Observation List
Get a list of Observation objects.
GET /api/v3/observations/?format=api&offset=7300
{ "count": 10256, "next": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=7400", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=7200", "results": [ { "ob_id": 33310, "uuid": "dd6a312c701f47778390de50cd052071", "title": "Perturbed CO2 forecasts of the February 2019 European heatwave", "abstract": "This dataset contains a range of model output from both operational runs of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) CY45R1 model forecasts and hindcasts and output from specific experimental runs. \r\n\r\nThese data were produced to support the article \"Forecast-based attribution of a winter heatwave within the limit of predictability\" (see citation and link to paper elsewhere on this record).\r\n\r\nWithin the archived products the directory tree structure is as follows:\r\n\r\nTop level domain splitting:\r\n>EU\r\nModel data at 0.25 degree resolution for the region contained within (33 to 73.5N / -27 to 45E).\r\n\r\n>GLOB\r\nModel data at 0.25 degree resolution for the full globe.\r\n\r\nBelow which there are sub-directories as follows (note, M-climate is only available for the EU domain):\r\n\r\n>>ENS\r\nData from the operational Ensemble Prediction System (EPS) forecast.\r\n\r\n>>M-climate\r\nData from EPS hindcasts used to construct model climate. See https://confluence.ecmwf.int/display/FUG/M-climate%2C+the+ENS+Model+Climate.\r\n\r\n>>pi-CO2\r\nData from EPS forecast that replicates operations *but for* reduced CO2 concentrations, set to 285 ppm.\r\n\r\n>>incr-CO2\r\nData from EPS forecast that replicates operations *but for* increased CO2 concentrations, set to 600 ppm.\r\n\r\nWithin these runs the data are then available on the following level types:\r\n>>>pl\r\nData on pressure levels.\r\n\r\n>>>sfc\r\nData on single levels.\r\n\r\nEnsemble data are further split into sub-directories as follows:\r\n>>>>cf\r\nControl forecast member.\r\n\r\n>>>>pf\r\nPerturbed forecast members.\r\n\r\nThe specific variables available within each netCDF file can differ depending on the region and experiment type (eg. the operational ENS forecasts whose initialisation dates match those of the perturbed CO2 experiments have more variables available than the others). All the datasets & variables required to reproduce the analysis are described in the paper.\r\n\r\nForecast-based attribution of a winter heatwave within the limit of predictability. Nicholas J. Leach, Antje Weisheimer, Myles R. Allen, Tim Palmer. Proceedings of the National Academy of Sciences Dec 2021, 118 (49) e2112087118; DOI: 10.1073/pnas.2112087118", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-11-15T14:12:45", "updateFrequency": "notPlanned", "dataLineage": "Data were generated using the ECMWF IFS CY45R1 model at Tco639 resolution. Data are either operational forecast data, or experiments that use an identical model version and setup to operations in which the CO2 concentration has been perturbed. These experiments would replicate operations but for the CO2 perturbations. Data were post-processed using cdo1.9.8 and NCO4.7.2. The data were then sent to the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "CO2, ECMWF, European heatwave", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-11-17T12:05:32", "doiPublishedTime": "2021-12-14T11:30:43", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 33311, "dataPath": "/badc/deposited2021/perturbedCO2-forecasts/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 438235283339, "numberOfFiles": 173, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 9151, "startTime": "2019-01-14T00:00:00", "endTime": "2019-03-01T00:00:00" }, "resultQuality": { "ob_id": 3790, "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": "2021-11-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33313, "uuid": "00013d859c8d4164a0900bf0abbaa22c", "short_code": "comp", "title": "ECMWF Integrated Forecasting System (IFS) CY45R1 deployed on ECMWF computing facilities using allocation for ECMWF special project spgbleac.", "abstract": "ECMWF Integrated Forecasting System (IFS) CY45R1 deployed on ECMWF computing facilities using allocation for ECMWF special project spgbleac.\r\n\r\nThis involved running a series of experiments set up and run on the ECMWF system alongside use of operational forecast run data and subsequent hindcast data products. Model runs were run globally, though output were taken on both global and and European area domains for archiving. EU domain has more comprehensive range of parameters archived.\r\n\r\nFor details of the model runs see Leach et al. (2021) in the linked documentation on this record." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2521, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 33450, "uuid": "bcac810c563d45a898330179487b0947", "short_code": "proj", "title": "Numerical weather prediction based event attribution (an ECMWF special project)", "abstract": "The aim of this project, which began in 2021, was to explore how operational weather forecast models could be used to examine the human influence in individual extreme weather events. There are several benefits that this approach might provide compared to conventional climate model based attribution:\r\n\r\n- The use of a forecast model that was demonstrably able to predict particular weather event in question means providing confidence that the model is able to simulate the key processes involved in the development of the event.\r\n- Similarly, the use of a forecast model ensures unequivocally that the event in question is being analysed, rather than a mixture of similar events that share a particular characteristic.\r\n- Some recent extreme weather events are so exceptional that there is little precedent in either the observational record or climate model simulations. Using forecast models that were able to predict such events provides a meaningful approach for studying these events." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 51981, 59154, 59155, 68853, 68861, 69039, 75130, 89635, 89636, 89637, 89638, 89639, 89640, 89641 ], "vocabularyKeywords": [], "identifier_set": [ 10970 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 148780, 148782, 148783, 148784, 148785, 148781, 148790, 149396, 148791 ], "onlineresource_set": [ 43615, 43616 ] }, { "ob_id": 33315, "uuid": "ac333bc4d212442aabdccbf80a457a41", "title": "MSG: Dust imagery in the RGB channels over the full disc at 0 degrees (EEDA41)", "abstract": "The Meteosat Second Generation (MSG) satellites, operated by EUMETSAT (The European Organisation for the Exploitation of Meteorological Satellites), provide almost continuous imagery to meteorologists and researchers in Europe and around the world. These include visible, infra-red, water vapour, High Resolution Visible (HRV) images and derived cloud top height, cloud top temperature, fog, snow detection and volcanic ash products. These images are available for a range of geographical areas. \r\n\r\nThis dataset contains RBG dust images from MSG satellites over the full disc at 0 degrees. Imagery available from November 2021 onwards at a frequency of 15 minutes (some are hourly) and are at least 24 hours old.\r\n\r\nThe geographic extent for images within this datasets is available via the linked documentation 'MSG satellite imagery product geographic area details'. Each MSG imagery product area can be referenced from the third and fourth character of the image product name giving in the filename. E.g. for EEAO11 the corresponding geographic details can be found under the entry for area code 'AO' (i.e West Africa).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2025-07-18T16:05:06", "updateFrequency": "daily", "dataLineage": "Data obtained by satellite from March 2005, prepared by the Met Office and stored in the CEDA archive.", "removedDataReason": "", "keywords": "MSG, dust, rgb, Satellites, full disc", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-06-06T15:09:01", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 934, "bboxName": "DA MSG Geostationary (Full disc on 0E0N)", "eastBoundLongitude": 70.0, "westBoundLongitude": -70.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 33316, "dataPath": "/badc/msg/data/hritimages/EEDA41", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 79061438758, "numberOfFiles": 35984, "fileFormat": "Data are JPEG formatted image files" }, "timePeriod": { "ob_id": 9193, "startTime": "2021-12-02T12:00:00", "endTime": null }, "resultQuality": { "ob_id": 629, "explanation": "Operational satellite data.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-06-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 14637, "uuid": "7d6854321461450eb85b5568889c01c5", "short_code": "cmppr", "title": "MSG infrared", "abstract": "MSG infrared" }, "imageDetails": [ 45 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2545, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "msg", "label": "restricted: msg group", "licence": { "ob_id": 22, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/msg.pdf", "licenceClassifications": [ { "ob_id": 4, "classification": "academic" } ] } } ], "projects": [ { "ob_id": 11161, "uuid": "1b489cdd8b3ce6b4cd79fd3080a621f4", "short_code": "proj", "title": "Meteosat Second Generation (MSG)", "abstract": "Meteosat Second Generation is operated by EUMETSAT and provides almost continuous images to meteorologists and researchers in Europe and around the world. It incorporates significant enhancements in frequency and resolution to the previous generation of Meteosat. MSG measures in 12 spectral channels (compared to only 3 on the previous Meteosat) and records data in a 15 minute cycle (30 minutes on the previous Meteosat). The resolution of the high-resolution visible light channel measures 1 km at the sub-satellite point (compared to 2.5 km on the previous Meteosat).\r\n\r\nThe first Meteosat Second Generation satellite, MSG-1, came into operational service on 29th January 2004 and was renamed Meteosat-8. MSG-1 has a nominal lifetime of seven years. MSG-2 was launched on 21st December 2005 and future MSG units are planned.\r\n\r\nThe MSG payload also contains the Geostationary Earth Radiation Budget (GERB) instrument which provides important data for climate research. Data from the GERB instrument is now available at the BADC.\r\n\r\nA humanitarian Search and Rescue transponder that relays distress signals from ships, aircraft and others in need of rescue is also mounted on the MSG platform." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 2645, "uuid": "5fa2529b973e47ae38ab3557f2018ef4", "short_code": "coll", "title": "Meteosat Second Generation (MSG) Geostationnary Satellites: Visible, Infra-Red and Water Vapour Images and Derived Data Products over the world", "abstract": "Meteosat Second Generation is operated by EUMETSAT and provides almost continuous images to meteorologists and researchers in Europe and around the world. It incorporates significant enhancements in frequency and resolution to the previous generation of Meteosat. MSG measures in 12 spectral channels (compared to only 3 on the previous Meteosat) and records data in a 15 minute cycle (30 minutes on the previous Meteosat). The resolution of the high-resolution visible light channel measures 1 km at the sub-satellite point (compared to 2.5 km on the previous Meteosat).\r\n\r\nThis dataset collection includes visible, infra-red, water vapour, High Resolution Visible (HRV) images and the derived cloud top height, cloud top temperature, fog, snow detection, and volcanic ash products. These images are available for a range of geographical areas. Images are available from March 2005 onwards at a frequency of 15 minutes (some are hourly) and are at least 24 hours old.\r\n\r\nThe different geographic extents for images within this dataset collection are available via the linked documentation 'MSG satellite imagery product geographic area details'. Each MSG imagery product area can be referenced from the third and fourth character of the image product name giving in the filename. E.g. for EEAO11 the corresponding geographic details can be found under the entry for area code 'AO' (i.e West Africa)." } ], "responsiblepartyinfo_set": [ 148806, 148807, 148808, 148809, 148810, 149394, 148803, 148805, 148802 ], "onlineresource_set": [ 43617, 47737 ] }, { "ob_id": 33317, "uuid": "b1dacc09b42f4d8ab492c5d5c751efa9", "title": "MSG: Dust imagery in the RGB channels over the full disc at 41.5 degrees East (LEDF41, upto 0900 UTC 1st June 2022)", "abstract": "The Meteosat Second Generation (MSG) satellites, operated by EUMETSAT (The European Organisation for the Exploitation of Meteorological Satellites), provide almost continuous imagery to meteorologists and researchers in Europe and around the world. These include visible, infra-red, water vapour, High Resolution Visible (HRV) images and derived cloud top height, cloud top temperature, fog, snow detection and volcanic ash products. These images are available for a range of geographical areas. \r\n\r\nThis dataset contains RGB dust images from MSG satellites over the full disc at 41.5 degrees East. Imagery available from November 2021 until 0900 UTC 1st June 2022 (see following note) at a frequency of 15 minutes (some are hourly) and are at least 24 hours old.\r\n\r\nNOTE - from 1st February 2022 to 20th April 2022 Meteosat-9 was drifted from 3.5 E to 45.5E at a rate of 0.5 degree a day drift to a new observation location centred over 45.5 degrees East to take over as the prime IODC (Indian Ocean Data Coverage) satellite from 30th May 2022. This role was previously by Meteosat-8, which remains in place for emergency . Data were not made available during this drifting process. The Met Office production of the LEDF41 product switched to using Meteosat-9 from 0915 UTC on 1st June 2022. See linked dataset for the replacement dataset to continue provision of this product over this region of the globe.\r\n\r\nThe geographic extent for images within this datasets is available via the linked documentation 'MSG satellite imagery product geographic area details'. Each MSG imagery product area can be referenced from the third and fourth character of the image product name giving in the filename. E.g. for EEAO11 the corresponding geographic details can be found under the entry for area code 'AO' (i.e West Africa).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2022-06-10T10:02:04", "updateFrequency": "notPlanned", "dataLineage": "Data obtained by satellite from March 2005, prepared by the Met Office and stored in the CEDA archive.", "removedDataReason": "", "keywords": "MSG, dust, rbd, Satellites, full disc", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-06T10:21:30", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2919, "bboxName": "DF IODC Geostationary(full disk, 41.5 E)", "eastBoundLongitude": 131.5, "westBoundLongitude": -48.5, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 33318, "dataPath": "/badc/msg/data/hritimages/LEDF41", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8509683310, "numberOfFiles": 4326, "fileFormat": "Data are JPEG formatted images" }, "timePeriod": { "ob_id": 9194, "startTime": "2021-12-02T12:00:00", "endTime": "2022-06-01T09:00:00" }, "resultQuality": { "ob_id": 629, "explanation": "Operational satellite data.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-06-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 14637, "uuid": "7d6854321461450eb85b5568889c01c5", "short_code": "cmppr", "title": "MSG infrared", "abstract": "MSG infrared" }, "imageDetails": [ 45 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2545, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "msg", "label": "restricted: msg group", "licence": { "ob_id": 22, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/msg.pdf", "licenceClassifications": [ { "ob_id": 4, "classification": "academic" } ] } } ], "projects": [ { "ob_id": 11161, "uuid": "1b489cdd8b3ce6b4cd79fd3080a621f4", "short_code": "proj", "title": "Meteosat Second Generation (MSG)", "abstract": "Meteosat Second Generation is operated by EUMETSAT and provides almost continuous images to meteorologists and researchers in Europe and around the world. It incorporates significant enhancements in frequency and resolution to the previous generation of Meteosat. MSG measures in 12 spectral channels (compared to only 3 on the previous Meteosat) and records data in a 15 minute cycle (30 minutes on the previous Meteosat). The resolution of the high-resolution visible light channel measures 1 km at the sub-satellite point (compared to 2.5 km on the previous Meteosat).\r\n\r\nThe first Meteosat Second Generation satellite, MSG-1, came into operational service on 29th January 2004 and was renamed Meteosat-8. MSG-1 has a nominal lifetime of seven years. MSG-2 was launched on 21st December 2005 and future MSG units are planned.\r\n\r\nThe MSG payload also contains the Geostationary Earth Radiation Budget (GERB) instrument which provides important data for climate research. Data from the GERB instrument is now available at the BADC.\r\n\r\nA humanitarian Search and Rescue transponder that relays distress signals from ships, aircraft and others in need of rescue is also mounted on the MSG platform." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 2645, "uuid": "5fa2529b973e47ae38ab3557f2018ef4", "short_code": "coll", "title": "Meteosat Second Generation (MSG) Geostationnary Satellites: Visible, Infra-Red and Water Vapour Images and Derived Data Products over the world", "abstract": "Meteosat Second Generation is operated by EUMETSAT and provides almost continuous images to meteorologists and researchers in Europe and around the world. It incorporates significant enhancements in frequency and resolution to the previous generation of Meteosat. MSG measures in 12 spectral channels (compared to only 3 on the previous Meteosat) and records data in a 15 minute cycle (30 minutes on the previous Meteosat). The resolution of the high-resolution visible light channel measures 1 km at the sub-satellite point (compared to 2.5 km on the previous Meteosat).\r\n\r\nThis dataset collection includes visible, infra-red, water vapour, High Resolution Visible (HRV) images and the derived cloud top height, cloud top temperature, fog, snow detection, and volcanic ash products. These images are available for a range of geographical areas. Images are available from March 2005 onwards at a frequency of 15 minutes (some are hourly) and are at least 24 hours old.\r\n\r\nThe different geographic extents for images within this dataset collection are available via the linked documentation 'MSG satellite imagery product geographic area details'. Each MSG imagery product area can be referenced from the third and fourth character of the image product name giving in the filename. E.g. for EEAO11 the corresponding geographic details can be found under the entry for area code 'AO' (i.e West Africa)." } ], "responsiblepartyinfo_set": [ 148811, 148812, 148813, 148814, 148815, 148816, 148817, 149393, 148818 ], "onlineresource_set": [ 43618, 47736, 47755 ] }, { "ob_id": 33319, "uuid": "7bdb8604acc940f79a88433cc83904fc", "title": "Cape Verde Atmospheric Observatory: FLEXPART back trajectory (2007 onwards)", "abstract": "Ten day back trajectory calculations made using the FLEXPART model initiated from Cape Verde Atmospheric Observatory four times a day to show the origins of the air arriving at the observatory and support the analysis of the observations. The Cape Verde Atmospheric Observatory (CVAO) exists to advance understanding of climatically significant interactions between the atmosphere and ocean and to provide a regional focal point and long-term data. \r\n\r\nThe observatory is based on Calhau Island of São Vicente, Cape Verde at 16.848N, 24.871W, in the tropical Eastern North Atlantic Ocean, a region which is data poor but plays a key role in atmosphere-ocean interactions of climate-related and biogeochemical parameters including greenhouse gases. It is an open-ocean site that is representative of a region likely to be sensitive to future climate change, and is minimally influenced by local effects and intermittent continental pollution.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-02-10T15:27:11", "updateFrequency": "asNeeded", "dataLineage": "FLEXPART model output prepared by NCAS staff before archiving at BADC.", "removedDataReason": "", "keywords": "Cape Verde, FLEXPART, trajectories", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2021-11-19T09:14:26", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 12, "bboxName": "Cape Verde Atmospheric Observatory Site", "eastBoundLongitude": -24.871, "westBoundLongitude": -24.871, "southBoundLatitude": 16.848, "northBoundLatitude": 16.848 }, "verticalExtent": null, "result_field": { "ob_id": 33322, "dataPath": "/badc/capeverde/data/FLEXPART-back-traj/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 39033382172, "numberOfFiles": 20169, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 9153, "startTime": "2007-01-01T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 226, "explanation": "Data from Cape Verde observatory are for research", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-09-10" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33320, "uuid": "bd4188b70dd54fd0a70416025c690c2f", "short_code": "comp", "title": "Cape Verde: FLEXPART model trajectories", "abstract": "Cape Verde: FLEXPART model trajectories" }, "procedureCompositeProcess": null, "imageDetails": [ 13 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 875, "uuid": "d5422d54d519ed056cc17e97037732b8", "short_code": "proj", "title": "Cape Verde Atmospheric Observatory Measurements", "abstract": "Measurements conducted at Cape Verde Atmospheric Observatory (CVAO)\r\n\r\nThe CVAO (16° 51' 49 N, 24° 52' 02 W), exists to advance understanding of climatically-significant interactions between the atmosphere and ocean and to provide a regional focal point and long-term data context for field campaigns. Measurements of O3, CO, NO, NO2, NOy and VOCs began at the site in October 2006. Chemical characterisation of aerosol measurements and flask sampling of greenhouse gases began in November 2006, halocarbon measurements in May 2007, and physical measurements of aerosol in June 2008. On-line measurements of greenhouse gases began in October 2008." