Observation List
Get a list of Observation objects.
GET /api/v3/observations/?format=api&offset=9000
{ "count": 10256, "next": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=9100", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=8900", "results": [ { "ob_id": 39986, "uuid": "5352ffa477fa489094a7c0a4b32ff677", "title": "Copernicus Climate Change Service Dataset: L4 Sea Surface Temperature Analysis Integrated Climate Data Record (ICDR), version 2.0", "abstract": "This Sea Surface Temperature Level 4 Analysis Intermediate Climate Data Record (ICDR) provides a globally-complete daily analysis of sea surface temperature (SST) on a 0.05 degree regular latitude - longitude grid. It combines data from both the Advanced Very High Resolution Radiometer (AVHRR ) and Sea and Land Surface Temperature Radiometer (SLSTR) Intermediate Climate Data Records, using a data assimilation method to provide SSTs where there were no measurements. \r\n\r\nThis dataset was produced for the Copernicus Climate Change Service (C3S). V2.0 extends from 2017-2021.\r\n\r\nA historic Climate Data Record (CDR) has also been produced under the ESA Climate Change Initiative Sea Surface Temperature (CCI_sst). This is available as a separate dataset in the CEDA catalogue and through the ESA CCI Open Data Portal.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-04-28T00:00:00", "latestDataUpdateTime": "2023-04-28T00:00:00", "updateFrequency": "notPlanned", "dataLineage": "Data were processed as part of the Copernicus Climate Change project", "removedDataReason": "", "keywords": "", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 39987, "dataPath": "/neodc/c3s_sst/data/ICDR_v2/Analysis/L4/v2.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 30715456343, "numberOfFiles": 1828, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 11067, "startTime": "2017-01-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 4253, "explanation": "For information on the data quality see the associated documentation", "passesTest": true, "resultTitle": "C3S SST data quality", "date": "2023-04-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39988, "uuid": "c04a7f3c061d4c99ada6ca255a56a536", "short_code": "comp", "title": "Derivation of the Copernicus Climate Change Service Dataset: L4 Sea Surface Temperature Analysis Integrated Climate Data Record (ICDR), version 2", "abstract": "The L4 Sea Surface Temperature Analysis data contains daily, spatially complete estimated daily SST data, derived using the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) processing system. This creates the L4 data from the SLSTR and AVHRR Level 2 and Level 3 data sets also produced in the Copernicus Climate Change project.\r\n\r\nFor further information please see the associated documentation." }, "procedureCompositeProcess": null, "imageDetails": [ 137 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2556, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 28, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sst_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 39992, "uuid": "e710f839faa3436ead72b6ff7efef32d", "short_code": "proj", "title": "Copernicus Climate Change Service: Sea Surface Temperature data production", "abstract": "The Copernicus Climate Change project (.....) produced Sea Surface Temperature datasets from the AVHRR and SLSTR instruments. \r\n\r\nTo be written...." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 66257, 66259, 66260, 68037, 83836 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 194884, 194883, 194882, 194881, 194880, 194879, 194878 ], "onlineresource_set": [ 83378 ] }, { "ob_id": 39989, "uuid": "edc03b1f5c4f42dcbe5906dd3b5fd592", "title": "Copernicus Climate Change Service Dataset: L4 Sea Surface Temperature Analysis Integrated Climate Data Record (ICDR), version 2.1", "abstract": "This Sea Surface Temperature Level 4 Analysis Intermediate Climate Data Record (ICDR) provides a globally-complete daily analysis of sea surface temperature (SST) on a 0.05 degree regular latitude - longitude grid. It combines data from both the Advanced Very High Resolution Radiometer (AVHRR ) and Sea and Land Surface Temperature Radiometer (SLSTR) Intermediate Climate Data Records, using a data assimilation method to provide SSTs where there were no measurements. \r\n\r\nThis dataset was produced for the Copernicus Climate Change Service (C3S). V2.1 extends from 2017-2022.\r\n\r\nA historic Climate Data Record (CDR) has also been produced under the ESA Climate Change Initiative Sea Surface Temperature (CCI_sst). This is available as a separate dataset in the CEDA catalogue and through the ESA CCI Open Data Portal.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-04-28T00:00:00", "latestDataUpdateTime": "2023-04-28T00:00:00", "updateFrequency": "notPlanned", "dataLineage": "Data were processed as part of the Copernicus Climate Change project", "removedDataReason": "", "keywords": "", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 39990, "dataPath": "/neodc/c3s_sst/data/ICDR_v2/Analysis/L4/v2.1/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 36452399946, "numberOfFiles": 2192, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 11067, "startTime": "2017-01-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 4253, "explanation": "For information on the data quality see the associated documentation", "passesTest": true, "resultTitle": "C3S SST data quality", "date": "2023-04-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39988, "uuid": "c04a7f3c061d4c99ada6ca255a56a536", "short_code": "comp", "title": "Derivation of the Copernicus Climate Change Service Dataset: L4 Sea Surface Temperature Analysis Integrated Climate Data Record (ICDR), version 2", "abstract": "The L4 Sea Surface Temperature Analysis data contains daily, spatially complete estimated daily SST data, derived using the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) processing system. This creates the L4 data from the SLSTR and AVHRR Level 2 and Level 3 data sets also produced in the Copernicus Climate Change project.\r\n\r\nFor further information please see the associated documentation." }, "procedureCompositeProcess": null, "imageDetails": [ 137 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2556, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 28, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sst_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 39992, "uuid": "e710f839faa3436ead72b6ff7efef32d", "short_code": "proj", "title": "Copernicus Climate Change Service: Sea Surface Temperature data production", "abstract": "The Copernicus Climate Change project (.....) produced Sea Surface Temperature datasets from the AVHRR and SLSTR instruments. \r\n\r\nTo be written...." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50415, 50417, 52530, 52532, 66255, 66256, 66257, 66258, 66259, 66260 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 194887, 194893, 194892, 194891, 194890, 194889, 194888 ], "onlineresource_set": [ 83379 ] }, { "ob_id": 39991, "uuid": "b70e6ae10a9f463d88819eb981cd4d0f", "title": "UKESM1 diagnostics for CMIP6 ScenarioMIP and CMIP historical experiments", "abstract": "Model output from the UKESM1 Earth System Model for experiments described in Archer-Nicholls et al. DOI:10.5194/acp-2022-706. \r\n\r\nData from five experiments are included here: ScenarioMIP SSP1-2.6 (ssp126), SSP2-4.5 (ssp245), SSP3-7.0 (ssp370), SSP5-8.5(ssp585) experiments and the CMIP Historical experiment (hist). \r\nFor the ScenarioMIP experiments, data are archived for the years 2090-2094 inclusive. \r\nFor the CMIP Historical experiment, data are archived for the years 1850-1854 inclusive. \r\nThe data comprise monthly mean output from both environmental variables, tracer mass mixing ratio and chemical reaction tendencies in moles per gridbox per second so as to be able levels of the nitrate radical and its principal chemical sinks consistent with the analysis in Archer-Nicholls et al. \r\nOther relevant data may be found in the CMIP6 archive for CMIP and ScenarioMIP hosted at the Centre for Environmental Data Analysis (CEDA) and at the Earth System Federation Grid.\r\n\r\nThe experiments described in Archer-Nicholls et al. target the controlling factors for the gas phase nitrate (NO3) radical species, focusing on CMIP6 CMIP and ScenarioMIP experiments which have already been uploaded to CEDA and the Earth System Federation Grid (historical data are available via DOI:10.22033/ESGF/CMIP6.6113 and ScenarioMIP data via DOI:10.22033/ESGF/CMIP6.1567) and we provide here additional diagnostic output for these reference datasets/experiments, necessary to reproduce the figures in Archer-Nicholls et al. \r\n\r\nThe analysis in Archer-Nicholls et al. uses the CMIP Historical experiment, described in Eyring et al. (2016) (DOI:10.5194/gmd-9-1937-2016 and ScenarioMIP SSP1-26, SSP2-45, SSP3-70 and SSP5-85 experiments described in O'Neill et al. (2016) (DOI:10.5194/gmd-9-3461-2016), with emissions given in Gidden et al.(2019) (DOI:10.5194/gmd-12-1443-2019). \r\n\r\nThe data presented here are from experiments using UKESM1 described in Sellar et al. (2019) (DOI:10.1029/2019MS001739) run on UK supercomputing platform MONSooN.\r\n\r\nAcronyms\r\n---------------------\r\nCMIP6: the sixth phase of the Coupled Model Intercomparison Project.\r\nScenarioMIP: the Scenario Model Intercomparison Project simulates climate outcomes based on alternative plausible future scenarios. \r\nSSP1-26: experiment based on Shared Socioeconomic Pathway SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP2-45: experiment based on Shared Socioeconomic Pathway SSP2 with medium challenges to climate change mitigation and adaptation and RCP4.5, a future pathway with a radiative forcing of 4.5 W/m2 in the year 2100.\r\nSSP3-70: experiment based on Shared Socioeconomic Pathway SSP3 which is characterised by high challenges to both mitigation and adaptation and RCP7.0, a future pathway with a radiative forcing of 7.0 W/m2 in the year 2100.\r\nSSP5-85: experiment based on Shares Socioeconomic Pathway SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.", "creationDate": "2023-04-28T13:29:41.537242", "lastUpdatedDate": "2023-04-28T13:22:55", "latestDataUpdateTime": "2023-05-16T17:10:09", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CMIP6, UKESM1, NO3, ScenarioMIP, CMIP historical, nitrate radical", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-05-16T15:56:50", "doiPublishedTime": "2023-05-16T15:57:07", "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": 40058, "dataPath": "/badc/aphh/data/delhi/promote/UKESM1-climate-response-NO3/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 47413422337, "numberOfFiles": 90, "fileFormat": "Netcdf" }, "timePeriod": { "ob_id": 11070, "startTime": "1850-01-01T00:00:00", "endTime": "2094-12-31T23:59:59" }, "resultQuality": { "ob_id": 4254, "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 for UKESM1 data", "date": "2023-04-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39993, "uuid": "ce22aec236b44835acdc9914fecb930a", "short_code": "comp", "title": "UKESM1 deployed on UK supercomputing platform MONSooN", "abstract": "UKESM1 Earth System Model described in Sellar et al. (2019) (DOI:10.1029/2019MS001739) at N96 horizontal resolution over global domain run on UK supercomputing platform MONSooN." }, "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": 32039, "uuid": "35248b7da34244578261df6044f1b711", "short_code": "proj", "title": "APHH: Process analysis, observations and modelling - Integrated solutions for cleaner air for Delhi (PROMOTE)", "abstract": "Atmospheric Pollution and Human Health in an Indian Megacity is a four year research programme jointly funded by the Natural Environment Research Council (NERC), the Medical Research Council (MRC), the Newton–Bhabha Fund, and the Ministry of Earth Sciences (MoES) and Department of Biotechnology (DBT).\r\n\r\nOver 4 years, PROMOTE aims to reduce uncertainties in air quality prediction and forecasting for Delhi by undertaking process orientated observational and modelling analyses and to derive the most effective mitigation solutions for reducing air pollution over the urban and surrounding region. PROMOTE brings together a cross-disciplinary team of leading researchers from India and the UK to deliver the project aims. Its investigations will address three key questions: Q1 What contribution is made by aerosols to the air pollution burden in Delhi? Q2 How does the lower atmospheric boundary layer affect the long range transport of air pollution incoming into Delhi? Q3 What are the most effective emission controls for mitigation interventions that will lead to significant reductions in air pollution and exposure levels over Delhi and the wider National Capital Region?\r\n\r\nGrant Ref: NE/P016421/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 19039, 19043, 21990, 22005, 51186, 51187, 54871, 54872, 54873, 54874, 62353, 62529, 66241, 66242, 66243, 66244, 66245, 66246, 66247, 66248, 66249, 66250, 66251, 66252, 66253, 66254, 70293, 87726, 87727 ], "vocabularyKeywords": [], "identifier_set": [ 12499 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 194896, 194897, 194898, 194899, 194900, 194894, 194895, 195191, 194901, 194902, 194903, 194904, 194905 ], "onlineresource_set": [ 83382, 83383, 83384, 83385 ] }, { "ob_id": 40004, "uuid": "0ad1068f45724169afbe541b2525e81c", "title": "ICECAPS-ACE: MIXCRA retrievals of fog properties at Summit Station, Greenland", "abstract": "This dataset contains retrievals of bulk fog particle phase and effective radius generated using the mixed-phase cloud property retrieval algorithm (MIXCRA), during twelve case studies of supercooled radiation fog at Summit Station in central Greenland.\r\n\r\nMIXCRA uses optimal estimation to retrieve fog microphysical properties at 5-min intervals from downwelling spectral longwave radiation measured by an Atmospheric emitted radiance interferometer (AERI).\r\nThese data and retrievals were generated as part of the ICECAPS-ACE project (The Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit:\r\nerosol Cloud Experiment).\r\nSee linked references for more information about the implementation of MIXCRA, the AERI measurements and complementary datasets.