Observation List
Get a list of Observation objects.
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{ "count": 10256, "next": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=8600", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=8400", "results": [ { "ob_id": 38235, "uuid": "29a0282f3b494c54a5e6c59f61e9202b", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.19 v20220923", "abstract": "Data for Figure TS.19 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure TS.19 shows carbon sink response in a scenario with net carbon dioxide (CO2) removal from the atmosphere. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains data for 50-year periods during 2000-2300 for:\r\n \r\n - Atmospheric CO2 concentration\r\n - Net CO2 emissions (accumulated over 50 yer periods)\r\n - Net land flux (accumulated over 50 yer periods)\r\n - Net ocean flux (accumulated over 50 yer periods)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file: Data_Figure_5_33.csv:\r\n \r\n - row 1: x-axis values.\r\n - row 2: light blue bars.\r\n - row 3: orange bars.\r\n - row 4: green bars.\r\n - row 5: blue bars\r\n - row 6: relates with the values written in black over the corresponding arrows (row 2 values plus values written in black)\r\n - row 7: Standard deviation over orange bars.\r\n - row 8: Standard deviation over green bars.\r\n - row 9: Standard deviation over blue bars.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n This figure was created in Excel and the error bars (standard deviation) were added in Adobe Illustrator.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to the report component of the underlying chapter figures from which this figure was generated (Chapter 5)\r\n - Link to the Supplementary Material for Chapter 5, which contains details on the input data used in Table 5.SM.6", "creationDate": "2022-09-23T13:57:28.640973", "lastUpdatedDate": "2022-09-23T13:57:28", "latestDataUpdateTime": "2022-11-23T15:36:18", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, carbon fluxes, SSP1-2.6, carbon dioxide removal", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-06T09:35:07", "doiPublishedTime": "2023-08-02T09:54:37.323919", "removedDataTime": null, "geographicExtent": { "ob_id": 3649, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 187, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38236, "dataPath": "/badc/ar6_wg1/data/TS/ts_19/v20220923", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6698, "numberOfFiles": 4, "fileFormat": "Data are CSV formatted" }, "timePeriod": { "ob_id": 10536, "startTime": "2000-01-01T12:00:00", "endTime": "2300-12-31T12:00:00" }, "resultQuality": { "ob_id": 4087, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-09-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 38237, "uuid": "3187f9177421463c8d306ba6275886a4", "short_code": "comp", "title": "Caption for Figure TS.19 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Carbon sink response in a scenario with net carbon dioxide (CO2) removal from the atmosphere. Shown are CO2 flux components from concentration-driven Earth system model (ESM) simulations during different emissions stages of SSP1-2.6 and its long-term extension: (a) Large net positive CO2 emissions; (b) small net positive CO2 emissions; (c), (d) net negative CO2 emissions; (e) net zero CO2 emissions. Positive flux components act to raise the atmospheric CO2 concentration, whereas negative components act to lower the CO2 concentration. Net CO2 emissions, land and ocean CO2 fluxes represent the multi-model mean and standard deviation (error bar) of four ESMs (CanESM5, UKESM1, CESM2-WACCM, IPSL-CM6a-LR) and one Earth system model of intermediate complexity (UVic ESCM; Mengis et al., 2020). Net CO2 emissions are calculated from concentration-driven ESM simulations as the residual from the rate of increase in atmospheric CO2 and land and ocean CO2 fluxes. Fluxes are accumulated over each 50-year period and converted to concentration units (ppm). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6)." }, "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": [ 12659 ], "observationcollection_set": [ { "ob_id": 39213, "uuid": "7409c7ea8a5045f8ab08e44db9cccb1a", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Technical Summary", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Technical Summary.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure TS.1\r\n- data for Figure TS.9\r\n- input data for Figure TS.12 \r\n- data for Figure TS.13\r\n- data for Figure TS.15\r\n- data for Figure TS.17\r\n- data for Figure TS.19\r\n- data for Figure TS.22\r\n- input data for Figure TS.24\r\n- data for Figure TS.25\r\n- data for Box TS.2, Figure 1\r\n- data for Box TS.2, Figure 2\r\n- data for Box TS.4, Figure 1\r\n- input data for Box Ts.4, Figure 1\r\n- input data for Box TS.5, Figure 1\r\n- data for Box TS.6, Figure 1\r\n- data for Box TS.7, Figure 1\r\n- data for Box TS.13, Figure 1\r\n- data for Cross-Section Box TS.1, Figure 1" } ], "responsiblepartyinfo_set": [ 182076, 182077, 182078, 182079, 182080, 182081, 182083, 182082, 182084, 182085 ], "onlineresource_set": [ 52949, 52951, 52950, 82727 ] }, { "ob_id": 38238, "uuid": "56742f742ea14edf872c3eca99f4a61a", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Multi-Functional Transport Satellite (MTSAT) level 3C (L3C) product (2009-2015), version 1.00", "abstract": "This dataset contains monthly averaged land surface temperatures (LST) and their uncertainty estimates from the Japanese Advanced Meteorological Imager (JAMI) onboard the Multi-Functional Transport Satellite series (MTSAT1 and 2, also known as Himiwari-6 and 7). The original surface temperatures are generated every 3 hours and in this L3C product are monthly averaged at each time step and distributed on a regular latitude-longitude grid with a resolution of 0.05ºx0.05º. The coverage is limited to land surfaces within the MTSAT disk, which encompasses Australia and part of Asia.\r\n\r\nThe LSTs in this dataset are estimated from infrared measurements using a single channel algorithm, and, therefore, are only available under clear-sky conditions. The quality of single channel algorithms is generally lower than dual channel ones, and users are advised to read the respective Validation Report for more information on the expected quality of these LST estimates.\r\n\r\nThe dataset was produced by the Portuguese Institute for Sea and Atmosphere (IPMA) as part of the ESA Land Surface Temperature Climate Change Initiative. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-05-12T11:38:11", "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the Portuguese Institute for Sea and Atmosphere (IPMA) within the ESA Land Surface Temperature Climate Change Initiative (LST_cci) and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). Level 1 satellite observations were provided by the Satellite Applications Facility on Land Surface Analysis (LSA-SAF).\r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "land surface temperature, CCI, MTSAT", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-05-16T13:36:45", "doiPublishedTime": "2023-05-17T08:29:09", "removedDataTime": null, "geographicExtent": { "ob_id": 3468, "bboxName": "LST CCI - MTSAT", "eastBoundLongitude": -145.0, "westBoundLongitude": 75.0, "southBoundLatitude": -70.0, "northBoundLatitude": 70.0 }, "verticalExtent": null, "result_field": { "ob_id": 39838, "dataPath": "/neodc/esacci/land_surface_temperature/data/MTSAT_JAMI/L3C/v1.00/monthly", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9254379736, "numberOfFiles": 601, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 10321, "startTime": "2009-08-10T00:00:00", "endTime": "2015-12-02T12:00:00" }, "resultQuality": { "ob_id": 3951, "explanation": "For information on the data quality see the associated LST_cci documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-05-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 37379, "uuid": "b3aa750910c349198aaf4950d535c2b8", "short_code": "cmppr", "title": "Composite process for ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multi-Functional Transport Satellite (MTSAT) level 3 (L3U) product (2009-2015), version 1.00", "abstract": "Data has been derived from the JApanese Advanced Meteorological Imager (JAMI) onboard the Multi-Functional Transport Satellite (MTSAT1 and 2, also known as Himiwari-6 and 7).\r\n\r\nFor information on the retrieval algorithm used see the documentation on the LST CCI webpage." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2558, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 30, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_lst_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 33361, "uuid": "555149fdc3ef4e23a1de8ece93c29f5d", "short_code": "proj", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci)", "abstract": "The land surface temperature (LST) CCI project, which is funded by the European Space Agency (ESA) as part of the Agency’s Climate Change Initiative (CCI) Programme, aims to deliver a significant improvement on the capability of current satellite LST data records to meet the challenging Global Climate Observing System (GCOS) requirements for climate applications to realise the full potential of long-term LST data for climate science.\r\n\r\nAccurate knowledge of LST plays a key role in describing the physics of land-surface processes at regional and global scales as they combine information on both the surface-atmosphere interactions and energy fluxes within the Earth Climate System. LST provides a metric of surface state when combined with vegetation parameters and soil moisture and is one of the drivers of vegetation phenology. Furthermore, LST is an independent temperature data set for quantifying climate change complementary to the near-surface air temperature ECV based on in situ measurements and reanalyses.\r\n\r\nThe team uses data from a variety of satellites to provide an accurate view of temperatures across land surfaces globally over the past +20 years. This involves developing innovative techniques to merge data from different satellites into combined long-term satellite records for climate. These will all be evaluated by scientists working at leading climate centres." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62778, 62779, 62780, 62781, 62782, 62783, 62784, 62785, 62786, 62787, 62788, 62789, 62790, 62791, 62792 ], "vocabularyKeywords": [], "identifier_set": [ 12504 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 182090, 182088, 182089, 182091, 182092, 182093, 182094, 182095, 182096 ], "onlineresource_set": [ 52954, 52955, 52956, 83465 ] }, { "ob_id": 38239, "uuid": "b1ac413014ec4960b68e311e90eec9d1", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Spinning Enhanced Visible and Infrared Imager (SEVIRI) on MSG level 3 (L3C) product (2004-2020), version 1.00", "abstract": "This dataset contains monthly-averaged land surface temperatures (LST) and their uncertainty estimates from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation series (Meteosat 8 - 11, also known as MSG1-4). The original surface temperatures are generated every hour, and in this L3C dataset are provided as monthly averages and distributed on a regular latitude-longitude grid with a resolution of 0.05ºx0.05º. The coverage is limited to land surfaces within the MSG disk, which encompasses Europe, Africa and part of South America. \r\n\r\nLSTs are estimated from infrared measurements using a split-window algorithm and, therefore, are only available under clear-sky conditions. \r\n\r\nThe dataset was produced by the Portuguese Institute for Sea and Atmosphere (IPMA) as part of the ESA Land Surface Temperature Climate Change Initiative. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the Portuguese Institute for Sea and Atmosphere (IPMA) within the ESA Land Surface Temperature Climate Change Initiative (LST_cci) and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). Level 1 satellite observations were provided by the Satellite Applications Facility on Land Surface Analysis (LSA-SAF).", "removedDataReason": "", "keywords": "land surface temperature, CCI, GOES", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3466, "bboxName": "LST - SEVIRI", "eastBoundLongitude": 70.0, "westBoundLongitude": -70.0, "southBoundLatitude": -70.0, "northBoundLatitude": 70.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 10319, "startTime": "2004-01-19T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3951, "explanation": "For information on the data quality see the associated LST_cci documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-05-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 37376, "uuid": "4667b80536d240ec973127f856cd12fc", "short_code": "cmppr", "title": "Composite process for ESA Land Surface Temperature Climate Change Initiative (LST_cci): Spinning Enhanced Visible and Infrared Imager (SEVIRI) on MSG level 3 (L3U) product (2004-2020), version 3.00", "abstract": "Data has been retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation series (Meteosat 8 - 11, also known as MSG1-4). \r\n\r\nFor information on the retrieval algorithm used see the documentation on the LST CCI webpage." }, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 182097, 182098, 182099, 182100, 182101, 182102, 182103, 182104, 182105 ], "onlineresource_set": [ 52957, 52958, 52959 ] }, { "ob_id": 38240, "uuid": "c01275d56cba4970bda78ca79b5b1273", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Geostationary Operational Environmental Satellite (GOES) level 3C (L3C) product (2009-2020), version 1.00", "abstract": "This dataset contains monthly averaged land surface temperatures (LST) and their uncertainty estimates from the IMAGER onboard the Geostationary Operational Environmental Satellite (GOES-12 and GOES-13) and from the Advanced Baseline Imager (ABI) onboard GOES-16. The original surface temperatures are generated every 3 hours for GOES 12 and 13 and every hour for GOES 16, and in the L3C dataset a monthly average at each time step is provided. Data are distributed on a regular latitude-longitude grid with a resolution of 0.05ºx0.05º. The coverage is limited to land surfaces within the GOES disk, which encompasses North and South America. \r\n\r\nLSTs are estimated from infrared measurements using a single channel algorithm in the case of GOES 12 and 13, and a split-window algorithm in the case of GOES 16. Observations are only available under clear-sky conditions. Quality of single channel algorithms is generally lower than dual channel ones, users are advised to read the respective Validation Report for more information on expected quality of these LST estimates.\r\n\r\nThe dataset was produced by the Portuguese Institute for Sea and Atmosphere (IPMA) as part of the ESA Land Surface Temperature Climate Change Initiative. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-04-17T16:29:49", "updateFrequency": "", "dataLineage": "The data record has been produced by the Portuguese Institute for Sea and Atmosphere (IPMA) within the ESA Land Surface Temperature Climate Change Initiative (LST_cci) and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). Level 1 satellite observations were provided by the Satellite Applications Facility on Land Surface Analysis (LSA-SAF). \r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "land surface temperature, CCI, GOES", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-05-16T13:31:27", "doiPublishedTime": "2023-05-17T08:28:30", "removedDataTime": null, "geographicExtent": { "ob_id": 3460, "bboxName": "LST - GOES", "eastBoundLongitude": -5.0, "westBoundLongitude": -145.0, "southBoundLatitude": -70.0, "northBoundLatitude": 70.0 }, "verticalExtent": null, "result_field": { "ob_id": 40047, "dataPath": "/neodc/esacci/land_surface_temperature/data/GOES_IMAGER_ABI/L3C/v1.00/monthly/", "oldDataPath": [ 39995 ], "storageLocation": "internal", "storageStatus": "online", "volume": 25492050178, "numberOfFiles": 1538, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 10304, "startTime": "2009-08-10T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3951, "explanation": "For information on the data quality see the associated LST_cci documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-05-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 37319, "uuid": "117901813f274f678a643c35e6a0c03b", "short_code": "cmppr", "title": "Composite process for ESA Land Surface Temperature Climate Change Initiative (LST_cci): Geostationary Operational Environmental Satellite (GOES) level 3 (L3U) product (2009-2020), version 1.00", "abstract": "Data has been retrieved from the IMAGER onboard the Geostationary Operational Environmental Satellite (GOES-12 and GOES-13) and from the Advanced Baseline Imager (ABI) onboard GOES-16.\r\n\r\nFor information on the retrieval algorithm used see the documentation on the LST CCI webpage." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2558, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 30, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_lst_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 33361, "uuid": "555149fdc3ef4e23a1de8ece93c29f5d", "short_code": "proj", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci)", "abstract": "The land surface temperature (LST) CCI project, which is funded by the European Space Agency (ESA) as part of the Agency’s Climate Change Initiative (CCI) Programme, aims to deliver a significant improvement on the capability of current satellite LST data records to meet the challenging Global Climate Observing System (GCOS) requirements for climate applications to realise the full potential of long-term LST data for climate science.\r\n\r\nAccurate knowledge of LST plays a key role in describing the physics of land-surface processes at regional and global scales as they combine information on both the surface-atmosphere interactions and energy fluxes within the Earth Climate System. LST provides a metric of surface state when combined with vegetation parameters and soil moisture and is one of the drivers of vegetation phenology. Furthermore, LST is an independent temperature data set for quantifying climate change complementary to the near-surface air temperature ECV based on in situ measurements and reanalyses.\r\n\r\nThe team uses data from a variety of satellites to provide an accurate view of temperatures across land surfaces globally over the past +20 years. This involves developing innovative techniques to merge data from different satellites into combined long-term satellite records for climate. These will all be evaluated by scientists working at leading climate centres." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62778, 62779, 62780, 62781, 62782, 62783, 62784, 62785, 62786, 62787, 62788, 62789, 62790, 62791, 62792 ], "vocabularyKeywords": [], "identifier_set": [ 12503 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 182106, 182107, 182108, 182109, 182110, 182111, 182112, 182113, 182114 ], "onlineresource_set": [ 52961, 52962, 52960, 83464 ] }, { "ob_id": 38241, "uuid": "022d449b91eb453eb56228c17fdce725", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.20 (v20220928)", "abstract": "Data for Figure 6.20 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 6.20 shows projected changes in regional annual mean surface ozone (O3; ppb) from 2015 to 2100 in different shared socio-economic pathways (SSPs)\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Szopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 817–922, doi:10.1017/9781009157896.008.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figures 11 panels, with data provided for all panels in three files in the main directory.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains precomputed values of surface ozone concentrations across world regions for:\r\n - A 10-year mean period (2005 to 2014) from the historical simulation to represent present day regional mean values. Regional multi-model annual mean and standard deviation values are calculated across 5 different CMIP6 models\r\n - Annual multi-model mean values of surface ozone from 5 different CMIP6 models projected for 7 different future scenarios covering the period 2015 to 2100\r\n - Standard deviation values of surface ozone from 5 different CMIP6 models projected for 7 different future scenarios covering the period 2015 to 2100\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n All the data files provided are used to create the time series plots for each region. The numbers in each panel for each region are obtained from 'Surf_O3_data_05_14_mean_for_IPCC_figure_V1_5mods.csv', with the time series line for each scenario from 'Surf_O3_data_fut_mean_for_IPCC_figure_V1_5mods.csv' and the shading obtained by using the values in 'Surf_O3_SD_data_fut_mean_for_IPCC_figure_V1_5mods.csv'.