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46689, 46690, 46691, 46692, 46693, 46694 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 872, "uuid": "81693aad69409100b1b9a247b9ae75d5", "short_code": "coll", "title": "Continuous Cape Verde Atmospheric Observatory Observations", "abstract": "Data from observations made at the Cape Verde Atmospheric Observatory (CVAO) which exists to advance understanding of climatically significant interactions between the atmosphere and ocean and to provide a regional focal point and long-term data.\r\n\r\nThe observatory is based on Calhau Island of São Vicente Cape Verde at 16.848N, 24.871W, in the tropical Eastern North Atlantic Ocean, a region which is data poor but plays a key role in atmosphere-ocean interactions of climate-related and biogeochemical parameters including greenhouse gases. It is an open-ocean site that is representative of a region likely to be sensitive to future climate change, and is minimally influenced by local effects and intermittent continental pollution.\r\n\r\nThe dataset collection contains mixing ratio measurements of Ozone, CO, ethane, propane, iso-butane, acetylene, iso-pentane, and halocarbons. Meteorological measurements (wind speed, wind direction, atmospheric pressure, air temperature, relative humidity, solar radiation, rainfall) and aerosol concentrations are also contained in the data set. \r\n\r\nThe Cape Verde Observatory was previously used during the SOLAS (Surface Ocean / Lower Atmosphere Study) project, from which the present day continuous observations have evolved. As such the earlier SOLAS measurements are also included within this collection. Additionally, back trajectory plots for the site are also within this collection." } ], "responsiblepartyinfo_set": [ 148819, 148826, 148824, 148823, 148822, 148820, 148827, 148821 ], "onlineresource_set": [] }, { "ob_id": 33327, "uuid": "ba8180a9876a4ef1a127046a673d8864", "title": "ClearfLo: IOP Winter atmospheric chemistry and meteorology measurements and NAME Airmass Footprint dispersion model output across London sites", "abstract": "ClearfLo (Clean Air for London) Project was a collaborative scientific project involving several academic institutions in the UK, which aimed to set up air pollution monitoring sites alongside meteorological measurements to investigate boundary layer pollution across London.\r\n\r\nThis dataset contains NAME airmass footprint images and a broad suite of chemistry and meteorological measurements at Detling, North Kensington, and King's College, London during the winter Intensive Operations Period (IOP) in January and February 2012.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-11-19T15:39:54", "updateFrequency": "notPlanned", "dataLineage": "Data are collected and processed by the instrument operators and uploaded to the ClearfLo (Clean Air for London) data archive at CEDA.", "removedDataReason": "", "keywords": "ClearfLo, pollution, chemistry, meteorology", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-11-24T15:06:06", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2886, "bboxName": "ClearfLo Winter IOP", "eastBoundLongitude": 0.116, "westBoundLongitude": -0.5, "southBoundLatitude": 51.3, "northBoundLatitude": 51.51 }, "verticalExtent": null, "result_field": { "ob_id": 33328, "dataPath": "/badc/clearflo/data/iop/winter", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 702836713, "numberOfFiles": 3139, "fileFormat": "NASA-Ames and NetCDF" }, "timePeriod": { "ob_id": 9154, "startTime": "2012-01-01T00:00:00", "endTime": "2012-03-25T23:45:00" }, "resultQuality": { "ob_id": 985, "explanation": "", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-07-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 14545, "uuid": "75d6e53f3a8a4674a4069b371842436e", "short_code": "cmppr", "title": "Clearflo ITOP Winter", "abstract": "Clearflo ITOP Winter" }, "imageDetails": [ 18 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 3933, "uuid": "03cf72a33d1fcf00908bf9eca3be7eca", "short_code": "proj", "title": "ClearfLo (Clean Air for London) Project", "abstract": "The ClearfLo (Clean Air for London) Project is a collaborative scientific project involving several academic institutions in the UK, to set up air pollution monitoring sites alongside meteorological measurements to investigate boundary layer pollution across London.\r\n\r\nThe ambition of ClearfLo is to provide long-term integrated measurements of the meteorology, composition and particulate loading of London’s urban atmosphere, made at street level and at elevated sites, complemented by modelling to improve predictive capability for air quality.\r\n\r\nClearfLo is funded by the Natural Environment Research Council (NERC) for three years from Jan 2010, and is coordinated by the National Centre for Atmospheric Science (NCAS)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 4385, 13294, 50559, 50561, 60511, 60512, 60513, 60514, 60515, 60516, 60517, 60518, 60519, 60520, 60521, 60522, 60523, 60524, 60525, 60526, 60527, 60528, 60529, 60530, 60531, 60532 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 3930, "uuid": "cee49a1f044b79d5413b7a0282467508", "short_code": "coll", "title": "ClearfLo (Clean Air for London) Project: Meteorology, Composition and Particulate Loading Measurements of London's urban atmosphere", "abstract": "ClearfLo (Clean Air for London) Project was a collaborative scientific project involving several academic institutions in the UK, which aimed to set up air pollution monitoring sites alongside meteorological measurements to investigate boundary layer pollution across London.\r\n\r\nThis dataset collection contains meteorology, composition and particulate loading measurements of London's urban atmosphere.\r\n\r\nThe ambition of ClearfLo was to provide long-term integrated measurements of the meteorology, composition and particulate loading of London’s urban atmosphere, made at street level and at elevated sites, complemented by modelling to improve predictive capability for air quality.\r\n\r\nClearfLo was funded by the Natural Environment Research Council (NERC) for three years from Jan 2010, and was coordinated by the National Centre for Atmospheric Science (NCAS)." } ], "responsiblepartyinfo_set": [ 148851, 148852, 148853, 148854, 148855, 148856, 148857, 148858, 148882, 148883, 148884, 148885, 148886, 148887, 148888, 148889 ], "onlineresource_set": [] }, { "ob_id": 33333, "uuid": "38bac9051d064d4da183fff2361f5de8", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 3.27 (v20220621)", "abstract": "Input data for figure 3.27 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.27 shows maps of multi-decadal salinity trends for the near-surface ocean.\r\n\r\n ---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n ---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two subpanels, with input data provided for the upper panel.\r\n\r\n ---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains the global ocean salinity estimates from Durack & Wijffels (2010) based on observations from 01-01-1950 to 12-31-2019:\r\n \r\n - Mean salinity (for the Jan/1950 to Dec/2019 period, units in PSS).\r\n - Salinity change (for the same period, PSS/70-years).\r\n - Salinity change error (same period, PSS/70-years).\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThe observational data from here (top panel) is taken from the file:\r\nDurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc. The fields of interest are salinity_mean (shown as black contours) and salinity_change (shown in colourscale). DurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc is an updated file from Durack & Wijffels (2010).\r\n\r\nThe data file DurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc is an intermediate file used in 3.27, please refer to the code to generate the figure using the corresponding tools (see the link to the code in the 'Related document' section README_AR6_WG1_Chap3_Figure3_27_GlobalSeaSurfaceSalinitytrends.md at main ESMValGroup/ESMValTool-AR6-OriginalCode-FinalFigures).\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to input data figure 2.27.\r\n - Link to the figure on the IPCC AR6 website.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:45:03", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1 Degrees", "status": "ongoing", "dataPublishedTime": "2022-12-14T16:38:04", "doiPublishedTime": "2023-02-08T19:38:31.554442", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 74, "highestLevelBound": 0.0, "lowestLevelBound": 2000.0, "units": "1" }, "result_field": { "ob_id": 37606, "dataPath": "/badc/ar6_wg1/data/ch_03/inputdata_ch3_fig27/v20220621", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 40251095, "numberOfFiles": 4, "fileFormat": "netCDF" }, "timePeriod": { "ob_id": 9150, "startTime": "1950-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 3789, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-11-12" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33334, "uuid": "017b66c133db4791856804b74a85752c", "short_code": "comp", "title": "Caption for Figure 3.27 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Maps of multi-decadal salinity trends for the near-surface ocean. Units are Practical Salinity Scale 1978 [PSS-78] decade-1. (top) The best estimate (Section 2.3.3.2) observed trend (Durack and Wijffels, 2010–. (bottom) Simulated trend from the CMIP6 historical experiment multi-model mean (1950-–2014). Black contours show the climatological mean salinity in increments of 0.5 PSS-78 (thick lines 1 PSS-78). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1050, 1635, 1636, 9042, 9043, 9045, 49483, 49484, 49485, 49486, 49487, 49488, 49489, 49490 ], "vocabularyKeywords": [], "identifier_set": [ 12391 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 148869, 148870, 148871, 148872, 148873, 148874, 148875, 148876 ], "onlineresource_set": [ 43671, 43672, 52325, 82626, 82627, 89683, 89684, 89685, 89686, 89687, 89688, 89689, 89690, 89691, 89692, 89693, 89694, 89695, 89696, 89697, 89698, 89699, 89700, 89701, 89702, 89703, 89704, 89705 ] }, { "ob_id": 33339, "uuid": "51725f90c60a45e69f07a748a25b9729", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Vegetation Index (NDVI) v1", "abstract": "Sentinel-Hub NDVI description: \r\nNDVI is a simple, but effective index for quantifying green vegetation. It normalizes green leaf scattering in Near Infra-red wavelengths with chlorophyll absorption in red wavelengths.\r\n\r\nThe value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1 to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1). It is a good proxy for live green vegetation.\r\n\r\nNDVI = (NIR – Red) / (NIR + RED)\r\n\r\nSentinel-2 NDVI = (B8 - B4) / (B8 + B4)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Vegetation Index (NDVI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data. \r\n\r\nNDVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.\r\n\r\nVersion 1 contains masked index files (using the Defra and JNCC ARD cloud and topographic shadow masks).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-05-23T01:46:44", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "JNCC, ARD, NDVI, Sentinel 2, EO indices, Analysis Ready Data, Sentinel", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2022-05-11T14:02:16", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2609, "bboxName": "UK", "eastBoundLongitude": 2.02, "westBoundLongitude": -10.85, "southBoundLatitude": 49.82, "northBoundLatitude": 59.48 }, "verticalExtent": null, "result_field": { "ob_id": 33340, "dataPath": "/neodc/sentinel_ard/indices/sentinel_2/ndvi/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1993246894475, "numberOfFiles": 17657, "fileFormat": "These data are provided in Cloud Optimized GeoTIFF (COG) format." }, "timePeriod": { "ob_id": 9164, "startTime": "2015-07-12T00:00:00", "endTime": "2023-12-21T23:59:59" }, "resultQuality": { "ob_id": 3792, "explanation": "A representative subset of data are manually checked to ensure the indices files align with the ARD files, the value ranges are as expected, and the data format is correct. 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": "2021-11-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33348, "uuid": "f71b64ad4ab14213b6110b5e7f775799", "short_code": "cmppr", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Vegetation Index (NDVI) v1", "abstract": "All index equations from Sentinel-hub custom scripts (links in Related Documents).\r\nSentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. ARD granules are masked using the corresponding cloud and topographic shadow masks. NDVI index files are then generated in R 3.6.1 using the raster::calc function." }, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30197, "uuid": "14818d07b867480c90ed01fbf81d9346", "short_code": "proj", "title": "Defra and JNCC Analysis Ready Sentinel Data", "abstract": "Defra and JNCC aim to provide timely, cost-effective, and high-quality, Analysis Ready Sentinel Data (ARD) for a wide range of applications. These data are provided for the UK geographic area for this project. The indices datasets currently cover Yorkshire, UK, but coverage will be expanded in the future. These products have been produced using the Copernicus satellites, Sentinel 1 and Sentinel 2." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 148897, 148898, 148899, 148892, 148893, 148894, 148895, 148896 ], "onlineresource_set": [ 43629, 43628 ] }, { "ob_id": 33342, "uuid": "967313bee45c48998c5027896e3da53c", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.13 (v20211126)", "abstract": "Data for Figure 2.13 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.13 shows the global trends in surface specific humidity and surface relative humidity over 1973 - 2019\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for panel (b) and (d) in one single file (qrh2.csv). Code for plotting panels (a) and (c) is archived on zenodo and a link is provided in the Related Documents section of this catalogue record.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains time-series of observed global average surface specific and relative humidity during 1973-2019\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel b. Global average surface specific humidity annual anomalies (1981–2010 base period):\r\n \r\n - Data file: qrh2.csv - column 2 cyan solid line\r\n - Data file: qrh2.csv - column 3 orange solid line\r\n - Data file: qrh2.csv - column 4 blue solid line\r\n - Data file: qrh2.csv - column 5 black solid line\r\n\r\n\r\nPanel d. Global average surface relative humidity annual anomalies (1981–2010 base period):\r\n - Data file: qrh2.csv - column 7 cyan solid line\r\n - Data file: qrh2.csv - column 8 orange solid line\r\n - Data file: qrh2.csv - column 9 blue solid line\r\n - Data file: qrh2.csv - column 10 black solid line\r\n\r\nRH stands for relative humidity.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2).\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1.\r\n - Link to input data figure 2.13.\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": "2024-03-09T02:44:56", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 2, Changing state, Multi-millennial context, pre-industrial, Natural forcing, anthropogenic forcing, Radiative forcing, Large-scale indicators, observed changes, Modes of variability, Figure 2.13, global specific humidity, global relative humidity", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "5° Degrees", "status": "ongoing", "dataPublishedTime": "2023-05-11T15:19:34", "doiPublishedTime": "2023-07-03T19:50:51", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 76, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "5°" }, "result_field": { "ob_id": 39215, "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig13/v20211126", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 10455, "numberOfFiles": 4, "fileFormat": "csv" }, "timePeriod": { "ob_id": 9158, "startTime": "1973-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 3793, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-11-26" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33344, "uuid": "ed3d5ebac9344ebf99b384ae0589b574", "short_code": "comp", "title": "Caption for Figure 2.13 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Changes in surface humidity. (a) Trends in surface specific humidity over 1973–2019. Trends are calculated using OLS regression with significance assessed following AR(1) adjustment after Santer et al. (2008) ; ‘x’ marks denote non-significant trends). (b) Global average surface specific humidity annual anomalies (1981–2010 base period). (c) as (a) but for the relative humidity. (d) as (b) but for the global average surface relative humidity annual anomalies. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12615 ], "observationcollection_set": [ { "ob_id": 32717, "uuid": "3da412ad9912427d9bb808b57faa21a7", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1" } ], "responsiblepartyinfo_set": [ 148900, 148901, 148902, 148903, 148904, 148905, 148906, 148907, 148908, 196260 ], "onlineresource_set": [ 43630, 43631, 52436, 52407, 82829 ] }, { "ob_id": 33345, "uuid": "de41130346a04f97879352525d890577", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.14 (v20211126)", "abstract": "Data for Figure 2.14 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.14 provides global average time series of total column water vapour anomalies for a variety of data sources\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset cointains time series of global averaged total column water vapour anomalies from 1979-2019 for observations and reanalyses\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file: tcwv6.csv:\r\n \r\n - Column 2: cyan solid line\r\n - Column 3: orange solid line\r\n - Column 4: black solid line\r\n - Column 5: green solid line\r\n - Column 6: cyan dotted line\r\n - Column 7: blue line\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\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": "2024-03-09T02:44:58", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 2, Changing state, Multi-millennial context, pre-industrial, Natural forcing, anthropogenic forcing, Radiative forcing, Large-scale indicators, observed changes, Modes of variability, Figure 2.14, global total column water vapour", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-11T15:44:55", "doiPublishedTime": "2023-07-03T19:55:07.202045", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 77, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39216, "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig14/v20211126", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6944, "numberOfFiles": 4, "fileFormat": "CSV" }, "timePeriod": { "ob_id": 9159, "startTime": "1979-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 3794, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-11-26" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33347, "uuid": "3941a47cf1864d1d8437713005a4e3d0", "short_code": "comp", "title": "Caption for Figure 2.14 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Timeseries of global mean total column water vapour annual anomalies (mm) relative to a 1988–2008 base period. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12616 ], "observationcollection_set": [ { "ob_id": 32717, "uuid": "3da412ad9912427d9bb808b57faa21a7", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1" } ], "responsiblepartyinfo_set": [ 148910, 148911, 148912, 148913, 148914, 148915, 148916, 148917 ], "onlineresource_set": [ 43632, 43633, 52406, 82830 ] }, { "ob_id": 33350, "uuid": "70276cf6b04e4b638b4fe9b37f7651dd", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.15 (v20211126)", "abstract": "Data for Figure 2.15 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.15 provides global precipitation trend maps and time series for a variety of data sources.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has six panels, with data provided for panel c (precipglobalavedata2019_4.csv). Code for plotting all panels is archived on zenodo and a link is provided in the Related Documents section of this catalogue record.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains observed global precipitation data from a variety of sources covering the period 1891-2019.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel c:\r\n \r\n - Data file precipglobalavedata2019_4.csv: column 2 orange solid line (upper panel)\r\n - Data file precipglobalavedata2019_4.csv: column 3 cyan solid line (upper panel)\r\n - Data file precipglobalavedata2019_4.csv: column 4 black solid line (upper panel)\r\n - Data file precipglobalavedata2019_4.csv: column 5 black dotted line (upper panel)\r\n - Data file precipglobalavedata2019_4.csv: column 7 orange solid line (lower panel)\r\n - Data file precipglobalavedata2019_4.csv: column 8 cyan solid line (lower panel)\r\n - Data file precipglobalavedata2019_4.csv: column 9 black solid line (lower panel)\r\n - Data file precipglobalavedata2019_4.csv: column 10 black dotted line (lower panel)\r\n\r\nGCPP stands for the Global Precipitation Climatology Centre.\r\nCRU TS stands for Climatic Research Unit Timeseries.\r\nGPCP stands for the Global Precipitation Climatology Project.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2).\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1.\r\n - Link to input data figure 2.15.\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": "2024-03-09T02:44:59", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 2, Changing state, Multi-millennial context, pre-industrial, Natural forcing, anthropogenic forcing, Radiative forcing, Large-scale indicators, observed changes, Modes of variability, Figure 2.15, global precipitation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "2.5° Degrees", "status": "ongoing", "dataPublishedTime": "2023-05-11T15:33:29", "doiPublishedTime": "2023-07-03T19:58:18", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 78, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "2.5°" }, "result_field": { "ob_id": 39217, "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig15/v20211126", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 15230, "numberOfFiles": 4, "fileFormat": "CSV" }, "timePeriod": { "ob_id": 9160, "startTime": "1891-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 3795, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-11-26" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33352, "uuid": "0ff5f7effdbe4e72adfe45b2bf2f8bd0", "short_code": "comp", "title": "Caption for Figure 2.15 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Changes in observed precipitation. (a, b) Spatial variability of observed precipitation trends over land for 1901–2019 for two global in-situ products. Trends are calculated using OLS regression with significance assessed following AR(1) adjustment after Santer et al. (2008) (‘x’ marks denote non-significant trends). (c) Annual time series and decadal means from 1891 to date relative to a 1981–2010 climatology (note that different products commence at distinct times). (d, e) as (a, b), but for the periods starting in 1980. (f) is for the same period for the globally complete merged GPCP v2.3 product. