth: dataset = 2\r\n o NOAAGlobalTemp-Interim: dataset = 3\r\n o Kadow: dataset = 4\r\n - histogram of histssp_tas_aa_trend in historical_cmip6_gsat.nc: red shading\r\n - multimodel ensemble mean of histssp_tas_aa_trend in historical_cmip6_gsat.nc: red vertical line\r\n - histogram of picontrol_tas_aa_runtrend in piControl_cmip6_gsat.nc: blue shading\r\n - multimodel ensemble mean picontrol_tas_aa_runtrend in piControl_cmip6_gsat.nc: blue vertical line\r\n \r\n Panel c:\r\n - obs_tas_aa in obs_gmst.nc: grey curves, with their 5-year running means for black curves\r\n o HadCRUT5: dataset = 1\r\n o BerkeleyEarth: dataset = 2\r\n o NOAAGlobalTemp-Interim: dataset = 3\r\n o Kadow: dataset = 4\r\n\r\n\r\nAcronyms: CMIP - Coupled Model Intercomparison Project, GMST - Global mean surface temperature, GSAT - Global surface air temperature, BCC-CSM - Beijing Climate Center Climate System Model, CMMC CM - Centro Euro-Mediterraneo sui Cambiamenti Climatici Climate Model, CNRM - Centre National de Recherches Meteorologiques, IPSL - Institut Pierre-Simon Laplace, KIOST-ESM - Korea Institute of Ocean Science & Technology Earth System, CRU - Climatic Research Unit, NOAA - National Oceanic and Atmospheric Administration. \r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nMultimodel ensemble means and histograms of historical simulations are calculated after weighting individual members with the inverse of the ensemble size of the same model. ensemble_assign in each file provides the model number to which each ensemble member belongs. \r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:51:56", "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\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 I, Physical Science Basis, Chapter 3, Human influence, Large-scale indicators, Natural variability, Anthropogenically-forced change, Observed changes, Figure 3.6", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-03-09T13:34:10", "doiPublishedTime": "2023-02-08T17:20:06.304548", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 111, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 36808, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig06/v20220119", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 357356, "numberOfFiles": 7, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 9606, "startTime": "1850-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 3842, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-19" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 34662, "uuid": "7fe0b4bae54047a19017cd76d95376c7", "short_code": "comp", "title": "Caption for Figure 3.6 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Simulated internal variability of global surface air temperature (GSAT) versus observed changes. (a) Time series of five-year running mean GSAT anomalies in 45 CMIP6 pre-industrial control (unforced) simulations. The 10 most variable models in terms of five-year running mean GSAT are coloured according to the legend on Figure 3.4. (b) Histograms of GSAT changes in CMIP6 historical simulations (extended by using SSP2-4.5 simulations) from 1850–1900 to 2010–2019 are shown by pink shading in (c), and GSAT changes between the average of the first 51 years and the average of the last 20 years of 170-year overlapping segments of the pre-industrial control simulations shown in (a) are shown by blue shading. GMST changes in observational datasets for the same period are indicated by black vertical lines. (c) Observed GMST anomaly time series relative to the 1850–1900 average. Black lines represent the five-year running means while grey lines show unfiltered annual time series. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": [ 46958, 46959, 46960, 46961, 46962, 46963, 46964, 46965, 46966, 46967, 46968 ], "vocabularyKeywords": [], "identifier_set": [ 12353 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on 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- 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\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 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": [ 164476, 164470, 164471, 164472, 164473, 164474, 164475, 164477, 164478, 164479, 164480 ], "onlineresource_set": [ 47621, 52364, 47620, 82858, 88597, 94633, 94634 ] }, { "ob_id": 34664, "uuid": "5fb94c8e37f64c95b671278b0e55cdd4", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2021, v3.2.0.2021f", "abstract": "This is version v3.2.0.2021f 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_19310101-20220101_v3.2.1.2021f.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": "2022-01-21T15:03:03", "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": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2022-01-26T14:37:33", "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": 34665, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadISD/subdaily/HadISDTable/r1/v3-2-0-2021f", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 47620160822, "numberOfFiles": 9291, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 9607, "startTime": "1931-01-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 3104, "explanation": "CF-Compliant NetCDF, see documentation for quality control processes used to produce these data. 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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. 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The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 54776, 54777, 54778, 54779, 54780, 54781, 54782, 54783, 54784, 54785, 54786, 54787, 54788, 54789, 54790, 54791, 54792, 54793, 54794, 54795 ], "vocabularyKeywords": [], "identifier_set": [], "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": [ 168052, 168053, 168056, 168057, 168051, 168054, 168059, 168058, 168055, 168060, 168061, 168062 ], "onlineresource_set": [ 47627, 47625, 47624, 47630, 47626, 47628, 47629, 47631, 47632 ] }, { "ob_id": 34669, "uuid": "115d5e4ebf7148ec941423ec86fa9f26", "title": "HadEX3: Global land-surface climate extremes indices v3.0.4 (1901-2018)", "abstract": "HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive). \r\n\r\nSpatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010.\r\n\r\nAll indices are available as annual quantities, with a subset also available on a monthly basis. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110').\r\n\r\nVersion 3.0.4 was added due to an error in how the Rx1day and Rx5day data were being handled for one of the West African data sources. More details can be found in the HadEX3 blog under 'Details/Docs' tab.\r\n\r\nAdditionally, an extension to HadEX3, comprising additional indices recommended by the WMO Expert Team on Sector-specific Climate Indices (ET-SCI), has been produced. These data are available in a separate dataset connected to this record, marked as supplemental to this dataset.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-01-24T14:14:55", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). \r\n\r\nHadEX3 is a dataset of gridded land-surface temperature and precipitation extremes indices and was produced by the Met Office Hadley Centre in collaboration with the ARC Centre of Excellence for Climate Extremes at the University of New South Wales and many data contributors from institutes and organisations around the world. The extremes indices were developed by the former WMO Expert Team on Climate Change Detection and Indices (ETCCDI) and derived from daily, station-based observations. These have undergone quality control checks and then been blended into a gridded product using an angular distance weighting routine.", "removedDataReason": "", "keywords": "HadEX3, indices, temperature, monthly, annual, land, surface, climate, precipitation extremes", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "final", "dataPublishedTime": "2024-03-07T12:01:59", "doiPublishedTime": "2024-04-03T11:24:07.080005", "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": 34670, "dataPath": "/badc/ukmo-hadobs/data/derived/MOHC/HadOBS/HadEX3/v3-0-4/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4115603887, "numberOfFiles": 63, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 8718, "startTime": "1901-01-01T00:00:00", "endTime": "2018-12-31T23:59:59" }, "resultQuality": { "ob_id": 3531, "explanation": "CF-Compliant NetCDF files. The extremes indices have undergone quality control checks at the station level to ensure consistency. These data are quality controlled by the data provider and not the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "HadEX3 CEDA Data Quality Statement", "date": "2020-09-09" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 31943, "uuid": "dbe9ab9b8cfc4213865fdd3935013226", "short_code": "comp", "title": "HadEX3 data processing performed at the Met Office Hadley Centre", "abstract": "Data were taken from public-facing archives as well as by submission from co-authors. These came either as precalculated indices or as daily precipitation, maximum and minimum temperatures. Where necessary, the indices were calculated from the daily values using the Climpact2 code, or reformatted to standard outputs. We perform some quality control checks on the indices to identify erroneous values and remove these stations from further use.\r\n\r\nIn order to calculate the grid-box values, we use the Angular Distance Weighting scheme, which uses a search radius from the grid-box centre to identify stations that could contribute. This search radius is defined by the correlation structure of the station timeseries (a decorrelation length scale) and is determined within latitude bands. 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The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1633, 9042, 9043, 30375, 30376, 30377, 30378, 30379, 30380, 30381, 30382, 30383, 30384, 30385, 30386, 30387, 30388, 30389, 30390, 30391, 30392, 30393, 30394, 30395, 30396, 30397, 30398, 30399, 30400, 30401, 30402, 30403, 30404, 30405 ], "vocabularyKeywords": [], "identifier_set": [ 12852 ], "observationcollection_set": [ { "ob_id": 31940, "uuid": "caa9f45738d34e4cb1208ae0d72b5e79", "short_code": "coll", "title": "HadEX3: Global land-surface climate extremes indices", "abstract": "HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid covering 1901-2018. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Indices are available on an annual, and for some a monthly, basis. Some indices use a reference period to calculate thresholds, and for these, we provide versions using 1961-90 and 1981-2010.\r\n\r\nThe indices are available in NetCDF files, with one index per file and separate files for annual and monthly values, as well as the different reference periods if appropriate. The codes used to create the dataset are available online, and a wide number of analysis plots are on the dataset homepage. For a detailed description of the methods behind the dataset, please see the paper in Details/Docs." } ], "responsiblepartyinfo_set": [ 168136, 168138, 168139, 168142, 168135, 168137, 168140, 168141, 168143, 168144, 168145, 168146, 168147, 168148, 168149, 168150, 168151, 168152, 168153, 168154, 168155, 168156, 168157, 168158, 168159, 168160, 168161, 168162, 168163, 168164, 168165, 168166, 168167, 168168, 168169, 168170, 168171, 168172, 168173, 168174, 168175, 168176, 168177, 168178, 168179, 168180, 168181, 168182, 168183, 168184, 168185, 168186, 168187, 168188, 168189, 168190, 168191, 168192, 168193, 168194, 168195, 168196, 168197, 168198, 168199 ], "onlineresource_set": [ 47637, 47638, 47639, 47640, 47641, 92694 ] }, { "ob_id": 34672, "uuid": "a8ebefa818e44f5a8bd259f561ad1555", "title": "Legacy data from Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS)", "abstract": "Legacy data from Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS).\r\n\r\nThese data are as found. They are a legacy from the Geophysical Data Facility (GDF). There is some fortran software which may help decode it.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "1995-10-16T16:27:06", "updateFrequency": "", "dataLineage": "Data are a legacy from the Geophysical Data Facility (GDF) the predecessor of the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "SAMS", "publicationState": "old", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "historicalArchive", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2942, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 34671, "dataPath": "/badc/sams", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 66846932, "numberOfFiles": 101, "fileFormat": "Unknown" }, "timePeriod": { "ob_id": 9609, "startTime": "1978-12-24T00:00:00", "endTime": "1983-06-09T00:00:00" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 168205, 168206, 168207, 168208, 168209, 168211, 168212, 168217 ], "onlineresource_set": [] }, { "ob_id": 34674, "uuid": "f0f32cf3e7884950a585b7a4fda9dcb6", "title": "HadISD: Global sub-daily, surface meteorological station data, 1973-2015, v1.0.4.2015f", "abstract": "This is version 1.0.4.2015f of HadISD (27 April 2015) the Met Office Hadley Centre's global sub-daily data, extending v1.0.3.2014f to span 1/1/1973 - 31/12/2015. \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, their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed in the quality control process are also provided along with a station listing with IDs, names and location information. The data are provided as one NetCDF file per station. \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_19310101-20151231_v1-0-4-2015f.nc (note, the filenames incorrectly show the start date of 19310101, instead of 19730101). The station codes can be found under the docs tab or on the archive beside the station_data folder. 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 up to date with 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., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Climate of the Past\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": "2022-01-28T15:25:56", "updateFrequency": "notPlanned", "dataLineage": "HadISD the global sub-daily station dataset is produced by the Met Office Hadley Centre. It is based on the ISD dataset from NOAA's NCDC. HadISD has been passed to the BADC for archiving and distribution.", "removedDataReason": "", "keywords": "HadISD, cloud data", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2022-11-07T16:31:16", "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": 34675, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadISD/subdaily/HadISDTable/r1/v1-0-4-2015f", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 20345458505, "numberOfFiles": 6117, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 9610, "startTime": "1973-01-01T00:00:00", "endTime": "2015-12-31T23:59:59" }, "resultQuality": { "ob_id": 3612, "explanation": "Uncertainty estimates are provided as part of the dataset both at the station and gridbox level, this includes information covering station uncertainty (climatological, homogenisation and measurement uncertainty), gridbox spatial and temporal sampling uncertainty and combined station and sampling uncertainty. See dataset associated documentation for full details.", "passesTest": true, "resultTitle": "HadISDH 4.3.1.2020f quality statement", "date": "2021-04-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 3862, "uuid": "c8a8c946e63a421987db316acb8384f1", "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 to be merged to form composite stations using a hierarchical scoring system. Then stations were selected on the basis of their length of record and reporting frequency. A final set of 6103 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). Some final filtering was performed to select those stations which in our opinion are most useful for climate studies. Note: 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/) For further details see: Dunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Climate of the Past and Dunn, 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": [], "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": [ 50416, 54776, 54777, 54778, 54790, 54794, 54795, 89139, 89141, 89142, 89143, 89144, 89145, 89147, 89149, 89150, 89151, 90490, 90491, 90492, 90493 ], "vocabularyKeywords": [], "identifier_set": [], "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": [ 168584, 168582, 168596, 168583, 168587, 168581, 168588, 168586, 168585, 168590, 168592, 168594, 168595 ], "onlineresource_set": [ 47652, 47648, 47649, 47650, 47651 ] }, { "ob_id": 34686, "uuid": "2db9a5613f634329bc581d3e00045cf5", "title": "Iceland Greenland Seas Project (IGP): water isotope measurements from the University of Iceland vapour isotope analyzer at Húsavik and samples of precipitation and surface snow in Iceland and southern Norway within SNOWPACE", "abstract": "This dataset contains measurements of the water vapour isotope composition of ambient vapour obtained from a water vapour isotope analyzer manufactured by Los Gatos Research (LGR), and from discrete sampling of surface snow and precipitation and subsequent laboratory analysis. The analyzer was installed in the harbour of Húsavik, Iceland. Discrete sampling included precipitation and surface snow from two surface transects in northern Iceland and in several locations in southern Norway. The LGR LWIA analyzer is an off-axis cavity ring-down spectrometer using infrared absorption bands for the retrieval of the water isotope ratios for H216O, H218O, and HDO, quantified as mixing ratio of water vapour (w, ppmv), delta 18-O, and delta-D (permil). The data set for the Húsavik station is accompanied by automatic weather station data (air temperature, relative humidity, wind speed, wind direction, sea-level pressure) from the Icelandic weather service (vedur.is) for several nearby locations. For further details and figures for the vapour measurements, and the surface sample collection during the campaign, and processing thereafter, please read the attached documentation.