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP stands for Shared Socioeconomic Pathway.\r\nppb stands for parts per billion.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The plotting code that is provided along with this dataset should just be able to read in each of the precomputed regional mean .csv files and then reproduce the time series figures. A link to the code archived on Zenodo is provided in the Related Documents section of this catalogue record.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-09-28T10:21:41.892749", "lastUpdatedDate": "2022-09-28T10:21:41", "latestDataUpdateTime": "2024-03-09T02:57:35", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Air quality, Climate, Ozone, CMIP6, Models", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-14T10:13:54", "doiPublishedTime": "2023-03-22T10:22:06.889007", "removedDataTime": null, "geographicExtent": { "ob_id": 3650, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 188, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38242, "dataPath": "/badc/ar6_wg1/data/ch_06/ch6_fig20/v20220928", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 188639, "numberOfFiles": 6, "fileFormat": "Data are csv formatted" }, "timePeriod": { "ob_id": 10537, "startTime": "2015-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4088, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-09-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 38243, "uuid": "856d29c7a40f4f7082b3a3b2e21fb46e", "short_code": "comp", "title": "Caption for Figure 6.20 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in regional annual mean surface ozone (O3; ppb) from 2015 to 2100 in different shared socio-economic pathways (SSPs). Each panel represents values averaged over the corresponding land area (except for ‘Global’) shown on the map in Figure 6.7. Solid coloured lines and shading indicate the multi-model mean and ±1 σ across the available CMIP6 models (Turnock et al. 2020; Allen et al. 2021) for each scenario. Changes are relative to annual mean values calculated over the period 2005–2014 from the historical experiment as indicated in the top left of each regional panel along with ±1 σ. For each model all available ensemble members are averaged before being used to calculate the multi-model mean. Ozone changes are also displayed in the Interactive Atlas. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12422 ], "observationcollection_set": [ { "ob_id": 32721, "uuid": "18cf830c350e451a9002105294861dc0", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 6.3\r\n- data for Figure 6.12\r\n- input data for Figure 6.12\r\n- input data for Figure 6.14\r\n- data for Figure 6.16\r\n- data for Figure 6.17\r\n- data for Figure 6.20\r\n- data for Figure 6.21\r\n- data for Figure 6.22\r\n- input data for Figure 6.22\r\n- data for Figure 6.23\r\n- data for figure 6.24\r\n- input data for Figure 6.24\r\n- data for Figure 6.SM.3\r\n- data for Figure 6.SM.4\r\n- data for Figure 6.SM.5" } ], "responsiblepartyinfo_set": [ 182115, 182116, 182117, 182118, 182119, 182120, 182121, 182122, 182123, 182124 ], "onlineresource_set": [ 82740, 52965, 52964, 83025, 88614 ] }, { "ob_id": 38244, "uuid": "572c9744ddab47fc8a5b938c4a4f7387", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.21 (v20220928)", "abstract": "Data for Figure 6.21 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 6.21 shows future changes in regional five-year mean surface PM2.5 from 2015 to 2100 in different shared socio-economic pathways (SSPs).\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Szopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 817–922, doi:10.1017/9781009157896.008.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 11 panels with data provided for all panels in three files placed in the main directory.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains precomputed values of surface PM2.5 concentrations across world regions for:\r\n \r\n - A 10-year mean period (2005 to 2014) from the historical simulation to represent present day regional mean values. Regional multi-model annual mean and standard deviation values are calculated across 5 different CMIP6 models\r\n - Annual 5-year multi-model mean values of surface PM2.5 from 5 different CMIP6 models projected for 7 different future scenarios covering the period 2015 to 2100\r\n - Standard deviation values of surface PM2.5 from 5 different CMIP6 models projected for 7 different future scenarios covering 5-year mean periods from 2015 to 2100\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nPM2.5 refers to fine particulate matter air pollution with diameter of less than 2.5 microns.\r\nSSP stands for Shared Socioeconomic Pathway.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n All the data files provided are used to create the time series plots for each region. The numbers in each panel for each region are obtained from 'Surf_PM2pt5_data_05_14_mean_for_IPCC_figure_V1_5mods.csv', with the time series line for each scenario from 'Surf_PM2pt5_data_fut_mean_for_IPCC_figure_V1_5mods.csv' and the shading obtained by using the values in 'Surf_PM2pt5_SD_data_fut_mean_for_IPCC_figure_V1_5mods.csv'.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The plotting code that is provided along with this dataset should just be able to read in each of the precomputed regional mean .csv files and then reproduce the time series figures.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-09-28T11:29:50.562872", "lastUpdatedDate": "2022-09-28T11:29:50", "latestDataUpdateTime": "2024-03-09T02:57:37", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Air quality, Climate, PM2.5, CMIP6, Models", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-14T10:17:21", "doiPublishedTime": "2023-03-22T10:27:44.389307", "removedDataTime": null, "geographicExtent": { "ob_id": 3651, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 189, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38245, "dataPath": "/badc/ar6_wg1/data/ch_06/ch6_fig21/v20220928", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 44000, "numberOfFiles": 6, "fileFormat": "Data are csv formatted" }, "timePeriod": { "ob_id": 10538, "startTime": "2015-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4089, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-09-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 38246, "uuid": "09f2348d4391452abc9fec68bcaedd73", "short_code": "comp", "title": "Caption for Figure 6.21 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Future changes in regional five-year mean surface PM2.5 from 2015 to 2100 in different shared socio-economic pathways (SSPs). PM2.5 stands for micrograms per cubic meter of aerosols with diameter less than 2.5 μm and is calculated by summing up individual aerosol mass components from each model as: black carbon + particulate organic matter + sulphate + 0.25 × sea salt + 0.1 × dust. Since not all CMIP6 models reported nitrate aerosol, it is not included here. See Figure 6.20 for further details. PM2.5 changes are also displayed in the Interactive Atlas. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12423 ], "observationcollection_set": [ { "ob_id": 32721, "uuid": "18cf830c350e451a9002105294861dc0", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 6.3\r\n- data for Figure 6.12\r\n- input data for Figure 6.12\r\n- input data for Figure 6.14\r\n- data for Figure 6.16\r\n- data for Figure 6.17\r\n- data for Figure 6.20\r\n- data for Figure 6.21\r\n- data for Figure 6.22\r\n- input data for Figure 6.22\r\n- data for Figure 6.23\r\n- data for figure 6.24\r\n- input data for Figure 6.24\r\n- data for Figure 6.SM.3\r\n- data for Figure 6.SM.4\r\n- data for Figure 6.SM.5" } ], "responsiblepartyinfo_set": [ 182127, 182128, 182129, 182130, 182131, 182132, 182133, 182134, 182135, 182136 ], "onlineresource_set": [ 52966, 52967, 82741, 83026, 88615, 94641 ] }, { "ob_id": 38247, "uuid": "a1f46ad9f2644e60a87591c6b4537fef", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.23 v20220928", "abstract": "Data for Figure 6.23 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 6.23 shows contribution from regional emissions of short-lived climate forcers (SLCFs) to changes in global surface air temperature (GSAT) in 2040 and 2100, relative to 2020 for four Shared Socio-economic Pathways (SSPs).\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Szopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 817–922, doi:10.1017/9781009157896.008.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels with data provided for all panels in one single file.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains contribution from regional emissions of SLCFs to changes in GSAT relative to 2020 for four SSPs. \r\n - DGSAT 2040-2020\r\n - DGSAT 2040-2100\r\n \r\n Regions: North America, Europe, Southern Asia, Eastern Asia, Southeast Asia and Developing Pacific, Asia-Pacific Developed, Africa, Middle East, Latin America and Caribbean, Eurasia\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Upper panel:\r\n - Data file: fig6_23_data_dTscenarios_allcomp_data.nc. Variables dT_2040_* (one for every SLCF), x-axis refers to the scenarios, y-axis refers to the regions.\r\n \r\n Lower panel:\r\n - Data file: fig6_23_data_dTscenarios_allcomp_data.nc. Variables dT_2100_* (one for every SLCF), x-axis refers to the scenarios, y-axis refers to the regions\r\n\r\nSLCF stands for short-lived climate forcers. \r\nGSAT stands for global surface air temperature.\r\nSSP stands for Shared Socioeconomic Pathway.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-09-28T13:24:38.381709", "lastUpdatedDate": "2022-09-28T13:24:38", "latestDataUpdateTime": "2024-03-09T02:57:36", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, short-lived climate forcers, regional emissions, Shared Socio-economic Pathways, global temperature effect", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-24T16:40:07", "doiPublishedTime": "2023-03-22T11:00:39.838355", "removedDataTime": null, "geographicExtent": { "ob_id": 3652, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 190, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38248, "dataPath": "/badc/ar6_wg1/data/ch_06/ch6_fig23/v20220928", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 13629, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10539, "startTime": "2020-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4090, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-09-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 38249, "uuid": "76e51066582b4d5eac0cec4983b6980f", "short_code": "comp", "title": "Caption for Figure 6.23 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Contribution from regional emissions of short-lived climate forcers (SLCFs) to changes in global surface air temperature (GSAT) in 2040 (upper row) and 2100 (lower row), relative to 2020 for four Shared Socio-economic Pathways (SSPs). Adapted from Lund et al. (2020). NOx, CO, and NMVOC account for the impact through changes in ozone and methane, NOx additionally includes the impact through formation of nitrate aerosols. BC, SO2 and OC accounts for the direct aerosol effect (aerosol–radiation interactions), as well as an estimate of the semi-direct effect for BC due to rapid adjustments and indirect effect (aerosol–cloud interactions) of sulphate aerosols. Regions are the same as shown in the map in Figure 6.7. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 10750, 63537, 63538, 63539, 63540, 63541, 63542, 63543, 63544, 63545, 63546, 63547, 63548, 63549, 63550, 63551, 63552 ], "vocabularyKeywords": [], "identifier_set": [ 12428 ], "observationcollection_set": [ { "ob_id": 32721, "uuid": "18cf830c350e451a9002105294861dc0", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 6.3\r\n- data for Figure 6.12\r\n- input data for Figure 6.12\r\n- input data for Figure 6.14\r\n- data for Figure 6.16\r\n- data for Figure 6.17\r\n- data for Figure 6.20\r\n- data for Figure 6.21\r\n- data for Figure 6.22\r\n- input data for Figure 6.22\r\n- data for Figure 6.23\r\n- data for figure 6.24\r\n- input data for Figure 6.24\r\n- data for Figure 6.SM.3\r\n- data for Figure 6.SM.4\r\n- data for Figure 6.SM.5" } ], "responsiblepartyinfo_set": [ 182139, 182140, 182141, 182142, 182143, 182144, 182145, 182146 ], "onlineresource_set": [ 52968, 52969, 52977, 82743 ] }, { "ob_id": 38250, "uuid": "d059d2e28b6d4a7d88a6fe2d0d25ba93", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.3 v20220928", "abstract": "Data for Figure 6.3 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 6.3 shows relative regional and sectoral contributions to the present day (year 2014) anthropogenic emissions of short-lived climate forcers (SLCFs).\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Szopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 817–922, doi:10.1017/9781009157896.008.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels with data provided for all panels in one single file.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains emission data from the Community Emissions Data System (CEDS; Hoesly et al., 2018) aggregated into the following sectors:\r\n \r\n - fossil fuel production and distribution (coal mining, oil and gas production, upstream gas flaring, gas distribution networks),\r\n - fossil fuel combustion for energy (power plants),\r\n - residential and commercial (fossil and biofuel use for cooking and heating),\r\n - industry (combustion and production processes, solvent-use losses from production and end use),\r\n - transport (road and off-road vehicles),\r\n - shipping (including international shipping),\r\n - aviation (including international aviation),\r\n - agriculture (livestock and crop production),\r\n - waste management (solid waste, including landfills and open trash burning, residential and industrial waste water),\r\n - other.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - Datafile: 'Fig_6_3_numbers.csv' \r\n . Panel a: Regional contribution, rows 41 to 59 (10 regions+8 species)\r\n . Panel b: Sector contribution, rows 17 to 35. (10 sectors and 8 pollutants)\r\n \r\n In both panels:\r\n . Column 2: S02\r\n . Column 3: NOx\r\n . Column 4: BC\r\n . Column 5: OC\r\n . Column 6: CO\r\n . Column 7: VOC\r\n . Column 8: NH3\r\n . Column 9: CH4\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3", "creationDate": "2022-09-28T14:31:31.373859", "lastUpdatedDate": "2022-09-28T14:31:31", "latestDataUpdateTime": "2024-03-09T02:57:43", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Anthropogenic emissions, Short-lived climate forcers, regional emissions, sectoral emissions, SLCF", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-15T14:55:45", "doiPublishedTime": "2023-07-03T15:05:53.152197", "removedDataTime": null, "geographicExtent": { "ob_id": 3653, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 191, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38251, "dataPath": "/badc/ar6_wg1/data/ch_06/ch6_fig3/v20220928", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4819, "numberOfFiles": 4, "fileFormat": "Data are csv formatted" }, "timePeriod": { "ob_id": 10540, "startTime": "2014-01-01T12:00:00", "endTime": "2014-12-01T12:00:00" }, "resultQuality": { "ob_id": 4091, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-09-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 38252, "uuid": "d64540dbbf2446baa479651d7e7e8ab9", "short_code": "comp", "title": "Caption for Figure 6.3 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Relative regional and sectoral contributions to the present day (year 2014) anthropogenic emissions of short-lived climate forcers (SLCFs). Emission data are from the Community Emissions Data System (CEDS; Hoesly et al., 2018). Emissions are aggregated into the following sectors: fossil fuel production and distribution (coal mining, oil and gas production, upstream gas flaring, gas distribution networks), fossil fuel combustion for energy (power plants), residential and commercial (fossil and biofuel use for cooking and heating), industry (combustion and production processes, solvent-use losses from production and end use), transport (road and off-road vehicles), shipping (including international shipping), aviation (including international aviation), agriculture (livestock and crop production), waste management (solid waste, including landfills and open trash burning, residential and industrial waste water), and other. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12576 ], "observationcollection_set": [ { "ob_id": 32721, "uuid": "18cf830c350e451a9002105294861dc0", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 6.3\r\n- data for Figure 6.12\r\n- input data for Figure 6.12\r\n- input data for Figure 6.14\r\n- data for Figure 6.16\r\n- data for Figure 6.17\r\n- data for Figure 6.20\r\n- data for Figure 6.21\r\n- data for Figure 6.22\r\n- input data for Figure 6.22\r\n- data for Figure 6.23\r\n- data for figure 6.24\r\n- input data for Figure 6.24\r\n- data for Figure 6.SM.3\r\n- data for Figure 6.SM.4\r\n- data for Figure 6.SM.5" } ], "responsiblepartyinfo_set": [ 182149, 182150, 182151, 182152, 182153, 182154, 182155, 182156 ], "onlineresource_set": [ 52971, 52970, 82736 ] }, { "ob_id": 38253, "uuid": "cdabe9af5aa94608b227e6b9a96771f9", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.17 (v20220928)", "abstract": "Data for Figure 6.17 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 6.17 shows emission source-sector attribution of regional population-weighted mean concentrations of PM2.5 (fine particulate matter) and ozone for present-day emissions (year 2014).\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Szopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 817–922, doi:10.1017/9781009157896.008.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels with data provided for all panels in one single directory.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains emission source-sector attribution of regional population-weighted mean concentrations for present-day emissions of:\r\n \r\n - Air pollutant (PM2.5)\r\n - Ozone\r\n\r\n\r\nRegions: Eastern Asia, Latin America and Caribbean, North America, Eurasia, Southeast Asia and Developing Pacific, Europe, Africa, Middle East, Southern Asia, Asia-Pacific Developed.\r\n\r\nSource sector: Energy, Industry, Land and transportation, Shipping, Aviation, Residential and commercial, Agriculture, Waste management, Open biomass burning, Natural sources\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Left panel:\r\n - Data file: PM25_regions_sectors_v210214.txt\r\n \r\n Right panel:\r\n - Data file: O3_regions_sectors_v210214.txt\r\n \r\nAdditional file: country-mapping_ISO-10Regions.csv: description of countries included in each region of the figure.