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-05-03T16:23:41", "updateFrequency": "notPlanned", "dataLineage": "AERI data were collected by the ICECAPS team and published here: https://www.doi.org/10.18739/A2TB0XW2V\r\nVertical temperature and water vapor profiles were genetrated using the TROPoe algorithm and archived at https://doi.org/10.5439/1880028 \r\nThese two datasets were used as input to the MIXCRA algorithm (v 1.12 2022/03/16).\r\nThe resulting retrievals were passed to CEDA for data archiving.", "removedDataReason": "", "keywords": "ICECAPS-ACE, fog, Summit station, Greenland Ice Sheet", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-05-04T07:04:48", "doiPublishedTime": "2023-05-05T09:18:37", "removedDataTime": null, "geographicExtent": { "ob_id": 2625, "bboxName": "Summit station greenland", "eastBoundLongitude": -38.46, "westBoundLongitude": -38.46, "southBoundLatitude": 72.575, "northBoundLatitude": 72.575 }, "verticalExtent": null, "result_field": { "ob_id": 40005, "dataPath": "/badc/icecaps-ace/data/MIXCRA_1_fog/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4558239, "numberOfFiles": 62, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 11074, "startTime": "2019-06-08T00:00:00", "endTime": "2019-09-30T23:59:59" }, "resultQuality": { "ob_id": 4258, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-03" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 40008, "uuid": "fc7b4590a2164451b0f5069adda8abee", "short_code": "cmppr", "title": "Composite process for ICECAPS-ACE MIXCRA", "abstract": "AERI data were collected by the ICECAPS team and published here: https://www.doi.org/10.18739/A2TB0XW2V\r\nVertical temperature and water vapor profiles were genetrated using the TROPoe algorithm\r\nand archived at https://doi.org/10.5439/1880028\r\nThese two datasets were used as input to the MIXCRA algorithm (v 1.12 2022/03/16)." }, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30502, "uuid": "65eaacda00a244328b944a1b76fbfd4f", "short_code": "proj", "title": "ICECAPS-ACE: Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit, Greenland - Aerosol Cloud Experiment", "abstract": "Since 2010, the ICECAPS project has been monitoring cloud-atmosphere-energy interactions at Summit Station, in the centre of the Greenland Ice Sheet (GrIS), using a comprehensive suite of ground-based remote sensing instruments and twice daily radiosonde profiles. In 2018, the Aerosol Cloud Experiment (ACE) expansion of ICECAPS saw the addition of a new series of instruments to measure surface aerosol concentrations and turbulent heat fluxes over the ice sheet. Combined with the original ICECAPS instrumentation, the ACE instruments allow for the study of cloud-aerosol-energy interactions over the central GrIS. ICECAPS-ACE is jointly funded by the Natural Environmental Research Council (NERC) and US National Science Foundation (NSF). Award numbers: NERC: NE/S00906X/1. NSF award numbers: 1801318, 1801477, 1801764.\r\n\r\nAdditional data generated as part of ICECAPS-ACE can be accessed at the Arctic Data Center doi:10.18739/A2S17SV6X" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 60616, 66202, 66203, 66204, 66205, 66206, 66207, 66208, 66209, 66210, 66211, 66212, 66213, 66214, 66215, 66216, 66217, 66218, 66219, 66220, 66221, 66222, 66223, 66224, 66225, 66226, 66227, 66228, 66229, 66230, 66231, 66232, 66233, 66234, 66235, 66236, 66237, 66238, 66239, 66240, 79783, 79784, 79785, 79786 ], "vocabularyKeywords": [], "identifier_set": [ 12475 ], "observationcollection_set": [ { "ob_id": 30507, "uuid": "f06c6aa727404ca788ee3dd0515ea61a", "short_code": "coll", "title": "ICECAPS-ACE: Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit, Greenland - Aerosol Cloud Experiment measurements", "abstract": "This dataset collection contains in situ atmospheric and aerosol measurements collected at Summit Station, Greenland.\r\n\r\nThese data were collected as part of the joint Natural Environmental Research Council (NERC) and US National Science Foundation (NSF) -funded Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit - Aerosol Cloud Experiment (ICECAPS-ACE) project. Since 2010, the ICECAPS project has been monitoring cloud-atmosphere-energy interactions at Summit Station, in the centre of the Greenland Ice Sheet (GrIS), using a comprehensive suite of ground-based remote sensing instruments and twice daily radiosonde profiles. In 2018, the Aerosol Cloud Experiment (ACE) expansion of ICECAPS saw the addition of a new series of instruments to measure surface aerosol concentrations and turbulent heat fluxes over the ice sheet. Combined with the original ICECAPS instrumentation, the ACE instruments allow for the study of cloud-aerosol-energy interactions over the central GrIS.\r\n\r\nThis dataset collection contains the measurements collected as part of the ACE component of ICECAPS-ACE, which includes the following:\r\n1) Surface-temperature-profile: A near surface temperature profile from four temperature/ humidity sensors distributed on the 15 m tower at Summit.\r\n2) Surface-moisture-profile: A near surface moisture profile from four temperature/ humidity sensors distributed on the 15 m tower at Summit.\r\n3) Surface-winds-profile: A near surface wind profile from four sonic anemometers distributed on the 15 m tower at Summit.\r\n4) Snow-height: The distance to the snow surface from the lowest level of instruments on the 15 m tower at Summit, detected by a sonic-ranging sensor.\r\n5) Skin-temperature: The brightness temperature of the snow surface as detected by an infrared radiation thermometer.\r\n6) Aerosol-concentration: The concentration of condensation nuclei (> 5nm diameter) measured at the surface using a Condensation Particle Counter.\r\n7) Aerosol-size-distribution: The size-resolved concentration of surface aerosol particles between 0.25 and 6.5 um in diameter measured using an Optical Particle Counter.\r\n8) Flux-components: High resolution temperature, humidity and wind fluctuations that can be used to estimate turbulent fluxes using eddy covariance, located at two levels on the 15 m tower at Summit.\r\n9) Flux-estimates: Estimates of turbulent heat and momentum fluxes by applying the eddy covariance technique to flux-components.\r\n\r\nOther ICECAPS data are available here:\r\nhttps://psl.noaa.gov/arctic/observatories/summit/\r\n\r\nFrom August 2022 to August 2025, these measurements were supported by the ICECAPS-MELT project (Measurements along a Transect)." } ], "responsiblepartyinfo_set": [ 194939, 194945, 194935, 194936, 194938, 194941, 194942, 194937, 194950, 194951, 194952, 194953, 194946, 194954, 194944 ], "onlineresource_set": [ 83394, 83395, 83390, 83396, 83397, 83392, 83398, 83393, 83399, 83400, 83401, 87633 ] }, { "ob_id": 40013, "uuid": "edf66239c70c426e9e9f19da1ac8ba87", "title": "Physical Marine Climate Projections for the North West European Shelf Seas: NWSPPE", "abstract": "A Perturbed Physics Ensemble (PPE) of the Met Office Global Coupled model version 3.05 (HadGEM3-GC3.05) has been downscaled with the shelf seas climate version of the Nucleus for European Modelling of the Ocean (NEMO) Coastal Ocean model (CO9). Each of the 12 ensemble members have been downscaled as transient simulations (from 1990-2098) under RCP8.5 scenario, and we refer to the resultant downscaled PPE as the North West Shelf Perturbed Parameter Ensemble (NWSPPE). The HadGEM3-GC3.05 PPE was designed to span the range of uncertainty associated with model parameter uncertainty in the atmosphere of the driving global climate model. CO9 was run at a 7 km resolution, with 51 vertical levels using s-coordinates. This data collection includes 2D fields of monthly mean output for the full period, for each ensemble member, as well as pre-processed climatologies. Regional mean time series are also included for each ensemble member.\r\n\r\nNEMO has three model grids, the T, U and V grids, and we output variables in three files, respecting their native model grid. In practice, all our variables are on the T grid, apart from the Eastward and Northward components of the depth mean velocities (DMU, DMV), which are in the U and V grid files respectively. The T grid files have Sea Surface, Near Bed, and the Difference between the surface and bed Temperature and Salinity (SST, NBT, DFT, SSS, NBS, DFS), Potential Energy Anomaly (PEA), Mixed Layer Depth (MLD), the barotropic current magnitude interpolated onto the T grid (DMUV).", "creationDate": "2023-05-04T13:25:41.627251", "lastUpdatedDate": "2023-05-04T13:16:19", "latestDataUpdateTime": "2023-05-18T15:33:24", "updateFrequency": "", "dataLineage": "The UKCP18 HadGEM3 GC3.05 Perturbed Parameter Ensemble was dynamically downscaled with shelf seas climate version of NEMO 4.0.4 (CO9) to give a set of climate projections.\r\n \r\nModel output from HadGEM3 GC3.05 was processed into model input for CO9. CO9 was run for each of the 12 ensemble members as transient simulations. The results were assessed against a range of observations, which will be described in Tinker et al. (2023, in prep). The model output was then post-processed using the python package available at https://github.com/hadjt/NWS_simulations_postproc. After data and code evaluation, the dataset was supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "UKCP, Marine Climate, Uncertainty, Climate Downscaling, NW European Shelf Seas, Shelf Seas, Temperature, SST, Salinity, Stratification, North Sea, Celtic Sea, Irish Sea, English Channel", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "7 km", "status": "completed", "dataPublishedTime": "2023-06-01T13:24:37", "doiPublishedTime": "2023-07-20T16:32:03", "removedDataTime": null, "geographicExtent": { "ob_id": 3813, "bboxName": "", "eastBoundLongitude": 13.0, "westBoundLongitude": -19.88888, "southBoundLatitude": 40.06667, "northBoundLatitude": 65.00125 }, "verticalExtent": null, "result_field": { "ob_id": 40061, "dataPath": "/badc/deposited2023/marine-nwsclim/NWSPPE", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 88644184799, "numberOfFiles": 6385, "fileFormat": "Data are provided in NetCDF format." }, "timePeriod": { "ob_id": 11075, "startTime": "1990-01-01T00:00:00", "endTime": "2099-01-01T23:59:59" }, "resultQuality": { "ob_id": 4295, "explanation": "The data have been thoroughly evaluated against observational data, including comparisons of the Sea Surface Temperature (SST) to the OSTIA analysis (Roberts-Jones et al. 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(2013) https://doi.org/10.2112/JCOASTRES-D-12-00175.1)", "passesTest": true, "resultTitle": "NWS Data Quality Statement", "date": "2023-06-01" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40012, "uuid": "9d5496e08abc416aaeec18630613fa59", "short_code": "comp", "title": "NEMO Shelf Coastal Ocean Model 9 (CO9) based on NEMO4.0.4", "abstract": "The shelf seas model used in these climate projections is available on github:\r\nhttps://github.com/hadjt/NEMO_4.0.4_CO9_shelf_climate" }, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2521, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 40121, "uuid": "7d6c30d625664d4d805e26b385e65964", "short_code": "proj", "title": "Physical Marine Climate Projections for the North West European Shelf Seas", "abstract": "This project has created a set of ensemble climate projections for the physical marine environment of the Northwest European Shelf Seas (NWS), with a consistent present day control simulation. The projections are an update to the Maritime INdustries Environmental Risk and Vulnerability Assessment (MINERVA) projections, and are consistent with global climate model simulations performed as part of the United Kingdom’s Climate Projections of 2018 (UKCP18).\r\n\r\nThe projections created in this project are designed to provide a new and complementary evidence base to inform the fourth UK Climate Change Risk Assessment (CCRA4) and other climate change studies. While they use updated modelling systems and techniques, and represent a much larger dataset, the projections are structurally the same. These projections include Sea Surface, Near Bed, and the Difference between the surface and bed Temperature and Salinity (SST, NBT, DFT, SSS, NBS, DFS), Potential Energy Anomaly (PEA), Mixed Layer Depth (MLD), the barotropic currents (their U and V components (DMU, DMV), as well as their magnitude, DMUV)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 27599, 50415, 50417, 62369, 62478, 62479, 62480, 62481, 62482, 62483, 62484, 62485, 62486, 62487, 62488, 63971, 80035, 80036, 80037, 80038, 80039, 80040, 80041, 80042, 80043, 80044, 80045 ], "vocabularyKeywords": [], "identifier_set": [ 12649 ], "observationcollection_set": [ { "ob_id": 40017, "uuid": "832677618370457f9e0a85da021c1312", "short_code": "coll", "title": "Physical Marine Climate Projections for the North West European Shelf Seas based on the UKCP18 Perturbed Parameter Ensemble.", "abstract": "A set of ensemble climate projections for the physical marine environment of the Northwest European Shelf Seas (NWS), with a consistent present day control simulation. This data set updates the Maritime INdustries Environmental Risk and Vulnerability Assessment (MINERVA) projections, and is consistent with global climate model simulations performed as part of the United Kingdom’s Climate Projections of 2018 (UKCP18). \r\n\r\nThe UKCP18 Perturbed Parameter Ensemble (PPE) of the Met Office Global Coupled model version 3.05 (HadGEM3-GC3.05) has been downscaled with a North West European Shelf seas climate configuration of the Nucleus for European Modelling of the Ocean (NEMO) Coastal Ocean model. The UKCP18 HadGEM3-GC3.05 PPE was designed to span the range of uncertainty associated with model parameter uncertainty in the atmosphere of the driving global climate model. Each of the 12 PPE ensemble members have been downscaled as transient simulation for the period 1990-2098 under the RCP8.5 climate change scenario. We refer to the downscaled ensemble as the North West Shelf Perturbed Parameter Ensemble (NWSPPE). \r\n\r\nThe NEMO configuration for NWSPPE has a horizontal resolution of 7 km with 51 vertical levels using terrain-following s-coordinates. This data collection includes 2D fields of monthly mean output over the full simulation period for every ensemble member, as well as pre-processed climatologies and ensemble statistics (for an early-century (2000-2019) and late-century (2079-2098) period). Regional mean time series are also included for each ensemble member at monthly time resolution.