\r\n\r\nThis research is funded by the Research Council of Norway under the Sources of the Norwegian winter season snow pack constrained by stable water isotopes - SNOWPACE project (Project Nr. 262710) and the Facility for advanced isotopic research and monitoring of weather, climate and biogeochemical cycling (FARLAB) project (Project Nr. 245907).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2022-02-14T16:42:32", "updateFrequency": "notPlanned", "dataLineage": "Data collected during the IGP field campaign, then processed and quality controlled at the University of Bergen and at University of Iceland.", "removedDataReason": "", "keywords": "", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-02-15T15:40:27", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2946, "bboxName": "", "eastBoundLongitude": 7.5, "westBoundLongitude": -18.1, "southBoundLatitude": 60.0, "northBoundLatitude": 66.1 }, "verticalExtent": null, "result_field": { "ob_id": 34687, "dataPath": "/badc/igp/data/SNOWPACE_stations/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4309997, "numberOfFiles": 5, "fileFormat": "Data are NetCDF and CSV formatted." }, "timePeriod": { "ob_id": 9618, "startTime": "2018-02-28T00:00:00", "endTime": "2018-04-28T00:00:00" }, "resultQuality": { "ob_id": 3847, "explanation": "This data has been quality controlled at the University of Bergen and at University of Iceland.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-31" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 34691, "uuid": "a46e2ab2b2c5468893e3273ab94809df", "short_code": "cmppr", "title": "Composite Process for Iceland Greenland Seas Project (IGP): water isotope measurements from the University of Iceland vapour isotope analyzer at Húsavik and samples of precipitation and surface snow in Iceland and southern Norway within SNOWPACE", "abstract": "Composite process covering Acquisition for: Iceland Greenland Seas Project (IGP): water isotope measurements from the University of Iceland vapour isotope analyzer at Húsavik and samples of precipitation and surface snow in Iceland and southern Norway within SNOWPACE and Post processing of station data at the University of Iceland and the University of Bergen." }, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2543, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: 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": 24899, "uuid": "2780d047461c42f0a12534ccf42f487a", "short_code": "proj", "title": "Iceland Greenland seas Project (IGP) including the Atmospheric Forcing of the Iceland Sea (AFIS)", "abstract": "The Iceland Greenland seas Project (IGP) is an international project involving the UK, US a Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1)\r\n\r\nThe Iceland Sea - to the north and east of Iceland - is arguably the least studied of the North Atlantic's subpolar seas. However new discoveries are forcing a redesign of our conceptual model of the North Atlantic's ocean circulation which places the Iceland Sea at the heart of this system and suggests that it requires urgent scientific focus. The recently discovered North Icelandic Jet is thought to be one of two pathways for dense water to pass through the Denmark Strait - the stretch of ocean between Iceland and Greenland - which is the main route for dense waters from the north to enter the Atlantic. Its discovery suggests a new paradigm for where dense water entering the North Atlantic originates. However at present the source of the North Icelandic Jet remains unknown. It is hypothesized that relatively warm Atlantic-origin water is modified into denser water in the Iceland Sea, although it is unclear precisely where, when or how this happens. \r\n\r\nThis project examined the wintertime atmosphere-ocean processes in the Iceland Sea by characterising its atmospheric forcing, i.e. observing the spatial structure and variability of surface heat, moisture and momentum fluxes in the region and the weather systems that dictate these fluxes. In situ observations of air-sea interaction processes from several platforms (an aircraft; and via project partners an unmanned airborne vehicle, a meteorological buoy and a research vessel) were made and used to evaluate meteorological analyses and reanalyses from operational weather forecasting centres. \r\n\r\nNumerical modelling experiments investigated the dynamics of selected weather systems which strongly influenced the region, but appear not to be well represented; for example, the boundary layers that develop over transitions between sea ice and the open ocean during cold-air outbreaks; or the jets and wakes that occur downstream of Iceland. The unique observations were used to improve model representation of these systems.\r\n\r\nThe project also carried out new high-resolution climate simulations. A series of experiments covered recent past and likely future situations; as well as some idealised situations such as no wintertime sea ice in the Iceland Sea region. This was done using a state-of-the-art atmospheric model with high resolution over the Iceland Sea to investigate changes in the atmospheric circulation and surface fluxes. \r\n\r\nFinally, in collaboration with the international partners, the project analysed new ocean observations and establish which weather systems are important for changing ocean properties in this region. The project used a range of ocean and atmospheric models to establish how current and future ocean circulation pathways function. In short, the project determined the role that atmosphere-ocean processes in the Iceland Sea play in creating the dense waters that flow through Denmark Strait and feed into the lower limb of the AMOC.\r\n\r\nThe subpolar region of the North Atlantic is crucial for the global climate system. It is where coupled atmosphere-ocean processes, on a variety of spatial scales, require an integrated approach for their improved understanding and prediction. This region has enhanced 'communication' between the atmosphere and ocean. Here large surface fluxes of heat and moisture make the surface waters colder, saltier and denser resulting in a convective overturning that contributes to the lower limb of the Atlantic Meridional Overturning Circulation (AMOC). The AMOC is an ocean circulation that carries warm water from the tropics northward with a return flow of cold water southwards at depth; it is instrumental in keeping Europe's climate relatively mild." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 65549, 85892, 85893, 85894, 85895, 85896, 85897, 85898, 85899, 85900, 85901, 85902, 85903 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 27445, "uuid": "b3e807b8df824a8ca83468ce2e5b54e5", "short_code": "coll", "title": "In situ observations of air-sea interaction processes from the Iceland Greenland seas Project (IGP)", "abstract": "This collection contains a range of in situ observations of meteorological and air-sea interaction processes from a range of instruments on several platforms (buoy, ship , radiosonde) from the Iceland Greenland seas Project (IGP). \r\n\r\n\r\nThe Iceland Greenland seas Project (IGP) was an international project involving the UK, US and Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1)" } ], "responsiblepartyinfo_set": [ 168678, 168679, 168680, 168681, 168682, 168683, 168684, 168685, 168686, 168687, 168688, 168689, 168690, 168691 ], "onlineresource_set": [ 47658 ] }, { "ob_id": 34692, "uuid": "705abc2d9988444ba79e942f22219bc0", "title": "Iceland Greenland Seas Project (IGP): water isotope measurements from the University of Bergen vapour isotope analyzer on board the NATO Research Vessel Alliance within SNOWPACE", "abstract": "This dataset contains measurements of the water vapour isotope composition of ambient vapour, surface snow, ocean water, and precipitation obtained from water vapour isotope analyzers manufactured by Picarro Inc., as well as from discrete sampling and subsequent laboratory analysis. The analyzer was installed onboard the NATO Research Vessel Alliance during the Iceland Greenland Seas Project. Discrete sampling included precipitation, sea water, and standards from the R/V Alliance. The Picarro L2140-i analyzer is a cavity ring-down spectrometer that uses infrared absorption bands for the retrieval of the water isotope ratios for H216O, H218O, and HDO, quantified as mixing ratio of water vapour (w, ppmv), delta 18-O, and delta-D (permil) at 1 Hz time native resolution. The data set for the R/V Alliance has been joined with the meteorological data from R/V Alliance (https://catalogue.ceda.ac.uk/uuid/b4ba8f11459c422d84d7293b9211ccf7) at 60 s averaging. For further details and figures on the Picarro analyzer, and the sample collection during the campaign, and processing thereafter, please read the attached documentation. \r\n\r\nThis research is funded by the Research Council of Norway under the Sources of the Norwegian winter season snow pack constrained by stable water isotopes - SNOWPACE project (Project Nr. 262710) and the Facility for advanced isotopic research and monitoring of weather, climate and biogeochemical cycling (FARLAB) project (Project Nr. 245907).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2022-02-14T16:41:29", "updateFrequency": "notPlanned", "dataLineage": "Data collected during the IGP field campaign, then processed and quality controlled at the University of Bergen.", "removedDataReason": "", "keywords": "", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-02-15T15:39:28", "doiPublishedTime": "2022-03-09T16:10:26.906928", "removedDataTime": null, "geographicExtent": { "ob_id": 2947, "bboxName": "", "eastBoundLongitude": -10.0, "westBoundLongitude": -25.0, "southBoundLatitude": 66.0, "northBoundLatitude": 77.0 }, "verticalExtent": null, "result_field": { "ob_id": 34693, "dataPath": "/badc/igp/data/SNOWPACE_Alliance/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5312790, "numberOfFiles": 5, "fileFormat": "Data are NetCDF and CSV formatted." }, "timePeriod": { "ob_id": 9619, "startTime": "2018-02-25T00:00:00", "endTime": "2018-03-22T00:00:00" }, "resultQuality": { "ob_id": 3848, "explanation": "This data has been quality controlled at the University of Bergen.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-31" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 34697, "uuid": "9852714eb53f4851b4b7a5e22c971fc8", "short_code": "cmppr", "title": "Composite Process for Iceland Greenland Seas Project (IGP): water isotope measurements from the University of Bergen vapour isotope analyzer on board the NATO Research Vessel Alliance within SNOWPACE", "abstract": "Composite process covering Acquisition for: Iceland Greenland Seas Project (IGP): water isotope measurements from the University of Bergen vapour isotope analyzer on board the NATO Research Vessel Alliance within SNOWPACE and Post processing of ship data at University of Bergen." }, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2543, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: 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": 24899, "uuid": "2780d047461c42f0a12534ccf42f487a", "short_code": "proj", "title": "Iceland Greenland seas Project (IGP) including the Atmospheric Forcing of the Iceland Sea (AFIS)", "abstract": "The Iceland Greenland seas Project (IGP) is an international project involving the UK, US a Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1)\r\n\r\nThe Iceland Sea - to the north and east of Iceland - is arguably the least studied of the North Atlantic's subpolar seas. However new discoveries are forcing a redesign of our conceptual model of the North Atlantic's ocean circulation which places the Iceland Sea at the heart of this system and suggests that it requires urgent scientific focus. The recently discovered North Icelandic Jet is thought to be one of two pathways for dense water to pass through the Denmark Strait - the stretch of ocean between Iceland and Greenland - which is the main route for dense waters from the north to enter the Atlantic. Its discovery suggests a new paradigm for where dense water entering the North Atlantic originates. However at present the source of the North Icelandic Jet remains unknown. It is hypothesized that relatively warm Atlantic-origin water is modified into denser water in the Iceland Sea, although it is unclear precisely where, when or how this happens. \r\n\r\nThis project examined the wintertime atmosphere-ocean processes in the Iceland Sea by characterising its atmospheric forcing, i.e. observing the spatial structure and variability of surface heat, moisture and momentum fluxes in the region and the weather systems that dictate these fluxes. In situ observations of air-sea interaction processes from several platforms (an aircraft; and via project partners an unmanned airborne vehicle, a meteorological buoy and a research vessel) were made and used to evaluate meteorological analyses and reanalyses from operational weather forecasting centres. \r\n\r\nNumerical modelling experiments investigated the dynamics of selected weather systems which strongly influenced the region, but appear not to be well represented; for example, the boundary layers that develop over transitions between sea ice and the open ocean during cold-air outbreaks; or the jets and wakes that occur downstream of Iceland. The unique observations were used to improve model representation of these systems.