\r\n\r\nPM2.5 refers to fine particulate matter air pollution with diameter of less than 2.5 microns\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-09-28T15:16:09.019343", "lastUpdatedDate": "2022-09-28T15:16:09", "latestDataUpdateTime": "2024-03-09T02:57:37", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, PM2.5, ozone, concentrations, sectoral attribution, regional attribution", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-14T10:38:23", "doiPublishedTime": "2023-03-22T10:46:48.297076", "removedDataTime": null, "geographicExtent": { "ob_id": 3654, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 192, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38254, "dataPath": "/badc/ar6_wg1/data/ch_06/ch6_fig17/v20220928", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 19793, "numberOfFiles": 6, "fileFormat": "Data are txt formatted" }, "timePeriod": { "ob_id": 10541, "startTime": "2014-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 4092, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-09-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 38255, "uuid": "0aaee65d405342a58cde4381a8f98440", "short_code": "comp", "title": "Caption for Figure 6.17 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Emission source-sector attribution of regional population-weighted mean concentrations of PM2.5 and ozone for present-day emissions (year 2014). Regional concentrations and source apportionment calculated with the TM5-FASST model (Van Dingenen et al., 2018) for the 2014 emission data from the Community Emissions Data System (CEDS) (Hoesly et al., 2018) and van Marle et al. (2017) for open-biomass burning. PM2.5 dust and sea salt are monthly mean climatological averages over 2010–2018 from CAMS global reanalysis (EAC4) (Inness et al., 2019), generated using Copernicus Climate Change Service information (January 2020). Anthropogenic sectors are similar to those in Figures 6.2 and 6.16, except the grouping of fossil fuel production, distribution and combustion for energy under ‘Energy’ and the grouping of use of fossil fuel and biofuel use for cooking and heating under ‘Residential and Commercial’. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12426 ], "observationcollection_set": [ { "ob_id": 32721, "uuid": "18cf830c350e451a9002105294861dc0", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 6.3\r\n- data for Figure 6.12\r\n- input data for Figure 6.12\r\n- input data for Figure 6.14\r\n- data for Figure 6.16\r\n- data for Figure 6.17\r\n- data for Figure 6.20\r\n- data for Figure 6.21\r\n- data for Figure 6.22\r\n- input data for Figure 6.22\r\n- data for Figure 6.23\r\n- data for figure 6.24\r\n- input data for Figure 6.24\r\n- data for Figure 6.SM.3\r\n- data for Figure 6.SM.4\r\n- data for Figure 6.SM.5" } ], "responsiblepartyinfo_set": [ 182159, 182160, 182161, 182162, 182163, 182164, 182165, 182166, 182167 ], "onlineresource_set": [ 82739, 52973, 82993, 52972 ] }, { "ob_id": 38257, "uuid": "cc32622e68ad40bcac14fc6cd69ae4b7", "title": "MOSAiC: Wind profiles from Galion G4000 Lidar Wind Profiler - Version 2", "abstract": "Wind profiles from a Galion G4000 Doppler lidar for the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) project, derived from conical scans at 30 degree and 50 degree beam elevation angles.\r\n\r\nThe University of Leeds participation in the project- MOSAiC Boundary Layer -was funded by the Natural Environment Research Council (NERC, grant: NE/S002472/1) and involved instrumentation from the Atmospheric Measurement and Observations Facility of the UK's National Centre for Atmospheric Science (NCAS AMOF). This was a year-long project on the German icebreaker Polarstern to study Arctic climate focused on measurements of atmospheric boundary layer dynamics and turbulent structure. The Galion wind profiler provides high resolution (~15m vertical and 5 minute temporal) measurements of wind profiles. Data are only available where sufficient particles are available to backscatter the laser light - in the clean arctic environment, this requires cloud or precipitation.\r\n\r\nThis is version 2 of this dataset.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-09-29T14:29:43", "latestDataUpdateTime": "2022-09-29T14:50:31", "updateFrequency": "notPlanned", "dataLineage": "Data were collected, quality controlled and prepared for archiving by the instrument scientists before upload to the Centre for Environmental Data Analysis (CEDA) for long term archiving.", "removedDataReason": "", "keywords": "MOSAiC, NE/S002472/1, AMOF,NCAS", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-11-18T14:15:41", "doiPublishedTime": "2022-11-18T14:46:43", "removedDataTime": null, "geographicExtent": { "ob_id": 3419, "bboxName": "", "eastBoundLongitude": 148.38, "westBoundLongitude": -165.07, "southBoundLatitude": 78.34, "northBoundLatitude": 89.99 }, "verticalExtent": null, "result_field": { "ob_id": 42818, "dataPath": "/badc/ncas-mobile/data/ncas-lidar-wind-profiler-1/20191005_mosaic/v2.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1581135587, "numberOfFiles": 597, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 10877, "startTime": "2019-10-05T00:00:00", "endTime": "2020-09-20T00:00:00" }, "resultQuality": { "ob_id": 3908, "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": "2022-03-24" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37048, "uuid": "ce2aa0607afc45c1a24abd5ddee091f0", "short_code": "acq", "title": "Acquisition for: Multidisciplinary drifting Observatory for Study of Arctic Climate (MOSAiC): Wind profiles from Galion G4000 Lidar Wind Profiler", "abstract": "Acquisition for: Multidisciplinary drifting Observatory for Study of Arctic Climate (MOSAiC): Wind profiles from Galion G4000 Lidar Wind Profiler" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 223 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37021, "uuid": "35a5a43ae2fa4289af0a3e5e2ca92a5a", "short_code": "proj", "title": "MOSAiC:The Multidisciplinary drifting Observatory for the Study of Arctic Climate", "abstract": "The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) initiative was a major international programme motivated by the rapid changes in Arctic climate observed over the last few decades. This is driven by an accelerated rise in the mean temperature of the Arctic; which is warming at 2-3 times the mean global rate. The most visible change is the dramatic reduction in sea ice extent, particularly of the summer minimum, which is decreasing at a rate of 13% per decade.\r\n\r\nThese rapid changes are the result of a combination of feedback processes - the best known is the ice albedo feedback, whereby the loss of ice exposes the land or sea surface beneath, lowering the area mean albedo and allowing more solar radiation to be absorbed, which warms the surface and enhances ice melt. Other feedbacks relate to the vertical profiles of atmospheric temperature and humidity, cloud properties, and large-scale atmospheric circulation.\r\n\r\nWhile climate models also show enhanced warming in the Arctic, they do not reproduce many of the observed details of the change; for example they do not reproduce the very rapid decline in the summer sea ice minimum observed over the last 10 years, and there are big differences between models. This has a significant impact on our ability to predict the future state of climate system. Poor model performance results from multiple leading-order deficiencies in their representation of physical processes in the Arctic system. MOSAiC aims to address these through a large-scale coordinated approach, making simultaneous measurements of the many interdependent processes relevant to climate over a full calendar year. This approach is necessary because of the strong linkages and feedbacks between different parts of the Arctic climate system and the strong seasonality in many processes. \r\nThe MOSAiC Boundary layer is a Natural Environment Research Council (NERC, grant: NE/S002472/1) funded contribution to this international project focused on measurements of atmospheric boundary layer dynamics and turbulent structure. This observational campaign took place on, and around, the icebreaker Polarstern, which was frozen in at the edge of the pack ice at the end of the summer melt. This provided ready access to both multi-year ice within the pack and to freshly forming ice just outside it. Measurements were made of all components of the surface energy budget on both the upper and lower sides of the ice, along with ice thickness, temperature, physical properties, topography, and deformation over time. The processes controlling the energy budget, including synoptic-scale forcing, cloud properties, turbulent mixing, and the interactions between them, will be studied in detail.\r\n\r\nGrantRef: NE/S002472/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 56577, 62763, 62764, 62765, 62766, 62767, 62768, 62769, 62770, 62771, 62772, 62773, 62774, 62775, 62776, 62777 ], "vocabularyKeywords": [], "identifier_set": [ 12294 ], "observationcollection_set": [ { "ob_id": 39477, "uuid": "a46fffe939cf4430b9ce812ffc5e03da", "short_code": "coll", "title": "Meteorological Observations for the Multidisciplinary drifting Observatory for Study of Arctic Climate (MOSAiC) project", "abstract": "This collection contains a range of meteorological observations made by instruments on board the German Icebreaker ship Polarstern for the Multidisciplinary drifting Observatory for Study of Arctic Climate (MOSAiC) project.\r\n\r\nThe University of Leeds participation in MOSAiC was funded by the Natural Environment Research Council (NERC, grant: NE/S002472/1) and involved instrumentation from the Atmospheric Measurement and Observations Facility of the UK's National Centre for Atmospheric Science (NCAS AMOF)." } ], "responsiblepartyinfo_set": [ 182170, 182171, 182172, 182173, 182174, 182175, 182176, 182177 ], "onlineresource_set": [] }, { "ob_id": 38260, "uuid": "59617e4c032543d48364f9ff2711372c", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.SM.3 (v20220930)", "abstract": "Data for Figure 6.SM.3 from the Chapter 6 Supplementary Material of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 6.SM.3 shows global mean temperature response 20 and 100 years following one year of present-day (year 2014) emissions.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Szopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers Supplementary Material. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Available from https://www.ipcc.ch/\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three panels with data provided for all panels in the main directory\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains global-mean temperature response after 20 and 100 years following one year pulse of present-day (year 2014) for different emissions for:\r\n \r\n - total anthropogenic emissions\r\n - sectoral emissions\r\n - regional emissions\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Top panel: \r\n - Data file dT20_100_CEDS_total_v2_wHFCs_v210214.txt (column 0, reference to 20 or 100 years, columns 1 to 13, coloured bars)\r\n \r\n Left panel: \r\n - Data file dT20_CEDS_sectors_v2_wHFCs_v210214.txt (column 0, reference to several sectors, columns 1 to 13, coloured bars)\r\n - Data file dT100_CEDS_sectors_v2_wHFCs_v210214.txt (column 0, reference to several sectors, columns 1 to 13, coloured bars)\r\n - Data file errorbar_dT20_sectors.txt (column 1: Lower bound of the uncertainty in the 20-year sectoral global-mean temperature responses. column 3: Upper bound of the uncertainty in the 20-year sectoral global-mean temperature responses.)\r\n - Data file errorbar_dT100_sectors.txt (column 1: Lower bound of the uncertainty in the 100-year sectoral global-mean temperature responses. column 3: Upper bound of the uncertainty in the 100-year sectoral global-mean temperature responses.)\r\n \r\n Right panel:\r\n - Data file dT20_CEDS_regions_v2_wHFCs_v210214.txt (column 0, reference to several regions, columns 1 to 13, coloured bars)\r\n - Data file dT100_CEDS_regions_v2_wHFCs_v210214.txt (column 0, reference to several regions, columns 1 to 13, coloured bars)\r\n - Data file errorbar_dT20_regions.txt (column 1: Lower bound of the uncertainty in the 20-year regional global-mean temperature responses. column 3: Upper bound of the uncertainty in the 20-year sectoral global-mean temperature responses.)\r\n - Data file errorbar_dT100_regions.txt (column 1: Lower bound of the uncertainty in the 100-year regional global-mean temperature responses. column 3: Upper bound of the uncertainty in the 100-year sectoral global-mean temperature responses.)\r\n \r\n Acronyms:\r\n AGR: agriculture\r\n ENE_C: Fossil fuel combustion for energy\r\n ENE_P: Fossil fuel production and distribution\r\n IND: Industry\r\n TRA: Land transportation\r\n RES_FF: Residential and commercial\r\n RES_BF: Residential and commercial (biofuel use only)\r\n WST: Waste management\r\n SHP: Shipping\r\n BIO: Open biomass burning\r\n AVIA: Aviation\r\n -----\r\n LAM: Latin America and Caribbean\r\n SAS: Southern Asia\r\n AFR: Africa\r\n EUR: Europe\r\n CAS: Eurasia\r\n MDE: Middle East\r\n SEA: Southeast Asia and Developing Pacific\r\n PAN: Asia-Pacific Developed\r\n NAM: North America\r\n EAS: Eastern Asia\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 report component related to the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3", "creationDate": "2022-09-30T11:02:27.273688", "lastUpdatedDate": "2022-09-30T11:02:27", "latestDataUpdateTime": "2022-09-30T15:50:37", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Short-lived Climate Forcers, Long-lived Greenhouse Gases, Regions, Sectors, Global Temperature Response, Shared Socio-economic Pathways, Time Horizons", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-10T15:22:37", "doiPublishedTime": "2023-07-03T14:49:44.851360", "removedDataTime": null, "geographicExtent": { "ob_id": 3655, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 193, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38261, "dataPath": "/badc/ar6_wg1/data/ch_06/ch6_fig6sm3/v20220930", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 30787, "numberOfFiles": 12, "fileFormat": "Data are in .txt format" }, "timePeriod": { "ob_id": 10544, "startTime": "2014-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4093, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-09-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 38262, "uuid": "e998cf18b659491bae8c55103d3cef21", "short_code": "comp", "title": "Caption for Figure 6.SM.3 from Chapter 6 Supplementary Material of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global mean temperature response 20 and 100 years following one year of present-day (year 2014) emissions. " }, "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": [ 12573 ], "observationcollection_set": [ { "ob_id": 32721, "uuid": "18cf830c350e451a9002105294861dc0", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 6.3\r\n- data for Figure 6.12\r\n- input data for Figure 6.12\r\n- input data for Figure 6.14\r\n- data for Figure 6.16\r\n- data for Figure 6.17\r\n- data for Figure 6.20\r\n- data for Figure 6.21\r\n- data for Figure 6.22\r\n- input data for Figure 6.22\r\n- data for Figure 6.23\r\n- data for figure 6.24\r\n- input data for Figure 6.24\r\n- data for Figure 6.SM.3\r\n- data for Figure 6.SM.4\r\n- data for Figure 6.SM.5" } ], "responsiblepartyinfo_set": [ 182186, 182187, 182188, 182189, 182190, 182191, 182192, 182193, 182194 ], "onlineresource_set": [ 52979, 52978 ] }, { "ob_id": 38263, "uuid": "dc93cf482acb4dff8d7baa01dfa1fa29", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.16 (v20223009)", "abstract": "Data for Figure 6.16 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 6.16 shows global mean temperature response 10 and 100 years following one year of present-day (year 2014) emissions.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Szopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 817–922, doi:10.1017/9781009157896.008.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three panels with data provided for all panels in the main directory\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains global-mean temperature response after 10 and 100 years following one year of present-day (year 2014) for different emissions for:\r\n \r\n - total anthropogenic emissions\r\n - sectoral emissions\r\n - regional emissions\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Top panel: \r\n - Data file 'dT10_100_CEDS_total_v2_wHFCs_v210214.txt' (column 0, reference to 10 or 100 years, columns 1 to 13, coloured bars)\r\n \r\n Left panel: \r\n - Data file 'dT10_CEDS_sectors_v2_wHFCs_v210214.txt' (column 0, reference to several sectors, columns 1 to 13, coloured bars)\r\n - Data file 'dT100_CEDS_sectors_v2_wHFCs_v210214.txt' (column 0, reference to several sectors, columns 1 to 13, coloured bars)\r\n - Data file 'errorbar_dT10_sectors.txt' (column 1: Lower bound of the uncertainty in the 10-year sectoral global-mean temperature responses. column 3: Upper bound of the uncertainty in the 10-year sectoral global-mean temperature responses.)\r\n - Data file 'errorbar_dT100_sectors.txt' (column 1: Lower bound of the uncertainty in the 100-year sectoral global-mean temperature responses. column 3: Upper bound of the uncertainty in the 100-year sectoral global-mean temperature responses.)\r\n \r\n Right panel: \r\n - Data file 'dT10_CEDS_regions_v2_wHFCs_v210214.txt' (column 0, reference to several regions, columns 1 to 13, coloured bars)\r\n - Data file 'dT100_CEDS_regions_v2_wHFCs_v210214.txt' (column 0, reference to several regions, columns 1 to 13, coloured bars)\r\n - Data file 'errorbar_dT10_regions.txt' (column 1: Lower bound of the uncertainty in the 10-year regional global-mean temperature responses. column 3: Upper bound of the uncertainty in the 10-year sectoral global-mean temperature responses.)\r\n - Data file 'errorbar_dT100_regions.txt' (column 1: Lower bound of the uncertainty in the 100-year regional global-mean temperature responses. column 3: Upper bound of the uncertainty in the 100-year sectoral global-mean temperature responses.)\r\n \r\n Acronyms:\r\n AGR: agriculture\r\n ENE_C: Fossil fuel combustion for energy\r\n ENE_P: Fossil fuel production and distribution\r\n IND: Industry\r\n TRA: Land transportation\r\n RES_FF: Residential and comercial\r\n RES_BF: Residential and commercial (biofuel use only)\r\n WST: Waste management\r\n SHP: Shipping\r\n BIO: Open biomass burning\r\n AVIA: Aviation\r\n -----\r\n LAM: Latin America and Caribbean\r\n SAS: Southern Asia\r\n CAS: Eurasia\r\n SEA: Southeast Asia and Developing Pacific\r\n PAN: Asia-Pacific Developed\r\n AFR: Africa\r\n EUR: Europe\r\n MDE: Middle East\r\n NAM: North America\r\n EAS: Eastern Asia\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-09-30T11:05:39.579604", "lastUpdatedDate": "2022-09-30T11:05:39", "latestDataUpdateTime": "2024-03-09T02:57:58", "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\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis,Short-lived climate forcers, long-lived greenhouse gases, regions, sectors, global temperature response, shared socio-economic pathways, time horizons", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-14T10:35:45", "doiPublishedTime": "2023-03-22T10:42:36.406434", "removedDataTime": null, "geographicExtent": { "ob_id": 3656, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 194, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38264, "dataPath": "/badc/ar6_wg1/data/ch_06/ch6_fig16/v20220930", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 29758, "numberOfFiles": 12, "fileFormat": "Data are txt formatted" }, "timePeriod": { "ob_id": 10545, "startTime": "2014-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4094, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-09-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 38265, "uuid": "9af2e68318284e96927a177ea82596d8", "short_code": "comp", "title": "Caption for Figure 6.16 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global mean temperature response 10 and 100 years following one year of present-day (year 2014) emissions. The temperature response is broken down by individual species and shown for total anthropogenic emissions (top), sectoral emissions (left) and regional emissions (right). Sectors and regions are sorted by (high-to-low) net temperature effect on the 10-year time scale. Error bars in the top panel show uncertainty (5–95% interval) in net temperature effect due to uncertainty in radiative forcing only (calculated using a Monte Carlo approach and best estimate uncertainties from the literature – see Lund et al. (2020) for details). CO2 emissions are excluded from open biomass burning and residential biofuel use due to their unavailability in the Community Emissions Data System (CEDS) and uncertainties around non-sustainable emission fraction. Emissions for 2014 originate from the CEDS (Hoesly et al., 2018), except for HFCs which are from Purohit et al. (2020), open biomass burning from van Marle et al. (2017), and aviation H2O which is from Lee et al. (2021). The split of fossil fuel production and distribution and combustion for energy and residential and commercial fuel use into fossil fuel and biofuel components is obtained from the GAINS model (ECLIPSE version 6b dataset). Open biomass burning emissions are not included for the regions. Emissions are aggregated into fossil fuel production and distribution (coal mining, oil and gas production, upstream gas flaring and gas distribution networks), agriculture (livestock and crop production), fossil fuel combustion for energy (power plants), industry (combustion and production processes, solvent-use losses from production and end use), residential and commercial (fossil fuel use for cooking and heating as well is HFCs leakage from A/C and refrigeration), waste management (solid waste, including landfills and open trash burning, residential and industrial waste water), transport (road and off-road vehicles, and HFC leakage from A/C and refrigeration equipment), residential and commercial (biofuels use for cooking and heating), open biomass burning (forest, grassland, savanna fires and agricultural waste burning), shipping (including international shipping), and aviation (including international aviation). Further details on data sources and processing are available in the chapter data table (Table 6.SM.3)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12425 ], "observationcollection_set": [ { "ob_id": 32721, "uuid": "18cf830c350e451a9002105294861dc0", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 6.3\r\n- data for Figure 6.12\r\n- input data for Figure 6.12\r\n- input data for Figure 6.14\r\n- data for Figure 6.16\r\n- data for Figure 6.17\r\n- data for Figure 6.20\r\n- data for Figure 6.21\r\n- data for Figure 6.22\r\n- input data for Figure 6.22\r\n- data for Figure 6.23\r\n- data for figure 6.24\r\n- input data for Figure 6.24\r\n- data for Figure 6.SM.3\r\n- data for Figure 6.SM.4\r\n- data for Figure 6.SM.5" } ], "responsiblepartyinfo_set": [ 182197, 182198, 182199, 182200, 182201, 182202, 182203, 182204, 182205 ], "onlineresource_set": [ 82738, 52980, 52981, 82992 ] }, { "ob_id": 38269, "uuid": "56c283c79666449ebe0235e809bdb69f", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.SM.4 (v20220930)", "abstract": "Data for Figure 6.SM.4 from Chapter 6 Supplementary Material of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 6.SM.4 shows future global and regional changes in annual mean surface PM2.5, relative to the 2005-2014 mean, for the different SSPs used in CMIP6. Each line represents a multi-model mean across the region with shading representing the ±1 standard deviation in the mean.