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12617 ], "observationcollection_set": [ { "ob_id": 32717, "uuid": "3da412ad9912427d9bb808b57faa21a7", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1" } ], "responsiblepartyinfo_set": [ 148921, 148922, 148923, 148924, 148925, 148926, 148927, 148928, 196261 ], "onlineresource_set": [ 43637, 43636, 52435, 52405, 82831 ] }, { "ob_id": 33357, "uuid": "09198c58032d4b8197fd7c6748b92785", "title": "Thermospheric cooling under RCP8.5 and high solar activity due to carbon dioxide increases only, select years from 1975-2095", "abstract": "This dataset contains WACCM-X model results under RCP8.5 (Representative Concentration Pathway) carbon dioxide increases and high solar activity (F10.7 = 200 sfu). These cover ground level to a pressure level of 4e-10 hPa (~300 km altitude) on a global 144x96 longitude-latitude grid .\r\n\r\nData is given in daily instantaneous (.h2.) and monthly average (.h0.) files in the netCDF format. Default WACCM-X outputs remain, along with the following additional variables:\r\n\r\n'PS' - Surface Pressure\r\n'Z3' - Geopotential Height (above sea level)\r\n'T' - Temperature\r\n'U' - Zonal Wind\r\n'V' - Meridional Wind\r\n'CO2' - Carbon Dioxide Concentration\r\n'CO' - Carbon Monoxide Concentration\r\n'NO' - Nitric Oxide Concentration\r\n'H2O' - Water Vapour Concentration\r\n'O' - Atomic Oxygen Concentration\r\n\r\nMore detail on each variable is given within the netCDF files and the readme file.\r\n\r\n64 month runs from edited initial files at 10 year intervals from 2015 to 2095 under RCP8.5. There is also a 28 month 1975 run and 64 month 2000 run. All of these cyclically repeat the initial year. These are separated into individual folders with the RCP8.5 CO2 concentration listed. The first 4 months of each of these datasets have been ignored in processing as the model spins up, but are included here for completeness.\r\n\r\nThis data was collected to understand the density drop at low earth orbit altitudes as carbon dioxide concentrations increase.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-11-27T01:29:21", "updateFrequency": "notPlanned", "dataLineage": "Input data other than default is obtained from the subversion input data repository at https://svn-ccsm-inputdata.cgd.ucar.edu/trunk/\r\n\r\nThese files changed in user_nl_cam include\r\nsolar_parms_file = '/.../inputdata/atm/waccm/phot/wa_smin_quiet_c071210.nc'\r\n\r\nsrf_emis_specifier = 'CH2O -> /.../inputdata/atm/cam/chem/2000-2100_RCP45/IPCC_emissions_houw_CH2O_2000-2100_1.9x2.5.nc',\r\n 'CO -> /.../inputdata/atm/cam/chem/2000-2100_RCP45/IPCC_emissions_houw_CO_2000-2100_1.9x2.5.nc',\r\n 'NO -> /.../inputdata/atm/cam/chem/2000-2100_RCP45/IPCC_emissions_houw_NOx_2000-2100_1.9x2.5.nc'\r\n\r\naero_file = 'aero_rcp45_v1_1.9x2.5_L26_1995-2105_c100316.nc'\r\n\r\nsolar_data_file = '/.../inputdata/atm/cam/solar/spectral_irradiance_Lean_1950-2100_daily_CCMI_Leap_c150727.nc'\r\n\r\n1975 run used all default input files. \r\n\r\nInitial conditions are those of the default year 2000 run, with CO2 and CO concentrations in the initial files edited as described within the paper (Brown et al, 2021).\r\n\r\nThe files uploaded here are the raw files output by WACCM-X with no editing.", "removedDataReason": "", "keywords": "thermospheric, cooling, model", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-12-03T15:05:09", "doiPublishedTime": "2022-01-12T14:35:11.412657", "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": 33358, "dataPath": "/badc/deposited2021/ThermoCooling_SA_WACCMX/data/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 859638421317, "numberOfFiles": 20847, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 9162, "startTime": "1975-01-01T00:00:00", "endTime": "2095-12-31T00:00:00" }, "resultQuality": { "ob_id": 3797, "explanation": "", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-11-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33360, "uuid": "6d5769c3e5f24ba3b66b58fb701949c9", "short_code": "comp", "title": "WACCM-X as part of CESM v1.2.2", "abstract": "WACCM-X as part of CESM v1.2.2 - more details on request" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 33359, "uuid": "4a831e324dfb4b418a17aeafb3bae9a3", "short_code": "proj", "title": "ThermosphericCoolingWACCM-X", "abstract": "Studying the future drop in thermospheric densities under increasing carbon dioxide concentrations. The rationale behind this is to understand the impact on the future space debris environment in low earth orbit." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 28669, 28670, 51981, 52192, 52193, 54920, 54921, 54922, 54923, 54924, 54925, 54926, 54927, 54928, 54929, 54930, 54931, 54932, 54933, 54934, 54935, 54936, 54937, 54938, 54939, 54940, 54941, 54942, 54943, 54944, 54945, 54946, 54947, 54948, 54949, 54950, 54951, 54952, 54953, 54954, 54955, 54956, 54957, 54958, 54959, 54960, 54961, 54962, 54963, 54964, 54965, 54966, 54999, 55043, 91580, 91581, 91582, 91583 ], "vocabularyKeywords": [], "identifier_set": [ 10976 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 148939, 148940, 148941, 148942, 148943, 148944, 148949, 148950, 168810 ], "onlineresource_set": [] }, { "ob_id": 33362, "uuid": "058d3ad17a084f0bb8dc9b5dc5efdb7f", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): All-weather MicroWave Land Surface Temperature (MW-LST) global data record (1996-2020)", "abstract": "MW-LST is a data record of land surface temperature (LST) derived from the microwave instruments Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager / Sounder (SSMIS). Observations available at frequencies close to 18, 22, 26, and 85 GHz are used as an input to a retrieval algorithm that produces LST over all continental surfaces, twice per day (6 am/pm), at a spatial resolution of ~25 km, and over 25 years (1996-2020). \r\n\r\nThe data record has been produced by the company Estellus working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci). Compared with the remaining infrared LST data records of the LST_cci, the spatial resolution of the MW-LST is coarser, and the associated retrieval errors are larger. However, it offers LST estimates for clear-sky and cloudy conditions, therefore complementing the IR LST data records, which can only provide LST for clear skies. The data record is temporally and spatially complete, although in rare occasions some data can be missing due to missing observations, e.g., due to satellite maintenance operations or anomalous behavior. The data record is provided on a regular grid of 0.25x0.25 degrees, saved as daily, monthly, and yearly netcdf files. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-01-21T20:21:15", "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the company Estellus working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). ). The source of satellite Level 1 observations is the Fundamental Climate Data Record of Microwave Imager Radiances (https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V003) kindly provided by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF).\r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2022-02-25T09:29:51", "doiPublishedTime": "2022-02-25T09:59:52", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 34866, "dataPath": "/neodc/esacci/land_surface_temperature/data/SSMI_SSMIS/L3C/v2.23/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 66236137474, "numberOfFiles": 18913, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9163, "startTime": "1996-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33373, "uuid": "96cc894239c341d182941c9205e62ca6", "short_code": "cmppr", "title": "Composite process for ESA Land Surface Temperature Climate Change Initiative (LST_cci): All-weather daily MicroWave Land Surface Temperature (MW-LST) global data record (1996-2020)", "abstract": "The land surface temperature (LST) data has been derived from the microwave instruments Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager / Sounder (SSMIS). Observations available at frequencies close to 18, 22, 26, and 85 GHz are used as an input to a retrieval algorithm that produces LST over all continental surfaces, twice per day (6 am/pm), at a spatial resolution of ~25 km, and over 25 years (1996-2020)." }, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2558, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 30, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_lst_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 33361, "uuid": "555149fdc3ef4e23a1de8ece93c29f5d", "short_code": "proj", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci)", "abstract": "The land surface temperature (LST) CCI project, which is funded by the European Space Agency (ESA) as part of the Agency’s Climate Change Initiative (CCI) Programme, aims to deliver a significant improvement on the capability of current satellite LST data records to meet the challenging Global Climate Observing System (GCOS) requirements for climate applications to realise the full potential of long-term LST data for climate science.\r\n\r\nAccurate knowledge of LST plays a key role in describing the physics of land-surface processes at regional and global scales as they combine information on both the surface-atmosphere interactions and energy fluxes within the Earth Climate System. LST provides a metric of surface state when combined with vegetation parameters and soil moisture and is one of the drivers of vegetation phenology. Furthermore, LST is an independent temperature data set for quantifying climate change complementary to the near-surface air temperature ECV based on in situ measurements and reanalyses.\r\n\r\nThe team uses data from a variety of satellites to provide an accurate view of temperatures across land surfaces globally over the past +20 years. This involves developing innovative techniques to merge data from different satellites into combined long-term satellite records for climate. These will all be evaluated by scientists working at leading climate centres." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62779, 62780, 62781, 62790, 62791, 66302, 66303, 66304, 66305, 66306, 66307, 66308, 66309, 66310, 66311, 66312, 66313, 66314, 82154 ], "vocabularyKeywords": [], "identifier_set": [ 11009 ], "observationcollection_set": [ { "ob_id": 34871, "uuid": "57cfc8b38d914abc8de02b647e879e66", "short_code": "coll", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Collection 1", "abstract": "This set of data comprises the first collection of land surface temperature datasets released by the ESA Land Surface Temperature Climate Change Initiative (LST_cci). \r\n\r\nData have been derived globally from a range of different satellites instruments.\r\n\r\nThe 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." } ], "responsiblepartyinfo_set": [ 148955, 148956, 148957, 148958, 148959, 148960, 148961, 148962, 169509 ], "onlineresource_set": [ 43644, 50440, 50438, 50439, 50437, 94737, 94738, 94739 ] }, { "ob_id": 33363, "uuid": "6babb8d9a8d247bcb3da6aed42f4b59a", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from MODIS (Moderate resolution Infra-red Spectroradiometer) on Aqua, level 3 collated (L3C) global product (2002-2018), version 3.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Aqua (Aqua). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the daytime and night-time Aqua equator crossing times which are 13:30 and 01:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 4th July 2002 and ends on 31st December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-02-01T09:57:28", "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). \r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-02-25T09:38:33", "doiPublishedTime": "2022-02-25T09:59:10", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 34704, "dataPath": "/neodc/esacci/land_surface_temperature/data/AQUA_MODIS/L3C/0.01/v3.00/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8680638173212, "numberOfFiles": 12009, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9622, "startTime": "2002-07-04T00:00:00", "endTime": "2018-12-31T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 34706, "uuid": "d117a265c9d24309b895d62c168d39ef", "short_code": "cmppr", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Moderate resolution Infra-red Spectroradiometer (MODIS) on Aqua level 3 collated (L3C) global product (2002-2018), version 3.00", "abstract": "Data has been derived from the Moderate resolution Infra-red Spectroradiometer (MODIS) on the Aqua\r\n satellite.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "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, 62790, 62791, 66303, 66305, 66307, 66308, 66310, 66311, 66312, 66751, 66754, 75124, 82154 ], "vocabularyKeywords": [], "identifier_set": [ 11006 ], "observationcollection_set": [ { "ob_id": 34871, "uuid": "57cfc8b38d914abc8de02b647e879e66", "short_code": "coll", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Collection 1", "abstract": "This set of data comprises the first collection of land surface temperature datasets released by the ESA Land Surface Temperature Climate Change Initiative (LST_cci). \r\n\r\nData have been derived globally from a range of different satellites instruments.\r\n\r\nThe 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." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 148963, 148965, 148966, 148967, 148968, 148969, 148970, 168978, 168979, 168980 ], "onlineresource_set": [ 43645, 47653, 47654, 47655, 50436, 90925, 90926, 90927, 90928, 90929, 90930, 94747, 94748, 94749, 94750, 94751 ] }, { "ob_id": 33364, "uuid": "1115d8946ba74c7f8a9fc3bfee5513a0", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from AATSR (Advanced Along-Track Scanning Radiometer), level 3 collated (L3C) global product (2002-2012), version 3.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Envisat equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. AATSR achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 25th July 2002 and ends on 8th April 2012. There is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-02-02T20:07:41", "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "land surface temperature, ESA, CCI, AATSR", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.01 degree", "status": "completed", "dataPublishedTime": "2022-02-25T09:54:14", "doiPublishedTime": "2022-02-25T09:56:08", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 34725, "dataPath": "/neodc/esacci/land_surface_temperature/data/ENVISAT_ATSR/L3C/0.01/v3.00/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1692010964766, "numberOfFiles": 6911, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9627, "startTime": "2002-07-25T00:00:00", "endTime": "2012-04-08T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 34728, "uuid": "d0c234bef9ce4607bb252fe87053a3a7", "short_code": "cmppr", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Advanced Along-Track Scanning Radiometer (AATSR) level 3 collated (L3C) global product (2002-2012), version 3.00\\", "abstract": "Data has been derived from the Advanced Along-Track Scanning Radiometer 2 (AATSR) on the Envisat satellite.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "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": [ 45321, 45322, 45323, 45324, 45325, 45326, 45327, 45328, 45329, 45330, 45331, 45332, 45333, 45334, 45335, 45336, 45337 ], "vocabularyKeywords": [], "identifier_set": [ 11001 ], "observationcollection_set": [ { "ob_id": 34871, "uuid": "57cfc8b38d914abc8de02b647e879e66", "short_code": "coll", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Collection 1", "abstract": "This set of data comprises the first collection of land surface temperature datasets released by the ESA Land Surface Temperature Climate Change Initiative (LST_cci). \r\n\r\nData have been derived globally from a range of different satellites instruments.\r\n\r\nThe 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." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 148972, 148973, 148974, 148975, 148976, 148977, 169072, 148971, 169073, 169075, 169074 ], "onlineresource_set": [ 43646, 47756, 47757, 47758, 50435, 90959, 90960, 90961, 90962, 90963, 90964, 90965, 94772, 94773, 94774, 94775, 94776 ] }, { "ob_id": 33365, "uuid": "f1445bde2f1249c99bb5a59b71e9a9d7", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2), level 3 collated (L3C) global product (1995-2013), version 3.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time. \r\n\r\nPer pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.\r\n\r\nAlso provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml.\r\n\r\nLSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st August 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-02-02T16:11:35", "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "land surface temperature, ESA, CCI, ATSR-2", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-02-25T09:54:28", "doiPublishedTime": "2022-02-25T09:56:23", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 34712, "dataPath": "/neodc/esacci/land_surface_temperature/data/ERS-2_ATSR/L3C/0.01/v3.00/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1183992251953, "numberOfFiles": 4915, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9608, "startTime": "1995-08-01T00:00:00", "endTime": "2003-06-22T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 34667, "uuid": "6c409ce178ec4d5eab40c1d6feaa070b", "short_code": "cmppr", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Along-Track Scanning Radiometer 2 (ATSR-2) level 3 collated (L3C) global product (1995-2003), version 3.00", "abstract": "Data has been derived from the Along-Track Scanning Radiometer 2 (ATSR-2) on the European Remote-sensing Satellite (ERS-2)\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "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, 62790, 62791, 66303, 66305, 66307, 66308, 66310, 66311, 66312, 66751, 66754, 75124, 82154 ], "vocabularyKeywords": [], "identifier_set": [ 11002 ], "observationcollection_set": [ { "ob_id": 34871, "uuid": "57cfc8b38d914abc8de02b647e879e66", "short_code": "coll", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Collection 1", "abstract": "This set of data comprises the first collection of land surface temperature datasets released by the ESA Land Surface Temperature Climate Change Initiative (LST_cci). \r\n\r\nData have been derived globally from a range of different satellites instruments.\r\n\r\nThe 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." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 148978, 148979, 148980, 148981, 148982, 148983, 148984, 168063, 168064, 168065 ], "onlineresource_set": [ 43647, 47633, 50434, 47635, 47634, 90953, 90954, 90955, 90956, 90957, 90958, 94767, 94768, 94769, 94770, 94771 ] }, { "ob_id": 33366, "uuid": "330b7c922a37420fabb3425671d7d7c6", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2020), version 3.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st May 2016 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-02-02T13:10:42", "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "land surface temperature, ESA, CCI, SLSTR", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.01 degree", "status": "completed", "dataPublishedTime": "2022-02-25T09:54:52", "doiPublishedTime": "2022-02-25T09:56:35", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 34731, "dataPath": "/neodc/esacci/land_surface_temperature/data/SENTINEL3A_SLSTR/L3C/0.01/v3.00/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1354795145452, "numberOfFiles": 3291, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9628, "startTime": "2016-05-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 34732, "uuid": "4b56444be9cd4f4e99d8244ccf5a66f2", "short_code": "cmppr", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A level 3 collated (L3C) global product (2016-2020), version 3.00", "abstract": "Data has been derived from the Sea and Land Surface Temperature Radiometer (SLSTR) on the Sentinel 3A satellite.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "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, 62790, 62791, 66303, 66305, 66307, 66308, 66310, 66311, 66312, 66751, 66754, 75124, 82154 ], "vocabularyKeywords": [], "identifier_set": [ 11003 ], "observationcollection_set": [ { "ob_id": 34871, "uuid": "57cfc8b38d914abc8de02b647e879e66", "short_code": "coll", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Collection 1", "abstract": "This set of data comprises the first collection of land surface temperature datasets released by the ESA Land Surface Temperature Climate Change Initiative (LST_cci). \r\n\r\nData have been derived globally from a range of different satellites instruments.\r\n\r\nThe 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." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 148985, 148986, 148987, 148988, 148989, 148990, 148991, 169094, 169095, 169097, 169096 ], "onlineresource_set": [ 43648, 47760, 47761, 47762, 50433, 90945, 90946, 90947, 90948, 90949, 90950, 90951, 90952, 94762, 94763, 94764, 94765, 94766 ] }, { "ob_id": 33367, "uuid": "5f66a881adf846bfaad58b0e6068f0ea", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land Surface Temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2020), version 3.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 17th November 2018 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-02-02T11:47:35", "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). \r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "land surface temperature, ESA, CCI, SLSTR", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-02-25T09:55:22", "doiPublishedTime": "2022-02-25T09:56:42", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 34735, "dataPath": "/neodc/esacci/land_surface_temperature/data/SENTINEL3B_SLSTR/L3C/0.01/v3.00/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 595761744858, "numberOfFiles": 1473, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9629, "startTime": "2018-11-17T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 34736, "uuid": "0969e83cf7134b0c89011f5c4ac8e8ca", "short_code": "cmppr", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B level 3 collated (L3C) global product (2018-2020), version 3.00", "abstract": "Data has been derived from the Sea and Land Surface Temperature Radiometer (SLSTR) on the Sentinel 3B satellite.