\r\n\r\nThe project also carried out new high-resolution climate simulations. A series of experiments covered recent past and likely future situations; as well as some idealised situations such as no wintertime sea ice in the Iceland Sea region. This was done using a state-of-the-art atmospheric model with high resolution over the Iceland Sea to investigate changes in the atmospheric circulation and surface fluxes. \r\n\r\nFinally, in collaboration with the international partners, the project analysed new ocean observations and establish which weather systems are important for changing ocean properties in this region. The project used a range of ocean and atmospheric models to establish how current and future ocean circulation pathways function. In short, the project determined the role that atmosphere-ocean processes in the Iceland Sea play in creating the dense waters that flow through Denmark Strait and feed into the lower limb of the AMOC.\r\n\r\nThe subpolar region of the North Atlantic is crucial for the global climate system. It is where coupled atmosphere-ocean processes, on a variety of spatial scales, require an integrated approach for their improved understanding and prediction. This region has enhanced 'communication' between the atmosphere and ocean. Here large surface fluxes of heat and moisture make the surface waters colder, saltier and denser resulting in a convective overturning that contributes to the lower limb of the Atlantic Meridional Overturning Circulation (AMOC). The AMOC is an ocean circulation that carries warm water from the tropics northward with a return flow of cold water southwards at depth; it is instrumental in keeping Europe's climate relatively mild." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 9487, 85896, 85934, 85935, 85936, 85937, 85938, 85939, 85941, 85942, 85944, 85946, 85948, 86851, 86852, 86853, 86854, 86855, 86856, 86857, 86858, 86859, 86860, 86861, 86862, 86863, 86864, 86865, 86866, 86867, 86868, 86869, 86870, 86871, 86872, 86873, 86874, 86875, 86876, 86877, 86878, 86879, 86880, 86881, 86882, 86883 ], "vocabularyKeywords": [], "identifier_set": [ 12058 ], "observationcollection_set": [ { "ob_id": 27445, "uuid": "b3e807b8df824a8ca83468ce2e5b54e5", "short_code": "coll", "title": "In situ observations of air-sea interaction processes from the Iceland Greenland seas Project (IGP)", "abstract": "This collection contains a range of in situ observations of meteorological and air-sea interaction processes from a range of instruments on several platforms (buoy, ship , radiosonde) from the Iceland Greenland seas Project (IGP). \r\n\r\n\r\nThe Iceland Greenland seas Project (IGP) was an international project involving the UK, US and Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1)" } ], "responsiblepartyinfo_set": [ 168700, 168701, 168702, 168703, 168704, 168705, 168706, 168707, 168708 ], "onlineresource_set": [ 47742, 87702, 92708, 94674, 94675 ] }, { "ob_id": 34698, "uuid": "7c8ce1c47da548a08146fa62158303c5", "title": "Iceland Greenland Seas Project (IGP): water isotope measurements from the University of Bergen vapour isotope analyzer on board the BAS research aircraft MASIN within SNOWPACE", "abstract": "This dataset contains measurements of the water vapour isotope composition of ambient vapour obtained from a water vapour isotope analyzers manufactured by Picarro Inc, as well as discrete samples of water standards used for calibration. The analyzer was installed onboard the research aircraft MASIN of the British Antarctic Survey. Discrete sampling included standards from the on-board calibration system on the MASIN aircraft. The Picarro L2130-i analyzer is a cavity ring-down spectrometer that uses infrared absorption bands for the retrieval of the water isotope ratios for H216O, H218O, and HDO, quantified as mixing ratio of water vapour (w, ppmv), delta 18-O, and delta-D (permil) at 1 Hz time resolution. The water isotope data set for the MASIN aircraft is joined with the airborne meteorological observations data set from the MASIN aircraft at different averaging times. For further details and figures on the Picarro analyzers, and processing thereafter, please read the attached documentation. \r\n\r\nThis research is funded by the Research Council of Norway under the Sources of the Norwegian winter season snow pack constrained by stable water isotopes - SNOWPACE project (Project Nr. 262710) and the Facility for advanced isotopic research and monitoring of weather, climate and biogeochemical cycling (FARLAB) project (Project Nr. 245907).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2022-02-14T16:42:18", "updateFrequency": "notPlanned", "dataLineage": "Data collected during the IGP field campaign, then processed and quality controlled at the University of Bergen.", "removedDataReason": "", "keywords": "", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-02-15T15:41:39", "doiPublishedTime": "2022-03-09T16:11:04.230019", "removedDataTime": null, "geographicExtent": { "ob_id": 2948, "bboxName": "", "eastBoundLongitude": -9.0, "westBoundLongitude": -25.0, "southBoundLatitude": 64.0, "northBoundLatitude": 72.0 }, "verticalExtent": null, "result_field": { "ob_id": 34699, "dataPath": "/badc/igp/data/SNOWPACE_MASIN/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 58105846, "numberOfFiles": 48, "fileFormat": "Data are NetCDF and CSV formatted." }, "timePeriod": { "ob_id": 9620, "startTime": "2018-03-04T00:00:00", "endTime": "2018-03-19T00:00:00" }, "resultQuality": { "ob_id": 3849, "explanation": "This data has been quality controlled at the University of Bergen.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-31" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 34700, "uuid": "11fa1282c74a4ce798239356afb448d1", "short_code": "acq", "title": "Acquisition for: Iceland Greenland Seas Project (IGP): water isotope measurements from the University of Bergen vapour isotope analyzer on board the BAS research aircraft MASIN within SNOWPACE", "abstract": "The Picarro instrument installed on board of MASIN was a flight-enabled Picarro L2130-i (Ser. No. HIDS2254), provided by FARLAB, University of Bergen. This particular L2130-i is a custom-made, flight-enabled modification that has an additional laser, allowing faster switching between wavelengths and thus faster measurement frequency, regulation of the flow rate through the cavity at changing ambient pressure, and a measurement cavity certified down to 200 ppmv H2O, lower than regular instruments of the same type. In addition to mixing ratio, isotope delta values and a range of internal control parameters, the instrument records ambient pressure, which allows for an alignment of the vapour isotope measurements with meteorological measurements onboard the aircraft. A backward-facing inlet was installed for vapour measurements. Inlet tubing was heated to about 60 deg C by self-regulating heat trace (Thermon Inc., USA). Less than 1 m stainless steel tubing (1/4 inch diameter) was used from the point where a T-valve allowed to draw air from either the forward or backward facing inlet line. From there, another 2.5 m of 3/8 inch stainless steel tubing with Sulfinert coating (Silcotek Inc., USA) for lower moisture retention led to a connection with a T-valve, that allowed to bypass the KNF manifold pump (N022AN.18) with a flow rate of about 5–10 lpm. During two of the flights, this valve was by mistake turned such that the pump did not flush the inlet, such that only the CRDS analyzer with a flow rate of about 40 sccm provided flow through the inlet. Less than 1 m of 1/4 inch stainless steel tubing then branched off to the analyzer, connected to an critical orifice required for low-flow mode, and protected by a check valve against reverse flow. An additional T-valve allowed for switching between flow from the inlet or from the calibration unit. These last parts of tubing had a flow rate of about 30–40 sccm, as the analyzer was operated in low-flow mode.\"\r\n\r\nPost processing of aircraft data at University of Bergen\r\nData files recorded by the analyzer in *.dat format are converted to netCDF format using apython routine. The raw data are then processed using the calibration routines FaVaCal, in use at FARLAB, University of Bergen, Norway. Calibration periods are identified and removed for separate processing with plots and quality evaluation. Water vapour isotope measurements are corrected for the humidity-isotope ratio dependency, as documented by Weng et al., 2020. The complete data processing is described in more detail in the data report for stable water isotope measurements from aircraft during IGP. The vapour isotope data are joined with the meteorological data from the MASIN aircraft obtained during IGP at a 2s, 10s, 30s and 60 s averaging time using the processing tool isofuse. Output from this conversion is stored as in separate datafiles for each averaging time and flight (naming: MASIN_isotopes_IGP2018_V3.3_20180304_f295_02s_final.nc; folder: IGP2018_SNOWPACE_MASIN_flight_data_V3.3/02s; format: netcdf). Results from liquid sample analysis for water isotopes from MASIN are available in one datafile (naming:IGP2018_SNOWPACE_water_isotope_samples_MASIN.csv, format: csv).Post processing was done by Harald Sodemann (UiB), who also acts as data contact." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2543, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: 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": 24899, "uuid": "2780d047461c42f0a12534ccf42f487a", "short_code": "proj", "title": "Iceland Greenland seas Project (IGP) including the Atmospheric Forcing of the Iceland Sea (AFIS)", "abstract": "The Iceland Greenland seas Project (IGP) is an international project involving the UK, US a Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1)\r\n\r\nThe Iceland Sea - to the north and east of Iceland - is arguably the least studied of the North Atlantic's subpolar seas. However new discoveries are forcing a redesign of our conceptual model of the North Atlantic's ocean circulation which places the Iceland Sea at the heart of this system and suggests that it requires urgent scientific focus. The recently discovered North Icelandic Jet is thought to be one of two pathways for dense water to pass through the Denmark Strait - the stretch of ocean between Iceland and Greenland - which is the main route for dense waters from the north to enter the Atlantic. Its discovery suggests a new paradigm for where dense water entering the North Atlantic originates. However at present the source of the North Icelandic Jet remains unknown. It is hypothesized that relatively warm Atlantic-origin water is modified into denser water in the Iceland Sea, although it is unclear precisely where, when or how this happens. \r\n\r\nThis project examined the wintertime atmosphere-ocean processes in the Iceland Sea by characterising its atmospheric forcing, i.e. observing the spatial structure and variability of surface heat, moisture and momentum fluxes in the region and the weather systems that dictate these fluxes. In situ observations of air-sea interaction processes from several platforms (an aircraft; and via project partners an unmanned airborne vehicle, a meteorological buoy and a research vessel) were made and used to evaluate meteorological analyses and reanalyses from operational weather forecasting centres. \r\n\r\nNumerical modelling experiments investigated the dynamics of selected weather systems which strongly influenced the region, but appear not to be well represented; for example, the boundary layers that develop over transitions between sea ice and the open ocean during cold-air outbreaks; or the jets and wakes that occur downstream of Iceland. The unique observations were used to improve model representation of these systems.\r\n\r\nThe project also carried out new high-resolution climate simulations. A series of experiments covered recent past and likely future situations; as well as some idealised situations such as no wintertime sea ice in the Iceland Sea region. This was done using a state-of-the-art atmospheric model with high resolution over the Iceland Sea to investigate changes in the atmospheric circulation and surface fluxes. \r\n\r\nFinally, in collaboration with the international partners, the project analysed new ocean observations and establish which weather systems are important for changing ocean properties in this region. The project used a range of ocean and atmospheric models to establish how current and future ocean circulation pathways function. In short, the project determined the role that atmosphere-ocean processes in the Iceland Sea play in creating the dense waters that flow through Denmark Strait and feed into the lower limb of the AMOC.\r\n\r\nThe subpolar region of the North Atlantic is crucial for the global climate system. It is where coupled atmosphere-ocean processes, on a variety of spatial scales, require an integrated approach for their improved understanding and prediction. This region has enhanced 'communication' between the atmosphere and ocean. Here large surface fluxes of heat and moisture make the surface waters colder, saltier and denser resulting in a convective overturning that contributes to the lower limb of the Atlantic Meridional Overturning Circulation (AMOC). The AMOC is an ocean circulation that carries warm water from the tropics northward with a return flow of cold water southwards at depth; it is instrumental in keeping Europe's climate relatively mild." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 61213, 61214, 61215, 61216, 61217, 61218, 61219, 61220, 61222, 61223, 61224, 61225, 61226, 61227, 61228, 61230, 61231, 61232, 61233, 74369, 85934, 85935, 85936, 85937, 85938, 85939, 85940, 85941, 85942, 85943, 85944, 85945, 85946, 85947, 85948, 85949, 85950, 85951, 85952, 85953, 85954, 85955, 85956, 85957, 85958, 85959, 85960, 85961, 85962, 85963, 85964, 85965, 85966, 85967, 85968, 85969, 85970, 85971, 85972, 85973, 85974, 85975, 85976, 85977, 85978, 85979, 85980, 85981, 85982, 85983, 85984, 85985, 85986, 85987, 85988, 85989 ], "vocabularyKeywords": [], "identifier_set": [ 12059 ], "observationcollection_set": [ { "ob_id": 27445, "uuid": "b3e807b8df824a8ca83468ce2e5b54e5", "short_code": "coll", "title": "In situ observations of air-sea interaction processes from the Iceland Greenland seas Project (IGP)", "abstract": "This collection contains a range of in situ observations of meteorological and air-sea interaction processes from a range of instruments on several platforms (buoy, ship , radiosonde) from the Iceland Greenland seas Project (IGP). \r\n\r\n\r\nThe Iceland Greenland seas Project (IGP) was an international project involving the UK, US and Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1)" } ], "responsiblepartyinfo_set": [ 168719, 168720, 168721, 168722, 168723, 168724, 168725, 168726, 168727 ], "onlineresource_set": [ 47741, 92710, 94673 ] }, { "ob_id": 34713, "uuid": "96c912c7c0094da2a8627d446cb06708", "title": "TRANSITION Clean Air Network: Exposures to particles and volatile organic compounds across multiple transportation modes", "abstract": "This dataset contains volatile organic compounds (VOC) and ultrafine particle measurements collected onboard several vehicles for the TRANSITION Clean air Network during May 2021 in the London and surrounding area. \r\n\r\nThe particles measured include VOCs captured in the field using thermal desorption tubes, and then analysed into the component species using highly sensitive Markes International GCxGC-TOF-MS system, and ultrafines data captured using a V2000 sensor from National Air Quality Testing Services by Emissions Analytics. Therefore, a much wider range of pollutants have been tested than in standard air quality monitoring. \r\n\r\nThe harm caused by emissions from vehicles to air quality and the health of humans outside is increasing well understood and It is generally accepted that it is a policy priority to remove high-emitting vehicles from the road and to swap for low-emission vehicles or public transport. What is less well understood is the exposure of the occupants in various transportation modes. Aggregate time spent in vehicles is significant, and can be measured in hours per day for certain commuters and professional drivers. Existing research by Emissions Analytics shows that the worst-performing cars can have particle number concentrations more than three times than in the ambient air. With Net Zero, particles are likely to be the dominant traffic pollutant.