\r\n\r\nA 200-year “present day” control simulation (for the year 2000) has also been downscaled with the shelf seas climate version of the NEMO Coastal Ocean model. HadGEM3 GC3.05 was run for 200 years with the atmospheric constituents fixed to the values of the year 2000. The present-day control simulation provides an estimate of the internal variability in the climate system that can arise in the absence of time-varying external forcings. Details of this simulation, which formed part of the core UKCP18 project, are available in Tinker et al. (2020).\r\n\r\nThese projections represent an update to the MINERVA projections, designed to provide a new and complementary evidence base to inform the fourth UK Climate Change Risk Assessment (CCRA4) and other climate change studies. While they use updated modelling systems and techniques, and represent a much larger dataset, the projections are structurally the same. These projections include Sea Surface, Near Bed, and the Difference between the surface and bed Temperature and Salinity (SST, NBT, DFT, SSS, NBS, DFS), Potential Energy Anomaly (PEA), Mixed Layer Depth (MLD), the barotropic currents (their U and V components (DMU, DMV), as well as their magnitude, DMUV)." } ], "responsiblepartyinfo_set": [ 194977, 194978, 194979, 194980, 194981, 194982, 194992, 194993 ], "onlineresource_set": [ 83419, 83420 ] }, { "ob_id": 40015, "uuid": "66e39885a60e4b6386752b1a295f268a", "title": "Physical Marine Climate Projections for the North West European Shelf Seas: PDCtrl", "abstract": "A 200-year present-day control simulation (for the year 2000) has been downscaled with the shelf seas climate version of the Nucleus for European Modelling of the Ocean (NEMO) Coastal Ocean model (CO6), and we refer to the downscaled present-day control simulation as PDCtrl. The Met Office Global Coupled model version 3.05 (HadGEM3-GC3.05) was run for 200 years with the atmospheric constituents fixed to the values of the year 2000. The present-day control simulation provides an estimate of the unforced internal variability in the climate system that can arise in the absence of time-varying external forcings. The PDCtrl simulation was performed as part of the United Kingdom’s Climate Projections of 2018 (UKCP18) with full details available in Tinker et al. (2020). CO6 was run at a 7 km resolution, with 51 vertical levels using s-coordinates. This data collection includes 2D fields of monthly mean output for the full period, as well as regional mean time series for the full 200 year period.\r\n\r\nNEMO has three model grids, the T, U and V grids, and we output variables in three files, respecting their native model grid. In practice, all our variables are on the T grid, apart from the Eastward and Northward components of the depth mean velocities (DMU, DMV), which are in the U and V grid files respectively. The T grid files have Sea Surface, Near Bed, and the Difference between the surface and bed Temperature and Salinity (SST, NBT, DFT, SSS, NBS, DFS), Potential Energy Anomaly (PEA), Mixed Layer Depth (MLD), the barotropic current magnitude interpolated onto the T grid (DMUV).", "creationDate": "2023-05-04T13:31:36.779771", "lastUpdatedDate": "2023-05-04T13:26:41", "latestDataUpdateTime": "2023-05-17T15:55:39", "updateFrequency": "", "dataLineage": "The UKCP18 HadGEM3 GC3.05 Present Day Control Simulation was dynamically downscaled with shelf seas climate version of NEMO 3.6 (CO6) to give a set of climate projections.\r\n \r\nModel output from HadGEM3 GC3.05 was processed into model input for CO6. The results were the basis of the UKCP18 present day sea level variability section (Tinker et al. 2018), and were assessed against a range of observations, which is described in Tinker et al. (2018). The model output was then post-processed using the python package available at https://github.com/hadjt/NWS_simulations_postproc. After data and code evaluation, the dataset was supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "UKCP, Marine Climate, Uncertainty, Climate Downscaling, NW European Shelf Seas, Shelf Seas, Temperature, SST, Salinity, Stratification, North Sea, Celtic Sea, Irish Sea, English Channel", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "7 km", "status": "completed", "dataPublishedTime": "2023-06-01T13:24:52", "doiPublishedTime": "2023-07-20T16:32:34", "removedDataTime": null, "geographicExtent": { "ob_id": 3814, "bboxName": "", "eastBoundLongitude": 13.0, "westBoundLongitude": -19.88888, "southBoundLatitude": 40.06667, "northBoundLatitude": 65.00125 }, "verticalExtent": null, "result_field": { "ob_id": 40062, "dataPath": "/badc/deposited2023/marine-nwsclim/PDCtrl", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 12930509896, "numberOfFiles": 605, "fileFormat": "Data are provided in NetCDF format." }, "timePeriod": { "ob_id": 11076, "startTime": "1990-01-01T00:00:00", "endTime": "2099-01-01T23:59:59" }, "resultQuality": { "ob_id": 4298, "explanation": "The data have been thoroughly evaluated against observational data, including comparisons of the Sea Surface Temperature (SST) to the OSTIA analysis (Roberts-Jones et al. (2012) http://dx.doi.org/10.1175/JCLI-D-11-00648.1) the surface and bed temperatures (SST, NBT) and sea surface salinity (SSS) to the quality controlled EN4 profile dataset (Good et al. (2013) https://doi.org/doi:10.1002/2013JC009067) and the sea surface height to satellite altimetry products (Rio et al. (2014) https://doi.org/doi:10.1002/2014GL061773; Legeais et al. (2018) https://doi.org/10.5194/essd-10-281-2018) and tide gauge records (PSMSL, Holgate et al. (2013) https://doi.org/10.2112/JCOASTRES-D-12-00175.1) The evaluation of the PDCtrl dataset has been published in Tinker et al. (2020) - see 'Related Documents'.", "passesTest": true, "resultTitle": "NWS PDCtrl Data Quality Statement", "date": "2023-06-01" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40014, "uuid": "b752b12bc7a4462b8e9afc00ea184bc5", "short_code": "comp", "title": "NEMO Shelf Coastal Ocean Model 6 (CO6) based on NEMO3.6", "abstract": "The shelf seas model used in these climate projections is available on github:\r\nhttps://github.com/hadjt/NEMO_3.6_CO6_shelf_climate" }, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2521, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 40121, "uuid": "7d6c30d625664d4d805e26b385e65964", "short_code": "proj", "title": "Physical Marine Climate Projections for the North West European Shelf Seas", "abstract": "This project has created a set of ensemble climate projections for the physical marine environment of the Northwest European Shelf Seas (NWS), with a consistent present day control simulation. The projections are an update to the Maritime INdustries Environmental Risk and Vulnerability Assessment (MINERVA) projections, and are consistent with global climate model simulations performed as part of the United Kingdom’s Climate Projections of 2018 (UKCP18).\r\n\r\nThe projections created in this project are designed to provide a new and complementary evidence base to inform the fourth UK Climate Change Risk Assessment (CCRA4) and other climate change studies. While they use updated modelling systems and techniques, and represent a much larger dataset, the projections are structurally the same. These projections include Sea Surface, Near Bed, and the Difference between the surface and bed Temperature and Salinity (SST, NBT, DFT, SSS, NBS, DFS), Potential Energy Anomaly (PEA), Mixed Layer Depth (MLD), the barotropic currents (their U and V components (DMU, DMV), as well as their magnitude, DMUV)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 27599, 50415, 50417, 62369, 62478, 62479, 62480, 62481, 62482, 62483, 62484, 62485, 62486, 62487, 62488, 63971, 80035, 80036, 80037, 80038, 80039, 80040, 80041, 80042, 80043, 80044, 80045 ], "vocabularyKeywords": [], "identifier_set": [ 12650 ], "observationcollection_set": [ { "ob_id": 40017, "uuid": "832677618370457f9e0a85da021c1312", "short_code": "coll", "title": "Physical Marine Climate Projections for the North West European Shelf Seas based on the UKCP18 Perturbed Parameter Ensemble.", "abstract": "A set of ensemble climate projections for the physical marine environment of the Northwest European Shelf Seas (NWS), with a consistent present day control simulation. This data set updates the Maritime INdustries Environmental Risk and Vulnerability Assessment (MINERVA) projections, and is consistent with global climate model simulations performed as part of the United Kingdom’s Climate Projections of 2018 (UKCP18). \r\n\r\nThe UKCP18 Perturbed Parameter Ensemble (PPE) of the Met Office Global Coupled model version 3.05 (HadGEM3-GC3.05) has been downscaled with a North West European Shelf seas climate configuration of the Nucleus for European Modelling of the Ocean (NEMO) Coastal Ocean model. The UKCP18 HadGEM3-GC3.05 PPE was designed to span the range of uncertainty associated with model parameter uncertainty in the atmosphere of the driving global climate model. Each of the 12 PPE ensemble members have been downscaled as transient simulation for the period 1990-2098 under the RCP8.5 climate change scenario. We refer to the downscaled ensemble as the North West Shelf Perturbed Parameter Ensemble (NWSPPE). \r\n\r\nThe NEMO configuration for NWSPPE has a horizontal resolution of 7 km with 51 vertical levels using terrain-following s-coordinates. This data collection includes 2D fields of monthly mean output over the full simulation period for every ensemble member, as well as pre-processed climatologies and ensemble statistics (for an early-century (2000-2019) and late-century (2079-2098) period). Regional mean time series are also included for each ensemble member at monthly time resolution.\r\n\r\nA 200-year “present day” control simulation (for the year 2000) has also been downscaled with the shelf seas climate version of the NEMO Coastal Ocean model. HadGEM3 GC3.05 was run for 200 years with the atmospheric constituents fixed to the values of the year 2000. The present-day control simulation provides an estimate of the internal variability in the climate system that can arise in the absence of time-varying external forcings. Details of this simulation, which formed part of the core UKCP18 project, are available in Tinker et al. (2020).\r\n\r\nThese projections represent an update to the MINERVA projections, designed to provide a new and complementary evidence base to inform the fourth UK Climate Change Risk Assessment (CCRA4) and other climate change studies. While they use updated modelling systems and techniques, and represent a much larger dataset, the projections are structurally the same. These projections include Sea Surface, Near Bed, and the Difference between the surface and bed Temperature and Salinity (SST, NBT, DFT, SSS, NBS, DFS), Potential Energy Anomaly (PEA), Mixed Layer Depth (MLD), the barotropic currents (their U and V components (DMU, DMV), as well as their magnitude, DMUV)." } ], "responsiblepartyinfo_set": [ 194984, 194985, 194986, 194987, 194988, 194989, 194990, 194991 ], "onlineresource_set": [ 83417, 83418 ] }, { "ob_id": 40016, "uuid": "bd375134bd8c4990a1e9eb6d199cc723", "title": "Physical Marine Climate Projections for the North West European Shelf Seas: EnsStats", "abstract": "Ensemble statistics are calculated from the North West Shelf Perturbed Parameter Ensemble (NWSPPE) climatologies. The NWSPPE (https://catalogue.ceda.ac.uk/uuid/edf66239c70c426e9e9f19da1ac8ba87) provides climatological mean and climatological standard deviations for an early-century period (2000-2019) and a late-century period (2079-2098) for each of the 12 NWSPPE ensemble members, for the annual means, and for each month and season of the year. These have been processed into ensemble statistics, for each period (2000-2019, 2079-2098, and the difference 2079-2098minus2000-2019), and for each season and month and for annual means. For the early-century and late-century climatology periods, these ensemble statistics include the ensemble mean, ensemble variance, ensemble standard deviation and interannual variance. These describe the behaviour of the ensemble, including any present day ensemble spread. For the statistics of the difference between the periods (2079-2098minus2000-2019), we simply provide the difference between the ensemble statistics calculated in the near present and end of century period. To allow the user to simply use these data to provide projections, with the associated uncertainty, we also provide two additional statistics, the projected ensemble mean (projensmean) and the projected ensemble standard deviation (projensstd). We remove the early-century climatological mean from the late-century climatological mean, for each ensemble member of the NWEPPE to give an anomaly ensemble. We then calculate the resulting ensemble mean (projensmean) and its standard deviation (projensstd).\r\n\r\nNucleus for European Modelling of the Ocean (NEMO) has three model grids, the T, U and V grids, and we output variables in three files, respecting their native model grid. In practice, all our variables are on the T grid, apart from the Eastward and Northward components of the depth mean velocities (DMU, DMV), which are in the U and V grid files respectively. The T grid files have Sea Surface, Near Bed, and the Difference between the surface and bed Temperature and Salinity (SST, NBT, DFT, SSS, NBS, DFS), Potential Energy Anomaly (PEA), Mixed Layer Depth (MLD), the barotropic current magnitude interpolated onto the T grid (DMUV).", "creationDate": "2023-05-04T13:48:39.759094", "lastUpdatedDate": "2023-05-04T13:44:23", "latestDataUpdateTime": "2023-05-17T15:58:52", "updateFrequency": "", "dataLineage": "The UKCP18 HadGEM3 GC3.05 Perturbed Parameter Ensemble was dynamically downscaled with shelf seas climate version of NEMO 4.0.4 (CO9) to give a set of climate projections.