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "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": [ 45321, 45322, 45323, 45324, 45325, 45326, 45327, 45328, 45329, 45330, 45331, 45332, 45333, 45334, 45335, 45336, 45337 ], "vocabularyKeywords": [], "identifier_set": [ 11004 ], "observationcollection_set": [ { "ob_id": 34871, "uuid": "57cfc8b38d914abc8de02b647e879e66", "short_code": "coll", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Collection 1", "abstract": "This set of data comprises the first collection of land surface temperature datasets released by the ESA Land Surface Temperature Climate Change Initiative (LST_cci). \r\n\r\nData have been derived globally from a range of different satellites instruments.\r\n\r\nThe 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." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 148992, 148993, 148994, 148995, 148996, 148997, 148998, 169102, 169103, 169105, 169104 ], "onlineresource_set": [ 43649, 47765, 47764, 47766, 50432, 90938, 90939, 90940, 90941, 90942, 90943, 90944, 94757, 94758, 94759, 94760, 94761 ] }, { "ob_id": 33368, "uuid": "58a01734f841466daa1837353aee5ff8", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land Surface Temperature from MODIS (Moderate resolution Infra-red Spectroradiometer) on Terra, level 3 collated (L3C) global product (2000-2018), version 3.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Terra (Terra). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Terra equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 24th February 2000 and ends on 31st December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-01-07T01:52:47", "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). \r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.01 degree", "status": "completed", "dataPublishedTime": "2022-02-25T09:55:54", "doiPublishedTime": "2022-02-25T09:56:52", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 34708, "dataPath": "/neodc/esacci/land_surface_temperature/data/TERRA_MODIS/L3C/0.01/v3.00/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9991764026044, "numberOfFiles": 13651, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9623, "startTime": "2000-02-24T00:00:00", "endTime": "2018-12-31T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 34711, "uuid": "415abc3d33ef49a39b5383945f3fe220", "short_code": "cmppr", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Moderate resolution Infra-red Spectroradiometer (MODIS) on Terra level 3 collated (L3C) global product (2000-2018), version 3.00", "abstract": "Data has been derived from the Moderate resolution Infra-red Spectroradiometer (MODIS) on the Aqua\r\n satellite.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "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, 62790, 62791, 66303, 66305, 66307, 66308, 66310, 66311, 66312, 66751, 66754, 75124, 82154 ], "vocabularyKeywords": [], "identifier_set": [ 11005 ], "observationcollection_set": [ { "ob_id": 34871, "uuid": "57cfc8b38d914abc8de02b647e879e66", "short_code": "coll", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Collection 1", "abstract": "This set of data comprises the first collection of land surface temperature datasets released by the ESA Land Surface Temperature Climate Change Initiative (LST_cci). \r\n\r\nData have been derived globally from a range of different satellites instruments.\r\n\r\nThe 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." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 148999, 149000, 149001, 149002, 149003, 149004, 149005, 168991, 168992, 168993 ], "onlineresource_set": [ 43650, 47659, 47660, 47661, 50431, 90931, 90932, 90933, 90934, 90935, 90936, 90937, 94752, 94753, 94754, 94755, 94756 ] }, { "ob_id": 33369, "uuid": "6775e27575124407afeebb4bb1dfaaf5", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) land surface temperature (LST) level 3 supercollated (L3S) global product (2009-2020), version 1.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on satellites in Geostationary Earth Orbit (GEO) and Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nLST fields are provided at 3 hourly intervals each day (00:00 UTC, 03:00 UTC, 06:00 UTC, 09:00 UTC, 12:00 UTC, 15:00 UTC, 18:00 UTC and 21:00 UTC). Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and the solar geometry angles.\r\n\r\nThe product is based on merging of available GEO data and infilling with available LEO data outside of the GEO discs. Inter-instrument biases are accounted for by cross-calibration with the IASI instruments on METOP and LSTs are retrieved using a Generalised Split Window algorithm from all instruments. As data towards the edge of the GEO disc is known to have greater uncertainty, any datum with a satellite zenith angle of more than 60 degrees is discarded. All LSTs included have an observation time that lies within +/- 30 minutes of the file nominal Universal Time.\r\n\r\nData from the following instruments is included in the dataset: geostationary, Imagers on Geostationary Operational Environmental Satellite (GOES) 12 and GOES 13, Advanced Baseline Imager (ABI) on GOES 16, Spinning Enhanced Visible Infra-Red Imager (SEVIRI) on Meteosat Second Generation (MSG) 1, MSG 2, MSG 3, and MSG 4, Japanese Advanced Meteorological Imager (JAMI) on Multifunctional Transport Satellite MTSAT) 1, and MTSAT 2; and polar, Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat), Moderate-resolution Imaging Spectroradiometer (MODIS) on Earth Observation System (EOS) - Aqua and EOS - Terra, Sea and Land Surface Temperature Radiometer SLSTR on Sentinel-3A and Sentinel-3B. However, it should be noted that which instruments contribute to a particular product file depends on depends on mission start and end dates and instrument downtimes.\r\n\r\nDataset coverage starts on 1st January 2009 and ends on 31st December 2020. \r\n\r\nLSTs are provided on a global equal angle grid at a resolution of 0.05° longitude and 0.05° latitude. The dataset coverage is nominally global over the land surface but varies depending on satellite and instrument availability and coverage. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and data were processed in the UoL processing chain. The Geostationary data were produced by the Instituto Português do Mar e da Atmosfera (IPMA) before being merged into the final dataset.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-03-07T16:11:08", "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). \r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "land surface temperature, ESA, CCI", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-02-25T09:31:30", "doiPublishedTime": "2022-02-25T09:59:28", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 34757, "dataPath": "/neodc/esacci/land_surface_temperature/data/MULTISENSOR_IRMGP/L3S/0.05/v1.00/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 944844752958, "numberOfFiles": 35066, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9631, "startTime": "2009-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 34746, "uuid": "b6501a3e35d24202ad89d3d5544ee6f4", "short_code": "cmppr", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) Land surface temperature (LST) level 3 supercollated (L3S) global product (2009-2020), version 1.00", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2558, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 30, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_lst_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 33361, "uuid": "555149fdc3ef4e23a1de8ece93c29f5d", "short_code": "proj", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci)", "abstract": "The land surface temperature (LST) CCI project, which is funded by the European Space Agency (ESA) as part of the Agency’s Climate Change Initiative (CCI) Programme, aims to deliver a significant improvement on the capability of current satellite LST data records to meet the challenging Global Climate Observing System (GCOS) requirements for climate applications to realise the full potential of long-term LST data for climate science.\r\n\r\nAccurate knowledge of LST plays a key role in describing the physics of land-surface processes at regional and global scales as they combine information on both the surface-atmosphere interactions and energy fluxes within the Earth Climate System. LST provides a metric of surface state when combined with vegetation parameters and soil moisture and is one of the drivers of vegetation phenology. Furthermore, LST is an independent temperature data set for quantifying climate change complementary to the near-surface air temperature ECV based on in situ measurements and reanalyses.\r\n\r\nThe team uses data from a variety of satellites to provide an accurate view of temperatures across land surfaces globally over the past +20 years. This involves developing innovative techniques to merge data from different satellites into combined long-term satellite records for climate. These will all be evaluated by scientists working at leading climate centres." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62779, 62780, 62781, 62790, 62791, 66303, 66305, 66307, 66308, 66310, 66311, 66312, 66754, 82154 ], "vocabularyKeywords": [], "identifier_set": [ 11007 ], "observationcollection_set": [ { "ob_id": 34871, "uuid": "57cfc8b38d914abc8de02b647e879e66", "short_code": "coll", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Collection 1", "abstract": "This set of data comprises the first collection of land surface temperature datasets released by the ESA Land Surface Temperature Climate Change Initiative (LST_cci). \r\n\r\nData have been derived globally from a range of different satellites instruments.\r\n\r\nThe 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." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 149006, 149007, 149008, 149009, 149010, 149011, 149012, 169143, 169144, 169145, 169146, 169147 ], "onlineresource_set": [ 43651, 50401, 50399, 50430, 50400, 94745, 94746 ] }, { "ob_id": 33370, "uuid": "ef8ce37b6af24469a2a4bdc31d3db27d", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2020), version 2.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer on Sentinel 3A (SLSTRA); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra) to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from August 1995 to July 2002; AATSR from August 2002 to March 2012; MODIS Terra from April 2012 to July 2016; and SLSTRA from August 2016 to December 2020. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width.\r\n\r\nThe dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml).\r\n\r\nLSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. Full Earth coverage is achieved in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st August 1995 and ends on 31st December 2020. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies. \r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-02-03T22:36:21", "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "land surface temperature, ESA, CCI, infra-red", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-02-25T09:30:26", "doiPublishedTime": "2022-02-25T09:59:40", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 34739, "dataPath": "/neodc/esacci/land_surface_temperature/data/MULTISENSOR_IRCDR/L3S/0.01/v2.00/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4309554055171, "numberOfFiles": 17403, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9630, "startTime": "1995-08-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 34742, "uuid": "860f3c1333c44302bd01911d53cdaeea", "short_code": "cmppr", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) Land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2020), version 2.00", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "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": [ 45321, 45322, 45323, 45324, 45325, 45326, 45327, 45328, 45329, 45330, 45331, 45332, 45333, 45334, 45335, 45336, 45337, 45734 ], "vocabularyKeywords": [], "identifier_set": [ 11008 ], "observationcollection_set": [ { "ob_id": 34871, "uuid": "57cfc8b38d914abc8de02b647e879e66", "short_code": "coll", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Collection 1", "abstract": "This set of data comprises the first collection of land surface temperature datasets released by the ESA Land Surface Temperature Climate Change Initiative (LST_cci). \r\n\r\nData have been derived globally from a range of different satellites instruments.\r\n\r\nThe 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." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 149013, 149014, 149015, 149016, 149017, 149018, 149019, 169110, 169111, 169113, 169112 ], "onlineresource_set": [ 43652, 50395, 50396, 50394, 50429, 90915, 90916, 90917, 90918, 90919, 90920, 90921, 90922, 90923, 90924, 94740, 94741, 94742, 94743, 94744 ] }, { "ob_id": 33374, "uuid": "46f5d503ce284114b5925709258bacc5", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Moisture Index (NDMI) v1", "abstract": "Sentinel-Hub NDMI description: \r\n\r\nThe NDMI is a normalized difference moisture index, that uses NIR and SWIR bands to display moisture. The SWIR band reflects changes in both the vegetation water content and the spongy mesophyll structure in vegetation canopies, while the NIR reflectance is affected by leaf internal structure and leaf dry matter content but not by water content. The combination of the NIR with the SWIR removes variations induced by leaf internal structure and leaf dry matter content, improving the accuracy in retrieving the vegetation water content. The amount of water available in the internal leaf structure largely controls the spectral reflectance in the SWIR interval of the electromagnetic spectrum. SWIR reflectance is therefore negatively related to leaf water content. In short, NDMI is used to monitor changes in the water content of leaves and was proposed by Gao. NDWI is computed using the near-infrared (NIR) and the short wave infrared (SWIR) reflectances:\r\n\r\nNDMI = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NDMI = (B08 - B11) / (B08 + B11)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Moisture Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDMI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-23T02:02:32", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "JNCC, ARD, NDMI, Sentinel 2, EO indices, Analysis Ready Data, Sentinel", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-05-11T14:37:16", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2609, "bboxName": "UK", "eastBoundLongitude": 2.02, "westBoundLongitude": -10.85, "southBoundLatitude": 49.82, "northBoundLatitude": 59.48 }, "verticalExtent": null, "result_field": { "ob_id": 33375, "dataPath": "/neodc/sentinel_ard/indices/sentinel_2/ndmi/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2000492691567, "numberOfFiles": 17689, "fileFormat": "These data are provided in Cloud Optimized GeoTIFF (COG) format." }, "timePeriod": { "ob_id": 9166, "startTime": "2015-07-12T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3792, "explanation": "A representative subset of data are manually checked to ensure the indices files align with the ARD files, the value ranges are as expected, and the data format is correct. 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": "2021-11-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33381, "uuid": "0b93bf0463a54e3ba6e590552af19cfe", "short_code": "cmppr", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Moisture Index (NDMI) v1", "abstract": "All index equations from Sentinel-hub custom scripts (links in Related Documents).\r\nSentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. ARD granules are masked using the corresponding cloud and topographic shadow masks. NDMI index files are then generated in R 3.6.1 using the raster::calc function." }, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30197, "uuid": "14818d07b867480c90ed01fbf81d9346", "short_code": "proj", "title": "Defra and JNCC Analysis Ready Sentinel Data", "abstract": "Defra and JNCC aim to provide timely, cost-effective, and high-quality, Analysis Ready Sentinel Data (ARD) for a wide range of applications. These data are provided for the UK geographic area for this project. The indices datasets currently cover Yorkshire, UK, but coverage will be expanded in the future. These products have been produced using the Copernicus satellites, Sentinel 1 and Sentinel 2." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 149026, 149027, 149028, 149029, 149030, 149031, 149032, 149033 ], "onlineresource_set": [ 43653, 43654 ] }, { "ob_id": 33376, "uuid": "b42f524bc9cd4dd6850b2399b616f5c4", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v1", "abstract": "Sentinel-Hub NDWI description: The NDWI is used to monitor changes related to water content in water bodies. As water bodies strongly absorb light in visible to the infrared electromagnetic spectrum, NDWI uses green and near-infrared bands to highlight water bodies. It is sensitive to built-up land and can result in the over-estimation of water bodies. \r\nIndex values greater than 0.5 usually correspond to water bodies. Vegetation usually corresponds to much smaller values and built-up areas to values between zero and 0.2.\r\n\r\nNDWI = (GREEN – NIR) / (GREEN + NIR)\r\n\r\nSentinel-2 NDWI = (B03 - B08) / (B03 + B08)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Water Index (NDWI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDWI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-05-24T01:46:23", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "JNCC, ARD, NDWI, Sentinel 2, EO indices, Analysis Ready Data, Sentinel", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-05-11T14:39:30", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2609, "bboxName": "UK", "eastBoundLongitude": 2.02, "westBoundLongitude": -10.85, "southBoundLatitude": 49.82, "northBoundLatitude": 59.48 }, "verticalExtent": null, "result_field": { "ob_id": 33377, "dataPath": "/neodc/sentinel_ard/indices/sentinel_2/ndwi/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1971223782059, "numberOfFiles": 17689, "fileFormat": "These data are provided in Cloud Optimized GeoTIFF (COG) format." }, "timePeriod": { "ob_id": 9165, "startTime": "2015-07-12T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3792, "explanation": "A representative subset of data are manually checked to ensure the indices files align with the ARD files, the value ranges are as expected, and the data format is correct. 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": "2021-11-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33378, "uuid": "cb31c83f45104421b6866bc2f1a81259", "short_code": "cmppr", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v1", "abstract": "All index equations from Sentinel-hub custom scripts (links in Related Documents).\r\nSentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. ARD granules are masked using the corresponding cloud and topographic shadow masks. NDWI index files are then generated in R 3.6.1 using the raster::calc function." }, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30197, "uuid": "14818d07b867480c90ed01fbf81d9346", "short_code": "proj", "title": "Defra and JNCC Analysis Ready Sentinel Data", "abstract": "Defra and JNCC aim to provide timely, cost-effective, and high-quality, Analysis Ready Sentinel Data (ARD) for a wide range of applications. These data are provided for the UK geographic area for this project. The indices datasets currently cover Yorkshire, UK, but coverage will be expanded in the future. These products have been produced using the Copernicus satellites, Sentinel 1 and Sentinel 2." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 149039, 149040, 149041, 149034, 149035, 149036, 149037, 149038 ], "onlineresource_set": [ 43656, 43655 ] }, { "ob_id": 33382, "uuid": "6df6b803c2784b8ab9e03834bf9a4337", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR) v1", "abstract": "Sentinel Hub NBR description: To detect burned areas, the NBR-RAW index is the most appropriate choice. Using bands 8 and 12 it highlights burnt areas in large fire zones greater than 500 acres. To observe burn severity, you may subtract the post-fire NBR image from the pre-fire NBR image. Darker pixels indicate burned areas.\r\n\r\nNBR = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NBR = (B08 - B12) / (B08 + B12)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital & Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat condition at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDVI, NDMI, NDWI, NBR, and EVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-23T02:01:48", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "JNCC, ARD, NBR, Sentinel 2, EO indices, Analysis Ready Data, Sentinel", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-05-11T14:40:03", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2609, "bboxName": "UK", "eastBoundLongitude": 2.02, "westBoundLongitude": -10.85, "southBoundLatitude": 49.82, "northBoundLatitude": 59.48 }, "verticalExtent": null, "result_field": { "ob_id": 33383, "dataPath": "/neodc/sentinel_ard/indices/sentinel_2/nbr/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1969713317948, "numberOfFiles": 17451, "fileFormat": "These data are provided in Cloud Optimized GeoTIFF (COG) format." }, "timePeriod": { "ob_id": 9167, "startTime": "2015-07-12T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3792, "explanation": "A representative subset of data are manually checked to ensure the indices files align with the ARD files, the value ranges are as expected, and the data format is correct. 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": "2021-11-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33409, "uuid": "4e83f489b880411ab9eed49276a76476", "short_code": "cmppr", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR)", "abstract": "All index equations from Sentinel-hub custom scripts (links in Related Documents).