\r\n\r\nThis dataset contains interior air quality measurements made on a range of modes of transport including diesel and electric trains, the London Underground, diesel and electric buses, and old and new cars, including a battery electric.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2022-02-04T10:45:37", "updateFrequency": "notPlanned", "dataLineage": "VOC data was captured in the field using thermal desorption tubes, which were then analysed by Emissions Analytics on a Markes International GCxGC-TOF-MS system. \r\nParticle number data was captured using a V2000 from National Air Quality Testing Services\r\n\r\nThe data was delivered to CEDA from Emissions Analytics.", "removedDataReason": "", "keywords": "VOC, particles, Clean Air Network", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-02-09T16:20:11", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2949, "bboxName": "Transition Clean Air sites", "eastBoundLongitude": 0.14, "westBoundLongitude": -1.27, "southBoundLatitude": 51.6, "northBoundLatitude": 51.75 }, "verticalExtent": null, "result_field": { "ob_id": 34714, "dataPath": "/badc/deposited2022/TRANSITION_Exposure_to_VOCs/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 40489771, "numberOfFiles": 31, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 9624, "startTime": "2021-05-17T00:00:00", "endTime": "2021-05-25T23:59:59" }, "resultQuality": { "ob_id": 3851, "explanation": "Test methods internally developed at Emissions Analytics, validated by Markes Inter national. Data as supplied by the project team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-02-04" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 34718, "uuid": "555ea48b594044cba44b642f0d1374eb", "short_code": "acq", "title": "Acquisition for TRANSITION Clean Air Network: Exposures to particles and volatile organic compounds across multiple transportation modes", "abstract": "VOCs captured in the field using thermal desorption tubes, and then analysed into the component species using highly sensitive Markes International GCxGC-TOF-MS system, and ultrafines data captured using a V2000 sensor from National Air Quality Testing Services by Emissions Analytics." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 34719, "uuid": "2a655b0551434bc8a4dfb8ef02f94a8b", "short_code": "proj", "title": "Optimising air quality and health benefits associated with a low-emission transport and mobility revolution in the UK", "abstract": "The Government's 'Future of Mobility' and 'Road to Zero' strategies outline a second UK transport revolution, characterised by rapid decarbonisation, increased automation and enhanced connectivity. This radical transformation presents both opportunities and challenges for improving air quality over the next two decades, occurring in the context of disruptive changes in transport technology, increasing public environmental awareness and evolving transport behaviours. In this context, actions taken during the emerging transition phase will influence air pollutant sources and exposure patterns across indoor (i.e. vehicle, rail/bus) and outdoor (i.e. pavement, platform, bus station) land transport environments, with profound future implications for public health. This project will establish a diverse interdisciplinary network, connecting researchers across nine UK higher education and research institutions with >20 network partners, comprising commercial, public sector and non-profit organisations. It will establish sustainable connections to undertake co-definition of issues and opportunities and co-delivery of innovative, evidence-based solutions. It will deliver a varied portfolio of network activities including TRANSITION summits, problem-solving workshops, hackathons, discovery studies, site visits, policy engagement events and creative outreach activities at transport locations. Thus directly shaping future air quality, climate and transport policy, reflecting the ambitions of the UKRI SPF Clean Air Analysis and Solutions programme. \r\n\r\nThe TRANSITION network will identify, prioritise and tackle indoor and outdoor air quality challenges linked to the UK low-emission mobility revolution by bringing together academics, researchers, policymakers, business, civil society and the wider general public. The project will further the aims of the UKRI SPF Clean Air programme by: \r\n* Informing implementation of the Government's Clean Air, Future of Mobility, Road to Zero Strategies, Transport Decarbonisation Plan and other policy domains with cross-cutting implications for achieving UK objectives for air quality, climate change and public health. \r\n* Mobilising and transferring existing knowledge from across the UK land transport stakeholder community, SPF Clean Air programmes (including Clean Air Network communities), Clean Air Champions and policymaking arenas, at local, national and international levels. \r\n* Establishing a framework for generating and sharing knowledge, tools and technologies necessary to achieve transformational change across the land transport network; to achieve improved air quality, energy security and enhanced connectivity, for health and societal benefit \r\nThe project aims to: \r\n-Establish a network of experts with complimentary expertise across a range of specialist areas, geographical locations, career stages, and experience levels.\r\n-Facilitate focused, productive and sustainable interactions between a diverse stakeholder community \r\n-Create a shared learning environment spanning academia, industry, and policy communities, to organise and disseminate air quality knowledge and best practice \r\n-Provide a forum for supporting development of the next generation of researchers in the field of indoor and outdoor air quality, transport and public health. \r\n-Promote the development of professional skills in practice, research, consultancy and teaching/training related to the transport environment \r\n-Generate opportunities for creative industry-led innovation activities to co-deliver solutions, translate knowledge from other fields and/or change existing practise. \r\n-Connect existing research activities and outputs and identify gaps in scientific knowledge and policy translation to inform future UKRI Clean Air strategic focus.\r\n-Explore the interactions between air pollution and mobility and the synergies, co-benefits and potential conflicts between air quality, transport other public policies \r\n-Interact synergistically with other clean air research, engagement and innovation activities, identifying coordinated initiatives and linking nationally and internationally to related opportunities. \r\n-Generate a sustainable clean air legacy through sharing best practice and supporting translational and impact activities among project partners \r\nThis will be the first time that such a network at the intersection between air quality, transport and health has been established; occurring at a critical point for achieving a sustainable future in the context of climate change, demographic shift and rapid technology revolution. TRANSITION has significant potential to achieve significant multisectoral impacts across academic, commercial, policy communities, alongside increasing knowledge and awareness of clean air and low-emission science among the wider general public. The dynamic, flexible and responsive TRANSITION consortium will thereby provide a focal point for sustained coordination, dialogue and interaction, beyond the project lifetime. The guiding network principles will embrace diversity, encourage creativity, support career development and seek to establish consensus on research, commercial and policy priorities, whilst enhancing opportunities for uptake and impact. Adherence to these principles is critically important to achieve our stated objectives, operating within a frontier challenge environment and to deliver impacts at regional, national, international levels, across all sectors" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 86692, 86693, 86694, 86695, 86696, 86697, 86698, 86699, 86700, 86701, 86702, 86703, 86704, 86705, 86706, 86707, 86708, 86709, 86710, 86711, 86712, 86713, 86714, 86715, 86716, 86717, 86718, 86719, 86720, 86721, 86722, 86723, 86724, 86725, 86726, 86727, 86728, 86729, 86730, 86731, 86732, 86733, 86734, 86735, 86736, 86737, 86738, 86739, 86740, 86741, 86742, 86743, 86744, 86745, 86746, 86747, 86748, 86749, 86750, 86751, 86752, 86753, 86754, 86755, 86756, 86757, 86758, 86759, 86760, 86761, 86762, 86763, 86764, 86765, 86766, 86767, 86768, 86769, 86770, 86771, 86772, 86773, 86774, 86775, 86776, 86777, 86778, 86779, 86780, 86781, 86782, 86783, 86784, 86785, 86786, 86787, 86788, 86789, 86790, 86791, 86792, 86793, 86794, 86795, 86796, 86797, 86798 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 169015, 169016, 169017, 169018, 169019, 169020, 169028, 169093 ], "onlineresource_set": [ 47740 ] }, { "ob_id": 34720, "uuid": "906f6d3957f9439c98dfb662cda8a769", "title": "MSG: Dust imagery in the RGB channels over the full disc at 45.5 degrees East (LEDF41, from 1st June 2022)", "abstract": "The Meteosat Second Generation (MSG) satellites, operated by EUMETSAT (The European Organisation for the Exploitation of Meteorological Satellites), provide almost continuous imagery to meteorologists and researchers in Europe and around the world. These include visible, infra-red, water vapour, High Resolution Visible (HRV) images and derived cloud top height, cloud top temperature, fog, snow detection and volcanic ash products. These images are available for a range of geographical areas. \r\n\r\nThis dataset contains RGB dust images from MSG satellites over the full disc at 45.5 degrees East. Imagery available from 1000 UTC 1st June 2022 onwards at a frequency of 15 minutes (some are hourly) and are at least 24 hours old.\r\n\r\nNOTE - this dataset differs from the previous LEDF41 product produced using imagery from Meteosat-8 located at 41.5E. These new data are from Meteosat-9 which was drifted from previous operations over 3.5 E to 45.5 E between 1st February 2022 to 20th April 2022 to take over as the prime IODC (Indian Ocean Data Coverage) satellite by 1st June 2022. See linked EUMETNET web page regarding this change in operation. The Met Office switched to providing this LEDF41 product from this new satellite at 0915 UTC on 1st June 2022, this dataset. See linked datasets for previous data. These are treated as two distinct datasets due to the shift in locational coverage.\r\n\r\nThe geographic extent for images within this datasets is available via the linked documentation 'MSG satellite imagery product geographic area details'. Each MSG imagery product area can be referenced from the third and fourth character of the image product name giving in the filename. E.g. for EEAO11 the corresponding geographic details can be found under the entry for area code 'AO' (i.e West Africa).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2025-07-18T16:05:05", "updateFrequency": "daily", "dataLineage": "Data obtained by satellite from May 2022, prepared by the Met Office and stored in the CEDA Archive. This is a switch in the source satellite imagery for the LEDF41 product produced by the Met Office. See linked note regarding the switch in satellite providing the coverage for this region of the globe.", "removedDataReason": "", "keywords": "MSG, dust, rbd, Satellites, full disc", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-06-06T10:23:52", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2950, "bboxName": "DF IODC Geostationary(full disk, 45.5 E)", "eastBoundLongitude": 135.5, "westBoundLongitude": -44.5, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 34721, "dataPath": "/badc/msg/data/hritimages/LEDF41_45E", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 72967322249, "numberOfFiles": 31619, "fileFormat": "Images are PNG formatted." }, "timePeriod": { "ob_id": 9625, "startTime": "2022-06-01T10:00:00", "endTime": null }, "resultQuality": { "ob_id": 629, "explanation": "Operational satellite data.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-06-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 14637, "uuid": "7d6854321461450eb85b5568889c01c5", "short_code": "cmppr", "title": "MSG infrared", "abstract": "MSG infrared" }, "imageDetails": [ 45 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2545, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "msg", "label": "restricted: msg group", "licence": { "ob_id": 22, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/msg.pdf", "licenceClassifications": [ { "ob_id": 4, "classification": "academic" } ] } } ], "projects": [ { "ob_id": 11161, "uuid": "1b489cdd8b3ce6b4cd79fd3080a621f4", "short_code": "proj", "title": "Meteosat Second Generation (MSG)", "abstract": "Meteosat Second Generation is operated by EUMETSAT and provides almost continuous images to meteorologists and researchers in Europe and around the world. It incorporates significant enhancements in frequency and resolution to the previous generation of Meteosat. MSG measures in 12 spectral channels (compared to only 3 on the previous Meteosat) and records data in a 15 minute cycle (30 minutes on the previous Meteosat). The resolution of the high-resolution visible light channel measures 1 km at the sub-satellite point (compared to 2.5 km on the previous Meteosat).\r\n\r\nThe first Meteosat Second Generation satellite, MSG-1, came into operational service on 29th January 2004 and was renamed Meteosat-8. MSG-1 has a nominal lifetime of seven years. MSG-2 was launched on 21st December 2005 and future MSG units are planned.\r\n\r\nThe MSG payload also contains the Geostationary Earth Radiation Budget (GERB) instrument which provides important data for climate research. Data from the GERB instrument is now available at the BADC.\r\n\r\nA humanitarian Search and Rescue transponder that relays distress signals from ships, aircraft and others in need of rescue is also mounted on the MSG platform." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 2645, "uuid": "5fa2529b973e47ae38ab3557f2018ef4", "short_code": "coll", "title": "Meteosat Second Generation (MSG) Geostationnary Satellites: Visible, Infra-Red and Water Vapour Images and Derived Data Products over the world", "abstract": "Meteosat Second Generation is operated by EUMETSAT and provides almost continuous images to meteorologists and researchers in Europe and around the world. It incorporates significant enhancements in frequency and resolution to the previous generation of Meteosat. MSG measures in 12 spectral channels (compared to only 3 on the previous Meteosat) and records data in a 15 minute cycle (30 minutes on the previous Meteosat). The resolution of the high-resolution visible light channel measures 1 km at the sub-satellite point (compared to 2.5 km on the previous Meteosat).\r\n\r\nThis dataset collection includes visible, infra-red, water vapour, High Resolution Visible (HRV) images and the derived cloud top height, cloud top temperature, fog, snow detection, and volcanic ash products. These images are available for a range of geographical areas. Images are available from March 2005 onwards at a frequency of 15 minutes (some are hourly) and are at least 24 hours old.