\r\n \r\nModel output from HadGEM3 GC3.05 was processed into model input for CO9. CO9 was run for each of the 12 ensemble members as transient simulations. The results were assessed against a range of observations, which will be described in Tinker et al. (2023, in prep). The model output was then post-processed into ensemble statistics using the python package available at https://github.com/hadjt/NWS_simulations_postproc. After data and code evaluation, the dataset was supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "UKCP, Marine Climate, Uncertainty, Climate Downscaling, NW European Shelf Seas, Shelf Seas, Temperature, SST, Salinity, Stratification, North Sea, Celtic Sea, Irish Sea, English Channel", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-06-01T13:25:08", "doiPublishedTime": "2023-07-20T16:33:11", "removedDataTime": null, "geographicExtent": { "ob_id": 3815, "bboxName": "", "eastBoundLongitude": 13.0, "westBoundLongitude": -19.88888, "southBoundLatitude": 40.06667, "northBoundLatitude": 65.00125 }, "verticalExtent": null, "result_field": { "ob_id": 40063, "dataPath": "/badc/deposited2023/marine-nwsclim/EnsStats", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1296272351, "numberOfFiles": 154, "fileFormat": "Data are provided in NetCDF format." }, "timePeriod": { "ob_id": 11077, "startTime": "1990-01-01T00:00:00", "endTime": "2099-01-01T23:59:59" }, "resultQuality": { "ob_id": 4295, "explanation": "The data have been thoroughly evaluated against observational data, including comparisons of the Sea Surface Temperature (SST) to the OSTIA analysis (Roberts-Jones et al. 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(2013) https://doi.org/10.2112/JCOASTRES-D-12-00175.1)", "passesTest": true, "resultTitle": "NWS Data Quality Statement", "date": "2023-06-01" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40012, "uuid": "9d5496e08abc416aaeec18630613fa59", "short_code": "comp", "title": "NEMO Shelf Coastal Ocean Model 9 (CO9) based on NEMO4.0.4", "abstract": "The shelf seas model used in these climate projections is available on github:\r\nhttps://github.com/hadjt/NEMO_4.0.4_CO9_shelf_climate" }, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2521, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 40121, "uuid": "7d6c30d625664d4d805e26b385e65964", "short_code": "proj", "title": "Physical Marine Climate Projections for the North West European Shelf Seas", "abstract": "This project has created a set of ensemble climate projections for the physical marine environment of the Northwest European Shelf Seas (NWS), with a consistent present day control simulation. The projections are an update to the Maritime INdustries Environmental Risk and Vulnerability Assessment (MINERVA) projections, and are consistent with global climate model simulations performed as part of the United Kingdom’s Climate Projections of 2018 (UKCP18).\r\n\r\nThe projections created in this project are designed to provide a new and complementary evidence base to inform the fourth UK Climate Change Risk Assessment (CCRA4) and other climate change studies. While they use updated modelling systems and techniques, and represent a much larger dataset, the projections are structurally the same. These projections include Sea Surface, Near Bed, and the Difference between the surface and bed Temperature and Salinity (SST, NBT, DFT, SSS, NBS, DFS), Potential Energy Anomaly (PEA), Mixed Layer Depth (MLD), the barotropic currents (their U and V components (DMU, DMV), as well as their magnitude, DMUV)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 27599, 50415, 50417, 62369, 62370, 62371, 62372, 62373, 62374, 62375, 62376, 62377, 62378, 62379, 62380, 62381, 62382, 62383, 62384, 62385, 62386, 62387, 62388, 62389, 62390, 62391, 62392, 62393, 62394, 62395, 62396, 62397, 62398, 62399, 62400, 62401, 62402, 62403, 62404, 62405, 62406, 62407, 62408, 62409, 62410, 62411, 62412, 62413, 62414, 62415, 62416, 62417, 62418, 62419, 62420, 62421, 62422, 62423, 62424, 62425, 62426, 62427, 62428, 62429, 62430, 62431, 62432, 62433, 62434, 62435, 62436, 62437, 62438, 62439, 62440, 62441, 62442, 62443, 62444, 62445, 62446, 62447, 62448, 62449, 62450, 62451, 62452, 62453, 62454, 62455, 62456, 62457, 62458, 62459, 62460, 62461, 62462, 62463, 62464, 62465, 62466, 62467, 62468, 62469, 62470, 62471, 62472, 62473, 62474, 62475, 62476, 62477, 63908, 63909, 63910, 63911, 63912, 63913, 63914, 63915, 63916, 63917, 63918, 63919, 63920, 63921, 63922, 63923, 63924, 63925, 63926, 63927, 63928, 63929, 63930, 63931, 63932, 63933, 63934, 63935, 63936, 63937, 63938, 63939, 63940, 63941, 63942, 63943, 63944, 63945, 63946, 63947, 63948, 63949, 63950, 63951, 63952, 63953, 63954, 63955, 63956, 63957 ], "vocabularyKeywords": [], "identifier_set": [ 12651 ], "observationcollection_set": [ { "ob_id": 40017, "uuid": "832677618370457f9e0a85da021c1312", "short_code": "coll", "title": "Physical Marine Climate Projections for the North West European Shelf Seas based on the UKCP18 Perturbed Parameter Ensemble.", "abstract": "A set of ensemble climate projections for the physical marine environment of the Northwest European Shelf Seas (NWS), with a consistent present day control simulation. This data set updates the Maritime INdustries Environmental Risk and Vulnerability Assessment (MINERVA) projections, and is consistent with global climate model simulations performed as part of the United Kingdom’s Climate Projections of 2018 (UKCP18). \r\n\r\nThe UKCP18 Perturbed Parameter Ensemble (PPE) of the Met Office Global Coupled model version 3.05 (HadGEM3-GC3.05) has been downscaled with a North West European Shelf seas climate configuration of the Nucleus for European Modelling of the Ocean (NEMO) Coastal Ocean model. The UKCP18 HadGEM3-GC3.05 PPE was designed to span the range of uncertainty associated with model parameter uncertainty in the atmosphere of the driving global climate model. Each of the 12 PPE ensemble members have been downscaled as transient simulation for the period 1990-2098 under the RCP8.5 climate change scenario. We refer to the downscaled ensemble as the North West Shelf Perturbed Parameter Ensemble (NWSPPE). \r\n\r\nThe NEMO configuration for NWSPPE has a horizontal resolution of 7 km with 51 vertical levels using terrain-following s-coordinates. This data collection includes 2D fields of monthly mean output over the full simulation period for every ensemble member, as well as pre-processed climatologies and ensemble statistics (for an early-century (2000-2019) and late-century (2079-2098) period). Regional mean time series are also included for each ensemble member at monthly time resolution.\r\n\r\nA 200-year “present day” control simulation (for the year 2000) has also been downscaled with the shelf seas climate version of the NEMO Coastal Ocean model. HadGEM3 GC3.05 was run for 200 years with the atmospheric constituents fixed to the values of the year 2000. The present-day control simulation provides an estimate of the internal variability in the climate system that can arise in the absence of time-varying external forcings. Details of this simulation, which formed part of the core UKCP18 project, are available in Tinker et al. (2020).\r\n\r\nThese projections represent an update to the MINERVA projections, designed to provide a new and complementary evidence base to inform the fourth UK Climate Change Risk Assessment (CCRA4) and other climate change studies. While they use updated modelling systems and techniques, and represent a much larger dataset, the projections are structurally the same. These projections include Sea Surface, Near Bed, and the Difference between the surface and bed Temperature and Salinity (SST, NBT, DFT, SSS, NBS, DFS), Potential Energy Anomaly (PEA), Mixed Layer Depth (MLD), the barotropic currents (their U and V components (DMU, DMV), as well as their magnitude, DMUV)." } ], "responsiblepartyinfo_set": [ 194994, 194995, 194996, 194997, 194998, 194999, 195000, 195001 ], "onlineresource_set": [ 83421, 83422 ] }, { "ob_id": 40021, "uuid": "72381743f3294be0b3c00de0bef4c409", "title": "CALIPSO: Cloud and Aerosol Lidar Level 2 Vertical Feature Mask Version 4-21 Product (CAL_LID_L2_VFM-Standard-V4-21)", "abstract": "The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) was a joint mission between NASA and the French space agency Centre National d'Etudes Spatiales. The main objective of the mission was to supply a unique data set of vertical cloud and aerosol profiles.\r\n\r\nThis dataset contains cloud and aerosol lidar level 2 vertical feature mask version 4-21 data product describes the horizontal and vertical distribution of the cloud and the aerosol layers observed by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). CAL_LID_L2_VFM-Standard-V4-21 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Vertical Feature Mask (VFM), Version 4-21 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The version of this product was changed from 4-20 to 4-21 to account for a change in the operating system of the CALIPSO production cluster. Data collection for this product is ongoing.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:20:08", "updateFrequency": "", "dataLineage": "These data were obtained from the NASA Langley Research Center Atmospheric Science Data Center and mirrored by CEDA.", "removedDataReason": "", "keywords": "NASA, CALIPSO, CALIOP, Cloud, Aerosol", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-16T09:13:50", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40024, "dataPath": "/neodc/caliop/data/l2_vfm/v4-21", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 691238827564, "numberOfFiles": 15283, "fileFormat": "Data are HDF4 formatted" }, "timePeriod": { "ob_id": 11081, "startTime": "2020-07-01T00:00:00", "endTime": "2022-01-19T23:59:59" }, "resultQuality": { "ob_id": 2219, "explanation": "", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-08-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 8391, "uuid": "b6975cc607fe40b2a7a9e0f9b3e40f61", "short_code": "cmppr", "title": "Composite Process for: CALIPSO Lidar Level 2 Vertical Feature Mask Version 3-30 Product (CAL_LID_L2_VFM-ValStage1-V3-30)", "abstract": "This process is comprised of multiple procedures: 1. Acquisition: Acquisition Process for: CALIPSO Lidar Level 2 Vertical Feature Mask Version 3-30 Product (CAL_LID_L2_VFM-ValStage1-V3-30); \n2. Computation: DETAILS NEEDED - COMPUTATION CREATED FOR SATELLITE COMPOSITE. deployed on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Satellite; \n" }, "imageDetails": [ 123 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2559, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 31, "licenceURL": "https://eosweb.larc.nasa.gov/citing-asdc-data", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 8349, "uuid": "46cc8da20687aa95febda281bebb4526", "short_code": "proj", "title": "Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Mission", "abstract": "The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) was a joint-mission between NASA and the French space agency Centre National d'Etudes Spatiales. The main objectives of the mission was to supply unique data set of vertical cloud and aerosol profiles. This was to investigate direct and indirect aerosol forcings; to create better surface and atmospheric radiation flux datasets; and to analyse cloud-climate feedbacks in conjunction with other missions which take part in the A-Train, a group of polar-orbiting satellites passing through equator around 13:30 and 01:30. The satellite of this mission was launched in 28th April, 2006." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64153, 64154, 64155, 64156, 64157, 64158, 64159, 64160, 64161, 64162, 64163, 64164, 64165, 64166, 64167, 64168, 64169 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 195018, 195019, 195020, 195021, 195022, 195023, 195024, 195025, 195026 ], "onlineresource_set": [ 83445, 83447, 83446, 83444 ] }, { "ob_id": 40026, "uuid": "14dd5580eab9410fb3696340711b1d67", "title": "Modelled Nearshore Wave Conditions, Happisburgh, UK (March to December 2019).", "abstract": "This dataset contains outputs from the SWAN (Simulated Waves Nearshore) model, which propagates wave conditions to the nearshore. The outputs are compressed into netcdf files, and contain time series of hourly sea states at the study site including wave height, direction, energy dissipation, and peak period. The latitude and longitude of the south-west grid corner is latitude 52.772629, longitude 1.344987. The series are given hourly, from (March 23, 2019-December 31, 2019) inclusive. These data are validated against an offshore buoy. The full year’s series is split into smaller time periods, numbered from 8 to 25. The data are given on a 26 km x 26 km grid, the coarse grid has a resolution of 0.1 km (matrix size 261x261), and the nested grid has a resolution of 10 m (matrix size 601x401). The hourly times corresponding to each variable is contained in an accompanying .csv file. The SWAN model was forced with bathymetry from the OceanWise 1 Arc Second Digital Elevation Model, and climate conditions from the ERA5 dataset. The data was collected to provide corresponding wave conditions to the LiDAR (Light Detection And Ranging) scans, to help determine the relationship between the wave conditions and the behaviour of the foreshore. The British Geological Survey (BGS) and Nottingham University were responsible for processing the model data, funded by the UK Natural Environment Research Council (NE/M004996/1; BLUE-coast project) and NERC’s ENVISION Doctoral Training Programme (NE/S007423/1).", "creationDate": "2023-05-11T09:38:44.673098", "lastUpdatedDate": "2023-05-11T09:38:44", "latestDataUpdateTime": "2025-07-18T02:00:28", "updateFrequency": "notPlanned", "dataLineage": "The bathymetry used to force the model is from the OceanWise 1 Arc Second Digital Elevation Model. The wave conditions at the corners of the grid, used to force the model, come from the ERA5 global climate variable estimates. The outputs are archived on the British Oceanographic Data Centre (BODC)'s space at the Centre for Environmental Data Analysis (CEDA) and assigned a DOI. No quality control procedures were applied by BODC.", "removedDataReason": "", "keywords": "coast, cliff, beach, shore, platform, Happisburgh, LiDAR, model, meteorology, oceanography", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-08-11T10:21:02", "doiPublishedTime": "2023-10-11T12:34:42", "removedDataTime": null, "geographicExtent": { "ob_id": 3816, "bboxName": "", "eastBoundLongitude": 1.723434, "westBoundLongitude": 1.344987, "southBoundLatitude": 52.772629, "northBoundLatitude": 53.011051 }, "verticalExtent": null, "result_field": { "ob_id": 40027, "dataPath": "/bodc/BGS220061", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 47869431270, "numberOfFiles": 441, "fileFormat": "Data are CF-Compliant NetCDF formatted and .csv data files" }, "timePeriod": { "ob_id": 11082, "startTime": "2019-03-23T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 4260, "explanation": "The data- specifically the significant wave height, spectral period m02, mean direction - were validated against a Channel Coast Observatory buoy. UTM (Universal Transverse Mercator) co-ordinates Easting: 402309, Northing: 5853906.