\r\nSentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. ARD granules are masked using the corresponding cloud and topographic shadow masks. NBR index files are then generated in R 3.6.1 using the raster::calc function." }, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30197, "uuid": "14818d07b867480c90ed01fbf81d9346", "short_code": "proj", "title": "Defra and JNCC Analysis Ready Sentinel Data", "abstract": "Defra and JNCC aim to provide timely, cost-effective, and high-quality, Analysis Ready Sentinel Data (ARD) for a wide range of applications. These data are provided for the UK geographic area for this project. The indices datasets currently cover Yorkshire, UK, but coverage will be expanded in the future. These products have been produced using the Copernicus satellites, Sentinel 1 and Sentinel 2." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 149046, 149047, 149048, 149049, 149050, 149051, 149052, 149053 ], "onlineresource_set": [ 43660, 43659 ] }, { "ob_id": 33384, "uuid": "102909b1d169469f85c099a6f1686bad", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index (EVI)", "abstract": "EVI is a development on Normalised Difference Vegetation Index (NDVI).\r\n\r\nSentinel-Hub EVI description: \r\nIn areas of dense canopy cover, where leaf area index (LAI) is high, the blue wavelengths can be used to improve the accuracy of NDVI, as it corrects for soil background signals and atmospheric influences. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\r\n\r\nEVI is calculated: EVI = 2.5 * ((NIR – RED) / ((NIR + (6 * RED) – (7.5 * BLUE)) + 1))\r\n\r\nSentinel 2 EVI = 2.5 * ((B8 – B4) / ((B8 + (6 * B4) – (7.5 * B2)) + 1))\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Enhanced Vegetation Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nEVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "These data have been removed due to errors in the processing. At present there are no plans to replace them.", "keywords": "JNCC, ARD, EVI, Sentinel 2, EO indices, Analysis Ready Data, Sentinel", "publicationState": "removed", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "obsolete", "dataPublishedTime": "2022-05-11T14:41:31", "doiPublishedTime": null, "removedDataTime": "2023-02-10T14:00:30", "geographicExtent": { "ob_id": 2907, "bboxName": "Yorkshire ARD", "eastBoundLongitude": 1.733, "westBoundLongitude": -3.0, "southBoundLatitude": 53.071, "northBoundLatitude": 55.047 }, "verticalExtent": null, "result_field": { "ob_id": 33385, "dataPath": "/neodc/sentinel_ard/indices/sentinel_2/evi/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 970, "numberOfFiles": 1, "fileFormat": "These data are provided in Cloud Optimized GeoTIFF (COG) format." }, "timePeriod": { "ob_id": 9168, "startTime": "2015-07-12T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3792, "explanation": "A representative subset of data are manually checked to ensure the indices files align with the ARD files, the value ranges are as expected, and the data format is correct. 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": "2021-11-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33406, "uuid": "b842c42d1ad74be9a5d0ebb8afa3ee66", "short_code": "cmppr", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index (EVI)", "abstract": "All index equations from Sentinel-hub custom scripts (links in Related Documents).\r\nSentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. ARD granules are masked using the corresponding cloud and topographic shadow masks. EVI index files are then generated in R 3.6.1 using the raster::calc function." }, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30197, "uuid": "14818d07b867480c90ed01fbf81d9346", "short_code": "proj", "title": "Defra and JNCC Analysis Ready Sentinel Data", "abstract": "Defra and JNCC aim to provide timely, cost-effective, and high-quality, Analysis Ready Sentinel Data (ARD) for a wide range of applications. These data are provided for the UK geographic area for this project. The indices datasets currently cover Yorkshire, UK, but coverage will be expanded in the future. These products have been produced using the Copernicus satellites, Sentinel 1 and Sentinel 2." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 149061, 149054, 149055, 149056, 149057, 149058, 149059, 149060 ], "onlineresource_set": [ 43662, 43661 ] }, { "ob_id": 33390, "uuid": "b51b72736ff943bb830e3c241d032621", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.17 (v20211203)", "abstract": "Data for Figure 2.17 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.17 shows temporal evolution of the Hadley Cell extent and intensity since 1979 from a variety of reanalyses.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, with data provided for all panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains the annual average of Northern Hemisphere and Southern Hemisphere latitude of the Hadley Cell edge, as well as the intensity of the Hadley Cell for each hemisphere\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n \r\n - Data file: HCextent_2.csv, column 2 cyan line, upper panel\r\n - Data file: HCextent_2.csv, column 3 orange line, upper panel\r\n - Data file: HCextent_2.csv, column 4 grey line, upper panel\r\n - Data file: HCextent_2.csv, column 5 black line, upper panel\r\n - Data file: HCextent_2.csv, column 6 cyan line, lower panel\r\n - Data file: HCextent_2.csv, column 7 orange line, lower panel\r\n - Data file: HCextent_2.csv, column 8 grey line, lower panel\r\n - Data file: HCextent_2.csv, column 9 black line, lower panel\r\n\r\n\r\nPanel b:\r\n \r\n - Data file: HCintensity_2.csv, column 2 cyan line, upper panel\r\n - Data file: HCintensity_2.csv, column 3 orange line, upper panel\r\n - Data file: HCintensity_2.csv, column 4 grey line, upper panel\r\n - Data file: HCintensity_2.csv, column 5 black line, upper panel\r\n - Data file: HCintensity_2.csv, column 6 cyan line, lower panel\r\n - Data file: HCintensity_2.csv, column 7 orange line, lower panel\r\n - Data file: HCintensity_2.csv, column 8 grey line, lower panel\r\n - Data file: HCintensity_2.csv, column 9 black line, lower panel\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:44:59", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 2, Changing state, Multi-millennial context, pre-industrial, Natural forcing, anthropogenic forcing, Radiative forcing, Large-scale indicators, observed changes, Modes of variability, Figure 2.17, Hadley Cell, tropical expansion", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-11T15:49:58", "doiPublishedTime": "2023-07-03T20:01:32", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 81, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39218, "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig17/v20211203", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 14412, "numberOfFiles": 5, "fileFormat": "CSV" }, "timePeriod": { "ob_id": 9170, "startTime": "1980-01-01T12:00:00", "endTime": "2018-12-31T12:00:00" }, "resultQuality": { "ob_id": 3800, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-12-03" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33392, "uuid": "1dd1d4bf42dc465a902141e41cc400bb", "short_code": "comp", "title": "Caption for Figure 2.17 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Time series of the annual mean Northern Hemisphere (NH, top curves) and Southern Hemisphere (SH, bottom curves) Hadley cell extent (a) and Hadley cell intensity (b) since 1979. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12618 ], "observationcollection_set": [ { "ob_id": 32717, "uuid": "3da412ad9912427d9bb808b57faa21a7", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1" } ], "responsiblepartyinfo_set": [ 149102, 149103, 149104, 149105, 149106, 149107, 149101, 149108, 149109, 196262 ], "onlineresource_set": [ 43669, 52403, 43670, 82832 ] }, { "ob_id": 33394, "uuid": "f7c3f3cbf65447b9a43207dcc30219d9", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 3.28 (v20220621)", "abstract": "Input Data for Figure 3.28 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.28 shows long-term trends in halosteric and thermosteric sea level in CMIP6 models and observations. \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\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The data is used in left upper and left lower panels (scatter panels), as well as right upper panels (D&W, EN4, Ishii) \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n 210127_DurackandWijffels_V1.0_70yr_steric_1950-2019_0-2000db_210122-205355_beta.nc is input data for D&W. The variables steric_height_halo_anom_depthInterp and steric_height_thermo_anom_depthInterp are used.\r\n 210201_EN4.2.1.g10_annual_steric_1950-2019_5-5350m.nc is input data for EN4\r\n 210201_Ishii17_v7.3_annual_steric_1955-2019_0-3000m.nc is input data for Ishii\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data.\r\n ---------------------------------------------------\r\n This data is an input observational data for the Figure 3.28. It is used for scatter plots and contour maps.\r\n In addition, shapefiles are required to calculate the regional boundaries: Pacific.shp, Atlantic.shp. These regions should be standarised throught AR6.\r\n\r\n\r\nThe following changes to filenames were made to archive the data (due to filenaming restrictions). To use the data with any associated figure code, the filenames should be reverted.\r\n\r\n 210127_DurackandWijffels_V1_0_70yr_steric_1950-2019_0-2000db_210122-205355_beta.nc -> 210127_DurackandWijffels_V1.0_70yr_steric_1950-2019_0-2000db_210122-205355_beta.nc \r\n 210201_EN4_2_1_g10_annual_steric_1950-2019_5-5350m.nc -> 210201_EN4.2.1.g10_annual_steric_1950-2019_5-5350m.nc \r\n 210201_Ishii17_v7_3_annual_steric_1955-2019_0-3000m.nc -> 210201_Ishii17_v7.3_annual_steric_1955-2019_0-3000m.nc \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 containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:44:57", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.28, Sea Level, Ocean, Halosteric Sea Level Rise, Observations", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-07-01T11:58:53", "doiPublishedTime": "2023-02-08T19:39:23.555140", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 82, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37700, "dataPath": "/badc/ar6_wg1/data/ch_03/inputdata_ch3_fig28/v20220620", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 574024475, "numberOfFiles": 21, "fileFormat": "txt, netCDF, cpg, dbf, prj, shp, shx" }, "timePeriod": { "ob_id": 9171, "startTime": "1950-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 3801, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-12-03" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33396, "uuid": "e7b9ab58d21047c1b9a515faab5625e6", "short_code": "comp", "title": "Caption for Figure 3.28 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Long-term trends in halosteric and thermosteric sea level in CMIP6 models and observations. Units are mm year-1. In the right column, three observed maps of 0 to 2000 m halosteric sea level trends (right column) top from (Durack and Wijffels, 2010, 1950–2019 updated – D&W), middle from (Good et al., 2013, 1950-2019 updated- EN4), and lower from (Ishii et al., 2017, 1955-2019 updated – Ishii), and bottom, the CMIP6 historical multi-model mean (1950-2014). Red and orange colours show a halosteric contraction (enhanced salinity) and blue and green a halosteric expansion (reduced salinity). In the left column, basin-integrated halosteric (top) and thermosteric (bottom) trends for the Atlantic and Pacific, the two largest ocean basins, where Pacific anomalies are presented on the x-axis and Atlantic on the y-axis. Observational estimates are presented in black, CMIP6 historical (all forcings) simulations are shown in orange squares, with the multi-model mean shown as a dark orange diamond with a black bounding box. CMIP6 hist-nat (historical natural forcings only) simulations are shown in green squares with the multi-model mean as a dark green diamond with a black bounding box. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1). " }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 9042, 9043, 9045, 50542, 50543, 50577, 63169, 63170, 63171, 63172, 63173, 63174, 63175, 63176, 63177, 63178, 63179, 63180, 63181, 63182, 63183, 63184, 63185, 63186, 63187, 63188, 63189, 63190, 63191, 63192, 63193, 63194, 63195, 63196, 63197, 63198, 63199 ], "vocabularyKeywords": [], "identifier_set": [ 12392 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 149112, 149113, 149114, 149115, 149116, 149117, 149118, 149119 ], "onlineresource_set": [ 43668, 43667, 82628, 82629, 89662, 89658, 89659, 89660, 89661, 89663, 89664, 89665, 89678, 89679, 89680, 89681, 89682, 89666, 89667, 89668, 89669, 89670, 89671, 89672, 89673, 89674, 89675, 89676, 89677 ] }, { "ob_id": 33399, "uuid": "a4cbbffe1bd44c7ba3e8608ee9c54547", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.31 (v20211203)", "abstract": "Data for Figure 3.31 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.31 shows evaluation of historical emission-driven CMIP6 simulations for 1850-2014. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for all panels in subdirectories named panel_a, panel_b, panel_c and panel_d.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Observed and simulated change in global mean atmospheric CO2 concentration (1850-2014)\r\n - Observed and simulated air surface temperature anomaly (1850-2014)\r\n - Observed and simulated change in land carbon uptake (1850-2014)\r\n - Observed and simulated change in ocean carbon uptake (1850-2014)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n panel_a/fig_3_31_panel_a.nc:\r\n \r\n - dim0 = 0: 'ACCESS-ESM1-5 ', (turquoise solid line), Australian Community Climate and Earth System Simulator - Earth System Model\r\n - dim0 = 1: 'CNRM-ESM2-1', (light green solid line), National Centre for Meteorological Research\r\n - dim0 = 2: 'CanESM5-CanOE ', (orange solid line), Canadian Earth System Model - Canadian Ocean Ecosystem model\r\n - dim0 = 3: 'CanESM5', (dark green solid line).\r\n - dim0 = 4: 'MIROC-ES2L', (light purple solid line), Japan Agency for Marine-Earth Science and Technology (JAMSTEC) and Centre for Climate System Research / National Institute for Environmental Studies, Japan.\r\n - dim0 = 5: 'MPI-ESM1-2-LR ', (teal solid line), Max Planck Institute Earth System Model \r\n - dim0 = 6: 'MRI-ESM2-0', (lime solid line), Meteorological Research Institute of the Japan Meteorological Agency\r\n - dim0 = 7: 'NorESM2-LM', (pink solid line), The Norwegian Earth System Model\r\n - dim0 = 8: 'UKESM1-0-LL', (dark purple solid line), UK Earth System Model\r\n - dim0 = 9: 'MultiModelMean', (red solid line).\r\n - dim0 = 10: 'ESRL' (OBS), (black solid line).\r\n\r\npanel_b/fig_3_31_panel_b.nc\r\n \r\n - dim0_0 = 0: 'ACCESS-ESM1-5',\r\n - dim0_0 = 1: 'ACCESS-ESM1-5_historical'.\r\n - dim0_0 = 2: 'CNRM-ESM2-1'.\r\n - dim0_0 = 3: 'CNRM-ESM2-1_historical'.\r\n - dim0_0 = 4: 'CanESM5-CanOE '.\r\n - dim0_0 = 5: 'CanESM5-CanOE_historical'.\r\n - dim0_0 = 6: 'CanESM5'.\r\n - dim0_0 = 7: 'CanESM5_historical'.\r\n - dim0_0 = 8: 'MIROC-ES2L'.\r\n - dim0_0 = 9: 'MIROC-ES2L_historical'.\r\n - dim0_0 = 10: 'MPI-ESM1-2-LR '.\r\n - dim0_0 = 11: 'MPI-ESM1-2-LR_historical '.\r\n - dim0_0 = 12: 'MRI-ESM2-0'.\r\n - dim0_0 = 13: 'MRI-ESM2-0_historical'.\r\n - dim0_0 = 14: 'NorESM2-LM'.\r\n - dim0_0 = 15: 'NorESM2-LM_historical'.\r\n - dim0_0 = 16: 'UKESM1-0-LL'.\r\n - dim0_0 = 17: 'UKESM1-0-LL_historical'.\r\n - dim0_0 = 18: 'HadCRUT5' (OBS), Met Office Hadley Centre\r\n\r\n\r\npanel_c/fig_3_31_panel_c.nc\r\n \r\n - dim0 = 0: 'ACCESS-ESM1-5 '.\r\n - dim0 = 1: 'CNRM-ESM2-1'.\r\n - dim0 = 2: 'CanESM5-CanOE '.\r\n - dim0 = 3: 'CanESM5'.\r\n - dim0 = 4: 'MIROC-ES2L'.\r\n - dim0 = 5: 'MPI-ESM1-2-LR '.\r\n - dim0 = 6: 'MRI-ESM2-0'.\r\n - dim0 = 7: 'NorESM2-LM'.\r\n - dim0 = 8: 'UKESM1-0-LL'.\r\n - dim0 = 9: 'MultiModelMean'.\r\n - dim0 = 10: 'GCP' (OBS), Global Carbon Project (GCP)\r\n\r\n\r\npanel_d/fig_3_31_panel_d.nc\r\n \r\n - dim0 = 0: 'ACCESS-ESM1-5 '.\r\n - dim0 = 1: 'CNRM-ESM2-1'.\r\n - dim0 = 2: 'CanESM5-CanOE '.\r\n - dim0 = 3: 'CanESM5'.\r\n - dim0 = 4: 'MIROC-ES2L'.\r\n - dim0 = 5: 'MPI-ESM1-2-LR '.\r\n - dim0 = 6: 'MRI-ESM2-0'.\r\n - dim0 = 7: 'NorESM2-LM'.\r\n - dim0 = 8: 'UKESM1-0-LL'.\r\n - dim0 = 9: 'MultiModelMean'.\r\n - dim0 = 10: 'GCP' (OBS).\r\n\r\n\r\nLabels and colors for all figures are the same as for panel a. Historical values in panel b are plotted with the same colors as the corresponding simulation, but using dotted lines.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:44:56", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.31, global mean atmospheric CO2 concentration, air surface temperature anomaly, land carbon uptake, ocean carbon uptake, historical emission-driven CMIP6 simulations", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-04-01T15:35:50", "doiPublishedTime": "2023-02-08T19:20:06.641357", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 84, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37117, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig31/v20211203", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 122031, "numberOfFiles": 7, "fileFormat": "netCDF" }, "timePeriod": { "ob_id": 9173, "startTime": "1850-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3803, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-12-03" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33401, "uuid": "a4811a3fb39444048b5e0b4cc3bef8ad", "short_code": "comp", "title": "Caption for Figure 3.31 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Figure 3.31 | Evaluation of historical emissions-driven CMIP6 simulations for 1850–2014. Observations (black) are compared to simulations of global mean (a) atmospheric CO2 concentration (ppmv), with observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL; Dlugokencky and Tans, 2020); (b) surface air temperature anomaly (°C) with respect to the 1850–1900 mean, with observations from HadCRUT4 (Morice et al., 2012); (c) land carbon uptake (PgC yr–1), (d) ocean carbon uptake (PgC yr–1), both with observations from the Global Carbon Project (GCP; Friedlingstein et al., 2019) and grey shading indicating the observational uncertainty. Land and ocean carbon uptakes are plotted using a 10-year running mean for better visibility. The ocean uptake is offset to 0 in 1850 to correct for pre-industrial riverine-induced carbon fluxes. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50510, 63167, 63168 ], "vocabularyKeywords": [], "identifier_set": [ 12377 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 149131, 149132, 149133, 149134, 149135, 149136, 149138, 168259, 149139, 149137 ], "onlineresource_set": [ 43666, 43665, 52388, 82634, 88582, 94620, 94619 ] }, { "ob_id": 33410, "uuid": "299b1bb28eaa440f9a36e9786adfe398", "title": "BICEP/NCEO: Monthly global Particulate Organic Carbon (POC), between 1997-2020 at 4 km resolution (produced from the Ocean Colour Climate Change Initiative v4.2 dataset), version 2", "abstract": "The BICEP/NCEO: Monthly global Particulate Organic Carbon (POC) v4.2 datasets contain POC concentrations (mg m^-3) with per pixel uncertainties estimates gridded on both geographic and sinusoidal projections at 4 km spatial resolution for the period of 1997 to 2020. The POC products were generated as part of the European Space Agency (ESA) Biological Pump and Carbon Exchange Processes (BICEP) project with support from the National Centre of Earth Observation (NCEO). \r\n\r\nThe POC concentrations were estimated using an empirical Remote Sensing Reflectance (Rrs) band ratio algorithm by Stramski et al. (2008): 203.2*Rrs(443)/Rrs(555)^-1.034. This algorithm has shown a relatively good performance in the recent global inter-comparison study conducted by Evers-King et al. (2017). Additional variables that were used for the calculation of the POC products are also provided in the datasets, including the Rrs at 443 nm and 555 nm obtained from the ESA Ocean Colour Climate Change Initiative version 4.2 dataset (OC-CCI v4.2)(Sathyendranath et al., 2020). In addition to the papers by Stramski et al. (2008) and Evers-king et al. (2017), for more details on the algorithm and its validation, please see the BICEP Algorithm Theoretical Basis Document (ATBD) and validation report (https://bicep-project.org/Home) \r\n\r\nThis version of the dataset is an updated version of the previous 'NCEO: Monthly global Particulate Organic Carbon (POC) (produced from the Ocean Colour Climate Change Initiative, Version 4.2 dataset)'.\r\n\r\nA related product based on the Ocean Colour Climate Change Initiative v5.0 data is also available (see the link in the related records section).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-12-06T16:26:10", "updateFrequency": "", "dataLineage": "Data were produced by the Plymouth Marine Laboratory and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). The research underpinning the work was supported by the European Space Agency (ESA) Biological Pump and Carbon Export Processes (BICEP) project and the product generation was supported by the National Centre for Earth Observation (NCEO).", "removedDataReason": "", "keywords": "NCEO, BICEP, Ocean, Particulate Organic Carbon, Ocean Colour Climate Change Initiative", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-12-21T14:45:49", "doiPublishedTime": "2022-01-12T17:38:06", "removedDataTime": null, "geographicExtent": { "ob_id": 2711, "bboxName": "NCEO Ocean POC", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 33418, "dataPath": "/neodc/bicep/data/particulate_organic_carbon/v4.2_update/monthly/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 256688997806, "numberOfFiles": 543, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 8748, "startTime": "1997-09-01T00:00:00", "endTime": "2020-03-31T00:00:00" }, "resultQuality": { "ob_id": 3804, "explanation": "BICEP/NECO: The algorithm used to generate the Particulate Organic Carbon (POC) v4.2 products has been selected based on a previous validation study (Evers-King et al., 2017). The Remote Sensi ng Reflectance (Rrs) of 443 and 555 nm band ratio algorithm by Stramski et al. (2008) has shown a satisfactory levels of performance from several algorithms (please see Evers-King et al., 2017 paper for detail). Additional validation has been performed through the ESA Biological Pump and Carbon Export Processes (BICEP) project using a large collection of in situ POC data matched up with the Ocean Colour Climate Change Initiative version v4.2 (OC-CCI v4.2) (1997-2020). The selected algorithm statistically performed well across different optical water classes, with r^2 of 0.83 and low uncertainties. For more details of this algorithm's performance, please see the BICEP Algorithm Theoretical Basis Document (ATBD) and validation report (https://bicep-project.org/Home)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-12-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33411, "uuid": "6e4809d45260433a8bf2a9c7c4476965", "short_code": "comp", "title": "Computation of the Particular Organic Carbon product produced from the Ocean Colour Climate Change Initiative v4.2 dataset", "abstract": "Computation of the Particulate Organic Carbon (POC) v4.2 products on geographic and sinusoidal projections at 4 km resolution are based on the empirical remote sensing reflectance (Rrs) band ratio algorithm by Stamski et al. (2008): 203.2*Rrs(443)/Rrs(555)^-1.034. The Rrs at 443 and 555 nm were obtained from the European Space Agency (ESA) Ocean Colour Climate Change Initiative version 4.2 dataset (OC-CCI v4.2) (Sathyendranath et al., 2020). The bias and root-mean-square-difference of log_10 transformed POC data in each pixel were computed by weighting the uncertainties in each CCI optical water class by their membership (Brewin et al., 2017; Jackson et al., 2017). For more details about algorithm and validations, please see BICEP Algorithm Theoretical Basis Document (ATBD) and validation report (https://bicep-project.org/Home)" }, "procedureCompositeProcess": null, "imageDetails": [ 130 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 5002, "uuid": "60e718d3f2957f742c89b2b4fc159718", "short_code": "proj", "title": "National Centre for Earth Observation (NCEO)", "abstract": "The National Centre for Earth Observation is a partnership of scientists and institutions, from a range of disciplines, who are using data from Earth observation satellites to monitor global and regional changes in the environment and to improve understanding of the Earth system so that we can predict future environmental conditions.