\r\n\r\nThe different geographic extents for images within this dataset collection are available via the linked documentation 'MSG satellite imagery product geographic area details'. Each MSG imagery product area can be referenced from the third and fourth character of the image product name giving in the filename. E.g. for EEAO11 the corresponding geographic details can be found under the entry for area code 'AO' (i.e West Africa)." } ], "responsiblepartyinfo_set": [ 169043, 169037, 169041, 169038, 169039, 169040, 169042, 169044, 169045 ], "onlineresource_set": [ 47743, 47744, 47754 ] }, { "ob_id": 34724, "uuid": "ddcad11fd71245d3b9d0e669b3fd9169", "title": "Global dataset of co-incident TLS-derived and harvested tree biomass", "abstract": "This dataset contains aboveground biomass estimates generated using terrestrial laser scanning (TLS) techniques for different species of tree. It was used to produce the figures and statistics of the publication \"Estimating forest aboveground biomass with terrestrial laser scanning: current status and future directions\".\r\n\r\nThis dataset contains 391 entries. Each entry is a tree that was terrestrial laser scanned and consecutively harvested to assess its aboveground biomass (AGB). AGB was also obtained from allometric scaling equations. Several ancillary tree properties such as stem diameter, foliage conditions,... and scan metadata (type of scanner, pattern) are included. We refer to the tab 'headers' for an explanation and units of the respective columns. Elaborate method descriptions can be found in the publication or in the following publications, which can be found in the documentation sections", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-02-08T14:12:41", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "AGB, TLS, biomass, trees", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-06-05T14:43:07", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2951, "bboxName": "Global TLS", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 34722, "dataPath": "/neodc/tls/data/global/tls_tree_biomass/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 52290, "numberOfFiles": 2, "fileFormat": "Excel spreadsheet" }, "timePeriod": { "ob_id": 9626, "startTime": "2015-10-01T00:00:00", "endTime": "2019-02-28T00:00:00" }, "resultQuality": { "ob_id": 3852, "explanation": "Data were validated by the NCEO tls project team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-02-08" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 34723, "uuid": "1a9bf108e75f466ea6451c73c3a12eec", "short_code": "acq", "title": "Terrestrial Laser Scans of individual trees", "abstract": "A terrestrial laser scanner was used to calculate the above-ground biomass of different tree species. Instrument type can be found in the data file and detailed methods have been described in the documentation section." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": 26683, "uuid": "70b2a6b0163747778ee85b4f7f86d8c0", "short_code": "proj", "title": "Weighing Trees with Lasers", "abstract": "Measuring the volume and structure of a tree accurately allows us to calculate the total above-ground carbon (C) stored in the tree, a very important property. Trees remove CO2 from the atmosphere during photosynthesis and can store this C for decades or even centuries until the tree dies, when some of it is released back to the atmosphere through decomposition. Tropical forests store around half of all above-ground terrestrial C, but are at particular risk due to deforestation and degradation, as well as from changing rainfall and temperature patterns. Surprisingly, our knowledge of tropical forest C stocks is quite poor, and errors in these stocks are large and uncertain. This uncertainty feeds into estimates of CO2 emissions due to deforestation, degradation and land use change. We will address this major uncertainty in the terrestrial C cycle by deploying a new, NERC-funded terrestrial laser scanner (TLS) to scan 1000s of trees in tropical forests on three continents: Amazonia, the Congo Basin and SE Asia. The laser data will allow us to measure 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing. The current, large uncertainties arise because weighing a tree is extremely difficult: tropical trees may be over 50m tall, and weigh 100 tonnes or more. Harvesting also precludes revisiting trees over time to measure change. In practice, a small sample of trees that have been harvested and weighed are related to easy-to-measure parameters of diameter and height, using empirical 'allometric' (size-to-mass) relationships. These relationships are then used to translate diameter and height measurements made over wider areas into estimates of biomass. Allometry is also the only way to infer biomass at very large (pan-tropical) scales, from remote sensing measurements. Unfortunately, the sample of harvested trees underpinning global allometric relationships is geographically limited, and contains very few large trees. Current estimates of tropical forest C stocks from satellite and ground data, all based on these very limited allometry samples, diverge significantly in size and pattern, leading to heated debate as to why this should be.\r\n\r\nThe project hopes to settle this debate, given that our lidar-derived estimates of biomass are completely independent of allometry and unbiased in terms of tree size. We will 'weigh' more trees than are currently included in all global pan-tropical allometries and quantify uncertainty in the allometry models. We will also test assumptions made in allometric models regarding tree shape and wood density. Our measurements will also answer fundamental questions about geographical differences in structural characteristics across tropical forests. Our data will be vital for testing new estimates of biomass from remote sensing; the UK-led ESA BIOMASS RADAR and NASA GEDI laser missions will both estimate pan-tropical C stocks by relying on allometric relationships between forest height and biomass. Our work will feed into these two missions through long-standing collaborations with the lead scientists. More generally, the large number of tree measurements we will collect would be of great interest to researchers in tropical ecology, forestry, biodiversity, remote sensing and C mapping, among others.\r\n\r\nA key aim of the project is to ensure the widest use of our results, by making our data and tools publicly available. We will work with partners to explore routes for commercial developments and input into government policy, particularly relating to forest management and C mapping and mitigation. Lastly, we will make our work accessible through a range of outreach activities, including developing links between a school in the Amazon and UK schools, to raise awareness of scientific, conservation and policy issues surrounding tropical forests.\r\n\r\nThis project was funded by NERC through grant: NE/N00373X/1" }, { "ob_id": 43969, "uuid": "f39d15602d404756988c225a188096f2", "short_code": "proj", "title": "Forest Degradation Experiment (FODEX)", "abstract": "It is know how to map tropical forest biomass using an array of satellite and aircraft sensors with reasonable accuracy (±15-40 %). However, we do not know how to map biomass change. Simply differencing existing biomass maps produces noisy and biased results, with confidence intervals unknowable using existing static field plots. Thus the potential for using plentiful free satellite data for biomass change mapping is being wasted. The FODEX project provides the first experimental arrays of biomass change plots. In total 52 large plots will be located in logging concessions in Gabon and Peru, where biomass will be assessed before and after logging, and during recovery. In addition to traditional field inventory, terrestrial laser scanning (TLS) data will give the precise 3D shape of thousands of trees before and after disturbance, allowing biomass change to be estimated without bias. The project’s unmanned aerial vehicle (UAV) will collect LiDAR data 4 times over each concession over 4 years, scaling up the field data to give thousands of hectares of biomass change data. In tandem, data from all potentially useful satellites (17+) flying over the field sites over the study period will be ordered and processed. These data will enable the development of new methods for mapping carbon stock changes, with known uncertainty, enabling upscaling across the Amazon basin and west/central Africa. For the first time we will have the methods to assess the balance of regrowth and anthropogenic disturbance across tropical forests, informing us about the status and resilience of the land surface carbon sink. As well as of scientific interest, these results are urgently needed for forest conservation: the Paris Agreement relies on paying countries to reduce losses and enhance gains in forest carbon stocks, but we do not currently have the tools to map forest carbon stock changes. Without accurate monitoring it is not possible to target resources nor assess success. FODEX addresses this problem.\r\n\r\nThis project was funded by EC H2020 program under grant_number: 757526" }, { "ob_id": 44328, "uuid": "f062fb741e364210835882e71b74eb6c", "short_code": "proj", "title": "RINGO project", "abstract": "The aims of the “Readiness of ICOS for Necessities of integrated Global Observations” (RINGO) project were the further development of Integrated Carbon Observation System (ICOS) Research Infrastructure (RI) and ICOS Ruropean Research Infrastructure Committee (ERIC) and foster its sustainability. The challenges are to further develop the readiness of ICOS RI along five principal objectives: \r\n\r\n1. Scientific readiness. To support the further consolidation of the observational networks and enhance their quality. This objective is mainly science-guided and will increase the readiness of ICOS RI to be the European pillar in a global observation system on greenhouse gases. \r\n\r\n2. Geographical readiness. To enhance ICOS membership and sustainability by supporting interested countries to build a national consortium, to promote ICOS towards the national stakeholders, to receive consultancy e.g. on possibilities to use EU structural fund to build the infrastructure for ICOS observations and also to receive training to improve the readiness of the scientists to work inside ICOS.\r\n\r\n3. Technological readiness. To further develop and standardize technologies for greenhouse gas observations necessary to foster new knowledge demands and to account for and contribute to technological advances.\r\n\r\n4. Data readiness. To improve data streams towards different user groups, adapting to the developing and dynamic (web) standards.\r\n\r\n5. Political and administrative readiness. To deepen the global cooperation of observational infrastructures and with that the common societal impact." }, { "ob_id": 44329, "uuid": "0b0c5a2c0a7b466ab4286a7fc8bda179", "short_code": "proj", "title": "3D-FOGROD: Understanding forest growth dynamics using novel 3D measurements and modelling approaches", "abstract": "Forest ecosystems are an essential terrestrial carbon sink, and deforestation and forest degradation account for about 12% of global anthropogenic carbon emissions. However, estimates of the global distribution of terrestrial carbon sinks and sources are highly uncertain. Constraining the inaccuracy of carbon estimates is essential to support effective forest management and future climate mitigation. A better understanding of forest growth dynamics will improve our understanding of the carbon cycle and mechanisms responsible for terrestrial carbon sources and sinks, reducing uncertainties on their magnitude and distribution. The 3D-FOGROD project aimed to improve our understanding of forest growth dynamics and evaluate the role of elevated CO2 levels on forest growth. It achieved this by using novel 3D laser scanning (LiDAR) techniques, unique datasets and state-of-the-art modelling approaches to:\r\n\r\n(1) accurately quantify forest growth using terrestrial LiDAR data in a free-air CO2 enrichment experiment; \r\n(2) improve historical and future simulated forest growth dynamics using LiDAR derived forest structure for a range of forest ecosystems; \r\n(3) develop and disseminate recommendations for climate mitigation actions to policy makers based on new insights in forest growth dynamics and carbon cycling." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 169047, 169048, 169049, 169050, 169051, 169052, 169053, 169071, 169054, 169055, 169056, 169057, 169058, 169059, 169060, 169061, 169062, 169063, 169064, 169065, 169066, 169067, 169068, 169069, 169070 ], "onlineresource_set": [ 47745, 47746, 47748, 47749, 47750, 47751, 47752, 47753, 93237, 47747 ] }, { "ob_id": 34729, "uuid": "3aaaaf94813e48f18f2b83242a8dacbe", "title": "ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area Grid product, version 1.0", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The Sentinel-3 SYN Fire_cci v1.0 grid product described here contains gridded data on global burned area derived from surface reflectance data from the OLCI and SLSTR instruments (combined as the Synergy (SYN) product) onboard the Sentinel-3 A&B satellites, complemented by VIIRS thermal information. This product, called FireCCIS310 for short, is currently available for 2019, but it is foreseen to be extended for additional years.\r\n\r\nThis gridded dataset has been derived from the FireCCIS310 pixel product (also available) by summarising its burned area information into a regular grid covering the Earth at 0.25 x 0.25 degrees resolution and at monthly temporal resolution. Information on burned area is included in 22 individual quantities: sum of burned area, standard error, fraction of burnable area, fraction of observed area, and the burned area for 18 land cover classes, as defined by the Copernicus Climate Change Initiative(C3S) Land Cover v2.1.1 product. For further information on the product and its format see the Product User Guide in the linked documentation.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-06-06T01:53:13", "updateFrequency": "asNeeded", "dataLineage": "Data was produced by the ESA Fire CCI team as part of the ESA Climate Change Initiative (CCI) and is being held on the CEDA (Centre for Environmental Data Analysis) archive as part of the ESA CCI Open Data Portal.", "removedDataReason": "", "keywords": "ESA, CCI, Grid, Burned Area, Fire Disturbance, Climate Change, GCOS", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2022-04-19T15:17:55", "doiPublishedTime": "2022-04-19T15:18:55", "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": 37144, "dataPath": "/neodc/esacci/fire/data/burned_area/Sentinel3_SYN/grid/v1.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 25977800, "numberOfFiles": 13, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 10263, "startTime": "2019-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 3931, "explanation": "See the associated dataset documentation at https://climate.esa.int/projects/fire/key-documents/", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-04-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 37148, "uuid": "f90d5bdc7c3e415b9ae4bd9e8dcda9eb", "short_code": "cmppr", "title": "Composite process for the ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area products, version 1.0", "abstract": "For more information see the documentation at https://climate.esa.int/projects/fire/key_documents/" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2539, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 19, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_fire_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13255, "uuid": "6c3584d985bd484e8beb23ff0df91292", "short_code": "proj", "title": "ESA Fire Climate Change Initiative Project (Fire CCI)", "abstract": "The European Space Agency (ESA) Fire Climate Change Initiative (Fire CCI) project, led by University of Alcala (Spain), is part of ESA's Climate Change Initiative (CCI) to produce long term datasets of Essential Climate Variables derived from global satellite data.