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-11" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40029, "uuid": "a71d72717a5a4d2498bbfcff8f4c4567", "short_code": "comp", "title": "Simulating Waves Nearshore (SWAN)", "abstract": "SWAN propagates offshore wave conditions to the nearshore, and can account for: Wave propagation in time and space, shoaling, refraction due to current and depth, frequency shifting due to currents and non-stationary depth. Wave generation by wind. Three- and four-wave interactions. Whitecapping, bottom friction and depth-induced breaking. Dissipation due to aquatic vegetation, turbulent flow and viscous fluid mud. Wave-induced set-up. Propagation from laboratory up to global scales. Transmission through and reflection (specular and diffuse) against obstacles. Diffraction." }, "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": 40028, "uuid": "db9835be84214bdd92a6c2cca5f7aae0", "short_code": "proj", "title": "Physical and biological dynamic coastal processes and their role in coastal recovery (BLUE-coast)", "abstract": "BLUE-coast aims to inform coastal management by reducing uncertainties in the prediction of medium-term (years) and long-term (decadal and longer) regional sediment budgets, morphological change and how the coast recovers after sequences of storms. Approach: Our teams are undertaking observations and experiments to develop modelling tools that will be used to evaluate coastal resilience and scope alternative management options. BLUE-coast combines the expertise of biologists, coastal engineers, geologists, geographers, and oceanographers with complementary field, laboratory and numerical skills. Areas: As it is not feasible to quantify all the relevant morphodynamic processes at high spatial resolution across the entire UK coast, we focus on a number of representative coastal systems." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49858, 66193, 66194, 66195, 66196, 66197, 66198, 66199, 66200, 66201 ], "vocabularyKeywords": [], "identifier_set": [ 12717 ], "observationcollection_set": [ { "ob_id": 40060, "uuid": "2c6f3201f01d4346a97ff8f08a8c15c9", "short_code": "coll", "title": "LiDAR (Light Detection And Ranging) images and model output from cliffs at Happisburgh, Norfolk, UK, 2019, from BLUE-coast and ScanLAB projects.", "abstract": "A colour LiDAR (Light Detection And Ranging) dataset was obtained at the cliffs at Happisburgh, Norfolk, UK, over a period of 9 months (April 6, 2019 to December 23, 2019). The scans were taken daily for 90% of the study period using a FARO S350 TLS (Terrestrial LiDAR Scanner). Scans were carried out from two locations consecutively, positioned at around 40 m from the cliffs. The full scans are also split into smaller subsets: \"slices\", 1 m wide bands oriented perpendicular to the shoreline, and \"grids\", smaller areas of the beach, to assist analysis. The numerical model SWAN (Simulated Waves Nearshore) (v41.31a), run in non-stationary mode, was used to simulate hourly sea states at the study site to aid in the context of environmental conditions. Wind parameters from the ERA5 reanalysis and bathymetry from the OceanWise 1 arc second digital elevation model (DEM) were used to force the SWAN model, and obtained wave parameters in 4x6 km rectangular grid around the scanning site, with a 10m interval, and a 26x26 km square grid encompassing the smaller grid, with a 100 m interval. The LiDAR scans were also projected into both colour and intensity images, viewing the shoreline from above. This research was funded by the UK Natural Environment Research Council (NE/M004996/1; BLUE-coast project). The on-location LiDAR Scanning and Technical R&D operated by ScanLAB Projects Ltd was funded by Innovate UK's Audience of the Future Program (Multiscale 3D Scanning with Framerate for TV and Immersive Applications project). The first 6 months of LiDAR scans (April to September 2019) were funded by Innovate UK, and this project was continued by the NERC BLUE-coast funding for the last 3 months (October to December 2019)." } ], "responsiblepartyinfo_set": [ 195052, 195056, 195054, 195053, 195055, 195062, 195063, 195057, 195064, 195065, 195066, 195067, 195068, 195069 ], "onlineresource_set": [ 83930, 83933 ] }, { "ob_id": 40037, "uuid": "da8e669a74334c82a56e0b470bc4ef04", "title": "ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area Grid product, version 1.1", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The Sentinel-3 SYN Fire_cci v1.1 grid product described here contains gridded data on global burned area derived from surface reflectance data from the OLCI and SLSTR instruments (combined as the Synergy (SYN) product) onboard the Sentinel-3 A&B satellites, complemented by VIIRS thermal information. This product, called FireCCIS311 for short, is available for the years 2019 to 2024.\r\n\r\nThis gridded dataset has been derived from the FireCCIS311 pixel product (also available) by summarising its burned area information into a regular grid covering the Earth at 0.25 x 0.25 degrees resolution and at monthly temporal resolution. Information on burned area is included in 22 individual quantities: sum of burned area, standard error, fraction of burnable area, fraction of observed area, and the burned area for 18 land cover classes, as defined by the Copernicus Climate Change Initiative (C3S) Land Cover v2.1.1 product. For further information on the product and its format see the Product User Guide in the linked documentation.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2024-02-07T13:51:29", "latestDataUpdateTime": "2025-10-09T11:48:32", "updateFrequency": "", "dataLineage": "Data was produced by the ESA Fire CCI team as part of the ESA Climate Change Initiative (CCI) and is being held on the CEDA (Centre for Environmental Data Analysis) archive as part of the ESA CCI Open Data Portal.", "removedDataReason": "", "keywords": "ESA, CCI, Grid, Burned Area, Fire Disturbance, Climate Change, GCOS", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2024-02-28T15:22:52", "doiPublishedTime": "2024-02-29T11:51:51", "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": 41453, "dataPath": "/neodc/esacci/fire/data/burned_area/Sentinel3_SYN/grid/v1.1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 153355990, "numberOfFiles": 74, "fileFormat": "The data are in netCDF format." }, "timePeriod": { "ob_id": 11556, "startTime": "2019-01-01T00:00:00", "endTime": "2024-12-31T23:59:59" }, "resultQuality": { "ob_id": 3931, "explanation": "See the associated dataset documentation at https://climate.esa.int/projects/fire/key-documents/", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-04-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41451, "uuid": "ae2139c2bfff4e6b9659ea46e03c6bb9", "short_code": "cmppr", "title": "Composite process for the ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area products, version 1.1", "abstract": "For more information see the documentation at https://climate.esa.int/en/projects/fire/" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2539, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 19, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_fire_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13255, "uuid": "6c3584d985bd484e8beb23ff0df91292", "short_code": "proj", "title": "ESA Fire Climate Change Initiative Project (Fire CCI)", "abstract": "The European Space Agency (ESA) Fire Climate Change Initiative (Fire CCI) project, led by University of Alcala (Spain), is part of ESA's Climate Change Initiative (CCI) to produce long term datasets of Essential Climate Variables derived from global satellite data.\r\n\r\nThe Fire CCI focuses on the following issues relating to Fire Disturbance: Analysis and specification of scientific requirements relating to climate; Development and improvement of pre-processing and burned area algorithms; Inter-comparison and selection of burned area algorithms; System prototyping and production of burned area datasets; Product validation and product assessment\r\n" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 8143, 8144, 12066, 27599, 57314, 57317, 59231, 59234, 59235, 59236, 59237, 60438, 68093, 68094 ], "vocabularyKeywords": [], "identifier_set": [ 12832 ], "observationcollection_set": [ { "ob_id": 12683, "uuid": "bcef9e87740e4cbabc743d295afbe849", "short_code": "coll", "title": "ESA Fire Climate Change Initiative (Fire CCI) Dataset Collection", "abstract": "The ESA Fire Climate Change Initiative (Fire_cci) project is producing long-term datasets of burned area information from satellites, as part of the ESA Climate Change Initiative. The data is of use for those interested in historical burned patterns, fire management and emissions analysis and climate change research, by providing a consistent burned area time series. \r\n\r\nCurrent datasets consist of maps of global burned area for the years 1982 to 2019. Products are available at different spatial resolutions: the Pixel product (at the original resolution of the sensor data) and the Grid product (0.25 degrees resolution), the latter of which is produced from the Pixel product. They are based upon spectral information from different sensors, and in many cases also thermal information from active fires.\r\n\r\nGlobal products: \r\n\r\nFireCCI41: Medium Resolution Imaging Spectrometer (MERIS) reflectance, on board the ENVISAT ESA satellite, 300m spatial resolution, and MODIS active fires. Temporal resolution: 2005 – 2011.\r\nFireCCI50: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2016.\r\nFireCCI51: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2019.\r\n\r\nFireCCILT10 (beta product): Advanced Very High Resolution Radiometer (AVHRR) Land Long Term Data Record (LTDR) reflectance. Provided only as grid product. Temporal resolution: 1982-2017.\r\n\r\nContinental products:\r\n\r\nFireCCISFD11: Multispectral Instrument (MSI) reflectance, on board the Sentinel-2A satellite, 20 spatial resolution, and MODIS active fires. Temporal resolution: 2016, spatial coverage: Sub-Saharan Africa." } ], "responsiblepartyinfo_set": [ 195112, 195114, 195115, 195116, 195117, 195118, 195119, 195113, 195120, 195121, 195122, 195123, 195124, 195125 ], "onlineresource_set": [ 83458, 83459, 86043, 86046, 83457 ] }, { "ob_id": 40038, "uuid": "d441079fc77f49fabeb41330612b252f", "title": "ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area Pixel product, version 1.1", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The Sentinel-3 SYN Fire_cci v1.1 pixel product is distributed as 6 continental tiles and is based upon surface reflectance data from the OLCI and SLSTR instruments (combined as the Synergy (SYN) product) onboard the Sentinel-3 A&B satellites. This information is complemented by VIIRS thermal information. This product, called FireCCIS311 for short, is available for the years 2019 to 2024.\r\n\r\nThe FireCCIS311 Pixel product described here includes maps at 0.002777-degree (approx. 300m) resolution. Burned area (BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Copernicus Climate Change Service (C3S) Land Cover v2.1.1 product. An unpacked version of the data is also available. For further information on the product and its format see the Product User Guide in the linked documentation.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2024-02-07T13:53:56", "latestDataUpdateTime": "2025-10-09T11:48:16", "updateFrequency": "", "dataLineage": "Data was produced by the ESA Fire CCI team as part of the ESA Climate Change Initiative (CCI) and is being held on the CEDA (Centre for Environmental Data Analysis) archive as part of the ESA CCI Open Data Portal.", "removedDataReason": "", "keywords": "ESA, CCI, Pixel, Burned Area, Fire Disturbance, Climate Change, GCOS", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2024-02-28T15:27:52", "doiPublishedTime": "2024-02-29T11:52:43", "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": 41454, "dataPath": "/neodc/esacci/fire/data/burned_area/Sentinel3_SYN/pixel/v1.1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 80639207048, "numberOfFiles": 2234, "fileFormat": "The data are in GeoTiff format." }, "timePeriod": { "ob_id": 11557, "startTime": "2019-01-01T00:00:00", "endTime": "2024-12-31T23:59:59" }, "resultQuality": { "ob_id": 3932, "explanation": "See the associated dataset documentation at https://climate.esa.int/projects/fire/key-documents/", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-04-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41451, "uuid": "ae2139c2bfff4e6b9659ea46e03c6bb9", "short_code": "cmppr", "title": "Composite process for the ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area products, version 1.1", "abstract": "For more information see the documentation at https://climate.esa.int/en/projects/fire/" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2539, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 19, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_fire_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13255, "uuid": "6c3584d985bd484e8beb23ff0df91292", "short_code": "proj", "title": "ESA Fire Climate Change Initiative Project (Fire CCI)", "abstract": "The European Space Agency (ESA) Fire Climate Change Initiative (Fire CCI) project, led by University of Alcala (Spain), is part of ESA's Climate Change Initiative (CCI) to produce long term datasets of Essential Climate Variables derived from global satellite data.