\r\n\r\nNCEO's Vision is to unlock the full potential of Earth observation to monitor, diagnose and predict climate and environmental changes, ensuring that these scientific advances are delivered to the wider community embedded in world class science." }, { "ob_id": 31968, "uuid": "cd161a8305384eb38109036a74d7e2b9", "short_code": "proj", "title": "ESA Biological Pump and Carbon Export Processes (BICEP) Project", "abstract": "The ESA Biological Pump and Carbon Export Processes (BICEP) project is an ESA project led by the Plymouth Marine Laboratory. The objective of the BICEP project is to further advance our capacity to better characterise the different components of the ocean biological carbon pump, its pools and fluxes, its variability in space and time and the understanding of its processes and interactions with the earth system, from a synergetic use of space data, in-situ measurements and model outputs.\r\n\r\nThe development of the BICEP datasets was also supported the Simons Foundation grant 'Computational Biogeochemical Modeling of Marine Ecosystems' (CBIOMES, number 549947)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1633, 3301, 3302, 43486, 43487, 43488, 43489, 43490 ], "vocabularyKeywords": [], "identifier_set": [ 10978 ], "observationcollection_set": [ { "ob_id": 30127, "uuid": "82b29f96b8c94db28ecc51a479f8c9c6", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Core datasets", "abstract": "This NCEO Core data set collection contains data generated by the National Centre for Earth Observation core scientific programmes. NCEO is a National Environment Research Council (NERC) research centre with more than 80 scientists distributed across leading UK universities and research organisations and led by Professor John Remedios at the University of Leicester.\r\n\r\nNCEO provides the UK with core expertise in Earth Observation science, data sets and merging techniques, and model evaluation to underpin Earth System research and the UK’s international contribution to environmental science. NCEO scientists work strategically with space agencies, play significant roles in mission planning, and generate internationally-recognised data products from 20 different satellite instruments." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 149176, 149177, 149170, 149173, 149172, 149171, 149178, 149174, 149180, 149181 ], "onlineresource_set": [ 43681, 43682, 43683, 43721, 47622 ] }, { "ob_id": 33412, "uuid": "5006f2c553cd4f26a6af0af2ee6d7c94", "title": "BICEP/NCEO: Monthly global Particulate Organic Carbon (POC), between 1997-2020 at 4 km resolution (produced from the Ocean Colour Climate Change Initiative v5.0 dataset)", "abstract": "The BICEP/NCEO: Monthly global Particulate Organic Carbon (POC) v5 datasets contain POC concentrations (mg m^-3) with per pixel uncertainties estimates gridded on both geographic and sinusoidal projections at 4 km spatial resolution for the period of 1997 to 2020. The POC products were generated as part of the European Space Agency (ESA) Biological Pump and Carbon Exchange Processes (BICEP) project with support from the National Centre of Earth Observation (NCEO). \r\n\r\nThe POC datasets have been produced by using a modified empirical band ratio algorithm by Stramski et al. (2008): 292*Rrs(490)/Rrs(560)^-1.49. Additional variables that were used for the calculation of the POC products are also provided in the datasets, including the Remote Sensing Reflectance (Rrs) at 490 nm and 560 nm obtained from the ESA Ocean Colour Climate Change Initiative version 5 dataset (OC-CCI v5). For more details on the algorithm and its validation, please see the BICEP Algorithm Theoretical Basis Document (ATBD) and validation report (https://bicep-project.org/Home).\r\n\r\nA related dataset based on the ESA Ocean Colour Climate Change Initiative v4.2 data is also available (see link in the related records section).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-12-07T10:24:22", "updateFrequency": "", "dataLineage": "Data were produced by the Plymouth Marine Laboratory and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). The research underpinning the work was supported by the European Space Agency (ESA) Biological Pump and Carbon Export Processes (BICEP) project and the product generation was supported by the National Centre for Earth Observation (NCEO).", "removedDataReason": "", "keywords": "NCEO, BICEP, Ocean, Particulate Organic Carbon, Ocean Colour Climate Change Initiative", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-12-21T14:42:15", "doiPublishedTime": "2022-01-12T17:36:21", "removedDataTime": null, "geographicExtent": { "ob_id": 2711, "bboxName": "NCEO Ocean POC", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 33419, "dataPath": "/neodc/bicep/data/particulate_organic_carbon/v5.0/monthly/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 265400374948, "numberOfFiles": 561, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 9174, "startTime": "1997-09-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3805, "explanation": "BICEP/NCEO: Four key satellite-based approaches (Stramski et al, 2008; Loisel et al., 2002; Le et al., 2018; Tran et al., 2019) were evaluated to estimate the global ocean surface Particulate Organic Carbon (POC) using a large collection of in situ data matched up with the Ocean Colour Climate Change Initiative version 5 (OC-CCI v5) (1997-2020), with the objective of providing an improved satellite-derived global surface POC algorithm. The candidate algorithms were carefully evaluated using the core variables suggested by the algorithm developers. Of these candidate algorithms, the remote sensing reflectance (Rrs) band ratio of algorithm by Stramski et al. (2008) showed a relatively good performance with lowest uncertainties. This algorithm was then re-calibrated to provide an accurate estimation of global POC concentration from the OC-CCI v5. For more details of the modified algorithm, please see the BICEP Algorithm Theoretical Basis Document (ATBD) and validation report (https://bicep -project.org/Home).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-12-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33413, "uuid": "2d7c9e299ebf4483aa0576f48e7a68e6", "short_code": "comp", "title": "Computation of the Particular Organic Carbon product produced from the Ocean Colour Climate Change Initiative v5.0 dataset", "abstract": "Computation of the Particulate Organic Carbon (POC) v5 products on sinusoidal and geographic projections at 4 km resolution are based on a modified remote sensing reflectance (Rrs) band ratio algorithm by Stamski et al. (2008): 292*Rrs(490)/Rrs(560)^-1.49. The Rrs at 490 and 560 nm were obtained from the European Space Agency (ESA) Ocean Colour Climate Change Initiative version 5 dataset (OC-CCI v5) (Sathyendranath et al., 2021). The coefficient parameters of the algorithm were estimated by applying Type II linear regression model to a global in-situ and OC-CCI v5 satellite match-up POC products. Uncertainties of log_10 transformed POC in each pixel were computed by weighting the uncertainties in each OC-CCI optical water class by their membership (Brewin et al., 2017; Jackson et al., 2017). For moredetails about algorithm and validations, please see BICEP Algorithm Theoretical Basis Document (ATBD) and validation report (https://bicep-project.org/Home)" }, "procedureCompositeProcess": null, "imageDetails": [ 130 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 5002, "uuid": "60e718d3f2957f742c89b2b4fc159718", "short_code": "proj", "title": "National Centre for Earth Observation (NCEO)", "abstract": "The National Centre for Earth Observation is a partnership of scientists and institutions, from a range of disciplines, who are using data from Earth observation satellites to monitor global and regional changes in the environment and to improve understanding of the Earth system so that we can predict future environmental conditions.\r\n\r\nNCEO's Vision is to unlock the full potential of Earth observation to monitor, diagnose and predict climate and environmental changes, ensuring that these scientific advances are delivered to the wider community embedded in world class science." }, { "ob_id": 31968, "uuid": "cd161a8305384eb38109036a74d7e2b9", "short_code": "proj", "title": "ESA Biological Pump and Carbon Export Processes (BICEP) Project", "abstract": "The ESA Biological Pump and Carbon Export Processes (BICEP) project is an ESA project led by the Plymouth Marine Laboratory. The objective of the BICEP project is to further advance our capacity to better characterise the different components of the ocean biological carbon pump, its pools and fluxes, its variability in space and time and the understanding of its processes and interactions with the earth system, from a synergetic use of space data, in-situ measurements and model outputs.\r\n\r\nThe development of the BICEP datasets was also supported the Simons Foundation grant 'Computational Biogeochemical Modeling of Marine Ecosystems' (CBIOMES, number 549947)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 62501, 89181, 89182, 89183, 89184, 89185 ], "vocabularyKeywords": [], "identifier_set": [ 10977 ], "observationcollection_set": [ { "ob_id": 30127, "uuid": "82b29f96b8c94db28ecc51a479f8c9c6", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Core datasets", "abstract": "This NCEO Core data set collection contains data generated by the National Centre for Earth Observation core scientific programmes. NCEO is a National Environment Research Council (NERC) research centre with more than 80 scientists distributed across leading UK universities and research organisations and led by Professor John Remedios at the University of Leicester.\r\n\r\nNCEO provides the UK with core expertise in Earth Observation science, data sets and merging techniques, and model evaluation to underpin Earth System research and the UK’s international contribution to environmental science. NCEO scientists work strategically with space agencies, play significant roles in mission planning, and generate internationally-recognised data products from 20 different satellite instruments." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 149185, 149190, 149184, 149187, 149186, 149192, 149189, 149188, 149194, 149195 ], "onlineresource_set": [ 43686, 43684, 47623 ] }, { "ob_id": 33414, "uuid": "a6fc730d88fd4935b59d64903715d891", "title": "BICEP / NCEO: Monthly global Oceanic Export Production, between 1998-2019 at 9 km resolution (derived from the Ocean Colour Climate Change Initiative v4.2 dataset)", "abstract": "This dataset contains monthly global data for Oceanic Export Production as part of the BICEP project. Data is provided between 1998-2019 at 9 km resolution. It has been derived from the Ocean Colour Climate Change Initiative v4.2 dataset.\r\n\r\nExport production can be defined as steady-state Net Community Production (NCP) with all temporal lags accounted for and with a well defined depth horizon over which the community production is integrated over. (Laws 1991). This is the net amount of carbon assimilated in the euphotic zone that will be exported to deeper waters. Export Production can by definition only vary on timescales significantly longer that any processes directly controlling production and respiration as to not violate the steady state assumption.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-12-20T14:37:29", "updateFrequency": "notPlanned", "dataLineage": "The data was produced as part of the BICEP project, and has been supplied to the Centre for Environmental Data Analysis for long term archiving.\r\n\r\nThe data has been derived from the following datasets:\r\n\r\nSST is based on reprojected fields from GHRSST/OSTIA (doi:10.5067/GHOST-4FK01), Chl and kd490 is from OC-CCI (https://catalogue.ceda.ac.uk/uuid/5400de38636d43de9808bfc0b500e863, https://catalogue.ceda.ac.uk/uuid/db32212d86f9431dae67076dd122565e) and primary production from BICEP (https://catalogue.ceda.ac.uk/uuid/69b2c9c6c4714517ba10dab3515e4ee6) The algorithms used are described in the papers listed in the documentation section.", "removedDataReason": "", "keywords": "Oceanic Export Production, BICEP", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": "2021-12-21T16:31:50", "doiPublishedTime": "2022-02-17T14:46:30.618600", "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": 33461, "dataPath": "/neodc/bicep/data/oceanic_export_production/v1.0/monthly/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 59142923301, "numberOfFiles": 265, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9191, "startTime": "1998-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 3818, "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": "2021-12-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33417, "uuid": "32ad63e558b84d7c8b391d464d45b99b", "short_code": "comp", "title": "Empirical Export Production algorithms for the dataset \"Oceanic Export Production Based on Satellite-Derived Properties\"", "abstract": "Oceanic Export Production has been calculated using Export Production algorithms, SST, Chl, kd490, and primary production. \r\n\r\nThe SST data used is based on reprojected fields from GHRSST/OSTIA (doi:10.5067/GHOST-4FK01 ), the Chl and kd490 is from the Ocean Colour Climate Change Initiative (https://catalogue.ceda.ac.uk/uuid/5400de38636d43de9808bfc0b500e863, https://catalogue.ceda.ac.uk/uuid/db32212d86f9431dae67076dd122565e) and the primary production data from BICEP (https://catalogue.ceda.ac.uk/uuid/69b2c9c6c4714517ba10dab3515e4ee6) The algorithms used are described in the papers listed in the docs section." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 5002, "uuid": "60e718d3f2957f742c89b2b4fc159718", "short_code": "proj", "title": "National Centre for Earth Observation (NCEO)", "abstract": "The National Centre for Earth Observation is a partnership of scientists and institutions, from a range of disciplines, who are using data from Earth observation satellites to monitor global and regional changes in the environment and to improve understanding of the Earth system so that we can predict future environmental conditions.\r\n\r\nNCEO's Vision is to unlock the full potential of Earth observation to monitor, diagnose and predict climate and environmental changes, ensuring that these scientific advances are delivered to the wider community embedded in world class science." }, { "ob_id": 31968, "uuid": "cd161a8305384eb38109036a74d7e2b9", "short_code": "proj", "title": "ESA Biological Pump and Carbon Export Processes (BICEP) Project", "abstract": "The ESA Biological Pump and Carbon Export Processes (BICEP) project is an ESA project led by the Plymouth Marine Laboratory. The objective of the BICEP project is to further advance our capacity to better characterise the different components of the ocean biological carbon pump, its pools and fluxes, its variability in space and time and the understanding of its processes and interactions with the earth system, from a synergetic use of space data, in-situ measurements and model outputs.\r\n\r\nThe development of the BICEP datasets was also supported the Simons Foundation grant 'Computational Biogeochemical Modeling of Marine Ecosystems' (CBIOMES, number 549947)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 4385, 18405, 18408, 43645, 43646, 43647 ], "vocabularyKeywords": [], "identifier_set": [ 10984 ], "observationcollection_set": [ { "ob_id": 30127, "uuid": "82b29f96b8c94db28ecc51a479f8c9c6", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Core datasets", "abstract": "This NCEO Core data set collection contains data generated by the National Centre for Earth Observation core scientific programmes. NCEO is a National Environment Research Council (NERC) research centre with more than 80 scientists distributed across leading UK universities and research organisations and led by Professor John Remedios at the University of Leicester.\r\n\r\nNCEO provides the UK with core expertise in Earth Observation science, data sets and merging techniques, and model evaluation to underpin Earth System research and the UK’s international contribution to environmental science. NCEO scientists work strategically with space agencies, play significant roles in mission planning, and generate internationally-recognised data products from 20 different satellite instruments." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 149198, 149199, 149200, 149201, 149202, 149203, 149204, 149518, 149205, 149206, 149207 ], "onlineresource_set": [ 43687, 43688, 43689 ] }, { "ob_id": 33416, "uuid": "22ae54ba3ab14ce8aa6a5271dfddaeb3", "title": "JNCC Sentinel-1 indices Analysis Ready Data (ARD) Radar Vegetation Index (RVI)", "abstract": "These data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra NCEA project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-1 Analysis Ready Data.\r\n\r\nRVI and RVIv files are generated for Sentinel-1 orbit 132 (ascending) every 12 days. \r\n\r\nIndices have been generated using the Defra and JNCC Sentinel-1 and Sentinel-2 ARD for the granules and scenes described above. As the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-12-02T13:48:27", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "JNCC, ARD, RVI, Sentinel 1, EO indices, Analysis Ready Data, Sentinel", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2609, "bboxName": "UK", "eastBoundLongitude": 2.02, "westBoundLongitude": -10.85, "southBoundLatitude": 49.82, "northBoundLatitude": 59.48 }, "verticalExtent": null, "result_field": { "ob_id": 33415, "dataPath": "/neodc/sentinel_ard/indices/sentinel_1/rvi/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 203305349, "numberOfFiles": 3, "fileFormat": "These data are provided in Cloud Optimized GeoTIFF (COG) format." }, "timePeriod": { "ob_id": 9176, "startTime": "2021-01-10T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3806, "explanation": "A representative subset of data are manually checked to ensure the indices files align with the ARD files, the value ranges are as expected, and the data format is correct. 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": "2021-12-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30197, "uuid": "14818d07b867480c90ed01fbf81d9346", "short_code": "proj", "title": "Defra and JNCC Analysis Ready Sentinel Data", "abstract": "Defra and JNCC aim to provide timely, cost-effective, and high-quality, Analysis Ready Sentinel Data (ARD) for a wide range of applications. These data are provided for the UK geographic area for this project. The indices datasets currently cover Yorkshire, UK, but coverage will be expanded in the future. These products have been produced using the Copernicus satellites, Sentinel 1 and Sentinel 2." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 149208, 149209, 149210, 149211, 149212, 149213, 149214, 149215 ], "onlineresource_set": [] }, { "ob_id": 33420, "uuid": "b953c9c8b41d4339b2a0a80fdc3cb840", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.11 (v20211207)", "abstract": "Data for Figure 2.11 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 2.11 includes mapped and time-series data showing global surface temperature relative to 1850 - 1900 over multiple time scales\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n---------------------------------------------------\r\n Figure has three panels, with data provided for panel (a) (center and right part), and panel (c).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n---------------------------------------------------\r\n Global surface temperature, relative to 1850 - 1900 for:\r\n\r\n Panel a: \r\n \r\n - 1000 to 1900 CE - from PAGES 2k Consortium (modified from the version 2019: 10.1038/s41561-019-0400-0)\r\n - 1850 to 2020 from AR6 assessed mean (same as Figure 2.11c).\r\n\r\n Panel c: \r\n \r\n - Annual and decadal means from instrumental data for 1850–2020, along with the uncertainty range from HadCRUT5.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n---------------------------------------------------\r\n Panel a:\r\n \r\n - Data file: Figure_2_11a-PAGES_2k_Consortium.csv (yearly data, 1000 to 1900); relates to the center part of the figure showing global surface temperature relative to 1850 -1900. (bold solid green line, column 2, median 10-yr smooth adjusted (+0.37°C), thin solid green lines: 5th (column 3) and 95th (column 4) percentiles of the ensemble members).\r\n - Data file: Figure2_11_panel_a.csv (yearly data, 1850 to 2020); relates to the right part of the figure showing global temperature anomaly AR6 assessed mean. (bold solid violet line, column 2)\r\n\r\nPanel c: \r\n \r\n - Data file: Figure_2_11c-land_and_ocean_time_series.csv (yearly data, 1850 to 2020); relates to the upper part of the figure showing global surface temperature relative to 1850 -1900. (Land, column 2, red line; Ocean, column 3, blue line).