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Szopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers Supplementary Material. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Available from https://www.ipcc.ch/\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 11 panels, with data provided for all panels in 4 files placed in the main directory.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains precomputed values of surface PM2.5 concentrations across world regions for:\r\n - A 10-year mean period (2005 to 2014) from the historical simulation to represent present day regional mean values. Regional multi-model annual mean and standard deviation values are calculated across 5 different CMIP6 models\r\n - Annual 5-year multi-model mean values of surface PM2.5 from 5 different CMIP6 models projected for 7 different future scenarios covering the period 2015 to 2100\r\n - Standard deviation values of surface PM2.5 from 5 different CMIP6 models projected for 7 different future scenarios covering 5-year mean periods from 2015 to 2100\r\n - Annual 10-year mean values of surface PM2.5 from the TM5-FASST model projected for 5 different future scenarios covering the period 2015 to 2100\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n All the data files provided are used to create the time series plots for each region. The numbers in each panel for each region are obtained from 'Surf_PM2pt5_data_05_14_mean_for_IPCC_figure_V1_5mods.csv', with the time series line for each scenario from 'Surf_PM2pt5_data_fut_mean_for_IPCC_figure_V1_5mods.csv' and the shading obtained by using the values in 'Surf_PM2pt5_SD_data_fut_mean_for_IPCC_figure_V1_5mods.csv'. The TM5-FASST data is included on the figure by reading in pre-computed regional mean values from the 'Regional_annual_mean_surface_PM2pt5_resp_values_CMIP6_Fut_Scens_from_TM5_FASST_on_AR6_reg_receptors_INCL_GLOB_2015_2100.txt' file.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nPM2.5 refers to fine particulate matter air pollution with diameter of less than 2.5 microns.\r\nSSP stands for Shared Socioeconomic Pathway.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The plotting code that is provided along with this dataset should just be able to read in each of the precomputed regional mean .csv files and then reproduce the time series figures.\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 report component related to the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-09-30T11:34:49.097162", "lastUpdatedDate": "2022-09-30T11:34:49", "latestDataUpdateTime": "2022-09-30T16:08:48", "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": "Air quality, Climate, PM2.5, CMIP6, Models, IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-16T15:05:46", "doiPublishedTime": "2023-07-03T14:53:26.387659", "removedDataTime": null, "geographicExtent": { "ob_id": 3658, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 196, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38270, "dataPath": "/badc/ar6_wg1/data/ch_06/ch6_fig6sm4/v20220930", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 54552, "numberOfFiles": 7, "fileFormat": "Data are csv and txt formatted" }, "timePeriod": { "ob_id": 10547, "startTime": "2015-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4096, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-09-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 38271, "uuid": "bd1ab29bf92249749a3b1376622ed597", "short_code": "comp", "title": "Caption for Figure 6.SM.4 from Chapter 6 Supplementary Material of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Future global and regional changes in annual mean surface PM2.5, relative to the 2005–2014 mean, for the different SSPs used in CMIP6. Each line represents a multi-model mean across the region with shading representing the ±1 standard deviation in the mean. Dots represent TM5-FASST results. The multi-model regional mean value (±1 standard deviation) for the year 2005–2014 is shown in the top-left corner of each panel. " }, "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": [ 12574 ], "observationcollection_set": [ { "ob_id": 32721, "uuid": "18cf830c350e451a9002105294861dc0", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 6.3\r\n- data for Figure 6.12\r\n- input data for Figure 6.12\r\n- input data for Figure 6.14\r\n- data for Figure 6.16\r\n- data for Figure 6.17\r\n- data for Figure 6.20\r\n- data for Figure 6.21\r\n- data for Figure 6.22\r\n- input data for Figure 6.22\r\n- data for Figure 6.23\r\n- data for figure 6.24\r\n- input data for Figure 6.24\r\n- data for Figure 6.SM.3\r\n- data for Figure 6.SM.4\r\n- data for Figure 6.SM.5" } ], "responsiblepartyinfo_set": [ 182219, 182220, 182221, 182222, 182223, 182224, 182225, 182226, 182227, 182228, 182229 ], "onlineresource_set": [ 52983, 52982, 83492, 88616, 94642 ] }, { "ob_id": 38274, "uuid": "c1e3dcebccfd43b4a65f8e446f9944e3", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.SM.5 (v20220930)", "abstract": "Data for Figure 6.SM.5 from Chapter 6 Supplementary Material of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 6.SM.5 shows future global and regional changes in annual mean surface O3, relative to the 2005-2014 mean, for the different SSPs used in CMIP6.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Szopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers Supplementary Material. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Available from https://www.ipcc.ch/\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 11 panels with data provided for all panels in 4 files placed in the directory.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains precomputed values of surface ozone concentrations across world regions for:\r\n \r\n - A 10-year mean period (2005 to 2014) from the historical simulation to represent present day regional mean values. Regional multi-model annual mean and standard deviation values are calculated across 5 different CMIP6 models\r\n - Annual multi-model mean values of surface ozone from 5 different CMIP6 models projected for 7 different future scenarios covering the period 2015 to 2100\r\n - Standard deviation values of surface ozone from 5 different CMIP6 models projected for 7 different future scenarios covering the time period from 2015 to 2100\r\n - Annual 10-year mean values of surface ozone from the TM5-FASST model projected for 5 different future scenarios covering the period 2015 to 2100\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n All the data files provided are used to create the time series plots for each region. The numbers in each panel for each region are obtained from 'Surf_O3_data_05_14_mean_for_IPCC_figure_V1_5mods.csv', with the time series line for each scenario from 'Surf_O3_data_fut_mean_for_IPCC_figure_V1_5mods.csv' and the shading obtained by using the values in 'Surf_O3 _SD_data_fut_mean_for_IPCC_figure_V1_5mods.csv'. The TM5-FASST data is included on the figure by reading in pre-computed regional mean values from the 'Regional_annual_mean_surface_O3_resp_values_CMIP6_Fut_Scens_from_TM5_FASST_on_AR6_reg_receptors_INCL_GLOB_2015_2100.txt' file.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The plotting code that is provided along with this dataset should just be able to read in each of the precomputed regional mean .csv files and then reproduce the time series figures.\r\n\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 report component related to the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-09-30T11:39:05.735357", "lastUpdatedDate": "2022-09-30T11:39:05", "latestDataUpdateTime": "2024-03-09T02:58:09", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Air quality, climate, ozone, CMIP6, models", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-16T15:08:30", "doiPublishedTime": "2023-07-03T14:59:32.396169", "removedDataTime": null, "geographicExtent": { "ob_id": 3660, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 198, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38275, "dataPath": "/badc/ar6_wg1/data/ch_06/ch6_fig6sm5/v20220930", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 199005, "numberOfFiles": 7, "fileFormat": "Data are csv formatted" }, "timePeriod": { "ob_id": 10549, "startTime": "2015-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4098, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-09-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 38276, "uuid": "b0a7512155794844aef236aaf5f443cd", "short_code": "comp", "title": "Caption for Figure 6.SM.5 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Future global and regional changes in annual mean surface O3, relative to the 2005–2014 mean, for the different SSPs used in CMIP6. Each line represents a multi-model mean across the region with shading representing the ± standard deviation in the mean. Dots represent TM5-FASST results. The multi-model regional mean value (±1 standard deviation) for the year 2005–2014 is shown in the top-left corner of each panel." }, "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": [ 12575 ], "observationcollection_set": [ { "ob_id": 32721, "uuid": "18cf830c350e451a9002105294861dc0", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 6.3\r\n- data for Figure 6.12\r\n- input data for Figure 6.12\r\n- input data for Figure 6.14\r\n- data for Figure 6.16\r\n- data for Figure 6.17\r\n- data for Figure 6.20\r\n- data for Figure 6.21\r\n- data for Figure 6.22\r\n- input data for Figure 6.22\r\n- data for Figure 6.23\r\n- data for figure 6.24\r\n- input data for Figure 6.24\r\n- data for Figure 6.SM.3\r\n- data for Figure 6.SM.4\r\n- data for Figure 6.SM.5" } ], "responsiblepartyinfo_set": [ 182243, 182244, 182245, 182246, 182247, 182248, 182249, 182250, 182251, 182252, 182253 ], "onlineresource_set": [ 52984, 52985, 83493, 88617 ] }, { "ob_id": 38277, "uuid": "39b789cb48694497941cd0c17f99bd69", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 6.14 v20220930", "abstract": "Input Data for Figure 6.14 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 6.14 shows multi-model annual mean change in surface O3 (ppb) concentrations at different warming levels.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Szopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 817–922, doi:10.1017/9781009157896.008.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with input data provided for all panels in the main directory.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains gridded monthly, annual and seasonal mean surface ozone concentrations from 5 different CMIP6 models covering period 2015 to 2099 for the ssp370SST and ssp370pdSST AerChemMIP scenarios.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n All of the individual CMIP6 model data files provided are used to create a mulit-model mean difference between the two different scenarios at the predetermined temperature thresholds for each model.\r\n\r\ndatafile: ‘MRI-ESM2-0_monthly_annual_seasonal_mean_surf_o3_for_CMIP6_ssp370pdSST_2015_2099_all.nc’\r\ndatafile: ‘MRI-ESM2-0_monthly_annual_seasonal_mean_surf_o3_for_CMIP6_ssp370SST_2015_2099_all.nc’\r\ndatafile: ‘GFDL-ESM4_monthly_annual_seasonal_mean_surf_o3_for_CMIP6_ssp370pdSST_2015_2099_all.nc’\r\ndatafile: ‘GFDL-ESM4_monthly_annual_seasonal_mean_surf_o3_for_CMIP6_ssp370SST_2015_2099_all.nc’\r\ndatafile: ‘GISS-E2-1-G_monthly_annual_seasonal_mean_surf_o3_for_CMIP6_ssp370pdSST_2015_2099_all.nc’\r\ndatafile: ‘GISS-E2-1-G_monthly_annual_seasonal_mean_surf_o3_for_CMIP6_ssp370SST_2015_2099_all.nc’\r\ndatafile: ‘UKESM1-0-LL_monthly_annual_seasonal_mean_surf_o3_for_CMIP6_ssp370pdSST_2015_2099_all.nc’\r\ndatafile: ‘UKESM1-0-LL_monthly_annual_seasonal_mean_surf_o3_for_CMIP6_ssp370pdSST_2015_2099_all.nc’\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The plotting code (CMIP6_global_2D_ann_O3_resp_at_diff_temp_thresholds.py) that is provided along with this dataset to reproduce the figure will firstly extract the annual mean surface ozone concentrations for all years for each model and scenario. Then it will extract the relevant 20 years worth of data for each scenario based on the pre-calculated year that model exceeded the relevant temperature threshold for each figure panel. A difference between the scenarios at this temperature threshold will be calculated for the particular model and then be used with the same data from all the models to create a multi-model mean difference that is used for each panel of the figure. Significance testing to applied to calculate where at least one model does not agree on the sign of the change between the two scenarios.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP370 is the Shared Socioeconomic Pathway which represents the upper-middle range of radiative forcing and development scenarios, consistent with RCP6.0 and SST stands for Sea Surface Temperature.\r\nppb stands for parts per billion.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-09-30T13:39:28.640422", "lastUpdatedDate": "2022-09-30T13:39:28", "latestDataUpdateTime": "2022-09-30T16:50:55", "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\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "Air quality, Climate, Ozone, IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-31T13:29:08", "doiPublishedTime": "2023-07-03T14:37:02.269132", "removedDataTime": null, "geographicExtent": { "ob_id": 3661, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 199, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38278, "dataPath": "/badc/ar6_wg1/data/ch_06/inputdata_ch6_fig14/v20220930", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1410241860, "numberOfFiles": 11, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10550, "startTime": "2015-01-01T12:00:00", "endTime": "2099-12-31T12:00:00" }, "resultQuality": { "ob_id": 4099, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-09-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 38279, "uuid": "94fd72aea4614c6d9a1ed7b2a271d3a8", "short_code": "comp", "title": "Caption for Figure 6.14 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Multi-model annual mean change in surface O3 (ppb) concentrations at different warming levels. Changes are shown for (a) 1.0°C, (b) 1.5°C, (c) 2.0°C and (d) 2.5°C increases in global mean surface air temperature. CMIP6 models include GFDL-ESM4, GISS-E2-1-G, MRI-ESM2-0 and UKESM1-0-LL. For each model, the change in surface O3 is calculated as the difference between two AerChemMIP experiments – one with evolving future emissions and sea surface temperatures (SSTs) under the SSP3-7.0 scenario and the other with the same setup but with fixed present-day SSTs. The difference is calculated as a 20-year mean in surface O3 around the year when the temperature threshold in each model is exceeded. The multi-model change in global annual mean surface O3 concentrations with ± 1 σ are shown within parentheses. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, three out of four models agree on sign of change; diagonal lines indicate regions with low model agreement, where three out of four models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 6255, 18925, 50415, 50417, 50420, 50553, 50559, 50561, 52744, 53144, 53146, 54211, 54691, 62750, 62752, 62753, 62754, 62755, 62756, 62757, 62758, 62759, 62760, 62762, 91952, 91953 ], "vocabularyKeywords": [], "identifier_set": [ 12571 ], "observationcollection_set": [ { "ob_id": 32721, "uuid": "18cf830c350e451a9002105294861dc0", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 6: Short-lived climate forcers.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 6.3\r\n- data for Figure 6.12\r\n- input data for Figure 6.12\r\n- input data for Figure 6.14\r\n- data for Figure 6.16\r\n- data for Figure 6.17\r\n- data for Figure 6.20\r\n- data for Figure 6.21\r\n- data for Figure 6.22\r\n- input data for Figure 6.22\r\n- data for Figure 6.23\r\n- data for figure 6.24\r\n- input data for Figure 6.24\r\n- data for Figure 6.SM.3\r\n- data for Figure 6.SM.4\r\n- data for Figure 6.SM.5" } ], "responsiblepartyinfo_set": [ 182256, 182257, 182258, 182259, 182260, 182261, 182262, 182263, 182264, 182265, 182266 ], "onlineresource_set": [ 52986, 52987, 82745, 83586 ] }, { "ob_id": 38285, "uuid": "5f8d2c32121a4885b20be2ae96aed72d", "title": "Atlas of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure Atlas.13 (v20221004)", "abstract": "Data for Figure Atlas.13 from Atlas of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure Atlas.13 shows changes in annual mean surface air temperature and precipitation from different lines of evidence (CMIP5 and CMIP6). \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citations:\r\nFor the report component from which the figure originates:\r\nGutiérrez, J.M., R.G. Jones, G.T. Narisma, L.M. Alves, M. Amjad, I.V. Gorodetskaya, M. Grose, N.A.B. Klutse, S. Krakovska, J. Li, D. Martínez-Castro, L.O. Mearns, S.H. Mernild, T. Ngo-Duc, B. van den Hurk, and J.-H. Yoon, 2021: Atlas. 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. 1927–2058, doi:10.1017/9781009157896.021\r\n\r\nIturbide, M. et al., 2021: Repository supporting the implementation of FAIR principles in the IPCC-WG1 Interactive Atlas. Zenodo. Retrieved from: http://doi.org/10.5281/zenodo.5171760\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has eight panels, with data provided for all panels in the master GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nThis dataset contains global monthly precipitation and near surface temperature aggregated by reference region for model output datasets: \r\n- CMIP5, CMIP6 (1850-2100)\r\n- CORDEX (1970-2100)\r\nThese are presented separately for land, sea, and land-sea gridboxes (a single run per model). Regional averages are weighted by the cosine of latitude in all cases. \r\nAn observation-based product (1979-2016) is also provided in the same format for reference: W5E5 (Lange, 2019).