\r\n\r\nThe Fire CCI focuses on the following issues relating to Fire Disturbance: Analysis and specification of scientific requirements relating to climate; Development and improvement of pre-processing and burned area algorithms; Inter-comparison and selection of burned area algorithms; System prototyping and production of burned area datasets; Product validation and product assessment\r\n" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 8143, 8144, 12066, 27599, 50416, 57314, 57317, 59231, 59234, 59235, 59236, 59237, 68093, 68094 ], "vocabularyKeywords": [ { "ob_id": 10663, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_fire", "resolvedTerm": "fire" } ], "identifier_set": [ 12077, 12111 ], "observationcollection_set": [ { "ob_id": 12683, "uuid": "bcef9e87740e4cbabc743d295afbe849", "short_code": "coll", "title": "ESA Fire Climate Change Initiative (Fire CCI) Dataset Collection", "abstract": "The ESA Fire Climate Change Initiative (Fire_cci) project is producing long-term datasets of burned area information from satellites, as part of the ESA Climate Change Initiative. The data is of use for those interested in historical burned patterns, fire management and emissions analysis and climate change research, by providing a consistent burned area time series. \r\n\r\nCurrent datasets consist of maps of global burned area for the years 1982 to 2019. Products are available at different spatial resolutions: the Pixel product (at the original resolution of the sensor data) and the Grid product (0.25 degrees resolution), the latter of which is produced from the Pixel product. They are based upon spectral information from different sensors, and in many cases also thermal information from active fires.\r\n\r\nGlobal products: \r\n\r\nFireCCI41: Medium Resolution Imaging Spectrometer (MERIS) reflectance, on board the ENVISAT ESA satellite, 300m spatial resolution, and MODIS active fires. Temporal resolution: 2005 – 2011.\r\nFireCCI50: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2016.\r\nFireCCI51: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2019.\r\n\r\nFireCCILT10 (beta product): Advanced Very High Resolution Radiometer (AVHRR) Land Long Term Data Record (LTDR) reflectance. Provided only as grid product. Temporal resolution: 1982-2017.\r\n\r\nContinental products:\r\n\r\nFireCCISFD11: Multispectral Instrument (MSI) reflectance, on board the Sentinel-2A satellite, 20 spatial resolution, and MODIS active fires. Temporal resolution: 2016, spatial coverage: Sub-Saharan Africa." } ], "responsiblepartyinfo_set": [ 169080, 169081, 169082, 169083, 169084, 169085, 177539, 177551, 177552, 177553, 177554, 177555, 177556, 177557 ], "onlineresource_set": [ 51966, 51967, 52087 ] }, { "ob_id": 34730, "uuid": "c98515f1934a4db68d2007b47c5a8d04", "title": "ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area Pixel product, version 1.0", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The Sentinel-3 SYN Fire_cci v1.0 pixel product is distributed as 6 continental tiles and is based upon surface reflectance data from the OLCI and SLSTR instruments (combined as the Synergy (SYN) product) onboard the Sentinel-3 A&B satellites. This information is complemented by VIIRS thermal information. This product, called FireCCIS310 for short, is currently available for 2019, but it is foreseen to be extended for additional years.\r\n\r\nThe FireCCIS310 Pixel product described here includes maps at 0.002777-degree (approx. 300m) resolution. Burned area (BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Copernicus Climate Change Service (C3S) Land Cover v2.1.1 product. An unpacked version of the data is also available. For further information on the product and its format see the Product User Guide in the linked documentation.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-06-06T01:55:39", "updateFrequency": "asNeeded", "dataLineage": "Data was produced by the ESA Fire CCI team as part of the ESA Climate Change Initiative (CCI) and is being held on the CEDA (Centre for Environmental Data Analysis) archive as part of the ESA CCI Open Data Portal.", "removedDataReason": "", "keywords": "ESA, CCI, Pixel, Burned Area, Fire Disturbance, Climate Change, GCOS", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2022-04-19T15:14:41", "doiPublishedTime": "2022-04-19T15:18:22", "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": 37145, "dataPath": "/neodc/esacci/fire/data/burned_area/Sentinel3_SYN/pixel/v1.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 12497999502, "numberOfFiles": 361, "fileFormat": "Data are in GeoTiff format" }, "timePeriod": { "ob_id": 10264, "startTime": "2019-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 3932, "explanation": "See the associated dataset documentation at https://climate.esa.int/projects/fire/key-documents/", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-04-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 37148, "uuid": "f90d5bdc7c3e415b9ae4bd9e8dcda9eb", "short_code": "cmppr", "title": "Composite process for the ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area products, version 1.0", "abstract": "For more information see the documentation at https://climate.esa.int/projects/fire/key_documents/" }, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2539, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 19, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_fire_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13255, "uuid": "6c3584d985bd484e8beb23ff0df91292", "short_code": "proj", "title": "ESA Fire Climate Change Initiative Project (Fire CCI)", "abstract": "The European Space Agency (ESA) Fire Climate Change Initiative (Fire CCI) project, led by University of Alcala (Spain), is part of ESA's Climate Change Initiative (CCI) to produce long term datasets of Essential Climate Variables derived from global satellite data.\r\n\r\nThe Fire CCI focuses on the following issues relating to Fire Disturbance: Analysis and specification of scientific requirements relating to climate; Development and improvement of pre-processing and burned area algorithms; Inter-comparison and selection of burned area algorithms; System prototyping and production of burned area datasets; Product validation and product assessment\r\n" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [ { "ob_id": 10663, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_fire", "resolvedTerm": "fire" } ], "identifier_set": [ 12076, 12110 ], "observationcollection_set": [ { "ob_id": 12683, "uuid": "bcef9e87740e4cbabc743d295afbe849", "short_code": "coll", "title": "ESA Fire Climate Change Initiative (Fire CCI) Dataset Collection", "abstract": "The ESA Fire Climate Change Initiative (Fire_cci) project is producing long-term datasets of burned area information from satellites, as part of the ESA Climate Change Initiative. The data is of use for those interested in historical burned patterns, fire management and emissions analysis and climate change research, by providing a consistent burned area time series. \r\n\r\nCurrent datasets consist of maps of global burned area for the years 1982 to 2019. Products are available at different spatial resolutions: the Pixel product (at the original resolution of the sensor data) and the Grid product (0.25 degrees resolution), the latter of which is produced from the Pixel product. They are based upon spectral information from different sensors, and in many cases also thermal information from active fires.\r\n\r\nGlobal products: \r\n\r\nFireCCI41: Medium Resolution Imaging Spectrometer (MERIS) reflectance, on board the ENVISAT ESA satellite, 300m spatial resolution, and MODIS active fires. Temporal resolution: 2005 – 2011.\r\nFireCCI50: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2016.\r\nFireCCI51: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2019.\r\n\r\nFireCCILT10 (beta product): Advanced Very High Resolution Radiometer (AVHRR) Land Long Term Data Record (LTDR) reflectance. Provided only as grid product. Temporal resolution: 1982-2017.\r\n\r\nContinental products:\r\n\r\nFireCCISFD11: Multispectral Instrument (MSI) reflectance, on board the Sentinel-2A satellite, 20 spatial resolution, and MODIS active fires. Temporal resolution: 2016, spatial coverage: Sub-Saharan Africa." } ], "responsiblepartyinfo_set": [ 169086, 169087, 169088, 169089, 169090, 169091, 177538, 177544, 177545, 177546, 177547, 177548, 177549, 177550 ], "onlineresource_set": [ 51968, 51969, 52086 ] }, { "ob_id": 34778, "uuid": "f01914b5dc2942029188d287322c67e5", "title": "BAS-MASIN flight 319: airborne meteorological observations from the ORCHESTRA project", "abstract": "In-situ airborne observations collected during flight 319 on 23 February 2019 by the Meteorological Airborne Science Instrumentation (MASIN) on board the British Antarctic Survey (BAS) Twin-otter aircraft for the ORCHESTRA - Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports project. This dataset contains the core meteorological data from the MASIN instrument suite. \r\n\r\nData were collected over the Falkland Islands area.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-09-16T11:30:15", "updateFrequency": "notPlanned", "dataLineage": "Data were collected and processed by British Antarctic Survey (BAS) and submitted to the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ORCHESTRA, aircraft, meteorology, BAS, MASIN, twin-otter", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-02-22T11:59:33", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2957, "bboxName": "", "eastBoundLongitude": -58.1330451965332, "westBoundLongitude": -60.19420623779297, "southBoundLatitude": -53.48469543457031, "northBoundLatitude": -51.81715774536133 }, "verticalExtent": null, "result_field": { "ob_id": 34779, "dataPath": "/badc/orchestra/data/twinotter/flight319_20190223", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 152742610, "numberOfFiles": 4, "fileFormat": "MASIN data are in NetCDF format" }, "timePeriod": { "ob_id": 9637, "startTime": "2019-02-23T12:28:49", "endTime": "2019-02-23T15:53:11" }, "resultQuality": { "ob_id": 3858, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-02-11" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 34780, "uuid": "dbb6b51c248e4e1fb733e7f1a1eb1fdc", "short_code": "acq", "title": "Acquisition for: BAS-MASIN flight 319 airborne meteorological observations from the ORCHESTRA project", "abstract": "" }, "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": 32626, "uuid": "c21cf5fb28c84cb2820a4785c503ea8c", "short_code": "proj", "title": "ORCHESTRA - Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports", "abstract": "The Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports (ORCHESTRA) is a £8.4 million, five year (2016-2021) research programme funded by the Natural Environment Research Council (NERC). The aim of the research is to to advance the understanding of, and capability to predict, the Southern Ocean's impact on climate change via its uptake and storage of heat and carbon. The programme will significantly reduce uncertainties concerning how this uptake and storage by the ocean influences global climate, by conducting a series of unique fieldwork campaigns and innovative model developments.\r\nORCHESTRA involves scientists from many NERC Centres. This ambitious 5 year project began in spring 2016 and will use a combination of data collection, analyses and computer simulations to radically improve our ability to understand and predict the circulation of the Southern Ocean and its role in the global climate, with particular emphasis on the way that the Southern Ocean absorbs and stores heat and carbon.\r\n\r\nThe intensive 5-year observational program will involve ORCHESTRA scientists undertaking 12 expeditions on research ships in the Southern Ocean, with US collaborators performing a 13th. Ten of these expeditions will be annual north/south transects across the Antarctic Circumpolar Current, to evaluate how the ocean changes from one year to the next. The other three will form ‘boxes’ around the Atlantic sector of the Southern Ocean so that budgets can be performed. All these data, along with historical measurements, will be combined and analysed together. 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This dataset contains the core meteorological data from the MASIN instrument suite. \r\n\r\nData were collected over the Falkland Islands area.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-09-16T11:30:12", "updateFrequency": "notPlanned", "dataLineage": "Data were collected and processed by British Antarctic Survey (BAS) and submitted to the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ORCHESTRA, aircraft, meteorology, BAS, MASIN, twin-otter", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-02-22T12:02:08", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2978, "bboxName": "", "eastBoundLongitude": -58.01833724975586, "westBoundLongitude": -59.11870193481445, "southBoundLatitude": -53.89120864868164, "northBoundLatitude": -51.99129104614258 }, "verticalExtent": null, "result_field": { "ob_id": 34863, "dataPath": "/badc/orchestra/data/twinotter/flight320_20190223", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 128095710, "numberOfFiles": 4, "fileFormat": "MASIN data are in NetCDF format" }, "timePeriod": { "ob_id": 9658, "startTime": "2019-02-23T17:46:43", "endTime": "2019-02-23T20:38:06" }, "resultQuality": { "ob_id": 3879, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-02-11" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 34864, "uuid": "17d319bb3a82415ab3ce5774406dec1c", "short_code": "acq", "title": "Acquisition for: BAS-MASIN flight 320 airborne meteorological observations from the ORCHESTRA project", "abstract": "" }, "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": 32626, "uuid": "c21cf5fb28c84cb2820a4785c503ea8c", "short_code": "proj", "title": "ORCHESTRA - Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports", "abstract": "The Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports (ORCHESTRA) is a £8.4 million, five year (2016-2021) research programme funded by the Natural Environment Research Council (NERC). The aim of the research is to to advance the understanding of, and capability to predict, the Southern Ocean's impact on climate change via its uptake and storage of heat and carbon. The programme will significantly reduce uncertainties concerning how this uptake and storage by the ocean influences global climate, by conducting a series of unique fieldwork campaigns and innovative model developments.\r\nORCHESTRA involves scientists from many NERC Centres. This ambitious 5 year project began in spring 2016 and will use a combination of data collection, analyses and computer simulations to radically improve our ability to understand and predict the circulation of the Southern Ocean and its role in the global climate, with particular emphasis on the way that the Southern Ocean absorbs and stores heat and carbon.