\r\n\r\nThe Fire CCI focuses on the following issues relating to Fire Disturbance: Analysis and specification of scientific requirements relating to climate; Development and improvement of pre-processing and burned area algorithms; Inter-comparison and selection of burned area algorithms; System prototyping and production of burned area datasets; Product validation and product assessment\r\n" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [ { "ob_id": 11136, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_olci", "resolvedTerm": "OLCI" }, { "ob_id": 10912, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_sentinel3b", "resolvedTerm": "Sentinel-3B" }, { "ob_id": 10663, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_fire", "resolvedTerm": "fire" }, { "ob_id": 10602, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/dataType/dtype_ba", "resolvedTerm": "burned area" }, { "ob_id": 10986, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/procLev/proc_level3S", "resolvedTerm": "Level 3S" }, { "ob_id": 10911, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_sentinel3a", "resolvedTerm": "Sentinel-3A" } ], "identifier_set": [ 12833, 12834 ], "observationcollection_set": [ { "ob_id": 12683, "uuid": "bcef9e87740e4cbabc743d295afbe849", "short_code": "coll", "title": "ESA Fire Climate Change Initiative (Fire CCI) Dataset Collection", "abstract": "The ESA Fire Climate Change Initiative (Fire_cci) project is producing long-term datasets of burned area information from satellites, as part of the ESA Climate Change Initiative. The data is of use for those interested in historical burned patterns, fire management and emissions analysis and climate change research, by providing a consistent burned area time series. \r\n\r\nCurrent datasets consist of maps of global burned area for the years 1982 to 2019. Products are available at different spatial resolutions: the Pixel product (at the original resolution of the sensor data) and the Grid product (0.25 degrees resolution), the latter of which is produced from the Pixel product. They are based upon spectral information from different sensors, and in many cases also thermal information from active fires.\r\n\r\nGlobal products: \r\n\r\nFireCCI41: Medium Resolution Imaging Spectrometer (MERIS) reflectance, on board the ENVISAT ESA satellite, 300m spatial resolution, and MODIS active fires. Temporal resolution: 2005 – 2011.\r\nFireCCI50: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2016.\r\nFireCCI51: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2019.\r\n\r\nFireCCILT10 (beta product): Advanced Very High Resolution Radiometer (AVHRR) Land Long Term Data Record (LTDR) reflectance. Provided only as grid product. Temporal resolution: 1982-2017.\r\n\r\nContinental products:\r\n\r\nFireCCISFD11: Multispectral Instrument (MSI) reflectance, on board the Sentinel-2A satellite, 20 spatial resolution, and MODIS active fires. Temporal resolution: 2016, spatial coverage: Sub-Saharan Africa." } ], "responsiblepartyinfo_set": [ 195126, 195128, 195129, 195130, 195131, 195132, 195133, 195127, 195134, 195135, 195136, 195137, 195138, 195139 ], "onlineresource_set": [ 83461, 83462, 83460, 86044, 86087, 87928 ] }, { "ob_id": 40039, "uuid": "5768b4e7462f4facbcf447c8cd3929b9", "title": "Daily Colour and Intensity Orthophotos of the Cliff and Beach at Happisburgh, Norfolk, UK (April-December 2019).", "abstract": "This dataset contains orthophotos collected daily along a 450 metre coastal stretch at Happisburgh, UK, over a time span of 9 months (April 6, 2019 to December 23, 2019). The dataset contains 190 colour images and 190 intensity images in .png format. The orthophotos are produced by projection of LiDAR (Light Detection And Ranging) scans of the coastal stretch. There are 190 images out of a possible 262 days, since only days when scans were performed from two locations are included, which didn't happen every day due to weather conditions. These data were collected to better understand the dynamic of beach-cliff and shore platform interaction along soft cliffed coasts. ScanLAB Projects Ltd and the British Geological Survey (BGS) were responsible for the collection of the data, funded by the UK Natural Environment Research Council (NE/M004996/1; BLUE-coast project).", "creationDate": "2023-05-12T13:50:25.198790", "lastUpdatedDate": "2023-05-12T13:50:25", "latestDataUpdateTime": "2025-07-18T02:00:28", "updateFrequency": "notPlanned", "dataLineage": "The orthophotos are produced by projecting LIDAR scans. The data are archived on the British Oceanographic Data Centre (BODC)'s space at the Centre for Environmental Data Analysis (CEDA) and assigned a DOI. No quality control procedures were applied by BODC.", "removedDataReason": "", "keywords": "coast, cliff, beach, shore, platform, Happisburgh, LiDAR, orthophotos, meteorology, oceanography", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2023-08-11T11:07:53", "doiPublishedTime": "2023-10-11T12:37:09", "removedDataTime": null, "geographicExtent": { "ob_id": 3817, "bboxName": "", "eastBoundLongitude": 1.540195603, "westBoundLongitude": 1.531080727, "southBoundLatitude": 52.82323871, "northBoundLatitude": 52.8288520697982 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 11085, "startTime": "2019-04-06T00:00:00", "endTime": "2019-12-23T23:59:59" }, "resultQuality": { "ob_id": 3897, "explanation": "The data are provided as-is with no quality control undertaken by the British Oceanographic Data Centre (BODC). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "BODC Data Quality Statement", "date": "2022-03-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 40041, "uuid": "5b837fba85c44c30af1e317cf198f5dd", "short_code": "acq", "title": "Acquisition for: Daily Colour and Intensity Orthophotos of the Cliff and Beach at Happisburgh, Norfolk, UK. (06/04/2019-23/12/2019)", "abstract": "" }, "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": 40028, "uuid": "db9835be84214bdd92a6c2cca5f7aae0", "short_code": "proj", "title": "Physical and biological dynamic coastal processes and their role in coastal recovery (BLUE-coast)", "abstract": "BLUE-coast aims to inform coastal management by reducing uncertainties in the prediction of medium-term (years) and long-term (decadal and longer) regional sediment budgets, morphological change and how the coast recovers after sequences of storms. Approach: Our teams are undertaking observations and experiments to develop modelling tools that will be used to evaluate coastal resilience and scope alternative management options. BLUE-coast combines the expertise of biologists, coastal engineers, geologists, geographers, and oceanographers with complementary field, laboratory and numerical skills. Areas: As it is not feasible to quantify all the relevant morphodynamic processes at high spatial resolution across the entire UK coast, we focus on a number of representative coastal systems." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49858, 66193, 66194, 66195, 66196, 66197, 66198, 66199, 66200, 66201 ], "vocabularyKeywords": [], "identifier_set": [ 12719 ], "observationcollection_set": [ { "ob_id": 40060, "uuid": "2c6f3201f01d4346a97ff8f08a8c15c9", "short_code": "coll", "title": "LiDAR (Light Detection And Ranging) images and model output from cliffs at Happisburgh, Norfolk, UK, 2019, from BLUE-coast and ScanLAB projects.", "abstract": "A colour LiDAR (Light Detection And Ranging) dataset was obtained at the cliffs at Happisburgh, Norfolk, UK, over a period of 9 months (April 6, 2019 to December 23, 2019). The scans were taken daily for 90% of the study period using a FARO S350 TLS (Terrestrial LiDAR Scanner). Scans were carried out from two locations consecutively, positioned at around 40 m from the cliffs. The full scans are also split into smaller subsets: \"slices\", 1 m wide bands oriented perpendicular to the shoreline, and \"grids\", smaller areas of the beach, to assist analysis. The numerical model SWAN (Simulated Waves Nearshore) (v41.31a), run in non-stationary mode, was used to simulate hourly sea states at the study site to aid in the context of environmental conditions. Wind parameters from the ERA5 reanalysis and bathymetry from the OceanWise 1 arc second digital elevation model (DEM) were used to force the SWAN model, and obtained wave parameters in 4x6 km rectangular grid around the scanning site, with a 10m interval, and a 26x26 km square grid encompassing the smaller grid, with a 100 m interval. The LiDAR scans were also projected into both colour and intensity images, viewing the shoreline from above. This research was funded by the UK Natural Environment Research Council (NE/M004996/1; BLUE-coast project). The on-location LiDAR Scanning and Technical R&D operated by ScanLAB Projects Ltd was funded by Innovate UK's Audience of the Future Program (Multiscale 3D Scanning with Framerate for TV and Immersive Applications project). The first 6 months of LiDAR scans (April to September 2019) were funded by Innovate UK, and this project was continued by the NERC BLUE-coast funding for the last 3 months (October to December 2019)." } ], "responsiblepartyinfo_set": [ 195143, 195140, 195142, 195141, 195154, 195146, 195145, 195144, 195148, 195149, 195150, 195151, 195152, 195153, 195147 ], "onlineresource_set": [ 83929 ] }, { "ob_id": 40044, "uuid": "b8cf940850164ebeb4cba343384f88b8", "title": "Daily Light Detection and Ranging (LiDAR) scans of an eroding soft cliff at Happisburgh, UK (October-December 2019)", "abstract": "This dataset contains 67 point-cloud elevation and colour intensity data collected daily along a 450 metre coastal stretch at Happisburgh, UK, over a time span of 3 months (October 2, 2019 to December 23, 2019). Also included are subsets of these point-clouds, named slices and grids. Scans were taken approximately daily, and on some days only one scanner was run resulting in half-size scans. A single FARO S350 LiDAR scanner was placed at two fixed locations on the beach, spaced 178 metres alongshore and between 30 to 40 metres from the 10 metre high cliff. The duration of the scanning at each location was around 30 minutes. These data were collected to better understand the dynamic of beach-cliff and shore platform interaction along soft cliffed coasts. ScanLAB Projects Ltd were responsible for the collection of the data, along with the British Geological Survey (BGS), funded by the UK Natural Environment Research Council (NE/M004996/1; BLUE-coast project). This was a continuation of an Innovate UK project undertaken by ScanLAB Projects Ltd.", "creationDate": "2023-05-12T14:09:27.756723", "lastUpdatedDate": "2023-05-12T14:09:27", "latestDataUpdateTime": "2024-06-12T14:27:57", "updateFrequency": "notPlanned", "dataLineage": "The raw data collected was further filtered and co-registered during post processing using FARO scene software. The global position of each TLS was recorded using a Leica GS15 at the end of the 9 month capture period. Then the exported scan data was transformed spatially to the GPS position and orientation recorded on-site, using ScanLABs proprietary processing software. All elevations are shown relative to Ordnance Datum Newlyn. The data are archived on the British Oceanographic Data Centre (BODC)'s space at the Centre for Environmental Data Analysis (CEDA) and assigned a DOI. No quality control procedures were applied by BODC.", "removedDataReason": "", "keywords": "coast, cliff, beach, shore, platform, Happisburgh, LiDAR, meteorology, oceanography", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-08-11T12:38:20", "doiPublishedTime": "2023-10-11T12:38:48", "removedDataTime": null, "geographicExtent": { "ob_id": 3818, "bboxName": "", "eastBoundLongitude": 1.540195603, "westBoundLongitude": 1.531080727, "southBoundLatitude": 52.82323871, "northBoundLatitude": 52.8288520697982 }, "verticalExtent": null, "result_field": { "ob_id": 40334, "dataPath": "/bodc/BGS230126", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1805497951523, "numberOfFiles": 4256, "fileFormat": ".xyz" }, "timePeriod": { "ob_id": 11212, "startTime": "2019-10-02T00:00:00", "endTime": "2019-12-23T23:59:59" }, "resultQuality": { "ob_id": 3897, "explanation": "The data are provided as-is with no quality control undertaken by the British Oceanographic Data Centre (BODC). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "BODC Data Quality Statement", "date": "2022-03-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 40046, "uuid": "c017edfc56bc4251919d2204d9bf7b75", "short_code": "acq", "title": "Acquisition for: Daily Light Detection and Ranging (LiDAR) scans of an eroding soft cliff at Happisburgh, UK (April-December 2019)", "abstract": "" }, "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": 40028, "uuid": "db9835be84214bdd92a6c2cca5f7aae0", "short_code": "proj", "title": "Physical and biological dynamic coastal processes and their role in coastal recovery (BLUE-coast)", "abstract": "BLUE-coast aims to inform coastal management by reducing uncertainties in the prediction of medium-term (years) and long-term (decadal and longer) regional sediment budgets, morphological change and how the coast recovers after sequences of storms. Approach: Our teams are undertaking observations and experiments to develop modelling tools that will be used to evaluate coastal resilience and scope alternative management options. BLUE-coast combines the expertise of biologists, coastal engineers, geologists, geographers, and oceanographers with complementary field, laboratory and numerical skills. Areas: As it is not feasible to quantify all the relevant morphodynamic processes at high spatial resolution across the entire UK coast, we focus on a number of representative coastal systems." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 49858, 66193, 66194, 66195, 66196, 66197, 66198, 66199, 66200, 66201 ], "vocabularyKeywords": [], "identifier_set": [ 12720 ], "observationcollection_set": [ { "ob_id": 40060, "uuid": "2c6f3201f01d4346a97ff8f08a8c15c9", "short_code": "coll", "title": "LiDAR (Light Detection And Ranging) images and model output from cliffs at Happisburgh, Norfolk, UK, 2019, from BLUE-coast and ScanLAB projects.", "abstract": "A colour LiDAR (Light Detection And Ranging) dataset was obtained at the cliffs at Happisburgh, Norfolk, UK, over a period of 9 months (April 6, 2019 to December 23, 2019). The scans were taken daily for 90% of the study period using a FARO S350 TLS (Terrestrial LiDAR Scanner). Scans were carried out from two locations consecutively, positioned at around 40 m from the cliffs. The full scans are also split into smaller subsets: \"slices\", 1 m wide bands oriented perpendicular to the shoreline, and \"grids\", smaller areas of the beach, to assist analysis. The numerical model SWAN (Simulated Waves Nearshore) (v41.31a), run in non-stationary mode, was used to simulate hourly sea states at the study site to aid in the context of environmental conditions. Wind parameters from the ERA5 reanalysis and bathymetry from the OceanWise 1 arc second digital elevation model (DEM) were used to force the SWAN model, and obtained wave parameters in 4x6 km rectangular grid around the scanning site, with a 10m interval, and a 26x26 km square grid encompassing the smaller grid, with a 100 m interval. The LiDAR scans were also projected into both colour and intensity images, viewing the shoreline from above. This research was funded by the UK Natural Environment Research Council (NE/M004996/1; BLUE-coast project). The on-location LiDAR Scanning and Technical R&D operated by ScanLAB Projects Ltd was funded by Innovate UK's Audience of the Future Program (Multiscale 3D Scanning with Framerate for TV and Immersive Applications project). The first 6 months of LiDAR scans (April to September 2019) were funded by Innovate UK, and this project was continued by the NERC BLUE-coast funding for the last 3 months (October to December 2019)." } ], "responsiblepartyinfo_set": [ 195162, 195159, 195163, 195161, 195160, 195173, 195165, 195164, 195167, 195168, 195169, 195170, 195171, 195172, 195166 ], "onlineresource_set": [ 83928 ] }, { "ob_id": 40049, "uuid": "9be6a7a9b2c4461e8977e2a18bf9c0c6", "title": "WCRP CMIP6: Norwegian Climate Centre (NCC) NorESM2-LM model output for the \"amip-p4K\" experiment", "abstract": "The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the Norwegian Climate Centre (NCC) NorESM2-LM model output for the \"AMIP with uniform 4K SST increase\" (amip-p4K) experiment. These are available at the following frequency: Amon. The runs included the ensemble member: r1i1p2f1.\n\nCMIP6 was a global climate model intercomparison project, coordinated by PCMDI (Program For Climate Model Diagnosis and Intercomparison) on behalf of the WCRP and provided input for the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6).\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.", "creationDate": "2023-05-15T11:33:43.764536", "lastUpdatedDate": "2023-05-15T11:33:43.764558", "latestDataUpdateTime": "2025-01-10T01:55:04", "updateFrequency": "asNeeded", "dataLineage": "Data were produced and verified by Norwegian Climate Centre (NCC) scientists before publication via the Earth Systems Grid Federation (ESGF) and a copy obtained by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CMIP6, WCRP, climate change, NCC, NorESM2-LM, amip-p4K, Amon", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-15T11:33:43.878106", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40050, "dataPath": "/badc/cmip6/data/CMIP6/CFMIP/NCC/NorESM2-LM/amip-p4K", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 572477439, "numberOfFiles": 71, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 11087, "startTime": "1979-01-16T12:00:00", "endTime": "2014-12-16T12:00:00" }, "resultQuality": { "ob_id": 3341, "explanation": "The CMIP6 data are copied to CEDA from international distributors. CEDA perform no quality control on these data. Where any CMIP6 data are identified as having a quality issue this is recorded in the CMIP6 errata service: https://errata.es-doc.org/static/index.html", "passesTest": true, "resultTitle": "CMIP6 Quality Statement - replica data", "date": "2017-02-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40051, "uuid": "ddc470549f214868ab49fc440e0f9be3", "short_code": "comp", "title": "Norwegian Climate Centre (NCC) running: experiment amip-p4K using the NorESM2-LM model.", "abstract": "Norwegian Climate Centre (NCC) running the \"AMIP with uniform 4K SST increase\" (amip-p4K) experiment using the NorESM2-LM model. See linked documentation for available information for each component." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2520, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 1, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/CMIP6_Terms_of_Use.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 28945, "uuid": "10aedcd6d43147cf83eb8ff70fdf34aa", "short_code": "proj", "title": "WCRP CMIP6: Norwegian Climate Centre (NCC) contribution", "abstract": "World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 contribution to the project by the Norwegian Climate Centre (NCC) team." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 50415, 50417, 50418, 50419, 50445, 50468, 50475, 50481, 50498, 50555, 50564, 50578, 50580, 60438 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 28952, "uuid": "69323c36acb840b284f670df8da693e2", "short_code": "coll", "title": "WCRP CMIP6: Norwegian Climate Centre (NCC) NorESM2-LM model output collection", "abstract": "World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6): Collection of simulations from the Norwegian Climate Centre (NCC) NorESM2-LM model.\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record." } ], "responsiblepartyinfo_set": [ 195176, 195177, 195178, 195179, 195180, 195181, 195182, 195184, 195185, 195183 ], "onlineresource_set": [ 83469, 83470, 83472, 83474, 83475, 83476 ] }, { "ob_id": 40053, "uuid": "503edf9eb68040c4a439fed88b81c8c9", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.22 (v20230206)", "abstract": "Data for Figure 9.22 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.22 shows simulated versus observed permafrost extent and volume change by warming level. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels in one central directory.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- (a) Diagnosed Northern Hemisphere permafrost extent (area with perennially frozen ground at 15 m depth, or at the deepest model soil level if this is above 15 m) for 1979–1998, for available CMIP5 and CMIP6 models, from the first ensemble member of the historical coupled run, and for CMIP6 AMIP (atmosphere+land surface, prescribed ocean) and land-hist (land only, prescribed atmospheric forcing) runs. \r\n\r\n- (b) Simulated global permafrost volume change between the surface and 3 m depth as a function of the simulated global surface air temperature (GSAT) change, from the first ensemble members of a selection of scenarios, for available CMIP6 models. \r\n\r\nEstimates of current permafrost extents based on physical evidence and reanalyses are indicated as black symbols – triangle: Obu et al. (2018); star: Zhang et al. (1999); circle: central value and associated range from Gruber (2012). \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.22\r\n \r\n- Data file: pf15m_CMIP5historical_NH_1979-1998.txt\r\n- Data file: pf15m_amip_NH_1979-1998.txt\r\n- Data file: pf15m_historical_NH_1979-1998.txt\r\n- Data file: pf15m_land-hist_NH_1979-1998.txt\r\n- Data file: pfv_ACCESS-CM2_historical_ssp126.nc\r\n- Data file: pfv_ACCESS-CM2_historical_ssp245.nc\r\n- Data file: pfv_ACCESS-CM2_historical_ssp370.nc\r\n- Data file: pfv_ACCESS-CM2_historical_ssp585.nc\r\n- Data file: pfv_ACCESS-ESM1-5_historical_ssp126.nc\r\n- Data file: pfv_ACCESS-ESM1-5_historical_ssp245.nc\r\n- Data file: pfv_ACCESS-ESM1-5_historical_ssp370.nc\r\n- Data file: pfv_ACCESS-ESM1-5_historical_ssp585.nc\r\n- Data file: pfv_BCC-CSM2-MR_historical_ssp126.nc\r\n- Data file: pfv_BCC-CSM2-MR_historical_ssp245.nc\r\n- Data file: pfv_BCC-CSM2-MR_historical_ssp370.nc\r\n- Data file: pfv_BCC-CSM2-MR_historical_ssp585.nc\r\n- Data file: pfv_CAMS-CSM1-0_historical_ssp126.nc\r\n- Data file: pfv_CAMS-CSM1-0_historical_ssp245.nc\r\n- Data file: pfv_CAMS-CSM1-0_historical_ssp370.nc\r\n- Data file: pfv_CAMS-CSM1-0_historical_ssp585.nc\r\n- Data file: pfv_CESM2-WACCM_historical_ssp126.nc\r\n- Data file: pfv_CESM2-WACCM_historical_ssp245.nc\r\n- Data file: pfv_CESM2-WACCM_historical_ssp370.nc\r\n- Data file: pfv_CESM2-WACCM_historical_ssp585.nc\r\n- Data file: pfv_CESM2_historical_ssp126.nc\r\n- Data file: pfv_CESM2_historical_ssp245.nc\r\n- Data file: pfv_CESM2_historical_ssp370.nc\r\n- Data file: pfv_CESM2_historical_ssp585.nc\r\n- Data file: pfv_CNRM-CM6-1-HR_historical_ssp126.nc\r\n- Data file: pfv_CNRM-CM6-1-HR_historical_ssp245.nc\r\n- Data file: pfv_CNRM-CM6-1-HR_historical_ssp370.nc\r\n- Data file: pfv_CNRM-CM6-1-HR_historical_ssp585.nc\r\n- Data file: pfv_CNRM-CM6-1_historical_ssp126.nc\r\n- Data file: pfv_CNRM-CM6-1_historical_ssp245.nc\r\n- Data file: pfv_CNRM-CM6-1_historical_ssp370.nc\r\n- Data file: pfv_CNRM-CM6-1_historical_ssp585.nc\r\n- Data file: pfv_CNRM-ESM2-1_historical_ssp126.nc\r\n- Data file: pfv_CNRM-ESM2-1_historical_ssp245.nc\r\n- Data file: pfv_CNRM-ESM2-1_historical_ssp370.nc\r\n- Data file: pfv_CNRM-ESM2-1_historical_ssp585.nc\r\n- Data file: pfv_CanESM5-CanOE_historical_ssp126.nc\r\n- Data file: pfv_CanESM5-CanOE_historical_ssp245.nc\r\n- Data file: pfv_CanESM5-CanOE_historical_ssp370.nc\r\n- Data file: pfv_CanESM5-CanOE_historical_ssp585.nc\r\n- Data file: pfv_CanESM5_historical_ssp126.nc\r\n- Data file: pfv_CanESM5_historical_ssp245.nc\r\n- Data file: pfv_CanESM5_historical_ssp370.nc\r\n- Data file: pfv_CanESM5_historical_ssp585.nc\r\n- Data file: pfv_EC-Earth3_historical_ssp126.nc\r\n- Data file: pfv_EC-Earth3_historical_ssp245.nc\r\n- Data file: pfv_EC-Earth3_historical_ssp370.nc\r\n- Data file: pfv_EC-Earth3_historical_ssp585.nc\r\n- Data file: pfv_FGOALS-g3_historical_ssp126.nc\r\n- Data file: pfv_FGOALS-g3_historical_ssp245.nc\r\n- Data file: pfv_FGOALS-g3_historical_ssp370.nc\r\n- Data file: pfv_FGOALS-g3_historical_ssp585.nc\r\n- Data file: pfv_GFDL-CM4_historical_ssp245.nc\r\n- Data file: pfv_GFDL-CM4_historical_ssp585.nc\r\n- Data file: pfv_GFDL-ESM4_historical_ssp126.nc\r\n- Data file: pfv_GFDL-ESM4_historical_ssp245.nc\r\n- Data file: pfv_GFDL-ESM4_historical_ssp370.nc\r\n- Data file: pfv_GFDL-ESM4_historical_ssp585.nc\r\n- Data file: pfv_GISS-E2-1-G_historical_ssp126.nc\r\n- Data file: pfv_GISS-E2-1-G_historical_ssp245.nc\r\n- Data file: pfv_GISS-E2-1-G_historical_ssp370.nc\r\n- Data file: pfv_GISS-E2-1-G_historical_ssp585.nc\r\n- Data file: pfv_HadGEM3-GC31-LL_historical_ssp126.nc\r\n- Data file: pfv_HadGEM3-GC31-LL_historical_ssp245.nc\r\n- Data file: pfv_HadGEM3-GC31-LL_historical_ssp585.nc\r\n- Data file: pfv_IPSL-CM6A-LR_historical_ssp126.nc\r\n- Data file: pfv_IPSL-CM6A-LR_historical_ssp245.nc\r\n- Data file: pfv_IPSL-CM6A-LR_historical_ssp370.nc\r\n- Data file: pfv_IPSL-CM6A-LR_historical_ssp585.nc\r\n- Data file: pfv_KACE-1-0-G_historical_ssp126.nc\r\n- Data file: pfv_KACE-1-0-G_historical_ssp245.nc\r\n- Data file: pfv_KACE-1-0-G_historical_ssp370.nc\r\n- Data file: pfv_KACE-1-0-G_historical_ssp585.nc\r\n- Data file: pfv_MIROC-ES2L_historical_ssp126.nc\r\n- Data file: pfv_MIROC-ES2L_historical_ssp245.nc\r\n- Data file: pfv_MIROC-ES2L_historical_ssp370.nc\r\n- Data file: pfv_MIROC-ES2L_historical_ssp585.nc\r\n- Data file: pfv_MIROC6_historical_ssp126.nc\r\n- Data file: pfv_MIROC6_historical_ssp245.nc\r\n- Data file: pfv_MIROC6_historical_ssp370.nc\r\n- Data file: pfv_MIROC6_historical_ssp585.nc\r\n- Data file: pfv_MPI-ESM1-2-HR_historical_ssp126.nc\r\n- Data file: pfv_MPI-ESM1-2-HR_historical_ssp245.nc\r\n- Data file: pfv_MPI-ESM1-2-HR_historical_ssp370.nc\r\n- Data file: pfv_MPI-ESM1-2-HR_historical_ssp585.nc\r\n- Data file: pfv_MPI-ESM1-2-LR_historical_ssp126.nc\r\n- Data file: pfv_MPI-ESM1-2-LR_historical_ssp245.nc\r\n- Data file: pfv_MPI-ESM1-2-LR_historical_ssp370.nc\r\n- Data file: pfv_MPI-ESM1-2-LR_historical_ssp585.nc\r\n- Data file: pfv_MRI-ESM2-0_historical_ssp126.nc\r\n- Data file: pfv_MRI-ESM2-0_historical_ssp245.nc\r\n- Data file: pfv_MRI-ESM2-0_historical_ssp370.nc\r\n- Data file: pfv_MRI-ESM2-0_historical_ssp585.nc\r\n- Data file: pfv_NorESM2-LM_historical_ssp126.nc\r\n- Data file: pfv_NorESM2-LM_historical_ssp245.nc\r\n- Data file: pfv_NorESM2-LM_historical_ssp370.nc\r\n- Data file: pfv_NorESM2-LM_historical_ssp585.nc\r\n- Data file: pfv_NorESM2-MM_historical_ssp126.nc\r\n- Data file: pfv_NorESM2-MM_historical_ssp245.nc\r\n- Data file: pfv_NorESM2-MM_historical_ssp370.nc\r\n- Data file: pfv_NorESM2-MM_historical_ssp585.nc\r\n- Data file: pfv_UKESM1-0-LL_historical_ssp126.nc\r\n- Data file: pfv_UKESM1-0-LL_historical_ssp245.nc\r\n- Data file: pfv_UKESM1-0-LL_historical_ssp370.nc\r\n- Data file: pfv_UKESM1-0-LL_historical_ssp585.nc\r\n\r\nIn the GitHub repository the filenames differ from that listed above, the final underscore is replaced with a '+'.\r\nFor example, ' pfv_ACCESS-CM2_historical_ssp126.nc' in the repository is called ' pfv_ACCESS-CM2_historical+ssp126.nc'\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nAMIP is the Atmospheric Modelling Intercomparison Project.\r\nGSAT stands for Global Surface Air Temperature.\r\nACCESS-CM2 is the Australian Community Climate and Earth System Simulator coupled climate model.\r\nACCESS-ESM1-5 is the Australian Community Climate and Earth System Simulator Earth system model version designed to participate in CMIP6 simulations.\r\nBCC-CSM2-MR is one of the Beijing Climate Center Climate System Models designed for use in CMIP6 simulations.\r\nCAMS-CSM1-0 is the Chinese Academy of Meteorological Sciences Climate System Model version 1.\r\nCESM is the Community Earth System Model. \r\nCESM2-WACCM is the Community System Model - Whole Atmosphere Community Climate Model.\r\nCNRM-CM6-1 is the Centre National de Recherches Météorologiques Climate Model for CMIP6.\r\nCNRM-CM6-1-HR is the Centre National de Recherches Météorologiques Climate Model for CMIP6 - altered Horizontal Resolution.\r\nCNRM-ESM2-1 is the Centre National de Recherches Météorologiques Earth System Model, derived from CNRM-CM6-1.\r\nCanESM5 is the Canadian Earth System Model version 5.\r\nCanESM5-CanOE is the Canadian Earth System Model version 5 - Canadian Ocean Ecosystem.\r\nEC-Earth3 is the European Community Earth-system model version 3.\r\nFGOALS-g3 is the Flexible Global Ocean-Atmosphere-Land System Model, Grid-point Version 3\r\nGFDL-ESM4 is the Geophysical Fluid Dynamics Laboratory - Earth System Model version 4.\r\nGISS-E2-1-G is the Goddard Institute for Space Studies - chemistry-climate model version E2.1, using the GISS Ocean v1 (G01) model.\r\nHadGEM3-GC31-LL is the Met Offfice Hadley Centre Global Environment Model - Global Coupled configuration 3.1 - using an atmosphere/ocean resolution for historical simulation N96/ORCA1.\r\nIPSL-CM6A-LR is the Institut Pierre-Simon Laplace Climate Model for CMIP6 - Low Resolution.\r\nKACE-1-0-G is the Korean Advanced Community Earth system model. \r\nMIROC-ES2L is the Model for Interdisciplinary Research on Climate - Earth System version 2 for Long-term simulations.\r\nMIROC6 is the Model for Interdisciplinary Research on Climate - version 6.