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nAll datasets of monthly precipitation and near surface temperature aggregated by region for CMIP5, CMIP6 and CORDEX models are provided in the labelled directories and are used for the production of this figure. \r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\nSSP1-2.6 is based on SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP5-8.5 is based on SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\nGWL stands for global warming levels.\r\nJJA and DJF stand for June, July, August and December, January, February respectively.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. To reproduce each panel in this figure using the 'regional-scatter-plots_R.ipynb' notebook, select the region: 'world', area: 'land' or 'landsea' and scatter.seasons: list of months by number e.g. boreal summer (JJA): list(c(12, 1, 2),6:8). \r\n\r\nThe notebooks describe step by step the basic process followed to generate some key figures of the AR6 WGI Atlas and some products underpinning the Interactive Atlas, such as reference regions, global warming levels, aggregated datasets. They include comments and hints to extend the analysis, thus promoting reusability of the results. These notebooks are provided as guidance for practitioners, more user friendly than the code provided as scripts in the reproducibility folder.\r\n\r\nSome of the notebooks require access to large data volumes out of this repository. To speed up the execution of the notebook, in addition to the full code to access the data, a data loading shortcut is provided, by storing intermediate results in the auxiliary-material folder in this repository. To test other parameter settings, the full data access instructions should be followed, which can take long waiting times.\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 (Atlas)\r\n - Link to the Supplementary Material for Atlas, which contains details on the input data used in Table Atlas.SM.15.\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the necessary notebooks for reproducing the figure from Github.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "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, WG1, WGI, Atlas, AR6, CMIP5, CMIP6, CORDEX, precipitation, temperature, regionally aggregated results, global, IPCC-DDC, Sixth Assessment Report, Working Group 1, Physical Science Basis", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-06-19T16:00:14", "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": 40833, "dataPath": "https://github.com/IPCC-WG1/Atlas/blob/main/notebooks/regional-scatter-plots_Figure-Atlas-13_R.ipynb", "oldDataPath": [], "storageLocation": "external", "storageStatus": "online", "volume": 0, "numberOfFiles": 0, "fileFormat": "notebook" }, "timePeriod": { "ob_id": 10554, "startTime": "1850-01-01T12:00:00", "endTime": "2099-12-31T12:00:00" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40181, "uuid": "6d9ffdeb02374eaf83a808bf9bb7224d", "short_code": "comp", "title": "Caption for Figure Atlas.13 from Chapter Atlas of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Changes in annual mean surface air temperature and precipitation averaged over the global land–sea (left) and global land areas (right) in each horizontal pair of panels. The top-left two panels show the median (dots) and 10th–90th percentile range across each model ensemble for temperature change, for two datasets (CMIP5 and CMIP6) and two scenarios (SSP1-2.6/RCP2.6 and SSP5-8.5/RCP8.5). The first 12 bars represent the projected changes over three time periods (near-term 2021–2040, mid-term 2041–2060 and long-term 2081–2100) compared to the baseline period of 1995–2014, and the remaining four bars represent the additional warming projected relative to the same baseline to reach four global warming levels (GWLs; 1.5°C, 2°C, 3°C and 4°C). The top-right two panels show scatter diagrams of temperature against precipitation changes, displaying the median (dots) and 10th–90th percentile ranges for the same four GWLs, again representing the additional changes for the global temperature to reach the respective GWL from the baseline period of 1995–2014. In all panels the dark (light) grey lines or dots represent the CMIP6 (CMIP5) simulated changes in temperature and precipitation between the 1850–1900 baseline used for calculating GWLs and the recent-past baseline of 1995–2014 used to calculate the changes in the bar diagrams and scatter plots. Changes are absolute for temperature and relative for precipitation. The script used to generate this figure is available online (Iturbide et al., 2021) and similar results can be generated in the Interactive Atlas for flexibly defined seasonal periods. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 32730, "uuid": "42b3dd3c88a04195b0ecaa3827dd7560", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Atlas", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Atlas.\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 Atlas.2\r\n- data for Figure Atlas.13\r\n- data for Figure Atlas.16\r\n- data for Figure Atlas.17\r\n- data for Figure Atlas.21\r\n- data for Figure Atlas.22\r\n- data for Figure Atlas.24\r\n- data for Figure Atlas.26\r\n- data for Figure Atlas.28\r\n- data for Figure Atlas.29" } ], "responsiblepartyinfo_set": [ 182295, 182294, 182288, 182289, 182290, 182291, 182292, 182293, 182344, 182343, 195731, 195732, 195733, 195734, 195735, 195736, 195737, 195738, 195739, 195740, 195741, 195742, 195743, 195744, 195806 ], "onlineresource_set": [ 53008, 52990, 52992, 52991, 82721, 88639 ] }, { "ob_id": 38286, "uuid": "fe02d5eef9ef4ad889d1917ccad3b35f", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track Integrated Sea State Parameters (ISSP) from SAR WV onboard Sentinel-1A & 1B, L2P product, release version 3", "abstract": "The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite synthetic aperture radar (SAR) integrated sea state parameters (ISSP) data from Sentinel-1 (referred to as SAR WV onboard Sentinel-1 Level 2P (L2P) ISSP data) with a particular focus for use in climate studies.\r\n \r\nThis dataset contains the Sentinel-1 SAR Remote Sensing Integrated Sea State Parameter product (v1.0), which forms part of the ESA Sea State CCI version 3.0 release. This product provides along-track primary significant wave height measurements and secondary sea state parameters, calibrated with CMEMS model data and reference in situ measurements at 20km resolution every 100km, processed using the Pleskachevsky et. al., 2021 emprical model, separated per satellite and pass, including all measurements with flags and uncertainty estimates. These are expert products with rich content and no data loss. The SAR Wave Mode data used in the Sea State CCI SAR WV onboard Sentinel-1 Level 2P (L2P) ISSP v3 dataset come from the Sentinel-1 satellite missions spanning from 2014 to 2021 (Sentinel-1 A, Sentinel-1 B).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-10-07T10:03:12", "updateFrequency": "notPlanned", "dataLineage": "Data was produced by the ESA Sea State CCI team and was transferred to CEDA as part of the ESA CCI Open Data Portal project.", "removedDataReason": "", "keywords": "CCI, Sea State, Integrated Sea State Parameters, ISSP, SAR", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-10-10T16:01:57", "doiPublishedTime": "2022-10-13T06:50:03", "removedDataTime": null, "geographicExtent": { "ob_id": 2576, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -80.0, "northBoundLatitude": 80.0 }, "verticalExtent": null, "result_field": { "ob_id": 38300, "dataPath": "/neodc/esacci/sea_state/data/v3_release/sar/l2p/sentinel1/issp/v1.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 14112320802, "numberOfFiles": 119659, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 10555, "startTime": "2014-12-01T00:00:00", "endTime": "2021-02-23T08:31:40" }, "resultQuality": { "ob_id": 3367, "explanation": "The data were quality checked and compared to in situ reference data. For more details on the validation process, refer to the product user guide.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-01-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 38297, "uuid": "d4cce7ac48b944bd9877ba67955c361e", "short_code": "cmppr", "title": "Composite process for the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track Integrated Sea State Parameters (ISSP) from SAR WV onboard Sentinel-1A & 1B, L2P product, version 3", "abstract": "The SAR Wave Mode data used in this dataset comes from Sentinel-1 satellite missions spanning from 2014 to 2021 (Sentinel-1 A, Sentinel-1 B).\r\n\r\nFor more information on the dataset see the product user guide." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2604, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 63, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sea_state_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 30004, "uuid": "7cfcd20428c3454fafa4e1afec2cf923", "short_code": "proj", "title": "ESA Sea State Climate Change Initiative Project", "abstract": "The European Space Agency (ESA) Sea State CCI+ project is part of ESA's Climate Change Initiative (CCI) programme. The CCI programme was launched by ESA in 2010, to produce long term datasets of Essential Climate Variables (ECV's) derived from global satellite data. In this context, the Sea State CCI+ project was kicked off in 2018 in order to produce a CDR for the new ECV \"Sea State\"." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 66521, 66522, 66523, 66526, 66527, 66528, 66529, 66530, 66531, 66536, 66537, 66538, 66539, 66540, 66541, 66542, 66543, 66544, 66545, 66546 ], "vocabularyKeywords": [], "identifier_set": [ 12214 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 182296, 182297, 182298, 182299, 182300, 182301, 182303, 182309, 182305, 182310 ], "onlineresource_set": [ 52993, 52995, 52996, 52994 ] }, { "ob_id": 38287, "uuid": "8cd1a9beabc648c8a2c91fa68791d9d7", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track Integrated Sea State Parameters (ISSP) from SAR Wave Mode onboard ENVISAT, L2P product, release version 3", "abstract": "The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite synthetic aperture radar (SAR) integrated sea state parameters (ISSP) data from ENVISAT (referred to as SAR Wave Mode onboard ENVISAT Level 2P (L2P) ISSP data) with a particular focus for use in climate studies.\r\n \r\nThis dataset contains the ENVISAT Remote Sensing Integrated Sea State Parameter product (version 1.1), which forms part of the ESA Sea State CCI version 3.0 release. This product provides along-track significant wave height (SWH) measurements at 5km resolution every 100km, processed using the Li et al., 2020 empirical model, separated per satellite and pass, including all measurements with flags and uncertainty estimates. These are expert products with rich content and no data loss. The SAR Wave Mode data used in the Sea State CCI SAR WV onboard ENVISAT Level 2P (L2P) ISSP v3 dataset come from the ENVISAT satellite mission spanning from 2002 to 2012.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-10-07T13:55:05", "updateFrequency": "notPlanned", "dataLineage": "Data was produced by the ESA Sea State CCI team and was transferred to CEDA as part of the ESA CCI Open Data Portal project.", "removedDataReason": "", "keywords": "CCI, Sea State, Integrated Sea State Parameters, ISSP, SAR", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-10-10T16:05:25", "doiPublishedTime": "2022-10-13T06:50:38", "removedDataTime": null, "geographicExtent": { "ob_id": 2576, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -80.0, "northBoundLatitude": 80.0 }, "verticalExtent": null, "result_field": { "ob_id": 38299, "dataPath": "/neodc/esacci/sea_state/data/v3_release/sar/l2p/envisat/issp/v1.1/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 16199592630, "numberOfFiles": 166980, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 10556, "startTime": "2002-12-10T21:45:05", "endTime": "2012-04-08T10:41:15" }, "resultQuality": { "ob_id": 3367, "explanation": "The data were quality checked and compared to in situ reference data. For more details on the validation process, refer to the product user guide.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-01-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 38298, "uuid": "a8c3649ece4f4f5098544c5acd54d6ed", "short_code": "cmppr", "title": "Composite process for the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track Integrated Sea State Parameters (ISSP) from SAR Wave Mode onboard ENVISAT, L2P product, version 3", "abstract": "The SAR Wave Mode data used in this dataset come from the ENVISAT satellite mission spanning from 2002 to 2012.\r\n\r\nFor more information on the dataset see the product user guide." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2604, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 63, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sea_state_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 30004, "uuid": "7cfcd20428c3454fafa4e1afec2cf923", "short_code": "proj", "title": "ESA Sea State Climate Change Initiative Project", "abstract": "The European Space Agency (ESA) Sea State CCI+ project is part of ESA's Climate Change Initiative (CCI) programme. The CCI programme was launched by ESA in 2010, to produce long term datasets of Essential Climate Variables (ECV's) derived from global satellite data. In this context, the Sea State CCI+ project was kicked off in 2018 in order to produce a CDR for the new ECV \"Sea State\"." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 66521, 66522, 66523, 66524, 66525, 66526, 66527, 66528, 66529, 66530, 66531, 66532, 66533, 66534, 66535, 66536, 66537, 66538 ], "vocabularyKeywords": [], "identifier_set": [ 12215 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 182318, 182321, 182311, 182313, 182314, 182315, 182316, 182317, 182319, 182322 ], "onlineresource_set": [ 52998, 53000, 53001, 52999 ] }, { "ob_id": 38302, "uuid": "2fa69cb20ac84cec9e16739e72fd7da1", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Seismic spectrograms from broadband seismometers, release version 3", "abstract": "This dataset provides microseism data, produced as part of the ESA Sea State Climate Change Initiative (Sea_State_cci). This microseism dataset is version 1.0, and forms part of the v3 release from the ESA Sea State Climate Change Initiative.\r\n\r\nMicroseism data is a new addition to this CCI Sea State version 3 Dataset. Microseisms are well known to contain rich spectral information about sea states and have been used as early as 1898 to locate typhoons . As such they are unique sources of information on sea states before the advent of surface-following buoys and satellites, with the potential to define long-term trends alongside Voluntary Observing Ship data . Microseisms can be a useful resource when looking for the occurrence of wave events or investigating trends, in particular in regions where no in situ data is available. The data here are spectra of vertical ground displacement at all the available land-based stations from 3 global seismic networks (Geoscope, IRIS USGS and IRIS IDA) from 1988 to 2019. So far microseism data was either handled by seismologists with specific data formats or processed by oceanographers with limited knowledge of the measurement (such as instrument corrections); in the frame of the CCI Sea State project, they have been transformed into yearly NetCDF files containing the seismic spectrograms (Power Spectral Density of displacement) for each station using the “LHZ” channel (vertical displacement sampled at 1 Hz). Note that some of these stations have recordings (with different instruments and stored on different media) that go back to the early part of the 20th century, hence their possible importance for extending climate time series to the pre-satellite era in a future release.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-10-21T19:37:04", "updateFrequency": "notPlanned", "dataLineage": "Data was produced by the ESA Sea State CCI team and was transferred to CEDA as part of the ESA CCI Open Data Portal project.", "removedDataReason": "", "keywords": "CCI, Sea State, Integrated Sea State Parameters, ISSP, SAR", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-11-25T18:18:55", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2576, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -80.0, "northBoundLatitude": 80.0 }, "verticalExtent": null, "result_field": { "ob_id": 38303, "dataPath": "/neodc/esacci/sea_state/data/v3_release/insitu/microseism/v1.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 204891845382, "numberOfFiles": 14220, "fileFormat": "Data are in NetCDF format. PNG images are also provided" }, "timePeriod": { "ob_id": 10557, "startTime": "1988-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 3367, "explanation": "The data were quality checked and compared to in situ reference data. For more details on the validation process, refer to the product user guide.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-01-20" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 38308, "uuid": "79165b664ddc4c9cb44c9aed415e34d1", "short_code": "acq", "title": "Acquisition for the ESA Sea State Climate Change Initiative (Sea_State_cci): Seismic spectrograms from broadband seismometers, release version 3", "abstract": "The data in this dataset are taken from all available land-based stations from 3 global seismic networks\r\n(Geoscope, IRIS USGS and IRIS IDA) from 1988 to 2019" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2604, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 63, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sea_state_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 30004, "uuid": "7cfcd20428c3454fafa4e1afec2cf923", "short_code": "proj", "title": "ESA Sea State Climate Change Initiative Project", "abstract": "The European Space Agency (ESA) Sea State CCI+ project is part of ESA's Climate Change Initiative (CCI) programme. The CCI programme was launched by ESA in 2010, to produce long term datasets of Essential Climate Variables (ECV's) derived from global satellite data. In this context, the Sea State CCI+ project was kicked off in 2018 in order to produce a CDR for the new ECV \"Sea State\"." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 66512, 66513, 66514, 66515, 66516, 66517, 66518, 66519, 66520 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 182356, 182358, 182359, 182360, 182361, 182362, 182363, 191678, 182364, 191679 ], "onlineresource_set": [ 53010, 53012, 53011 ] }, { "ob_id": 38310, "uuid": "3dfc1388681941cb973a0dbd6f3056f2", "title": "Sentinel 5P: Tropospheric Ozone Column (O3_TCL) level 2 data", "abstract": "The TROPOspheric Monitoring Instrument (TROPOMI) tropospheric ozone product is a level-2c product that represents three days of averaged tropospheric ozone columns on a 0.5° by 1° latitude-longitude grid for the tropical region between 20°N and 20°S. The TROPOMI tropospheric ozone column product uses the TROPOMI Level-2 total OZONE and CLOUD products as input.\r\n\r\nThe TROPOMI instrument onboard the Copernicus Sentinel-5 Precursor is a nadir-viewing, imaging spectrometer covering wavelength bands between the ultraviolet and the shortwave infrared. The instrument uses passive remote sensing techniques to attain its objective by measuring, at the Top Of Atmosphere (TOA), the solar radiation reflected by and radiated from the earth.