\r\n\r\nThe intensive 5-year observational program will involve ORCHESTRA scientists undertaking 12 expeditions on research ships in the Southern Ocean, with US collaborators performing a 13th. Ten of these expeditions will be annual north/south transects across the Antarctic Circumpolar Current, to evaluate how the ocean changes from one year to the next. The other three will form ‘boxes’ around the Atlantic sector of the Southern Ocean so that budgets can be performed. All these data, along with historical measurements, will be combined and analysed together. The observations will be used to inform and test a range of models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 65713, 65714, 65715, 65716, 65717, 65718, 65719, 65720, 65721, 65722, 65723, 65724, 65725, 65726, 65727, 65728, 65729, 65730, 65731, 65732, 65733, 65734, 65735, 65736, 65737, 65738, 65739, 65740, 65741, 65742, 65743, 65744, 65745, 65746, 65747, 65748, 65749, 65750, 65751, 65752, 65753, 65754, 65755, 65756, 65757, 65758, 65759, 65760, 65761, 65762, 65763, 65764, 65765, 65766, 65767, 65768, 65769, 65770, 65771, 65772, 65773, 65774, 65775, 65776, 65777, 65778, 65779, 65780, 65781, 65782, 65783, 65784, 65785, 65788, 65789, 65790, 65791, 65792, 65793, 65794, 65795, 65796, 65797, 65798, 65799, 65800, 65801, 65802, 65803, 65804, 65805 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 32624, "uuid": "16230d437d5941f0b370d5ead4193b15", "short_code": "coll", "title": "ORCHESTRA - Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports: Airborne meteorological measurements", "abstract": "Airborne in-situ observations of core meteorological data collected by the MASIN instruments on board the British Antarctic Survey instrumented Twin Otter aircraft for the Orchestra project. The core data was collected from Rothera Station Antarctica between 2nd and 16th December 2017 and from the Falkland Islands between 7th and 23rd February 2019. The data is from a variety of instruments (temperature, humidity, wind, surface temperature, radiation and position), it is stored in NetCDF format using CF conventions.\r\n\r\nORCHESTRA is a NERC-funded Long Term Science programme that involves scientists from many NERC Centres. This 5 year project began in spring 2016 and will use a combination of data collection, analyses and computer simulations to radically improve our ability to understand and predict the circulation of the Southern Ocean and its role in the global climate, with particular emphasis on the way that the Southern Ocean absorbs and stores heat and carbon." }, { "ob_id": 7571, "uuid": "8be3dd7cdf44090d89aeb8f105421506", "short_code": "coll", "title": "BAS Masin Twin-Otter aircraft data", "abstract": "Data is collected on board the British Antarctic Survey MASIN Twin Otter aircraft for a range of projects in the Antarctic and at other locations. These projects include atmospheric, boundary layer and cloud/aerosol studies.\r\n\r\n\r\nThe instrument suite includes standard temperature and water vapour sensors as well as a turbulence probe allowing full atmospheric profile measurements of temperature, dew point and winds. A DMT Cloud and aerosol spectrometer (CAPS) probe is used for cloud studies and a closed path Licor H2O/CO2 instrument, Grimm optical particle counter and cloud condensation nuclei counter are fed from simple Rosemount inlets." } ], "responsiblepartyinfo_set": [ 169484, 169485, 169486, 169487, 169488, 169489, 169490, 169491, 169492, 169493 ], "onlineresource_set": [ 50428, 51630 ] }, { "ob_id": 34868, "uuid": "e2aea49e9ff14bd185a7e4ffb91e6dda", "title": "TLS-ARCH terrestrial laser scanner data; Downfall Creek (AEP-02), North Queensland, Australia, July 2018", "abstract": "This dataset is comprised of raw data from the NERC-funded, full-waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations. Plot (A)EP-02 (Downfall Creek) is part of the CSIRO Rainforest Permanent Plots of North Queensland (Graham et al. 2006)\r\n\r\nThe TLS data were collected on a 10 m x 10 m grid where at each position the scanner captured data in an upright and tilted position. The scanner was set to an angular step of 0.04 degrees for all scans. In between each scan position, a set of retro-reflective targets were positioned to be used as tie-points between scans. For more information on TLS acquisition refer to Wilkes et al. (2017). Scan data were coregistered using RiSCAN Pro, the 4x4 rotation transformation matrices to transform the point cloud data into a common reference coordinate system can be found in the \"matrix\" directory.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-02-08T17:26:13", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "tls, biomass, trees, Downfall Creek, Australia", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-04-08T10:51:52", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2979, "bboxName": "TLS AOEP-02 site", "eastBoundLongitude": 145.5865, "westBoundLongitude": 145.5864, "southBoundLatitude": -17.1463, "northBoundLatitude": -17.1462 }, "verticalExtent": null, "result_field": { "ob_id": 43918, "dataPath": "/neodc/tls/data/raw/australia/AEP-02/2018-08-31.001.riproject", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 34172635329, "numberOfFiles": 1554, "fileFormat": "Point cloud data in RIEGL proprietry .rxp format, image data in .jpg format and matrix data in text file." }, "timePeriod": { "ob_id": 9660, "startTime": "2018-08-31T06:00:00", "endTime": "2018-09-01T00:00:00" }, "resultQuality": { "ob_id": 3880, "explanation": "Data were validated by the TLS-ARCH project team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-02-15" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 43911, "uuid": "ee225803d4ff4df0b3d40643b24fa078", "short_code": "acq", "title": "TLS-ARC Downfall Creek (AEP-02) July 2018", "abstract": "TLS data was collected on a 10 m x 10 m grid where at each position the scanner captured data in an upright and tilted position. The scanner was set to an angular step of 0.04 degrees and 0.02 degress for upright and tilted scans respectively. In between each scan position a set of retro-reflective targets were positioned to be used as tie-points between scans. For more information on TLS acquisition refer to Wilkes et al. (2017). Scan data was coregistered using RiSCAN Pro, the 4x4 rotation transformation matrices to trasnform the point cloud data into a common reference coordinate system can be found in the \"matrix\" directory." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": 43637, "uuid": "3d37d6d2993b4c6ea667593e63537b83", "short_code": "proj", "title": "Understanding tree architecture, form and function in the tropics", "abstract": "This project was funded by NERC under grant_number: NE/P011780/1. \r\n\r\nThe basic shape and branching structure of a tree can be distinctive and characteristic, yet there exists no consistent dataset quantifying how tree form varies across species and how it is related to other functional traits of a tree. Understanding the variation in structure and form of trees is important in order to link tree physiology to tree performance, scale fluxes of water and carbon within and among trees, and understand constraints on tree growth and mortality. These topics hold great importance in the field of ecosystem science, especially in light of current and future changes to climate. This project used 3D terrestrial laser scanning technologies (TLS) in combination with recently developed theoretical frameworks to measure and compare tree architecture." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 169497, 169498, 169499, 169500, 169501, 169502, 169506, 169507, 169508 ], "onlineresource_set": [ 50441, 50442 ] }, { "ob_id": 34897, "uuid": "e4eac61c06e348389f16bb863334dbf6", "title": "Water budget and Lagrangian analysis of tropical tropopause in simulations with Hadley Centre Global Environmental Model version 3 (HadGEM3)", "abstract": "This dataset contains water budget and Lagrangian analysis of the tropical tropopause from climate model simulations and Lagrangian trajectory calculations. This study was conducted to understand better the role of convection as water vapour enters the tropical stratosphere (above about 17.4km), in particular in future scenarios.\r\n\r\nThe atmosphere component of HadGEM3, Global Atmosphere (GA) 7.0, was run for three different scenarios. Based on the SPARC Quasi-Biennial Oscillation initiative (QBOi) experiments 2,3,4, these force the atmosphere model with year 2002 conditions (e.g. of solar radiation and sea surface temperatures) every year for 21 years, so that each year experiences identical boundary conditions. The first scenario has no modifications (as a control), the second has doubled CO2 concentrations and sea surface temperatures (SSTs) are increased by 2K, andthe third has quadrupled CO2 concentrations and SSTs are increased by 4K. Simulations were allowed 10 years to stabilise to their modified forcing conditions and the final 11 years were analysed further. These simulations were chosen because they give a simplified indication of how the atmosphere might change in the 21st century.\r\n\r\nA second component to this dataset is estimates of water vapour entering the stratosphere with the available output. For this, climate model output was used for Lagrangian calculations which were conducted with the OFFLINE trajectory model.\r\n\r\nRecords includes:\r\n-increments of all model processes that affect water vapour and ice (to get a full water budget) at grid points around the tropical tropopause (altitude of 17.4km and 18.0km, 40degS - 40degN and 180W - 180E) as monthly means of 6 hourly instantaneous values across the first two years after stabilisation. \r\n- locations and timing of model grid points above the minimum saturation mixing ratio in the vertical profile (the dry point) that exhibit convective ice injection (fast transport of ice by strong cloud processes)\r\n- monthly mean values of estimates of water vapour concentration above the tropical tropopause. These values include the HadGEM3 calculation, and proxies based on the dry point or on Lagrangian (trajectory-following) calculations of water vapour passing through the tropical tropopause.\r\n\r\nThese records are analysed in:\r\nSmith, J. W., Bushell, A. C., Butchart.,N. , Haynes, P. H., Maycock, A. C., The effect of convective injection of ice on stratospheric water vapor in a changing climate, Geophysical Research Letters, submitted 12/21.\r\n\r\nLinks for further information:\r\n\r\nHadGEM3:\r\nhttps://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/climate-models/hadgem3\r\n\r\nQBOi experiment:\r\nButchart, N., Anstey, J. A., Hamilton, K., Osprey, S., McLandress, C., Bushell, A. C., … Yukimoto, S. (2018). Overview of experiment design and comparison of models participating in phase 1 of the SPARC Quasi-Biennial Oscillation initiative (QBOi). Geoscientific Model Development, 11(3), 1009–1032. https://doi.org/10.5194/gmd-11-1009-2018\r\n\r\nOFFLINE trajectory model:\r\nhttp://www.met.reading.ac.uk/~swrmethn/offline/", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2022-03-02T10:10:31", "updateFrequency": "notPlanned", "dataLineage": "Climate model simulations were conducted at the UK Met Office. \r\nLagrangian calculations were then conducted with climate model output on JASMIN.\r\nOutput of the simulations and Lagrangian calculations was then analysed with the contained scripts.\r\nKey output has been processed and stored as netcdf files herein.", "removedDataReason": "", "keywords": "", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-03-04T08:36:39", "doiPublishedTime": "2022-03-04T08:41:57", "removedDataTime": null, "geographicExtent": { "ob_id": 2985, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -40.0, "northBoundLatitude": 40.0 }, "verticalExtent": null, "result_field": { "ob_id": 34898, "dataPath": "/badc/deposited2022/Lagrangian_H2Ostrat_projection/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 507485970, "numberOfFiles": 21, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 9666, "startTime": "2002-01-01T00:00:00", "endTime": "2002-12-31T00:00:00" }, "resultQuality": { "ob_id": 3888, "explanation": "Climate model simulations followed UK Met Office conventions and version control.\nKey output, as netcdf files, strives to follow the Climate and Forecast (CF) Metadata Convention.\nPython scripts strive to follow the PEP 8 style guide.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-02-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 34902, "uuid": "7688b3764f4e47dca47b3246f19b8924", "short_code": "comp", "title": "HadGEM3, Global Atmosphere (GA) 7.0", "abstract": "The atmosphere component of HadGEM3, Global Atmosphere (GA) 7.0, was run for three different scenarios. Based on QBOi experiments 2,3,4, these force the atmosphere model with year 2002 conditions (e.g. of solar radiation and sea surface temperatures) every year for 21 years. The first scenario has no modifications (as a control), the second has doubled CO2 concentrations and sea surface temperatures (SSTs) are increased by 2K, and the same again where CO2 concentrations are quadrupled and SSTs are increased by 4K. Simulations were allowed 10 years to stabilise to their modified forcing conditions and the final 11 years were analysed further." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 34899, "uuid": "ecd43b5f9bbb4b88aa99706ee83ba02c", "short_code": "proj", "title": "iCASE PhD studentship with the UK Met Office: Processes determining stratospheric water vapour", "abstract": "The co-operating partners in this project will be the University of Cambridge and the Met Office. Improving simulation of stratospheric water vapour remains a challenge for Earth System Models that are used for climate prediction. There are strong links between the water vapour distribution in the lower stratosphere and the tropopause temperatures which in turn determine water vapour, so positive feedbacks are possible that may significantly enhance the effects of modest errors in model representation of other relevant processes. The project will build on recent work in Cambridge and elsewhere that (a) has exploited trajectory techniques to examine the annual, interannual and longer-term links between tropopause temperatures and stratospheric water vapour and (b) has investigated the radiative coupling between water vapour and temperatures in the tropical tropopause region using a combination of offline radiative calculations and simple dynamical models. The focus of the project will be to analyse the variations of water vapour on monthly, annual, interannual and longer timescales simulated by the Met Office Unified Model (UM) and link these to the corresponding temperature and transport variations. (One component of this analysis would be use of a trajectory code which is already available for the UM.) The results will be compared against corresponding analysis of the recent history of the real atmosphere (some of which is already on record in scientific publications). In its later stages the project will consider the two-way coupling between tropical tropopause temperatures and water vapour concentrations in the UM and assess the possible implications for model predictions of long-term changes in these quantities. During visits to the Met Office the student will investigate these processes in long historical and scenario simulations of the new UKESM1 earth system model that will support future climate and ozone assessments.