\r\n - Data file: Figure_2_11c-lower_panel.csv, (annual and decadal mean, 1850 to 2020); relates to the lower part of the figure. (black line, column 2, HadCRUT 5.0; cyan line, column 3, NOAA Global Temp; pink line, column 4, Berkeley Earth; orange line, column 5, Kadow et al.; grey shadow, columns 6 and 7, HadCRUT confidence limit)\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:45:02", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 2, Changing state, Multi-millennial context, pre-industrial, Natural forcing, anthropogenic forcing, Radiative forcing, Large-scale indicators, observed changes, Modes of variability, Figure 2.11, Global surface temperature", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-03-31T14:48:05", "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": { "ob_id": 85, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37091, "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig11/v20211207/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 99809, "numberOfFiles": 8, "fileFormat": "csv, txt" }, "timePeriod": { "ob_id": 9177, "startTime": "1850-01-01T12:00:00", "endTime": "2020-12-31T12:00:00" }, "resultQuality": { "ob_id": 3807, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-12-07" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33422, "uuid": "f4219ba0c0f745a2bab48348a71721ba", "short_code": "comp", "title": "Caption for Figure 2.11 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Earth’s surface temperature history with key findings annotated within each panel. (a) GMST over the Holocene divided into three time scales: (i) 12 kyr–1 kyr in 100-year time steps; (ii) 1000–1900 CE, 10-year smooth; and (iii) 1900–2020 CE (from panel (c)). Median of the multi-method reconstruction (bold lines), with 5th and 95th percentiles of the ensemble members (thin lines). Vertical bars are the assessed medium confidence ranges of GMST for the Last Interglacial and mid-Holocene (Section 2.3.1.1). The last decade value and very likely range arises from Section 2.3.1.1.3. (b) Spatially resolved trends (C per decade) for HadCRUTv5 over (upper map) 1900–1980, and (lower map) 1981–2020.Significance is assessed following AR(1) adjustment after Santer et al. (2008), ‘x’ marks denote non-significant trends. (c) Temperature from instrumental data for 1850–2020, including (upper panel) multi-product mean annual timeseries assessed in Section 2.3.1.1.3 for temperature over the oceans (blue line) and temperature over the land (red line) and indicating the warming to the most recent 10 years; and annually (middle panel) and decadally (bottom panel) resolved averages for the GMST datasets assessed in Section 2.3.1.1.3. The grey shading in each panel shows the uncertainty associated with the HadCRUT5 estimate (Morice et al., 2021). All temperatures relative to the 1850–1900 reference period. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [ { "ob_id": 6597, "vocabService": "nerc_skos_vocab", "uri": "http://vocab.nerc.ac.uk/collection/P07/current/CFSN0226/", "resolvedTerm": "surface_temperature_anomaly" } ], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 149218, 149219, 149220, 149221, 149222, 149223, 149225, 168257, 149224, 149226, 149227, 149228, 149229 ], "onlineresource_set": [ 43691, 51918, 43690, 82827 ] }, { "ob_id": 33423, "uuid": "5aa32fb863a048f0b24c541639cfd990", "title": "RAL extended Infrared Microwave Sounder (IMS) retrievals of atmospheric and surface properties: subset of four selected months in 2018 from Suomi-NPP, v1", "abstract": "The Rutherford Appleton Laboratory (RAL) extended Infrared Microwave Sounder (IMS) data set retrieves vertical profiles of temperature, water vapour (H2O), ozone (O3), carbon monoxide (CO), together with cloud optical depth and effective radius and column amounds of minor gases, dust and sulfuric acid aerosol optical depth. The scheme also provides surface temperature and surface spectral emissivity spanning infrared and microwave. \r\n\r\nColumn amounts of the following minor gases are retrieved: Nitric acid (HNO3), ammonia (NH3), sulfur dioxide (SO2), methanol (CH3OH), formic acid (HCOOH) and (for Suomi-NPP only) isoprene (C5H8).\r\n\r\nThe retrieval scheme has been applied to the infra-red and microwave sounders on platforms Metop (IASI, AMSU and MHS) and Suomi-NPP (CrIS and ATMS). The data sub-set provided here comprises four months (April, July, September, December) of Suomi-NPP data in 2018 produced with the horizontal sampling of CrIS, ~18x18 km.\r\n\r\nDevelopment of the core IMS scheme was funded by the UK’s National Centre for Earth Observation (NCEO) under the Natural Environment Research Council (NERC), with additional funding from EUMETSAT contract EUM/CO/13/4600001252/THH. Development of the extended IMS scheme and production of this Suomi-NPP data sub-set were funded through NCEO. Data were produced by the Remote Sensing Group (RSG) at the Rutherford Appleton Laboratory (RAL).”", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-12-14T13:19:15", "updateFrequency": "", "dataLineage": "Development of the IMS scheme and data production were funded by the UK’s National Centre for Earth Observation (NCEO) under the Natural Environment Research Council (NERC), with additional funding from EUMETSAT contract EUM/CO/13/4600001252/THH", "removedDataReason": "", "keywords": "RAL, IMS, atmospheric and surface properties, Suomi-NPP", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-12-16T16:21:55", "doiPublishedTime": "2021-12-16T17:03:51", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 33449, "dataPath": "/neodc/ral_ims_ext/data/snpp_2018_subset/v1/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 936384359830, "numberOfFiles": 28615, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9178, "startTime": "2018-04-01T00:00:00", "endTime": "2018-12-31T23:59:59" }, "resultQuality": { "ob_id": 3813, "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": "2021-12-10" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33427, "uuid": "afe4dc7900e1420484f55e80345db858", "short_code": "cmppr", "title": "The RAL extended IMS retrieval scheme applied to the CrIS instrument on Suomi-NPP", "abstract": "The Rutherford Appleton Laboratory (RAL) extended Infrared Microwave Sounder (IMS) data set contains vertical profiles of temperature, water vapour (H2O), ozone (O3), carbon monoxide (CO), together with estimated total columns of other minor gases, cloud optical depth and effective radius, dust and sulfuric acid aerosol optical depth. The scheme also provides surface temperature and surface spectral emissivity spanning infrared and microwave. Data are retrieved from the infra-red and microwave sounders on platforms Metop (IASI, AMSU and MHS) and Suomi-NPP (CrIS and ATMS).\r\n\r\nColumn amounts of the following minor gases are retrieved: Nitric acid (HNO3), ammonia (NH3), sulfur dioxide (SO2), methanol (CH3OH), formic acid (HCOOH) and (for Suomi-NPP only) isoprene (C5H8).\r\n\r\nIn this dataset, the scheme has been applied to the CrIS instrument on the Suomi-NPP satellite." }, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 5002, "uuid": "60e718d3f2957f742c89b2b4fc159718", "short_code": "proj", "title": "National Centre for Earth Observation (NCEO)", "abstract": "The National Centre for Earth Observation is a partnership of scientists and institutions, from a range of disciplines, who are using data from Earth observation satellites to monitor global and regional changes in the environment and to improve understanding of the Earth system so that we can predict future environmental conditions.\r\n\r\nNCEO's Vision is to unlock the full potential of Earth observation to monitor, diagnose and predict climate and environmental changes, ensuring that these scientific advances are delivered to the wider community embedded in world class science." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58082, 58083, 69637, 69638, 69639, 69640, 69641, 69642, 69643, 69644, 69647, 69648, 69649, 69650, 69651, 69652, 69653, 69654, 69655, 69656, 69657, 69658, 69659, 69660, 69661, 69662, 69663, 69664, 69665, 69666, 69667, 69668, 69669, 69670, 69671, 69672, 69673, 69674, 69675, 69676, 69677, 69678, 69679, 69680, 69681, 69682, 69683, 69684, 69685, 69686, 69687, 69688, 69689, 69690, 69691, 69692, 69693, 69694, 69695, 69696, 69697, 69698, 69699, 69700, 69701, 69702, 69703, 69704, 69705, 69707, 86245, 86246, 86247, 86248, 86249, 86250, 86251, 86252, 86253, 86254, 86255, 86256, 86257, 86258, 86259, 86260, 86261, 86262, 86263, 86264, 86265, 86266, 86267, 86268, 86269, 86270, 86271, 86272, 86273, 86274, 86275, 86276, 86277, 86278, 86279, 86280, 86281, 86282, 86283, 86284, 86285, 86286, 86287, 86288, 86289, 86290, 86291, 86292, 86293, 86294, 86295, 86296, 86297, 86298, 86299, 86300, 86301, 86302, 86303, 86304, 86305, 86306, 86307, 86308, 86309, 86310, 86311, 86312, 86313, 86314, 86315, 86316, 86317, 86318, 86319, 86320, 86321, 86322, 86323, 86324, 86325, 86326, 86327, 86328, 86329, 86330 ], "vocabularyKeywords": [], "identifier_set": [ 10972 ], "observationcollection_set": [ { "ob_id": 30127, "uuid": "82b29f96b8c94db28ecc51a479f8c9c6", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Core datasets", "abstract": "This NCEO Core data set collection contains data generated by the National Centre for Earth Observation core scientific programmes. NCEO is a National Environment Research Council (NERC) research centre with more than 80 scientists distributed across leading UK universities and research organisations and led by Professor John Remedios at the University of Leicester.\r\n\r\nNCEO provides the UK with core expertise in Earth Observation science, data sets and merging techniques, and model evaluation to underpin Earth System research and the UK’s international contribution to environmental science. NCEO scientists work strategically with space agencies, play significant roles in mission planning, and generate internationally-recognised data products from 20 different satellite instruments." } ], "responsiblepartyinfo_set": [ 149231, 149232, 149233, 149234, 149235, 149236, 149237, 149241, 149238, 149239, 149240 ], "onlineresource_set": [ 43692, 87456 ] }, { "ob_id": 33428, "uuid": "71c2e401df5e4798b917ae4a353daff1", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.17 (v20211208)", "abstract": "Data for Figure 3.17 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.17 shows observed and simulated global monsoon domain, intensity, and circulation. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for all panels in a single file. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains\r\n \r\n - Observed and simulated global monsoon domain and summer minus winter precipitation and 850hPa wind velocity\r\n - Global land monsoon precipitation index and Northern Hemisphere summer monsoon circulation index.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n All data are given in global_monsoon.nc file.\r\n\r\nPanel a:\r\n \r\n - uRef & vRef: vector\r\n - prRef: shading\r\n - domainRef: domain (1 = monsoon, 0 = not monsoon)\r\n\r\nPanel b:\r\n \r\n - uMME & vMME: vector\r\n - prMME: shading\r\n - domainMME: monsoon domain (1 = monsoon, 0 = not monsoon)\r\n\r\nPanel c:\r\n \r\n - Multimodel ensemble mean and 5th-95th percentiles of GMprecip_cmip6: red curve and shading\r\n - Multimodel ensemble mean and 5th-95th percentiles of GMprecip_cmip5: blue curve and shading\r\n - Multimodel ensemble mean and 5th-95th percentiles of GMprecip_amip: yellow curve and shading\r\n - GMprecip_CMAP: black dotted curve\r\n - GMprecip_CRU-TS: black solid curve\r\n - GMprecip_GPCC: black dashed-dotted curve\r\n - GMprecip_GPCP-SG: black dashed curve\r\n\r\n\r\nPanel d:\r\n \r\n - Multimodel ensemble mean and 5th-95th percentiles of NHMcirc_cmip6: red curve and shading\r\n - Multimodel ensemble mean and 5th-95th percentiles of NHMcirc_cmip5: blue curve and shading\r\n - Multimodel ensemble mean and 5th-95th percentiles of NHMcirc_amip: yellow curve and shading\r\n - Max-min range of NHMcirc_20CRv3: grey hatching\r\n - NHMcirc_ERA-20C: black dash-dotted curve\r\n - NHMcirc_ERA5: black solid curve\r\n - NHMcirc_JRA-55: dashed curve\r\n - NHMcirc_MERRA2: dotted curve\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Multimodel ensemble means and percentiles are calculated after weighting individual members with the inverse of the ensemble size of the same model, which is given as the weight attribute of each variable.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains supporting information on the figure in Section and details on the input data used in Table 3.SM.1\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": "2024-03-09T02:45:14", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.17, Global monsoon domain, monsoon precipitation, monsoon circulation, global land monsoon precipitation, Northern Hemisphere summer monsoon circulation index, CMIP5, CMIP6, AMIP", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-04-01T15:01:25", "doiPublishedTime": "2023-02-08T18:01:19.397972", "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 86, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37116, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig17/v20211208", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1226552, "numberOfFiles": 4, "fileFormat": "netCDF" }, "timePeriod": { "ob_id": 9179, "startTime": "1870-01-01T12:00:00", "endTime": "2018-12-31T12:00:00" }, "resultQuality": { "ob_id": 3808, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-12-08" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33430, "uuid": "94e53a8b2ba642b081ec4a44ad3bcd36", "short_code": "comp", "title": "Caption for Figure 3.17 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Model evaluation of global monsoon domain, intensity, and circulation. (a, b) Climatological summer-winter range of precipitation rate, scaled by annual mean precipitation rate (shading) and 850 hPa wind velocity (arrows) based on (a) GPCP and ERA5 and (b) a multi-model ensemble mean of CMIP6 historical simulations for 1979–2014. Enclosed by red lines is the monsoon domain based on the definition by Wang and Ding (2008). (c, d) Five-year running mean anomalies of (c) global land monsoon precipitation index defined as the percentage anomaly of the summertime precipitation rate averaged over the monsoon regions over land, relative to its average for 1979–2014 (the period indicated by light grey shading) and (d) the tropical monsoon circulation index defined as the vertical shear of zonal winds between 850 and 200 hPa levels averaged over 0º–20ºN, from 120ºW eastward to 120ºE in NH summer (Wang et al., 2013; m s–1) in CMIP5 historical and RCP4.5 simulations, CMIP6 historical and AMIP simulations. Summer and winter are defined for individual hemispheres: May through September for NH summer and SH winter, and November through March for NH winter and SH summer. The number of models and ensembles are given in the legend. The multi-model ensemble mean and percentiles are calculated after weighting individual members with the inverse of the ensemble size of the same model, so that individual models are equally weighted irrespective of ensemble size. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 3301, 3302, 47688, 47689, 47690, 47691, 47692, 47693, 47694, 47695, 47696, 47697, 47698 ], "vocabularyKeywords": [], "identifier_set": [ 12363 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 149256, 149250, 149251, 149252, 149253, 149254, 149255, 149257, 149258 ], "onlineresource_set": [ 43693, 52374, 43694, 82869, 88566, 90039, 90040, 90041, 90042, 90043, 90044, 90045, 90046, 90047, 90048, 90049, 90050, 90051, 90052, 90053, 90054, 90055, 90056, 90057, 90058, 90059, 90060, 94610 ] }, { "ob_id": 33431, "uuid": "eac7485cce194194b6731cb41ae463b5", "title": "JNCC Sentinel-1 indices Analysis Ready Data (ARD) Radar Vegetation Index (RVIv)", "abstract": "These data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra NCEA project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-1 Analysis Ready Data.\r\n\r\nRVI and RVIv files are generated for Sentinel-1 orbit 132 (ascending) every 12 days. \r\n\r\nIndices have been generated using the Defra and JNCC Sentinel-1 and Sentinel-2 ARD for the granules and scenes described above. As the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-12-02T15:42:54", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "JNCC, ARD, RVIv, Sentinel 1, EO indices, Analysis Ready Data, Sentinel", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2609, "bboxName": "UK", "eastBoundLongitude": 2.02, "westBoundLongitude": -10.85, "southBoundLatitude": 49.82, "northBoundLatitude": 59.48 }, "verticalExtent": null, "result_field": { "ob_id": 33432, "dataPath": "/neodc/sentinel_ard/indices/sentinel_1/rviv/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 225513124, "numberOfFiles": 3, "fileFormat": "These data are provided in Cloud Optimized GeoTIFF (COG) format." }, "timePeriod": { "ob_id": 9176, "startTime": "2021-01-10T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3806, "explanation": "A representative subset of data are manually checked to ensure the indices files align with the ARD files, the value ranges are as expected, and the data format is correct. 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": "2021-12-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30197, "uuid": "14818d07b867480c90ed01fbf81d9346", "short_code": "proj", "title": "Defra and JNCC Analysis Ready Sentinel Data", "abstract": "Defra and JNCC aim to provide timely, cost-effective, and high-quality, Analysis Ready Sentinel Data (ARD) for a wide range of applications. These data are provided for the UK geographic area for this project. The indices datasets currently cover Yorkshire, UK, but coverage will be expanded in the future. These products have been produced using the Copernicus satellites, Sentinel 1 and Sentinel 2." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 149261, 149262, 149263, 149264, 149265, 149266, 149267, 149268 ], "onlineresource_set": [] }, { "ob_id": 33440, "uuid": "4fe1afacdc524c118989c16a1bccd51e", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.33 (v20211209)", "abstract": "Data for Figure 3.33 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.33 shows observed and simulated Northern Annular Mode (NAM), North Atlantic Oscillation (NAO) and Southern Annular Mode (SAM) in boreal winter. \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 Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has twelve panels, with data provided for panels (a), (d), (g) and (j) in the subdirectory named panel_adgj, panels (b), (e), (h) and (k) in the subdirectory named panel_behk, and panels (c), (f), (i) and (l) in the subdirectory named panel_cfil. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains: \r\n - Observed sea level pressure anomalies associated with NAM.\r\n - Observed sea level pressure anomalies associated with NAO.\r\n - Observed sea level pressure anomalies associated with SAM.\r\n - Simulated sea level pressure anomalies associated with NAM.\r\n - Simulated sea level pressure anomalies associated with NAO.\r\n - Simulated sea level pressure anomalies associated with SAM.\r\n - Taylor statistics of sea level pressure anomalies associated with NAM.\r\n - Taylor statistics of sea level pressure anomalies associated with NAO.\r\n - Taylor statistics of sea level pressure anomalies associated with SAM.\r\n - 1958-2014 trends of the NAM index.\r\n - 1958-2014 trends of the NAO index.\r\n - 1979-2014 trends of the SAM index.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - nam_patterns(0, :, :) in panel_adgj/nam.obs.nc; shading\r\n - nam_pattern_significance in panel_adgj/nam.obs.nc; cross marker\r\n \r\n Panel b:\r\n - nao_patterns(0, :, :) in panel_behk/nao.obs.nc; shading\r\n - nao_pattern_significance in panel_behk/nao.obs.nc; cross marker\r\n \r\n Panel c:\r\n - sam_patterns(0, :, :) in panel_cfil/sam.obs.nc; shading\r\n - sam_pattern_significance in panel_cfil/sam.obs.nc; cross marker\r\n\r\n Panel d:\r\n - nam_patterns in panel_adgj/nam.hist.cmip6.nc; multimodel ensemble mean for shading, and sign agreement for hatching\r\n \r\n Panel e: \r\n - nao_patterns in panel_behk/nao.hist.cmip6.nc; multimodel ensemble mean for shading, and sign agreement for hatching\r\n \r\n Panel f: \r\n - sam_patterns in panel_cfil/sam.hist.cmip6.nc; multimodel ensemble mean for shading, and sign agreement for hatching\r\n \r\n Panel g: \r\n - nam_tay_stat(:, 0:1) in panel_adgj/nam.amip.cmip6.nc: multimodel ensemble mean for the orange dot\r\n - nam_tay_stat(:, 0:1) in panel_adgj/nam.hist.cmip5.nc: blue crosses, with multimodel ensemble mean for the blue dot\r\n - nam_tay_stat(:, 0:1) in panel_adgj/nam.hist.cmip6.nc: red crosses, with multimodel ensemble mean for the red dot\r\n - nam_tay_stat(:, 0:1) in panel_adgj/nam.obs.nc: black dots\r\n \r\n Panel h: \r\n - nao_tay_stat(:, 0:1) in panel_behk/nao.amip.cmip6.nc: multimodel ensemble mean for the orange dot\r\n - nao_tay_stat(:, 0:1) in panel_behk/nao.hist.cmip5.nc: blue crosses, with multimodel ensemble mean for the blue dot\r\n - nao_tay_stat(:, 0:1) in panel_behk/nao.hist.cmip6.nc: red crosses, with multimodel ensemble mean for the red dot\r\n - nao_tay_stat(:, 0:1) in panel_behk/nao.obs.nc: black dots\r\n \r\n Panel i: \r\n - sam_tay_stat(:, 0:1) in panel_cfil/sam.amip.cmip6.nc: multimodel ensemble mean for the orange dot\r\n - sam_tay_stat(:, 0:1) in panel_cfil/sam.hist.cmip5.nc: blue crosses, with multimodel ensemble mean for the blue dot\r\n - sam_tay_stat(:, 0:1) in panel_cfil/sam.hist.cmip6.nc: red crosses, with multimodel ensemble mean for the red dot\r\n - sam_tay_stat(:, 0:1) in panel_cfil/sam.obs.nc: black dots\r\n \r\n Panel j: \r\n - nam_pc_trends in panel_adgj/nam.amip.cmip6.nc: multimodel ensemble mean for orange vertical line\r\n - nam_pc_trends in panel_adgj/nam.hist.cmip5.nc: multimodel ensemble mean for blue vertical line\r\n - nam_pc_trends in panel_adgj/nam.hist.cmip6.nc: histogram, with multimodel ensemble mean for red vertical line\r\n - nam_pc_trends in panel_adgj/nam.obs.nc: black vertical lines\r\n \r\n Panel k: \r\n - nao_pc_trends in panel_behk/nao.amip.cmip6.nc: multimodel ensemble mean for orange vertical line\r\n - nao_pc_trends in panel_behk/nao.hist.cmip5.nc: multimodel ensemble mean for blue vertical line\r\n - nao_pc_trends in panel_behk/nao.hist.cmip6.nc: histogram, with multimodel ensemble mean for red vertical line\r\n - nao_pc_trends in panel_behk/nao.obs.nc: black vertical lines\r\n \r\n Panel l: \r\n - sam_pc_trends in panel_cfil/sam.amip.cmip6.nc: multimodel ensemble mean for orange vertical line\r\n - sam_pc_trends in panel_cfil/sam.hist.cmip5.nc: multimodel ensemble mean for blue vertical line\r\n - sam_pc_trends in panel_cfil/sam.hist.cmip6.nc: histogram, with multimodel ensemble mean for red vertical line\r\n - sam_pc_trends in panel_cfil/sam.obs.nc: black vertical lines\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Multimodel ensemble means and histograms are obtained after weighting individual members with the inverse of the ensemble size of the same model. ensemble_assign in each file provides the model number to which each ensemble member belongs. This weighting does not apply to the sign agreement calculation.