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-10-11T16:57:12", "updateFrequency": "", "dataLineage": "Data collected and prepared by European Space Agency (ESA). Downloaded from the Collaborative Hub for use by CEDA users.", "removedDataReason": "", "keywords": "Sentinel, O3, Ozone, ESA, TCL", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3668, "bboxName": "Tropospheric Sentinel 5p", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -20.0, "northBoundLatitude": 20.0 }, "verticalExtent": null, "result_field": { "ob_id": 38311, "dataPath": "/neodc/sentinel5p/data/L2_O3_TCL/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1624763475, "numberOfFiles": 3228, "fileFormat": "These files are provided in netCDF format." }, "timePeriod": { "ob_id": 10746, "startTime": "2018-04-30T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 1, "explanation": "See dataset associated documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-08-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 26458, "uuid": "c02e46f0ea3a445bac6fc819639ef2aa", "short_code": "cmppr", "title": "Composite Process for: Level 2 data from the Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI) for total Ozone (O3) column data.", "abstract": "Composite process for Level 2 data from the TROPOspheric Monitoring Instrument (TROPOMI) deployed on Sentinel 5P. This consists of the Acquisition process for raw imaging data from the Sentinel 5P TROPOMI and the computation component to produce processed Level 2 Ozone (O3) total column data." }, "imageDetails": [ 148 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2586, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 49, "licenceURL": "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12321, "uuid": "7896ea1117dc4fa9bb95485ca9b1c6be", "short_code": "proj", "title": "Copernicus Programme", "abstract": "Copernicus, formerly known as the Global Monitoring for Environment and Security (GMES) programme, is headed by the European Commission (EC) in partnership with the European Space Agency (ESA). Within the Copernicus Space Component, ESA is developing a series of Sentinel satellite missions. Data from the Sentinel missions, as well as from Contributing Missions from other space agencies, are made freely available through a unified ground segment. Each Sentinel mission is comprised of a constallation of two or more satellites to fulfil the timeliness and reliability requirements of the Copernicus Services environmental monitoring and civil security activities. As well as operational and monitoring capabilities, the Sentinel missions will provide a wealth of Earth Observation data for scientific exploitation. The Sentinel 1 mission provides all weather, day and night radar imagery with scientific applications in sea-ice measurements, biomass observations and earthquake analysis. Sentinel 2 is a high resolution imaging mission to provide imagery of vegetation, soil and water cover, inland waterways and coastal areas. Sentinel 3 is a multi-instrument mission to measure sea-surface topography, sea- and land-surface temperature, ocean colour and land colour with high-end accuracy and reliability. Sentinel 4 is devoted to atmospheric monitoring and will be flown on a Meteosat Third Generation-Sounder (MTG-S) satellite in geostationary orbit. Sentinel 5 will monitor the atmosphere from polar orbit on board a MetOp Second Generation satellite. The Sentinel 5 precursor satellite mission is being developed to reduce data gaps between Envisat, in particular the Sciamachy instrument, and the launch of Sentinel 5. The Sentinel 5 mission will be dedicated to atmospheric monitoring. Sentinel 6 carries a radar altimeter to measure global sea-surface height, primarily for operational oceanography and for climate studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 66476, 66477, 66478, 66479, 66480, 66481, 66482, 66483, 66484, 66485, 66486, 66487, 66488, 66489, 66490, 66491, 66492, 66493, 66494, 66495, 66496, 66497, 66498, 66499, 66500, 66501, 66502, 66503, 66504, 66505, 66506, 66507, 66508, 66509, 66510, 66511 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26459, "uuid": "6accd46663bc4669afaac418f2bf498e", "short_code": "coll", "title": "Sentinel 5 Precursor: Level 2 data", "abstract": "Need some info!" }, { "ob_id": 30129, "uuid": "3b0630c7fa264164868d4da5c9f90bed", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Third Party Data", "abstract": "The National Centre for Earth Observation (NCEO) Third Party data contains a broad range remotely sensed data acquired by satellite for use by the Earth Observation Scientific community supported by NCEO. The Centre for Environmental Data Analysis (CEDA) has archived and provides access to extensive Earth observation datasets under strict licensing conditions. Please see the individual dataset records for conditions of use." } ], "responsiblepartyinfo_set": [ 182382, 182383, 182384, 182385, 182386, 182387, 182388, 182389, 182390, 182391 ], "onlineresource_set": [ 53015, 53016, 53017, 53018 ] }, { "ob_id": 38312, "uuid": "0f51318b226546c3a13e7d8a1451bbd3", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.5, November 2017 - December 2020", "abstract": "This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 µm spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD. This version of the product is version 1.5, and covers the period from November 2017 - December 2020. \r\n\r\nThe WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.\r\n\r\nThese data were produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.\r\n\r\nWhen citing this dataset, please also cite the following peer-reviewed publication: \r\nSchneising, O., Buchwitz, M., Reuter, M., Bovensmann, H., Burrows, J. P., Borsdorff, T., Deutscher, N. M., Feist, D. G., Griffith, D. W. T., Hase, F., Hermans, C., Iraci, L. T., Kivi, R., Landgraf, J., Morino, I., Notholt, J., Petri, C., Pollard, D. F., Roche, S., Shiomi, K., Strong, K., Sussmann, R., Velazco, V. A., Warneke, T., and Wunch, D.: A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor, Atmos. Meas. Tech., 12, 6771–6802, https://doi.org/10.5194/amt-12-6771-2019, 2019.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-10-12T13:54:28", "latestDataUpdateTime": "2024-03-09T03:19:23", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA Climate Change Initiative (CCI) Greenhouse Gas project team and supplied to CEDA as part of the ESA CCI Open Data Portal.", "removedDataReason": "", "keywords": "ESA, CCI, satellite, Sentinel-5 Precursor, TROPOMI, atmosphere, methane, carbon monoxide", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-11-17T15:51:23", "doiPublishedTime": "2022-11-25T18:52:27", "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": 39210, "dataPath": "/neodc/esacci/ghg/data/cci_plus/CH4_S5P_WFMD/v1.5/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 66233652133, "numberOfFiles": 1107, "fileFormat": "Data are in NetCDF format." }, "timePeriod": { "ob_id": 10560, "startTime": "2017-11-10T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3389, "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": "2020-02-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 39208, "uuid": "18e7eb7b90a04053ba14b2f0b55247e6", "short_code": "cmppr", "title": "Composite process for the ESA Greenhouse Gases Climate Change Initiative CH4_S5P_WFMD v1.5 product", "abstract": "The ESA Greenhouse Gases Climate Change Initiative CH4_S5P_WFMD v1.5 product has been derived from the TROPOMI instrument on the Sentinel-5P satellite, using the WFM-DOAS retrieval algorithm." }, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2564, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 34, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_ghg_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13295, "uuid": "f0c66ffa30514d2daee821286a014b16", "short_code": "proj", "title": "ESA Greenhouse Gases Climate Change Initiative Project", "abstract": "The European Space Agency Greenhouse Gases Climate Change Initiative (GHG CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs)\r\n\r\nCarbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases (GHGs) and a focus of international research activities related to a better understanding of the carbon cycle (see, for example, the Global Carbon Project (GCP)).\r\n \r\nWithin the GHG-CCI project the focus is on satellite data. Satellite observations combined with modelling can add important missing global information on regional CO2 and CH4 (surface) sources and sinks required for better climate prediction. The GHG CCI project started on the 1st September 2010." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1050, 4776, 4777, 11820, 11846, 11889, 11894, 11899, 12279, 12280, 12283, 25160, 25161, 26292, 28577, 28578, 28579, 28580, 28581, 28582, 28583, 28584, 28585, 28586, 28587, 28588, 28590, 50206, 50207, 50208, 50209 ], "vocabularyKeywords": [], "identifier_set": [ 12298 ], "observationcollection_set": [ { "ob_id": 12808, "uuid": "0508f3dd991144aa80346007a415fb07", "short_code": "coll", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci) dataset collection", "abstract": "The Greenhouse Gases Climate Change Initiative (GHG_cci) data products are near-surface-sensitive dry-air column-averaged mole fractions (mixing ratios) of methane (CH4) and carbon dioxide (CO2), created as part of the European Space Agency's (ESA) Greenhouses Gases Essential Climate Variable (ECV) CCI project. Denoted XCO2 (in ppmv) and XCH4 (in ppbv), the products have been retrieved from the SCIAMACHY instrument on ENVISAT and TANSO-FTS onboard GOSAT, using ECV Core Algorithms (ECAs). Other satellite instruments such as IASI, MIPAS and ACE-FTS have also been used to provide constraints for upper layers, with their corresponding retrieval algorithms referred to as Additional Constraints Algorithms (ACAs). The GHG data products are typically updated annually, the corresponding datasets being called Climate Research Data Packages (CRDP). \r\n\r\nThe products have each been generated from individual sensors, a single merged product not having yet been created \"combining\" the products from different sensors to cover the entire available satellite time series. One merged product has however been generated using the EMMA algorithm, covering a limited time period. This EMMA product is mainly used as a comparison tool for products generated using individual algorithms, making up the collection of products used by EMMA. \r\n\r\nTypically the same product (e.g. XCO2 from GOSAT) has been generated using different retrieval algorithms. A baseline algorithm has been used to generate one recommended baseline product, for users unsure which product to choose. Other products are called alternative products. However an alternative product's quality may equal that of the corresponding baseline product. It typically depends upon the application for which a product is required, which product is best to use as methods involved in producing them typically have varying strength and weaknesses. \r\n\r\nFor further information on the products, such as details on the SCIAMACHY and TANSO instruments, the algorithms used to generate the data and the data's format, please see the Product Specification Document (PSD) in the documentation section." } ], "responsiblepartyinfo_set": [ 182392, 182393, 182394, 182395, 182396, 182397, 182398, 182399, 182400, 182401, 182402, 182403, 182404 ], "onlineresource_set": [ 53021, 53024, 53025, 53019, 53022, 53023, 53050 ] }, { "ob_id": 38313, "uuid": "6ecb706ac16c4e05aab75ed2cc3ec119", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRPR (RemoTeC) proxy retrieval algorithm (CH4_GO2_SRPR), version 2.0.0", "abstract": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Proxy (SRPR) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.\r\n\r\nThese data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-10-12T15:28:38", "latestDataUpdateTime": "2022-11-22T16:08:52", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI GHG project team and supplied to CEDA as part of the ESA CCI Open Data Portal Project. 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This new high-resolution climate dataset will be useful in assessing future changes and variability in climate and in particular in driving richer impact assessment models. The data are available for the historical (1981-2014) and future (2015-2100) periods at 0.25 degree horizontal resolution and daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4OS and SSP5-8.5).\r\n\r\n\r\nSSP2-4.5 is based on Shared Socioeconomic Pathway SSP2 with intermediate climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 4.5 W/m2 in the year 2100.\r\nSSP5-8.5 is based on Shares Socioeconomic Pathway SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\nSSP5-3.4OS is an overshoot scenario where emissions follow the SSP5-8.5 pathway until 2040 before dramatically declining.", "creationDate": "2022-10-13T09:08:16.145138", "lastUpdatedDate": "2022-10-13T09:08:45", "latestDataUpdateTime": "2022-12-12T17:54:41", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the EVOFLOOD team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Climate change,CMIP6 GCMs,Statistical downscaling,Impact assessment,High-resolution,BCCAQ, SSP5-8.5, SSP2-4.5, SSP5-3.4OS, EVOFLOOD", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-12-14T14:05:50", "doiPublishedTime": "2022-12-14T14:15:04", "removedDataTime": null, "geographicExtent": { "ob_id": 3735, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 39496, "dataPath": "/badc/evoflood/data/Downscaled_CMIP6_Climate_Data", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 23104948174732, "numberOfFiles": 389, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 10565, "startTime": "1981-01-01T00:00:00", "endTime": "2100-12-31T00:00:00" }, "resultQuality": { "ob_id": 4100, "explanation": "Model output has been compared against observational datasets during the historical period (1981-2014) and checked for CF convention compliance.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-10-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39497, "uuid": "ec38cf000e0e401d82ed041c75bbc6e4", "short_code": "comp", "title": "Computation for High-resolution daily global climate dataset of BCCAQ statistically downscaled CMIP6 models for the EVOFLOOD project", "abstract": "A novel statistical downscaling model, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), is used to downscale daily precipitation, air temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6)." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38317, "uuid": "d43fa1f2c5d443b2b596db84f18a7cd0", "short_code": "proj", "title": "The Evolution of Global Flood Hazard and Risk (EVOFLOOD)", "abstract": "The EvoFlood project is a £3.7M NERC funded research programme addressing flooding, Earth's deadliest and most costly natural hazard, affecting societies across the globe." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 62501, 62748, 62749 ], "vocabularyKeywords": [], "identifier_set": [ 12314 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 182445, 182446, 182447, 182448, 182449, 182450, 182451, 182480, 182452, 182453, 182454, 182455 ], "onlineresource_set": [ 53066, 87677 ] }, { "ob_id": 38323, "uuid": "5d6b9c165edf4e69b624ddeb5d28f5ee", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for FAQ 7.3, Figure 1 (v20220721)", "abstract": "Data for FAQ 7.3 Figure 1, from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFAQ 7.3 Figure 1 shows equilibrium climate sensitivity and future warming.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- (left) Equilibrium climate sensitivities for the current generation (CMIP6) climate models, and the previous (CMIP5) generation. The assessed range in this Report (AR6) is also shown. \r\n\r\n- (right) Climate projections of CMIP5, CMIP6 and AR6 for the very high-emissions scenarios RCP8.5, and SSP5-8.5, respectively. \r\n\r\nThe thick horizontal lines represent the multi-model average and the thin horizontal lines represent the results of individual models. The boxes represent the model ranges for CMIP5 and CMIP6 and the range assessed in AR6.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to FAQ 7.3 Figure 1.\r\n \r\n- Data file: ECS_Proj_CMIP5_CMIP6.csv \r\n \r\n\r\nCMIP5 is the fifth stage of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth stage of the Coupled Model Intercomparison Project.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. Also listed on the 'master' GitHub page linked in the documentation of this catalogue record are external GitHub repositories and locations within the contributed directory where code for figures have been supplied by other authors. These are provided \"as-is\" and are not guaranteed to be reproducible within this environment. For external GitHub locations, check out the relevant repository READMEs.\r\n\r\nThe notebook used to plot this figure and the input data used in the code are linked in the 'Related Documents' section. The input data to this code is also archived at CEDA.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n - Link to the Jupyter notebook for plotting this figure from the Chapter 7 GitHub repository\r\n - Link to the code for the figure, archived on Zenodo", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:58:57", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, ECS, CMIP6, CMIP5", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-01T11:04:14", "doiPublishedTime": "2023-07-10T15:00:09.396444", "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": 39528, "dataPath": "/badc/ar6_wg1/data/ch_07/ch7_faq3_fig1/v20221019", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8041, "numberOfFiles": 5, "fileFormat": "Files are csv formatted" }, "timePeriod": null, "resultQuality": { "ob_id": 4291, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-06-01" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39598, "uuid": "db032a49ac10480c84f3844656f8f7f2", "short_code": "comp", "title": "Caption for FAQ 7.3, Figure 1 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Equilibrium climate sensitivity and future warming. (left) Equilibrium climate sensitivities for the current generation (Coupled Model Intercomparison Project Phase 6, CMIP6) climate models, and the previous (CMIP5) generation. The assessed range in this Report (AR6) is also shown. (right) Climate projections of CMIP5, CMIP6 and AR6 for the very high-emissions scenarios RCP8.5, and SSP5-8.5, respectively. The thick horizontal lines represent the multi-model average and the thin horizontal lines represent the results of individual models. The boxes represent the model ranges for CMIP5 and CMIP6 and the range assessed in AR6." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12644 ], "observationcollection_set": [ { "ob_id": 32722, "uuid": "b96ed5cbb8304a82bf1a2a4cad49bf6d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 7.3\r\n- input data for Figure 7.4\r\n- input data for Figure 7.5\r\n- data for Figure 7.6\r\n- input data for Figure 7.7\r\n- data for Figure 7.8\r\n- input data for Figure 7.10\r\n- data for Figure 7.11\r\n- input data for Figure 7.13\r\n- data for Figure 7.16\r\n- data for Figure 7.17\r\n- input data for Figure 7.18\r\n- data for Figure 7.19\r\n- input data for Figure 7.21\r\n- data for FAQ 7.3, Figure 1\r\n- input data for FAQ 7.3, Figure 1\r\n- data for 7.SM.1\r\n- input data for Box 7.2, Figure 1" } ], "responsiblepartyinfo_set": [ 182470, 182472, 182473, 182474, 182475, 182476, 182477, 182471 ], "onlineresource_set": [ 53067, 53068, 82709, 83592, 83593 ] }, { "ob_id": 38324, "uuid": "fa7519c41af742d696729b0ac2db8148", "title": "Sentinel 3A Ocean and Land Colour Instrument (OLCI) Land Reduced Resolution (LRR) Level 2 data", "abstract": "This dataset contains ocean and land colour imagery data from the Ocean and Land Colour Instrument (OLCI) aboard the European Space Agency (ESA) Sentinel 3A Satellite. Sentinel 3A was launched on the 16th February 2016. Data are provided by ESA and are made available via CEDA to any registered user. The OLCI radiometer images using 21 spectral bands ranging from 400nm to 1020nm.\r\n\r\nThe OLCI Level-2 Land Reduced (OL_2_LRR) Resolution products are outputs from the OLCI Level-2 processor and contain land and atmospheric geophysical products at Reduced resolution.\r\n\r\nThe content of these files depends on several switches included in the OLCI configuration parameters. Each geophysical parameter format is only triggered if the corresponding switch is set to ‘1'. Note that all pixels flagged as cloudy are discarded from OLCI Level-2 processing.\r\n\r\nOL_2_LRR products have a spatial sampling of approximately 1.2 km for Reduced Resolution. The products are assumed to be computed in Near Real Time (NRT) (i.e. delivered to users less than 3 hours after acquisition), in Non-Time Critical (NTC) (i.e. within 1 month after acquisition) or in re-processed NTC.\r\n\r\nEach product provides as measurement data files:\r\n\r\n- Surface product as Green Instantaneous Fraction of Absorbed Photosynthetically Active Radiation (GI-FAPAR) and Terrestrial Chlorophyll Index (OTCI)\r\n- Atmosphere by-products as Integrated Water Vapour (IWV) column (this product also contains information for water pixels and is identical to that included in OL_2_WRR and OL_2_WFR)\r\n- Error estimates for all products.