\r\nThe work in the studentship project will provide an opportunity for the student to make a contribution in a scientific area that is both of fundamental interest and of real practical interest to the Met Office earth-system modelling effort. TRAINING" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 9042, 9043, 18925, 19039, 19043, 50542, 50543, 53106, 54871, 66241, 86011, 86646, 86647, 86648, 86649, 86650, 86651, 86652, 86653, 86654, 86655, 86656, 86657, 86658, 86659, 86660, 86661, 86662, 86663, 86664, 86665, 86666, 86667, 86668, 86669, 86670, 86671, 86672, 86673, 86674, 86675, 86676, 86677, 86678, 86679, 86680, 86681, 86682, 86683, 86684, 86685, 86686, 86687 ], "vocabularyKeywords": [], "identifier_set": [ 11107 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 169696, 169697, 169698, 169699, 169700, 169701, 169706, 169707, 169708, 169709, 169710, 169711 ], "onlineresource_set": [ 90910, 90911, 90912, 90913, 90914 ] }, { "ob_id": 35039, "uuid": "5b0d2588954e404ab03e8f439d48a8d3", "title": "Multi-frequency, three-dimensional, four-component ocean bottom seismometer dataset acquired from an active fluid flow structure: Scanner Pockmark, North Sea.", "abstract": "A three-dimensional wide-angle active source seismic dataset was acquired during RRS James Cook cruise JC152 (August - September 2017), around the Scanner Pockmark Complex in the North Sea. Data were recorded on 25 four-component ocean bottom seismometers, recording at a sampling rate of 4 kHz. Four different seismic sources were used: \r\n\r\n1) a 700 ci (100, 200 and 400 ci) Bolt airgun array towed at 10-12 m below sea surface and fired at 10 s intervals; \r\n2) a 420 ci (2 x 105/105 ci) GI airgun array, towed at 2 m depth below sea surface and fired at 8 s intervals; \r\n3) a Squid sparker (1750 or 2000 J), towed at the sea surface and triggered at 2 s intervals; and, \r\n4) a Duraspark sparker (2000 J), towed at the sea surface and triggered at 2 s intervals. \r\n\r\nThis dataset complements Bayrakci et al. (2021; https://doi.org/10.1594/PANGAEA.932200) and updates instrument and shot corrections to fit a single, unique set of instrument positions, generated using a grid search algorithm. \r\n\r\nData are provided in standard SEG-Y format. The shot line geometry corrections were performed following the method described in the accompanying document CHIMNEY_OBS_corrections_summary.pdf. All location information is also present in the positioning directory. The data were acquired as part of the 'Characterization of major overburden leakage pathways above sub-seafloor CO2 storage reservoirs in the North Sea' (CHIMNEY) project, funded by the Natural Environment Research Council (NERC) under grant reference NE/N016130/1.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2022-03-02T10:41:17", "updateFrequency": "notPlanned", "dataLineage": "Three-dimensional wide-angle active source seismic data were acquired during NERC cruise JC152 from August-September 2017 in the North Sea. The raw data were processed by the science party and provided to the British Oceanographic Data Centre for long-term archive. Data were subsequently archived and catalogued at CEDA.", "removedDataReason": "", "keywords": "seismic reflection, ocean bottom seismograph, active source seismology, passive source seismology, fluid flow pathways", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-04-27T11:36:51", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3409, "bboxName": "", "eastBoundLongitude": 1.0784, "westBoundLongitude": 0.8824, "southBoundLatitude": 58.2266, "northBoundLatitude": 58.3438 }, "verticalExtent": null, "result_field": { "ob_id": 35040, "dataPath": "/bodc/USO220011/CHIMNEY_OBS_active", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 868369944141, "numberOfFiles": 924, "fileFormat": "Data are SEGY formatted data files" }, "timePeriod": { "ob_id": 10226, "startTime": "2017-08-28T00:00:00", "endTime": "2017-09-05T23:59:59" }, "resultQuality": { "ob_id": 3897, "explanation": "The data are provided as-is with no quality control undertaken by the British Oceanographic Data Centre (BODC). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "BODC Data Quality Statement", "date": "2022-03-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37080, "uuid": "f831bbf53427445aacbe1d0d491f759e", "short_code": "acq", "title": "Acquisition for: Multi-frequency, three-dimensional, four-component ocean bottom seismometer dataset acquired from an active fluid flow structure: Scanner Pockmark, North Sea.", "abstract": "Deployment aboard RRS James Cook cruise JC152 (August - September 2017), around the Scanner Pockmark Complex in the North Sea. Data were recorded on 25 four-component ocean bottom seismometers, recording at a sampling rate of 4 kHz.\r\nFour different seismic sources were used:\r\ni) a 700 ci (100, 200 and 400 ci) Bolt airgun array towed at 10-12 m below sea surface and fired at 10 s intervals;\r\n2) a 420 ci (2 x 105/105 ci) GI airgun array, towed at 2 m depth below sea surface and fired at 8 s intervals;\r\n3) a Squid sparker (1750 or 2000 J), towed at the sea surface and triggered at 2 s intervals, and;\r\n4) a Duraspark sparker (2000 J), towed at the sea surface and triggered at 2 s intervals." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37051, "uuid": "295469c1e48d44aeae896ff34b684b24", "short_code": "proj", "title": "Characterization of major overburden leakage pathways above sub-seafloor CO2 storage reservoirs in the North Sea (CHIMNEY)", "abstract": "Academics from the University of Southampton, the University of Edinburgh, and the National Oceanography Centre (NOC) worked together to understand more about the hazards involved in the storage of CO2 in depleted oil and gas reservoirs and saline aquifers in the North Sea. Carbon Capture and Storage (CCS) is recognised as an important way of reducing the amount of CO2 added to the atmosphere, and oil and gas reservoirs and saline aquifers are the preferred storage location of most European nations. However, the safety of such storage is dependent on fully exploring the risks of any leakage. The four-year CHIMNEY project developed better techniques to locate these sub-sea floor structures and determine the permeability of the pathways so that they can be better constrained and quantified. The team worked closely with GEOMAR, in Germany; the Lawrence Berkeley National Laboratory, in California; CGG, in the UK; and Applied Acoustics, in the UK. The project was funded by the Natural Environment Research Council (NERC) under grant reference NE/N016130/1. The project is complementary to the EU-funded Horizon 2020 project 'Strategies for Environmental Monitoring of Marine Carbon Capture and Storage' (STEMM-CCS)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 37081, "uuid": "0068c20aca6c43e786e737c9257179a2", "short_code": "coll", "title": "Seismic datasets acquired around an active fluid flow structure: Scanner Pockmark, North Sea as part of the CHIMNEY project", "abstract": "A multi-channel seismic dataset was acquired during RRS James Cook cruise JC152 (August - September 2017), around the Scanner Pockmark Complex in the North Sea. Data were recorded using a number of different seismic sources, comprising: 1) a GI airgun array, used in two different configurations for separate parts of the survey, i) a 420 ci (2 x 105/105 ci) array operated in harmonic mode and fired at 8 s intervals, and ii) a 300 ci (2 x 45/105 ci) array operated in true GI mode and fired at 6 s intervals, both towed at 2 m depth below sea surface; 2) an Applied Acoustic Engineering Squid sparker (1750 or 2000 J), towed at the sea surface and triggered at 2 s intervals, and; 3) a Duraspark sparker (2000 J), towed at the sea surface and triggered at 2 s intervals. Signals produced by the GI airguns and surface sparkers were recorded on two towed multi-channel streamers: a) a 60 channel, 1 m group interval streamer recorded on a Geometrics Strataview R60 recording system, and b), a 120 channel, 1.56 m group interval GeoEel streamer, at sampling rates between 0.125 and 0.5 ms depending on the streamer and source pairing. Data are provided in standard SEG-D format. The data were acquired as part of the 'Characterization of major overburden leakage pathways above sub-seafloor CO2 storage reservoirs in the North Sea' (CHIMNEY) project, funded by the Natural Environment Research Council (NERC) under grant reference NE/N016130/1." } ], "responsiblepartyinfo_set": [ 170195, 170197, 170196, 170194, 170193, 170192, 177019, 177025, 177020, 177021, 177022, 177023, 177024 ], "onlineresource_set": [ 51872, 51880, 51892, 51893, 51894, 51895, 51896, 51897, 51898, 51902 ] }, { "ob_id": 35144, "uuid": "91fe5a23aefa4ad59dc7288b0e3a46cd", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from ZAMG's Vaisala CL51 instrument deployed at Altmunster, Austria", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Zentralanstalt für Meteorologie und Geodynamik (ZAMG)'s Vaisala CL51 deployed at Altmunster, Austria.\n\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\n\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-11254.\n Details for this WIGOS station are presently unavailable 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.\n", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2023-09-29T00:08:51", "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). 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The main goal is to improve the overall usability of wind profiler data for operational meteorology and to provide support and expertise to both profiler operators and end users.\r\nDue to technical advances of the last years ceilometers (automatic low cost lidars) provide nowadays not only cloud base height but also information on the vertical distribution of aerosols derived from the backscatter profile. To make available this new observation capacity E-PROFILE is developing a framework to produce and exchange profiles of attenuated backscatter profiles. Automatic lidars and ceilometers of stations across Europe are added to the operational network." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46582, 46583, 46584, 46585, 46586, 46587, 46588, 46589, 46590, 46591, 46592, 46593, 46594, 46595, 46596, 46597, 46598 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 34905, "uuid": "345c47d378b64c75b7957aef0c09c81f", "short_code": "coll", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from a network covering most of Europe with additional sites worldwide", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from a network of instruments within EUMETNET's E-PROFILE ALC network.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nMost datasets are available to registered CEDA users. For those not available to CEDA users application for access to those datasets under restricted access can be made using the links on one of the associated records. All use is made in accordance with the Closed-Use Non-Commercial General Licence. See datasets for further licencing links and for individual dataset citations.\r\n\r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.\r\n\r\nNote - the datasets listed on this collection are daily concatenated files produced from single time-step files for each instrument. CEDA holds an older archive of single time-step files (not linked to from the datasets or this collection) which will be aggregated together over time to extend these datasets further back to the start of the E-PROFILE holdings in the CEDA archives. Access to the older single time-step files ahead of their concatenation into daily files can be made via : https://data.ceda.ac.uk/badc/eprofile/data/. As these data are processed single time-step files will be removed from the archive.\r\n\r\nIt is not possible to support any requests for data that predates the CEDA holdings nor to back-fill any data gaps." } ], "responsiblepartyinfo_set": [ 170541, 170542, 170543, 170544, 170545, 170546, 170547, 170548, 170549 ], "onlineresource_set": [ 50515 ] }, { "ob_id": 35147, "uuid": "622c657e3a3a49e0961153f9a03c8d43", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from RMI's Vaisala CL51 instrument deployed at Arlon, Belgium", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Meteorological Institute (RMI)'s Vaisala CL51 deployed at Arlon, Belgium.\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: 0-56-101-70003.\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\n \nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2025-01-18T04:30:21", "updateFrequency": "daily", "dataLineage": "Data were collected by instrument and transmitted to the central E-PROFILE processing hub at the UK's Met Office before preparation and delivery to the Centre for Environmental Data Analysis (CEDA). CEDA then produces daily concatenated files before ingestion into the CEDA Archive.", "removedDataReason": "", "keywords": "E-PROFILE, ceilometer measurements", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-01-14T04:08:44", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2994, "bboxName": "Arlon", "eastBoundLongitude": 5.82397985458374, "westBoundLongitude": 5.82397985458374, "southBoundLatitude": 49.68199920654297, "northBoundLatitude": 49.68199920654297 }, "verticalExtent": null, "result_field": { "ob_id": 35146, "dataPath": "/badc/eprofile/data/daily_files/belgium/arlon/rmi-vaisala-cl51_A", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3212752949, "numberOfFiles": 1230, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 9726, "startTime": "2022-01-12T00:00:00", "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). 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The main goal is to improve the overall usability of wind profiler data for operational meteorology and to provide support and expertise to both profiler operators and end users.\r\nDue to technical advances of the last years ceilometers (automatic low cost lidars) provide nowadays not only cloud base height but also information on the vertical distribution of aerosols derived from the backscatter profile. To make available this new observation capacity E-PROFILE is developing a framework to produce and exchange profiles of attenuated backscatter profiles. Automatic lidars and ceilometers of stations across Europe are added to the operational network." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46582, 46583, 46584, 46585, 46586, 46587, 46588, 46589, 46590, 46591, 46592, 46593, 46594, 46595, 46596, 46597, 46598 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 34905, "uuid": "345c47d378b64c75b7957aef0c09c81f", "short_code": "coll", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from a network covering most of Europe with additional sites worldwide", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from a network of instruments within EUMETNET's E-PROFILE ALC network.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nMost datasets are available to registered CEDA users. For those not available to CEDA users application for access to those datasets under restricted access can be made using the links on one of the associated records. All use is made in accordance with the Closed-Use Non-Commercial General Licence. See datasets for further licencing links and for individual dataset citations.\r\n\r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.\r\n\r\nNote - the datasets listed on this collection are daily concatenated files produced from single time-step files for each instrument. CEDA holds an older archive of single time-step files (not linked to from the datasets or this collection) which will be aggregated together over time to extend these datasets further back to the start of the E-PROFILE holdings in the CEDA archives. Access to the older single time-step files ahead of their concatenation into daily files can be made via : https://data.ceda.ac.uk/badc/eprofile/data/. As these data are processed single time-step files will be removed from the archive.\r\n\r\nIt is not possible to support any requests for data that predates the CEDA holdings nor to back-fill any data gaps." } ], "responsiblepartyinfo_set": [ 170552, 170553, 170554, 170555, 170556, 170557, 170558, 170559, 170560 ], "onlineresource_set": [ 50517, 50516 ] }, { "ob_id": 35150, "uuid": "44ebc02da2cf4d0a8b4de454643c0b34", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from UWO's Lufft CHM15k \"Nimbus\" instrument deployed at Western, Canada", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from University of Western Ontario (UWO)'s Lufft CHM15k \"Nimbus\" deployed at Western, Canada.\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: 0-20000-0-73009.\n Details for this WIGOS station are presently unavailable in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\n \nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2025-07-18T00:11:12", "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). 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