\r\n\r\nMultimodel ensemble mean of the pattern correlation in Taylor statistics is calculated via Fisher z-transformation and back transformation.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains supporting information on the figure in Section and details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-04-01T17:13:40", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.33, Annular Modes, NAM, SAM, NAO, modes of variability, CMIP5, CMIP6", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-04-01T16:19:28", "doiPublishedTime": "2023-02-08T19:21:40.702995", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 87, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37119, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig33/v20211209", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 247214943, "numberOfFiles": 15, "fileFormat": "netCDF" }, "timePeriod": { "ob_id": 9182, "startTime": "1959-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3811, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-12-09" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33442, "uuid": "9c71c89edc3d4f579cace2627683ae27", "short_code": "comp", "title": "Caption for Figure 3.33 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Model evaluation of NAM, NAO and SAM in boreal winter. Regression of Mean Sea Level Pressure (MSLP) anomalies (in hPa) onto the normalized principal component (PC) of the leading mode of variability obtained from empirical orthogonal decomposition of the boreal winter (December–February) MSLP poleward of 20ºN for the observed Northern Annular Mode (NAM, a), over 20ºN–80°N, 90°W–40°E for the North Atlantic Oscillation as shown by the black sector (NAO, b), and poleward of 20ºS for the Southern Annular Mode (SAM, c) for the JRA-55 reanalysis. Cross marks indicate regions where the anomalies are not significant at the 10% level based on t-test. The period used to calculate the NAO/NAM is 1958–2014 but 1979–2014 for the SAM. (d–f) Same but for the multi-model ensemble (MME) mean from CMIP6 historical simulations. Models are weighted in compositing to account for differences in their respective ensemble size. Diagonal lines stand for regions where less than 80% of the runs agree in sign. (g–i) Taylor diagram summarizing the representation of the modes in models and observations following Lee et al. (2019) for CMIP5 (light blue) and CMIP6 (red) historical runs. The reference pattern is taken from JRA-55 (a–c). The ratio of standard deviation (radial distance), spatial correlation (radial angle) and resulting root-mean-squared errors (solid isolines) are given for individual ensemble members (crosses) and for other observational products (ERA5 and NOAA 20CR version 3, black dots). Coloured dots stand for weighted multi-model mean statistics for CMIP5 (blue) and CMIP6 (light red) as well as for AMIP simulations from CMIP6 (orange). (j–l) Histograms of the trends built from all individual ensemble members and all the models (brown bars). Vertical lines in black show all the observational estimates. The orange, light red, and light blue lines indicate the weighted multi-model mean of CMIP6 AMIP, CMIP6 and CMIP5 historical simulations, respectively. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6019, 6020, 46697, 46959, 46960, 46966, 47634, 47635, 47636, 47637, 47638, 47639, 47640, 47641, 47642, 47643 ], "vocabularyKeywords": [], "identifier_set": [ 12379 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 149289, 149290, 149291, 149292, 149293, 149294, 149296, 168254, 149297, 149295, 149298, 149299 ], "onlineresource_set": [ 43700, 52390, 43699, 82632, 88584, 89836, 89837, 89838, 89839, 89840, 89841, 89842, 89843, 89844, 89845, 89846, 89847, 89848, 89849, 89850, 89851, 89852, 89853, 89854, 89855 ] }, { "ob_id": 33446, "uuid": "25d4a6597d954721bc4a616d094b0cda", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.32 (v20211210)", "abstract": "Data for Figure 3.32 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.32 shows relative change in the amplitude of the seasonal cycle of global land carbon uptake in the historical CMIP6 simulations from 1961-2014. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n - Observed seasonal cycle amplitude of global land carbon uptake\r\n - Simulated seasonal cycle amplitude of global land carbon uptake\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n fig_3_32_main.nc:\r\n \r\n - Multi-Model Mean: dim0 = 0, red solid line. [red shaded region: (dim0=0) +- (dim0=1))]\r\n - JMA-TRANSCOM: dim0 = 2, black dotted line.\r\n - CO2-MLO: dim0 = 3, black solid line. [black shaded region: (dim0=3) +- (dim0=4))]\r\n - CO2-GLOBAL: dim0 = 5, black dashed line.\r\n\r\nfig_3_32_inset.nc:\r\n \r\n - Multi-Model Mean for 1961-1970 (orange): dim0 = 0 (shaded region(dim0=0) +- (dim0=1))\r\n - Multi-Model Mean for 2005-2014 (green): dim0 = 2 (shaded region(dim0=2) +- (dim0=3))\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-04-01T16:54:17", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.32, amplitude of the seasonal cycle, global land carbon uptake", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-04-01T15:57:16", "doiPublishedTime": "2023-02-08T19:20:49.883918", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 88, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37118, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig32/v20211210", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 32114, "numberOfFiles": 5, "fileFormat": "netCDF" }, "timePeriod": { "ob_id": 9183, "startTime": "1961-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3814, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-12-10" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33448, "uuid": "2eac08a8afe642c38754f82819c7c8bf", "short_code": "comp", "title": "Caption for Figure 3.32 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Relative change in the amplitude of the seasonal cycle of global land carbon uptake in the historical CMIP6 simulations from 1961–2014. Net biosphere production estimates from 19 CMIP6 models (red), the data-led reconstruction JMA-TRANSCOM (Maki et al., 2010; dotted) and atmospheric CO2 seasonal cycle amplitude changes from observations (global as dashed line), Mauna Loa Observatory (MLO) (Dlugokencky et al., 2020) in bold black. Seasonal cycle amplitude is calculated using the curve fit algorithm package from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL). Relative changes are referenced to the 1961–1970 mean and for short time series adjusted to have the same mean as the model ensemble in the last 10 years. Interannual variation was removed with a nine-year Gaussian smoothing. Shaded areas show the one sigma model spread (grey) for the CMIP6 ensemble and the one sigma standard deviation of the smoothing (red) for the CO2 MLO observations. Inset: average seasonal cycle of ensemble mean net biosphere production and its one sigma model spread for 1961–1970 (orange dashed line, light orange shading) and 2005–2014 (solid green line, green shading). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 47699, 47700, 47701, 47702 ], "vocabularyKeywords": [], "identifier_set": [ 12378 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 149309, 149310, 149311, 149312, 149313, 149314, 149316, 168290, 149317, 149315 ], "onlineresource_set": [ 43705, 43704, 52389, 82633, 88583, 94621, 94622 ] }, { "ob_id": 33451, "uuid": "691c673c0d204911893659e10d4ddcba", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.37 (v20211215)", "abstract": "Data for Figure 2.37 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.37 shows indices of interannual climate variability from 1950-2019 based upon several sea surface temperature data products\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has five 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:\r\n \r\n - IOB.nc contains IOB index from COBE, ERSST, HADI, KAPL and OISST (yearly data, 1950-2019).\r\n - IOD.nc contains IOD index from COBE, ERSST, HADI, KAPL and OISST (yearly data, 1950-2019).\r\n - NINO34.nc contains Nino 3.4 index from COBE, ERSST, HADI, KAPL and OISST (yearly data, 1950-2019).\r\n - AMM.nc contains AMM index from COBE, ERSST, HADI, KAPL and OISST (yearly data, 1950-2019).\r\n - AZM.nc contains AZM index from COBE, ERSST, HADI, KAPL and OISST (yearly data, 1950-2019).\r\n \r\n Data acronyms:\r\n COBE [Objective Analyses of Sea-Surface Temperature and Marine Meteorological Variables for the 20th Century using ICOADS (International Comprehensive Ocean-Atmosphere Data Set) and the Kobe Collection].\r\n ERSST [NOAA Extended Reconstructed Sea Surface Temperature].\r\n HADI [Hadley Centre Sea Ice and Sea Surface Temperature data set].\r\n KAPL [Kaplan Extended SST].\r\n OISST [NOAA Optimum Interpolation Sea Surface Temperature].\r\n IOB [Indian Ocean Basin]\r\n IOD [Indian Ocean Dipole]\r\n AMM [Atlantic Meridional Mode]\r\n AZM [Atlantic Zonal Mode]\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n First panel:\r\n - Data file: IOB.nc\r\n \r\n Second panel:\r\n - Data file: IOD.nc\r\n \r\n Third panel:\r\n - Data file: Nino34.nc\r\n \r\n Fourth panel:\r\n - Data file: AMM.nc\r\n \r\n Fifth panel:\r\n - Data file: AZM.nc\r\n \r\n In all the cases:\r\n - Blue line corresponds to COBE data set\r\n - Red correspinds to ERSST data set\r\n - Skyblue corresponds to HADI data set\r\n - Green corresponds to KAPL data set\r\n - Yellow corresponds to OISST data set\r\n For all the nc files the sources are arranged in columns 1 to 5, respectively.\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 IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-01-24T17:19:50", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 2, Changing state, Multi-millennial context, pre-industrial, Natural forcing, anthropogenic forcing, Radiative forcing, Large-scale indicators, observed changes, Modes of variability, Figure 2.37, AZM, AMM, IOB, IOD, Niño 3.4", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-15T10:33:42", "doiPublishedTime": "2023-07-03T20:18:11", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 89, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 40048, "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig37/v20211215/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 27263, "numberOfFiles": 8, "fileFormat": "Net-CDF" }, "timePeriod": { "ob_id": 9186, "startTime": "1950-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 3815, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-12-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33453, "uuid": "0db8ef252cf44925b851f06c630693b2", "short_code": "comp", "title": "Caption for Figure 2.37 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Indices of interannual climate variability from 1950–2019 based upon several sea surface temperature data products. Shown are the following indices from top to bottom: IOB mode, IOD, Niño3.4, AMM and AZM. All indices are based on area-averaged annual data (see Annex IV). Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 21859, 85528, 85529, 85530, 85531, 85532 ], "vocabularyKeywords": [], "identifier_set": [ 12620 ], "observationcollection_set": [ { "ob_id": 32717, "uuid": "3da412ad9912427d9bb808b57faa21a7", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1" } ], "responsiblepartyinfo_set": [ 149406, 149407, 149408, 149409, 149410, 149411, 149412, 149413, 196264 ], "onlineresource_set": [ 43707, 43708, 82836 ] }, { "ob_id": 33460, "uuid": "4bbcb1722f2842c1b0a5ebc19160a863", "title": "STFC RAL methane retrievals from IASI on board MetOp-B, version 2.0", "abstract": "This Infrared Atmospheric Sounding Interferometer (IASI) methane dataset contains height-resolved and column-averaged volume mixing ratios of atmospheric methane (CH4) retrieved from the IASI instrument on the MetOp-B satellite. It also includes column-averaged water vapour (H2O), a scale factor for the HDO (water vapour isotopologue) volume mixing ratio profile, surface temperature, effective cloud fraction, effective cloud-top pressure and scale factors for two systematic residual spectra which are jointly retrieved from the spectral range 1232.25-1290.00 cm-1. This dataset was produced by Version 2.0 of the Rutherford Appleton Laboratory's (RAL's) IASI optimal estimation scheme to retrieve methane, which takes as input temperature and water vapour profiles and surface spectral emissivity pre-retrieved by RAL's Infrared and Microwave Scheme applied to IASI, MHS and AMSU-A on MetOp-B. The dataset additionally contains selected a priori values and uncertainties adopted in the optimal estimation scheme and retrieval output diagnostics such as the retrieval cost and the averaging kernels.\r\n\r\nDevelopment of the Version 2.0 scheme and its application to MetOp-A (2007-2017, http://dx.doi.org/10.5285/f717a8ea622f495397f4e76f777349d1) was funded by the National Centre for Earth Observation (NCEO) under the UK Natural Environment Research Council (NERC) with additional funding from EUMETSAT. Adaptation to MetOp-B and production of the IASI MetOp-B methane dataset 01/2018-03/2021 were funded by NCEO and ESA Contract No. 4000129987/20/I-DT Methane+.\r\n\r\nData were produced by the United Kingdom Research and Innovation (UKRI) Science and Technology Facilities Council (STFC) Remote Sensing Group (RSG) at the Rutherford Appleton Laboratory (RAL).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2022-01-16T18:27:09", "updateFrequency": "notPlanned", "dataLineage": "Data has been produced by the Rutherford Appleton Laboratory (RAL) Remote Sensing Group (RSG) at the United Kingdom Research and Innovation (UKRI) Science and Technology Facilities Council (STFC) and supplied to the Centre for Environmental Data Analysis for long term archiving.\r\n\r\nDevelopment of the Version 2.0 Infrared Atmospheric Sounding Interferometer (IASI) offline thermal infrared methane retrieval scheme and its application to MetOp-A (2007-2017, http://dx.doi.org/10.5285/f717a8ea622f495397f4e76f777349d1) were funded by the National Centre for Earth Observation (NCEO) under the UK Natural Environment Research Council (NERC) with additional funding from EUMETSAT. Adaptation to MetOp-B and production of the IASI MetOp-B methane dataset (01/2018-03/2021) were funded by NCEO and ESA Contract No. 4000129987/20/I-DT Methane+.", "removedDataReason": "", "keywords": "methane, IASI, optimal estimation, retrieval, averaging kernels, assimilation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-01-18T18:05:28", "doiPublishedTime": "2022-01-20T15:39:58.056345", "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 34646, "dataPath": "/neodc/iasi_ch4_ral/data/metop-b/l2/tir/v2.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 802812457119, "numberOfFiles": 242324, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9189, "startTime": "2018-01-01T00:00:00", "endTime": "2021-03-31T23:59:59" }, "resultQuality": { "ob_id": 3365, "explanation": "Data are as provided by the data providers. See the user guide for further information", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2019-12-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33464, "uuid": "c473bb55244b472285bb9d01545757bd", "short_code": "cmppr", "title": "STFC RAL methane retrieval from IASI on METOP B", "abstract": "Retrieval of methane from MetOp-B IASI" }, "imageDetails": [ 130 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 5002, "uuid": "60e718d3f2957f742c89b2b4fc159718", "short_code": "proj", "title": "National Centre for Earth Observation (NCEO)", "abstract": "The National Centre for Earth Observation is a partnership of scientists and institutions, from a range of disciplines, who are using data from Earth observation satellites to monitor global and regional changes in the environment and to improve understanding of the Earth system so that we can predict future environmental conditions.\r\n\r\nNCEO's Vision is to unlock the full potential of Earth observation to monitor, diagnose and predict climate and environmental changes, ensuring that these scientific advances are delivered to the wider community embedded in world class science." }, { "ob_id": 34648, "uuid": "ac4e0e51574a4488a1cad653cea9de5c", "short_code": "proj", "title": "ESA Methane+ project", "abstract": "The ESA Methane+ project aims at exploiting the Short Wave Infrared (SWIR) and Thermal Infrared (TIR) \r\n mthane (CH4) observations from different satellites in order to better differentiate between sources and sinks of CH4 on the regional and global scale. For this it uses the CH4 observations of TROPOMI on Copernicus Sentinel-5p, IASI on MetOp-B, and CrIS on Suomi NPP in combination with atmospheric inversion models.\r\n\r\nESA Contract No. 4000129987/20/I-DT Methane+." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 61970, 61971, 61972, 61973, 61977, 61978, 61979, 61980, 61981, 61984, 61985, 61986, 61987, 61988, 61989, 61990, 61991, 61992, 61993, 61994, 61995, 61996, 62000, 62001, 62002, 62004, 68548, 68549, 68550, 68551, 68552, 68553, 68554, 68555, 68558, 68559, 68560, 68561, 68562, 68563, 68564, 68565, 68566, 68567, 68570, 68571, 68573, 68579, 68580, 82408, 82409, 82410, 82411, 82412, 82413, 82414, 82415, 82416, 82417, 82418, 82419, 82420, 82421, 82422, 82423 ], "vocabularyKeywords": [], "identifier_set": [ 10981 ], "observationcollection_set": [ { "ob_id": 14469, "uuid": "bdea6532df314effb9addc6bca1e9f55", "short_code": "coll", "title": "STFC RAL methane retrievals from MetOp IASI", "abstract": "This dataset collection contains various versions of the STFC RAL Infrared Atmospheric Sounding Interferometer (IASI) methane dataset , which contains height-resolved and column-averaged volume mixing ratios of atmospheric methane (CH4). The dataset also includes column-averaged water vapour (H2O), a scale factor for the HDO (water vapour isotopologue) volume mixing ratio profile, surface temperature, effective cloud fraction, effective cloud-top pressure and scale factors for two systematic residual spectra which are jointly retrieved from the spectral range 1232.25-1290.00 cm-1 by the Rutherford Appleton Laboratory (RAL) IASI optimal estimation methane retrieval scheme. The dataset also contains selected a priori values and uncertainties adopted in the optimal estimation scheme and retrieval output diagnostics such as the retrieval cost and the averaging kernels. \r\n\r\nData were produced by the United Kingdom Research and Innovation (UKRI) Science and Technology Facilities Council (STFC) Remote Sensing Group (RSG) at the Rutherford Appleton Laboratory (RAL).\r\n\r\nThe development of the STFC RAL methane retrieval was funded by the Natural Environment Research Council (NERC) through its National Centre for Earth Observation (NCEO) with additional funding from EUMETSAT." }, { "ob_id": 30127, "uuid": "82b29f96b8c94db28ecc51a479f8c9c6", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Core datasets", "abstract": "This NCEO Core data set collection contains data generated by the National Centre for Earth Observation core scientific programmes. NCEO is a National Environment Research Council (NERC) research centre with more than 80 scientists distributed across leading UK universities and research organisations and led by Professor John Remedios at the University of Leicester.\r\n\r\nNCEO provides the UK with core expertise in Earth Observation science, data sets and merging techniques, and model evaluation to underpin Earth System research and the UK’s international contribution to environmental science. NCEO scientists work strategically with space agencies, play significant roles in mission planning, and generate internationally-recognised data products from 20 different satellite instruments." } ], "responsiblepartyinfo_set": [ 149468, 149472, 149471, 149470, 149474, 149473, 149475, 149476, 149480, 149469, 149521, 149477, 149478 ], "onlineresource_set": [ 43717, 47609, 47608, 88118 ] }, { "ob_id": 33462, "uuid": "acdfa050673c46d49d6a35bfa482762b", "title": "Mars Analysis Correction Data Assimilation (MACDA): MGS/TES v1.0 Reference Run Data", "abstract": "This dataset contains basic gridded atmospheric and surface variables for the planet Mars over three martian years (a martian year is 1.88 terrestrial years), produced as a reference run in association with the Mars Analysis Correction Data Assimilation (MACDA) v1.0 re-analysis. Each file in the dataset spans 30 martian mean solar days (sols) during the science mapping phase of the National Aeronautics and Space Administration's (NASA) Mars Global Surveyor (MGS) spacecraft, between May 1999 and August 2004.\r\n\r\nThis dataset is a reference run produced by re-analysis of Thermal Emission Spectrometer (TES) retrievals of only total dust opacities, using the MACDA scheme in a Mars global circulation model (MGCM). This reference dataset, therefore, should be used in association with the full re-analysis of TES retrievals of nadir thermal profiles and total dust opacities - see linked dataset.\r\n\r\nThe MGCM used is the UK spectral version of the model developed by the Laboratoire de Météorologie Dynamique in Paris, France.\r\n\r\nMACDA is a collaboration between the University of Oxford and The Open University in the UK.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-01-05T14:12:14", "updateFrequency": "notPlanned", "dataLineage": "Data provided to the Centre for Envirnomental Data Analysis (CEDA) by the project team members in December 2021. Mars Global Surveyor/ Thermal Emission Spectrometer retrievals have been provided by Michael D. 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