\r\n\r\nSeveral associated variables are also provided in the annotations data files:\r\n\r\n- Rectified reflectance for red and NIR channels (RC681 and RC865)\r\n- Classification, quality and science flags (LQSF)\r\n- Common data such as the ortho-geolocation of land pixels, solar and satellite angles, atmospheric and meteorological data, time stamp or instrument information. These variables are inherited from Level-1B products.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-06-17T13:06:18", "updateFrequency": "continual", "dataLineage": "Data collected and prepared by European Space Agency (ESA). Downloaded from the ESA hubs for use by the UK scientific community.", "removedDataReason": "", "keywords": "Sentinel, Ocean and Land Colour Instrument, OLCI", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-12-20T13:52:46", "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": 38325, "dataPath": "/neodc/sentinel3a/data/OLCI/L2_LRR/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 55368348523, "numberOfFiles": 922, "fileFormat": "These image files are in NETCDF4 format." }, "timePeriod": { "ob_id": 4980, "startTime": "2016-04-25T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4107, "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": "2022-11-08" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 38858, "uuid": "9bdb1c9d505d40049358c3ba34dd3e26", "short_code": "cmppr", "title": "Composite Process for: Level 2 data from the Sentinel 3A Ocean and Land Colour Instrument (OLCI)", "abstract": "Composite process for Level 2 data from the Ocean and Land Colour Instrument (OLCI) deployed on Sentinel 3A. This consists of the Acquisition process for raw data from the Sentinel 3 OLCI and the computation component to produce processed data." }, "imageDetails": [ 148 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2586, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 49, "licenceURL": "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12321, "uuid": "7896ea1117dc4fa9bb95485ca9b1c6be", "short_code": "proj", "title": "Copernicus Programme", "abstract": "Copernicus, formerly known as the Global Monitoring for Environment and Security (GMES) programme, is headed by the European Commission (EC) in partnership with the European Space Agency (ESA). Within the Copernicus Space Component, ESA is developing a series of Sentinel satellite missions. Data from the Sentinel missions, as well as from Contributing Missions from other space agencies, are made freely available through a unified ground segment. Each Sentinel mission is comprised of a constallation of two or more satellites to fulfil the timeliness and reliability requirements of the Copernicus Services environmental monitoring and civil security activities. As well as operational and monitoring capabilities, the Sentinel missions will provide a wealth of Earth Observation data for scientific exploitation. The Sentinel 1 mission provides all weather, day and night radar imagery with scientific applications in sea-ice measurements, biomass observations and earthquake analysis. Sentinel 2 is a high resolution imaging mission to provide imagery of vegetation, soil and water cover, inland waterways and coastal areas. Sentinel 3 is a multi-instrument mission to measure sea-surface topography, sea- and land-surface temperature, ocean colour and land colour with high-end accuracy and reliability. Sentinel 4 is devoted to atmospheric monitoring and will be flown on a Meteosat Third Generation-Sounder (MTG-S) satellite in geostationary orbit. Sentinel 5 will monitor the atmosphere from polar orbit on board a MetOp Second Generation satellite. The Sentinel 5 precursor satellite mission is being developed to reduce data gaps between Envisat, in particular the Sciamachy instrument, and the launch of Sentinel 5. The Sentinel 5 mission will be dedicated to atmospheric monitoring. Sentinel 6 carries a radar altimeter to measure global sea-surface height, primarily for operational oceanography and for climate studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30129, "uuid": "3b0630c7fa264164868d4da5c9f90bed", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Third Party Data", "abstract": "The National Centre for Earth Observation (NCEO) Third Party data contains a broad range remotely sensed data acquired by satellite for use by the Earth Observation Scientific community supported by NCEO. The Centre for Environmental Data Analysis (CEDA) has archived and provides access to extensive Earth observation datasets under strict licensing conditions. Please see the individual dataset records for conditions of use." }, { "ob_id": 32818, "uuid": "0bcc2457250548abb51a90f96772c374", "short_code": "coll", "title": "Sentinel 3A data", "abstract": "This dataset collection contains data products from all of the instruments on the European Space Agency (ESA) Sentinel 3A satellite. Sentinel 3A was launched on 16th February 2016.\r\n\r\nThe Sentinel 3 satellites have 3 main instruments on board: Sea and Land Surface Temperature Radiometer (SLSTR), Ocean and Land Colour Instrument (OLCI) and SAR Radar Altimeter (SRAL).\r\n\r\nData are provided by the European Space Agency (ESA) and are made available via CEDA to any registered user." } ], "responsiblepartyinfo_set": [ 182490, 182487, 182483, 182489, 182485, 182484, 182491, 186907, 182486, 182488 ], "onlineresource_set": [ 53102, 53103, 53101 ] }, { "ob_id": 38326, "uuid": "898c950f441e400d8b569216ebe41cab", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 06.2", "abstract": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.\r\n\r\nThe v06.2 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-10-27T16:22:09", "latestDataUpdateTime": "2024-09-11T12:54:10", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Soil Moisture project team and transferred to CEDA for the ESA CCI Open Data Portal Project", "removedDataReason": "", "keywords": "ESA, Soil Moisture, CCI, Active", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-03-10T12:27:21", "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": 39683, "dataPath": "/neodc/esacci/soil_moisture/data/daily_files/ACTIVE/v06.2", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9619425999, "numberOfFiles": 11108, "fileFormat": "Data are in NetCDF format." }, "timePeriod": { "ob_id": 10566, "startTime": "1991-08-05T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 3571, "explanation": "See the Soil Moisture CCI documentation for information on data quality", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-11-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 32300, "uuid": "d53529490ad14a5880add371873b79f7", "short_code": "cmppr", "title": "ESA Soil Moisture Climate Change Initiative: Retrieval of Soil Moisture using Active sensors for version 06.1 data.", "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture. The ACTIVE product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2535, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 15, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_soilmoisture_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13332, "uuid": "c256fcfeef24460ca6eb14bf0fe09572", "short_code": "proj", "title": "ESA Soil Moisture Climate Change Initiative Project", "abstract": "The European Space Agency Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project is part of the ESA Climate Change Initiative (CCI) programme, which aims to produce datasets of Essential Climate Variables (ECV's) from satellite datasets.\r\n\r\nThe Soil Moisture CCI project was set up to :\r\n - Analyse the needs of the climate research community in terms of soil moisture data.\r\n - Adapt soil moisture satellite measurements for their use by the climate research community.\r\n - Create a long-term consistent soil moisture time series, based on active and passive data, suitable for climate change studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 52664, 52665, 54641, 54644, 54645, 54646, 54647, 54648, 54649, 62501, 62745 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 38329, "uuid": "3ff7ae723d3542bab6525207a9092e2b", "short_code": "coll", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 06.2 data collection", "abstract": "Soil Moisture data (version 06.2) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets alongside ancillary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, AMSR2, SMOS, GPM and SMAP. The COMBINED product is generated from the Level 2 active and passive instruments.\r\n\r\nThe homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31 and the ACTIVE product covering 1991-08-05 to 2021-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Additional documentation and information relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product User Guide.\r\n\r\nThe data set should be cited using all of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\nIf using the COMBINED break-adjusted product, the following should also be cited in addition to the above:\r\n\r\n3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896." } ], "responsiblepartyinfo_set": [ 182493, 182492, 182499, 182494, 182495, 182496, 182497, 182498, 182501, 182500, 182502, 182503, 182504, 182505, 182506, 182507, 182508, 182509, 182510, 182511 ], "onlineresource_set": [ 53109, 53110, 53106, 53105, 53104, 53107, 53108, 53111 ] }, { "ob_id": 38327, "uuid": "4dd145a7060143cd875325390d3b01c8", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 06.2", "abstract": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. ACTIVE and COMBINED products have also been created.\r\n\r\nThe v06.2 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-10-27T16:22:18", "latestDataUpdateTime": "2024-09-11T12:54:07", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Soil Moisture project team and transferred to CEDA for the ESA CCI Open Data Portal Project", "removedDataReason": "", "keywords": "ESA, CCI, Passive, Soil Moisture", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-03-10T12:30:06", "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": 39686, "dataPath": "/neodc/esacci/soil_moisture/data/daily_files/PASSIVE/v06.2", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 16047379733, "numberOfFiles": 15768, "fileFormat": "Data are in NetCDF format." }, "timePeriod": { "ob_id": 10567, "startTime": "1978-11-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 3570, "explanation": "See the Soil Moisture CCI documentation for information on data quality", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-11-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 32307, "uuid": "80e3ac78b8e34eb994762120a2a73ca4", "short_code": "cmppr", "title": "ESA Soil Moisture Climate Change Initiative: Retrieval of Soil Moisture using Passive sensors for version 06.1 data.", "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture. The passive product has been created by fusing radiometer soil moisture products, merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2535, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 15, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_soilmoisture_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13332, "uuid": "c256fcfeef24460ca6eb14bf0fe09572", "short_code": "proj", "title": "ESA Soil Moisture Climate Change Initiative Project", "abstract": "The European Space Agency Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project is part of the ESA Climate Change Initiative (CCI) programme, which aims to produce datasets of Essential Climate Variables (ECV's) from satellite datasets.\r\n\r\nThe Soil Moisture CCI project was set up to :\r\n - Analyse the needs of the climate research community in terms of soil moisture data.\r\n - Adapt soil moisture satellite measurements for their use by the climate research community.\r\n - Create a long-term consistent soil moisture time series, based on active and passive data, suitable for climate change studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 52664, 52665, 54641, 54646, 54647, 54648, 54649, 54650, 54651, 62501, 62745 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 38329, "uuid": "3ff7ae723d3542bab6525207a9092e2b", "short_code": "coll", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 06.2 data collection", "abstract": "Soil Moisture data (version 06.2) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets alongside ancillary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, AMSR2, SMOS, GPM and SMAP. The COMBINED product is generated from the Level 2 active and passive instruments.\r\n\r\nThe homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31 and the ACTIVE product covering 1991-08-05 to 2021-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Additional documentation and information relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product User Guide.\r\n\r\nThe data set should be cited using all of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\nIf using the COMBINED break-adjusted product, the following should also be cited in addition to the above:\r\n\r\n3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896." } ], "responsiblepartyinfo_set": [ 182512, 182514, 182515, 182516, 182517, 182518, 182519, 182513, 182520, 182521, 182522, 182523, 182524, 182525, 182526, 182527, 182528, 182529, 182530, 182531 ], "onlineresource_set": [ 53112, 53113, 53114, 53115, 53117, 53118, 53119, 53116 ] }, { "ob_id": 38328, "uuid": "e83e62dd493447c5808f80c36b5acac7", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 06.2", "abstract": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.\r\n\r\nThe v06.2 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-10-27T16:22:26", "latestDataUpdateTime": "2024-09-11T12:54:24", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Soil Moisture project team and transferred to CEDA for the ESA CCI Open Data Portal Project", "removedDataReason": "", "keywords": "ESA, Soil Moisture, CCI, Combined", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-03-10T12:32:55", "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": 39685, "dataPath": "/neodc/esacci/soil_moisture/data/daily_files/COMBINED/v06.2", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 18967667820, "numberOfFiles": 15768, "fileFormat": "Data are in NetCDF format." }, "timePeriod": { "ob_id": 10568, "startTime": "1978-11-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 3572, "explanation": "See the Soil Moisture CCI documentation for information on data quality", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-11-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 32309, "uuid": "7c60f5393c5e4578a7e851de79b311e5", "short_code": "cmppr", "title": "ESA Soil Moisture Climate Change Initiative: Retrieval of Soil Moisture using combined active and passive sensors for version 06.1 data.", "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture. The combined product uses information from both active and passive sensors." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2535, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 15, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_soilmoisture_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13332, "uuid": "c256fcfeef24460ca6eb14bf0fe09572", "short_code": "proj", "title": "ESA Soil Moisture Climate Change Initiative Project", "abstract": "The European Space Agency Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project is part of the ESA Climate Change Initiative (CCI) programme, which aims to produce datasets of Essential Climate Variables (ECV's) from satellite datasets.\r\n\r\nThe Soil Moisture CCI project was set up to :\r\n - Analyse the needs of the climate research community in terms of soil moisture data.\r\n - Adapt soil moisture satellite measurements for their use by the climate research community.\r\n - Create a long-term consistent soil moisture time series, based on active and passive data, suitable for climate change studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 52664, 52665, 54641, 54646, 54647, 54648, 54649, 54650, 54651, 62501, 62745 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 38329, "uuid": "3ff7ae723d3542bab6525207a9092e2b", "short_code": "coll", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 06.2 data collection", "abstract": "Soil Moisture data (version 06.2) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets alongside ancillary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, AMSR2, SMOS, GPM and SMAP. The COMBINED product is generated from the Level 2 active and passive instruments.\r\n\r\nThe homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31 and the ACTIVE product covering 1991-08-05 to 2021-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Additional documentation and information relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product User Guide.\r\n\r\nThe data set should be cited using all of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\nIf using the COMBINED break-adjusted product, the following should also be cited in addition to the above:\r\n\r\n3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896." } ], "responsiblepartyinfo_set": [ 182534, 182532, 182533, 182535, 182536, 182537, 182538, 182539, 182540, 182541, 182542, 182543, 182544, 182545, 182546, 182547, 182548, 182549, 182550, 182551 ], "onlineresource_set": [ 53127, 53123, 53121, 53122, 53120, 53124, 53125, 53126 ] }, { "ob_id": 38330, "uuid": "56d43a34ec3e4a85b3f311470f58e664", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the ACTIVE, PASSIVE and COMBINED products, Version 07.1", "abstract": "These ancillary datasets were used in the production of the ACTIVE, PASSIVE and COMBINED soil moisture data products, created as part of the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. The set of ancillary datasets include datasets of Average Vegetation Optical Depth data from AMSR-E, Soil Porosity, Topographic Complexity and Wetland fraction, as well as a Land Mask. This version of the ancillary datasets were used in the production of the v07.1 Soil Moisture CCI data.\r\n\r\nThe ACTIVE, PASSIVE and COMBINED soil moisture products which these data were used to develop are fusions of scatterometer and radiometer soil moisture products, derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. To access these products or for further details on them please see their dataset records. Additional reference documents and information relating to them can also be found on the CCI Soil Moisture project website.\r\n\r\nSoil moisture CCI data should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-10-27T16:22:35", "latestDataUpdateTime": "2024-09-11T12:54:08", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Soil Moisture project team and transferred to CEDA for the ESA CCI Open Data Portal Project", "removedDataReason": "", "keywords": "ESA, Soil Moisture, CCI, Ancillary", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-03-14T09:03:18", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 39707, "dataPath": "/neodc/esacci/soil_moisture/data/ancillary/v07.1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1558698, "numberOfFiles": 6, "fileFormat": "Data are in NetCDF format." }, "timePeriod": null, "resultQuality": { "ob_id": 3547, "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": "2020-09-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2535, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 15, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_soilmoisture_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13332, "uuid": "c256fcfeef24460ca6eb14bf0fe09572", "short_code": "proj", "title": "ESA Soil Moisture Climate Change Initiative Project", "abstract": "The European Space Agency Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project is part of the ESA Climate Change Initiative (CCI) programme, which aims to produce datasets of Essential Climate Variables (ECV's) from satellite datasets.\r\n\r\nThe Soil Moisture CCI project was set up to :\r\n - Analyse the needs of the climate research community in terms of soil moisture data.\r\n - Adapt soil moisture satellite measurements for their use by the climate research community.\r\n - Create a long-term consistent soil moisture time series, based on active and passive data, suitable for climate change studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 12066, 52664, 52665, 54683, 54684, 54685, 54686, 60753, 60754, 63533, 63534, 63535, 63536 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 38335, "uuid": "ea3eb0714dc6402b905fe9f7ee50dbbc", "short_code": "coll", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 07.1 data collection", "abstract": "Soil Moisture data (version 07.1) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets alongside ancillary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP. The COMBINED product is generated from the Level 2 active and passive instruments.\r\n\r\nThe homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31 and the ACTIVE product covering 1991-08-05 to 2021-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. In addition to the main products, an experimental break-adjusted COMBINED product is also provided at v07.1. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Additional documentation and information relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product User Guide.\r\n\r\nThe data set should be cited using all of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\nIf using the COMBINED break-adjusted product, the following should also be cited in addition to the above:\r\n\r\n3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896." } ], "responsiblepartyinfo_set": [ 182572, 182573, 182574, 182576, 182577, 182578, 182579, 182575, 182580, 182581, 182582, 182583, 182584, 182585, 182586, 182587, 182588, 182589, 182590, 182591 ], "onlineresource_set": [ 53139, 53141, 53142, 83141, 83142, 83143, 83144, 83145 ] }, { "ob_id": 38331, "uuid": "e235d2980d6a441895f7221ff4787a6f", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 07.1", "abstract": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.\r\n\r\nThe v07.1 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-10-27T16:22:44", "latestDataUpdateTime": "2024-09-11T12:54:22", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Soil Moisture project team and transferred to CEDA for the ESA CCI Open Data Portal Project", "removedDataReason": "", "keywords": "ESA, Soil Moisture, CCI, Active", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-03-10T12:47:03", "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": 39690, "dataPath": "/neodc/esacci/soil_moisture/data/daily_files/ACTIVE/v07.1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9000184179, "numberOfFiles": 11108, "fileFormat": "Data are in NetCDF format." }, "timePeriod": { "ob_id": 10569, "startTime": "1991-08-05T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 3571, "explanation": "See the Soil Moisture CCI documentation for information on data quality", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-11-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 40778, "uuid": "74490fac41ac4499a0b090ff9a4376dc", "short_code": "cmppr", "title": "ESA Soil Moisture Climate Change Initiative: Retrieval of Soil Moisture using Active sensors for version 07.1 data.", "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture. The ACTIVE product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2535, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 15, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_soilmoisture_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13332, "uuid": "c256fcfeef24460ca6eb14bf0fe09572", "short_code": "proj", "title": "ESA Soil Moisture Climate Change Initiative Project", "abstract": "The European Space Agency Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project is part of the ESA Climate Change Initiative (CCI) programme, which aims to produce datasets of Essential Climate Variables (ECV's) from satellite datasets.\r\n\r\nThe Soil Moisture CCI project was set up to :\r\n - Analyse the needs of the climate research community in terms of soil moisture data.\r\n - Adapt soil moisture satellite measurements for their use by the climate research community.\r\n - Create a long-term consistent soil moisture time series, based on active and passive data, suitable for climate change studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 3894, 52664, 52665, 54646, 54647, 54648, 54649, 62742, 62745, 62746, 62747 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 38335, "uuid": "ea3eb0714dc6402b905fe9f7ee50dbbc", "short_code": "coll", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 07.1 data collection", "abstract": "Soil Moisture data (version 07.1) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets alongside ancillary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP. The COMBINED product is generated from the Level 2 active and passive instruments.\r\n\r\nThe homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31 and the ACTIVE product covering 1991-08-05 to 2021-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. In addition to the main products, an experimental break-adjusted COMBINED product is also provided at v07.1. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Additional documentation and information relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product User Guide.\r\n\r\nThe data set should be cited using all of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\nIf using the COMBINED break-adjusted product, the following should also be cited in addition to the above:\r\n\r\n3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896." } ], "responsiblepartyinfo_set": [ 182592, 182593, 182594, 182595, 182596, 182597, 182598, 182599, 182600, 182601, 182602, 182603, 182604, 182605, 182606, 182607, 182608, 182609, 182610, 182611 ], "onlineresource_set": [ 53149, 53150, 53146, 53148, 53145, 53144, 83127, 83128 ] }, { "ob_id": 38332, "uuid": "63e14c1e66124ccc857ce4e73ab601ed", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 07.1", "abstract": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. ACTIVE and COMBINED products have also been created.\r\n\r\nThe v07.1 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-10-27T16:22:54", "latestDataUpdateTime": "2025-01-10T01:57:51", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Soil Moisture project team and transferred to CEDA for the ESA CCI Open Data Portal Project", "removedDataReason": "", "keywords": "ESA, CCI, Passive, Soil Moisture", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-03-10T12:49:04", "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": 39691, "dataPath": "/neodc/esacci/soil_moisture/data/daily_files/PASSIVE/v07.1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 19645562869, "numberOfFiles": 15769, "fileFormat": "Data are in NetCDF format." }, "timePeriod": { "ob_id": 10570, "startTime": "1978-11-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 3570, "explanation": "See the Soil Moisture CCI documentation for information on data quality", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-11-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 40783, "uuid": "b7be06950fcf479f99743862e87737f6", "short_code": "cmppr", "title": "ESA Soil Moisture Climate Change Initiative: Retrieval of Soil Moisture using Passive sensors for version 07.1 data.", "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture. The passive product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2535, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 15, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_soilmoisture_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13332, "uuid": "c256fcfeef24460ca6eb14bf0fe09572", "short_code": "proj", "title": "ESA Soil Moisture Climate Change Initiative Project", "abstract": "The European Space Agency Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project is part of the ESA Climate Change Initiative (CCI) programme, which aims to produce datasets of Essential Climate Variables (ECV's) from satellite datasets.\r\n\r\nThe Soil Moisture CCI project was set up to :\r\n - Analyse the needs of the climate research community in terms of soil moisture data.\r\n - Adapt soil moisture satellite measurements for their use by the climate research community.\r\n - Create a long-term consistent soil moisture time series, based on active and passive data, suitable for climate change studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 3894, 52664, 52665, 54646, 54647, 54648, 54649, 62742, 62743, 62744, 62745 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 38335, "uuid": "ea3eb0714dc6402b905fe9f7ee50dbbc", "short_code": "coll", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 07.1 data collection", "abstract": "Soil Moisture data (version 07.1) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets alongside ancillary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP. The COMBINED product is generated from the Level 2 active and passive instruments.\r\n\r\nThe homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31 and the ACTIVE product covering 1991-08-05 to 2021-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. In addition to the main products, an experimental break-adjusted COMBINED product is also provided at v07.1. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Additional documentation and information relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product User Guide.\r\n\r\nThe data set should be cited using all of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\nIf using the COMBINED break-adjusted product, the following should also be cited in addition to the above:\r\n\r\n3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896." } ], "responsiblepartyinfo_set": [ 182612, 182614, 182615, 182616, 182617, 182618, 182619, 182613, 182620, 182621, 182622, 182623, 182624, 182625, 182626, 182627, 182628, 182629, 182630, 182631 ], "onlineresource_set": [ 53153, 53157, 53158, 53155, 83129, 83130, 83131, 83132 ] }, { "ob_id": 38333, "uuid": "c7e974411cfe4cf99cb077f7cb4d75d4", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 07.1", "abstract": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.\r\n\r\nThe v07.1 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": null, "latestDataUpdateTime": "2025-01-18T03:20:31", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Soil Moisture project team and transferred to CEDA for the ESA CCI Open Data Portal Project", "removedDataReason": "", "keywords": "ESA, Soil Moisture, CCI, Combined", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-03-10T11:30:29", "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": 39692, "dataPath": "/neodc/esacci/soil_moisture/data/daily_files/COMBINED/v07.1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 21939239703, "numberOfFiles": 15768, "fileFormat": "Data are in NetCDF format." }, "timePeriod": { "ob_id": 10571, "startTime": "1978-11-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 3572, "explanation": "See the Soil Moisture CCI documentation for information on data quality", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-11-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 40784, "uuid": "5179769763934169abdfaab1af1286e4", "short_code": "cmppr", "title": "ESA Soil Moisture Climate Change Initiative: Retrieval of Soil Moisture using combined active and passive sensors for version 07.1 data.", "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture. The combined product uses information from both active and passive sensors." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2535, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 15, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_soilmoisture_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13332, "uuid": "c256fcfeef24460ca6eb14bf0fe09572", "short_code": "proj", "title": "ESA Soil Moisture Climate Change Initiative Project", "abstract": "The European Space Agency Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project is part of the ESA Climate Change Initiative (CCI) programme, which aims to produce datasets of Essential Climate Variables (ECV's) from satellite datasets.\r\n\r\nThe Soil Moisture CCI project was set up to :\r\n - Analyse the needs of the climate research community in terms of soil moisture data.\r\n - Adapt soil moisture satellite measurements for their use by the climate research community.\r\n - Create a long-term consistent soil moisture time series, based on active and passive data, suitable for climate change studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 3894, 52664, 52665, 54646, 54647, 54648, 54649, 62742, 62743, 62744, 62745 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 38335, "uuid": "ea3eb0714dc6402b905fe9f7ee50dbbc", "short_code": "coll", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 07.1 data collection", "abstract": "Soil Moisture data (version 07.1) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets alongside ancillary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP. The COMBINED product is generated from the Level 2 active and passive instruments.\r\n\r\nThe homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31 and the ACTIVE product covering 1991-08-05 to 2021-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. In addition to the main products, an experimental break-adjusted COMBINED product is also provided at v07.1. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Additional documentation and information relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product User Guide.\r\n\r\nThe data set should be cited using all of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\nIf using the COMBINED break-adjusted product, the following should also be cited in addition to the above:\r\n\r\n3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896." } ], "responsiblepartyinfo_set": [ 182632, 182633, 182635, 182636, 182637, 182638, 182639, 182634, 182640, 182641, 182642, 182643, 182644, 182645, 182646, 182647, 182648, 182649, 182650, 182651 ], "onlineresource_set": [ 53160, 53161, 53166, 53162, 83133, 83134, 83135, 83136, 84911 ] }, { "ob_id": 38334, "uuid": "0ae6b18caf8a4aeba7359f11b8ad49ae", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Experimental Break-Adjusted COMBINED Product, Version 07.1", "abstract": "An experimental break-adjusted soil-moisture product has been generated by the ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project for their v07.1 data release. The product attempts to reduce breaks in the final CCI product by matching the statistics of the datasets between merging periods. At v07.1, the break-adjustment process (explained in Preimesberger et al. 2020) is applied only to the COMBINED product, using ERA5 soil moisture as a reference. The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.\r\n\r\nThe v07.1 COMBINED break-adjusted product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document and Preimesberger et al. 2020. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using all of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-10-27T16:23:20", "latestDataUpdateTime": "2024-09-11T12:53:55", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Soil Moisture project team and transferred to CEDA for the ESA CCI Open Data Portal Project", "removedDataReason": "", "keywords": "ESA, Soil Moisture, CCI, Combined", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-03-10T12:53:15", "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": 39693, "dataPath": "/neodc/esacci/soil_moisture/data/daily_files/break_adjusted_COMBINED/v07.1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 16728932319, "numberOfFiles": 15769, "fileFormat": "Data are in NetCDF format." }, "timePeriod": { "ob_id": 10572, "startTime": "1978-11-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 3572, "explanation": "See the Soil Moisture CCI documentation for information on data quality", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-11-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 40784, "uuid": "5179769763934169abdfaab1af1286e4", "short_code": "cmppr", "title": "ESA Soil Moisture Climate Change Initiative: Retrieval of Soil Moisture using combined active and passive sensors for version 07.1 data.", "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture. The combined product uses information from both active and passive sensors." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2535, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 15, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_soilmoisture_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13332, "uuid": "c256fcfeef24460ca6eb14bf0fe09572", "short_code": "proj", "title": "ESA Soil Moisture Climate Change Initiative Project", "abstract": "The European Space Agency Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project is part of the ESA Climate Change Initiative (CCI) programme, which aims to produce datasets of Essential Climate Variables (ECV's) from satellite datasets.\r\n\r\nThe Soil Moisture CCI project was set up to :\r\n - Analyse the needs of the climate research community in terms of soil moisture data.\r\n - Adapt soil moisture satellite measurements for their use by the climate research community.\r\n - Create a long-term consistent soil moisture time series, based on active and passive data, suitable for climate change studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 3894, 52664, 52665, 54646, 54647, 54648, 54649, 62742, 62745, 63532 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 38335, "uuid": "ea3eb0714dc6402b905fe9f7ee50dbbc", "short_code": "coll", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 07.1 data collection", "abstract": "Soil Moisture data (version 07.1) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets alongside ancillary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP. The COMBINED product is generated from the Level 2 active and passive instruments.\r\n\r\nThe homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31 and the ACTIVE product covering 1991-08-05 to 2021-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. In addition to the main products, an experimental break-adjusted COMBINED product is also provided at v07.1. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Additional documentation and information relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product User Guide.\r\n\r\nThe data set should be cited using all of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\nIf using the COMBINED break-adjusted product, the following should also be cited in addition to the above:\r\n\r\n3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896." } ], "responsiblepartyinfo_set": [ 182652, 182654, 182655, 182656, 182657, 182658, 182659, 182653, 182660, 182661, 182662, 182663, 182664, 182665, 182666, 182667, 182668, 182669, 182670, 182671 ], "onlineresource_set": [ 53172, 53173, 53174, 53169, 83137, 83138, 83139, 83140 ] }, { "ob_id": 38338, "uuid": "bfc2483537a744dca8e3239278b6e522", "title": "Continuous measurements of atmospheric carbon dioxide (CO2) and oxygen (O2) at Heathfield Tower 2021-2022", "abstract": "Continuous measurements of atmospheric carbon dioxide (CO2) and oxygen (O2) made at at Heathfield Tower, East Sussex, UK for the DARE-UK (Detection and Attribution of Regional greenhouse gas Emissions in the UK) project.\r\n\r\nThe measurement system was installed in June 2021 and measurements are ongoing. There were two inlet lines 100 meters above ground level, going up the tower, automatic switching between inlet lines every 2 hours. Data were sampled every 2 minutes, and gaps are present due to routine running of calibration cylinders, maintenance & removal of data with known technical issues.\r\nAtmospheric CO2 was observered using a Non-Dispersive InfraRed (NDIR) CO2 analyser from Siemens Corp., model Ultramat 6E (Units: ppm), whilst atmospheric O2 was measured using a dual lead fuel cell O2 analyser, Oxzilla from Sable Systems International Inc. (units: per meg). 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This also allows us to better understand whether we are meeting our emission reduction targets. This research supports the UKs emission reporting to the UN Climate Change Convention Secretariat. The project focuses on three major greenhouse gases responsible for global warming: carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). 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