\r\nMPI-ESM1-2-HR is the Max Planck Institute Earth System Model - version 2 - altered Horizontal Resolution.\r\nMPI-ESM1-2-LR is the Max Planck Institute Earth System Model - version 2 - Low Resolution.\r\nMRI-ESM2-0 is the Meteorological Research Institute Earth System Model version 2.0.\r\nNorESM2-LM is the Norwegian Earth System Model version 2 - 2 degree resolution for atmosphere and land components, 1 degree resolution for ocean and sea-ice components.\r\nNorESM2-MM is the Norwegian Earth System Model version 2 - 1 degree resolution for all model components.\r\nUKESM1-0-LL is the United Kingdom Earth System Modelling project - version 1 - 2 degree resolution for all model components.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThe panels were plotted using Python and shell scripts (BASH files) - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n - Link to the code and output data for this figure, contained in a dedicated GitHub repository.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-02-07T12:53:34", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, permafrost", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-16T16:17:40", "doiPublishedTime": "2023-05-16T16:21:54.759503", "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": 40057, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig22/v20230206", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5230594, "numberOfFiles": 112, "fileFormat": "NetCDF, txt" }, "timePeriod": { "ob_id": 10427, "startTime": "1979-01-01T00:00:00", "endTime": "2100-12-31T00:00:00" }, "resultQuality": { "ob_id": 4208, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39680, "uuid": "3c19a763a0564aa599355e87acee95fa", "short_code": "comp", "title": "Caption for Figure 9.22 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Simulated versus observed permafrost extent and volume change by warming level. (a) Diagnosed Northern Hemisphere permafrost extent (area with perennially frozen ground at 15 m depth, or at the deepest model soil level if this is above 15 m) for 1979–1998, for available Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) models, from the first ensemble member of the historical coupled run, and for CMIP6 Atmospheric Model Intercomparison Project (AMIP) (atmosphere+land surface, prescribed ocean) and land-hist (land only, prescribed atmospheric forcing) runs. Estimates of current permafrost extents based on physical evidence and reanalyses are indicated as black symbols – triangle: Obu et al. (2018); star: Zhang et al. (1999); circle: central value and associated range from Gruber (2012). (b) Simulated global permafrost volume change between the surface and 3 m depth as a function of the simulated global surface air temperature (GSAT) change, from the first ensemble members of a selection of scenarios, for available CMIP6 models. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "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": [ 62363, 62364, 62365, 62366, 62367, 62368, 62501 ], "vocabularyKeywords": [], "identifier_set": [ 12501 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 195196, 195197, 195198, 195199, 195200, 195201, 195195, 195194 ], "onlineresource_set": [ 83494, 83497, 83495, 83496, 83498, 88632, 94650 ] }, { "ob_id": 40064, "uuid": "229d671fe5524580838cc452ee7bef18", "title": "AMOF: cloud camera 2 imagery from Chilbolton, Hampshire (2016-present)", "abstract": "This dataset contains photographs taken by an all-sky camera located on the roof of the Receive Cabin (51.145168°N, -1.439750°E) at the National Centre for Atmospheric Science (NCAS) Chilbolton Atmospheric Observatory (CAO) in southern England, UK. Photos are taken at 5 minute intervals on a continuous basis in order to record general atmospheric conditions.\r\n\r\nThe camera is an AXIS M3027-PVE network camera, which is alternatively known as an AXIS 0556-001. In the frame of reference of the photos, the angular field of view is 187° in the horizontal and 168° in the vertical, i.e. essentially covering a hemisphere. The centre of the field of view is nominally directed towards the zenith, although it is not known with what level of accuracy. The azimuthal alignment (measured in degrees from North), ϕ_AZIMUTH, of each part of the photo can be estimated from the relationship:\r\n\r\n ϕ_AZIMUTH = 280 - ϕ_PHOTO\r\n\r\nwhere ϕ_PHOTO is the angle measured (in degrees) clockwise from the the 12 o'clock position in a polar coordinate system whose pole is at the centre of the photo. Note that ϕ_PHOTO decreases as ϕ_AZIMUTH increases. Details of how this relationship has been derived can be found in the publication available from https://doi.org/10.5281/zenodo.8096680 .\r\n\r\nThe camera synchronises its internal clock with Coordinated Universal Time (UTC). This clock is used to trigger the capture of photographs and to produce the time stamps used in the file names. For a reason that is not entirely clear, there is typically a 1 s delay between the time stamp shown in the overlay at the top of each photograph and the one used in the file name. Although the time stamp shown in the overlay is assumed to be the more appropriate one, it is not computer-readable without making use of image processing software. Consequently, the time stamp from the file name has been adopted as the official one and recorded in the embedded metadata.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "continual", "dataLineage": "Data are prepared by Chilbolton Facility for Atmospheric and Radio Research (CFARR) staff prior to submission to CEDA for archiving.", "removedDataReason": "", "keywords": "CFARR, cloud camera", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 59, "bboxName": "Chilbolton", "eastBoundLongitude": -1.427, "westBoundLongitude": -1.427, "southBoundLatitude": 51.145, "northBoundLatitude": 51.145 }, "verticalExtent": null, "result_field": { "ob_id": 42897, "dataPath": "/badc/ncas-cao/data/ncas-cam-9/20160510_longterm/v1.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4402937, "numberOfFiles": 3, "fileFormat": "Data are PNG formatted." }, "timePeriod": { "ob_id": 965, "startTime": "1996-07-04T23:00:00", "endTime": null }, "resultQuality": { "ob_id": 853, "explanation": "Data are checked by CFARR staff prior to submission to BADC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-21" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 20054, "uuid": "49ee31d83c0c443b88c7673b1161ee71", "short_code": "acq", "title": "Acquisition Process for: Chilbolton Facility for Atmospheric and Radio Research (CFARR) Cloud Camera 2 Data", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Chilbolton Facility for Atmospheric and Radio Research (CFARR) Cloud Camera; PLATFORMS: Chilbolton Facility for Atmospheric and Radio Research (CFARR), UK; " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 223 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 3464, "uuid": "493185a4f967ee2a34516d9c5da9331e", "short_code": "proj", "title": "Chilbolton Facility for Atmospheric and Radio Research (CFARR)", "abstract": "The STFC facility at Chilbolton, Hampshire (51.1445N, 1.4270W) is the site of several observation systems for meteorological studies. The main system is the 3 GHz Doppler radar (CAMRa). A supporting 94 GHz radar (Galileo) has been located close to the main dish to allow dual frequency studies of precipitating particles. The system is complemented by a 905 nm Vaisala CT75K lidar, a 355nm UV Raman Lidar, multiple raingauge and meteorological sensors. This dataset also holds attenuation time-series data from vertically polarised links from South Wonston to Sparsholt. Sparsholt meteorological sensor and raingauge data is also archived. Cloud camera data from the Chilbolton site is available for examining weather patterns." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 195214, 195212, 195218, 195211, 195215, 195216, 195217, 195220 ], "onlineresource_set": [ 83505, 83506 ] }, { "ob_id": 40082, "uuid": "ac43da11867243a1bb414e1637802dec", "title": "Hydro-JULES: Global high-resolution drought datasets from 1981-2022", "abstract": "These are global scale high-resolution drought indices developed from a combination of precipitation and potential evapotranspiration datasets for the Hydro-JULES project. Climate Hazards group InfraRed Precipitation with Station data (CHIRPS), Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation estimates, Global Land Evaporation Amsterdam Model (GLEAM) and Bristol Hourly potential evapotranspiration (hPET) estimates were used. The drought index is developed using the Standardized Precipitation-Evapotranspiration Index (SPEI). These high-resolution global scale drought indices are available from 1981-2022 at a monthly and 5km spatial resolution. The SPEI indices are available from 1-48 months. The datasets provide valuable information for the study and analysis of droughts at much higher resolution from global to local scale. \r\nThese data were produced for Hydro-Jules (NE/S017380/1) and REACH (Foreign, Commonwealth and Development Office): Programme Code 201880.", "creationDate": "2023-05-22T15:03:02.061547", "lastUpdatedDate": "2023-05-22T15:03:02", "latestDataUpdateTime": "2024-05-30T16:26:41", "updateFrequency": "notPlanned", "dataLineage": "Data were generated using Standardized Precipitation-Evapotranspiration Index (SPEI) at 5km horizontal resolution over the domain 180W-180E, 55S-85N. The dataset is available in NetCDF format. Data were produced by the project team before uploading to CEDA.\r\nAn error in model run of this data was identified post publishing and a replacement for the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) Global Land Evaporation Amsterdam Model (GLEAM) section of this dataset can be found here https://catalogue.ceda.ac.uk/uuid/e652f0109f21401680bc3c0ac834a96e/", "removedDataReason": "", "keywords": "NE/S017380/1, drought", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-05-23T09:12:08", "doiPublishedTime": "2023-07-07T09:35:20", "removedDataTime": null, "geographicExtent": { "ob_id": 3830, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -55.0, "northBoundLatitude": 85.0 }, "verticalExtent": null, "result_field": { "ob_id": 40083, "dataPath": "/badc/hydro-jules/data/Global_drought_indices/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1668153770350, "numberOfFiles": 389, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 11096, "startTime": "1981-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 4280, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-22" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40084, "uuid": "c4cb80ae80cd45d492ae8c27f606d878", "short_code": "comp", "title": "Computation for Hydro-JULES: Global Drought Indices", "abstract": "Data were generated using Standardized Precipitation-Evapotranspiration Index (SPEI) at 5km horizontal resolution over the domain 180W-180E, 55S-85N" }, "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": 40079, "uuid": "9f364df79edc4aa69735bfaec25b1c07", "short_code": "proj", "title": "Hydro-JULES: Next generation land surface and hydrological prediction", "abstract": "Hydro-JULES is a NERC-funded research programme which will build a three-dimensional community model of the terrestrial water cycle to underpin hydrological research in the United Kingdom. Hydro-JULES is delivered by UKCEH in partnership with BGS and NCAS.\r\n\r\nThe Hydro-JULES model and its associated datasets will enable the UK to tackle outstanding research questions in hydrological science and will provide a national resource to support research both specific to the Hydro-JULES project and beyond.\r\n\r\nHydro-JULES will support and enable collaborative work across the research and academic community to:\r\naddress important science questions in the fields of hydrology, land-atmosphere feedbacks, carbon and nutrient cycles, data science and integration with novel instrumentation and Earth observation technologies;\r\nquantify the risks of hydro-climatic extremes (e.g., floods and drought) in a changing environment to support long-range planning and policy decisions;\r\nimprove hydrological forecasting using new sensors and modelling technology.\r\nThe Hydro-JULES project covers topics in land-surface science and hydrology including: quantification of hydro-meteorological risks, using high-resolution climate predictions for hydrological applications, calculation the impacts of environmental change on evaporation, transpiration, and soil moisture, modelling flood inundation over large areas, representing anthropogenic interventions in the water cycle, and application of new techniques including Earth observation and data assimilation. NE/S017380/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 52192, 52193, 63011, 63233 ], "vocabularyKeywords": [], "identifier_set": [ 12636 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 195278, 195279, 195280, 195281, 195282, 195283, 195284, 195285, 196267, 196268, 196269, 196270, 196271, 196272, 196273, 196274, 195286 ], "onlineresource_set": [] }, { "ob_id": 40085, "uuid": "4f3d4c97d9be45419679fc498e7f6501", "title": "ATSR-2: Average Surface Temperature (AST) Product (AT2_AR__2P), v3.0.1", "abstract": "Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).\r\n\r\nThis dataset contains the Along-Track Scanning Radiometer on ESA ERS-2 satellite (ATSR-2) Average Surface Temperature (AST) Product. These data are the Level 2 spatially averaged geophysical product derived from Level 1B product and auxiliary data. This data is from the 3rd reprocessing and tagged v3.0.1\r\n\r\nThere are two types of averages provided: 10 arcminute cells and 30 arcminute cells. All cells are present regardless of the surface type. Hence, the sea (land) cells would also have the land (sea) records even though these would be empty. Cells containing coastlines will have both valid land and sea records; the land (sea) record only contains averages from the land (sea) pixels. The third reprocessing was done to implement the updated algorithms, processors, and auxiliary files.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-05-23T09:15:57", "latestDataUpdateTime": "2017-12-14T16:33:26.040454", "updateFrequency": "notPlanned", "dataLineage": "The data were acquired by the European Space Agency's (ESA) Envisat satellite, and the NERC Earth Observation Data Centre (NEODC) mirrors the data for UK users. The data has been periodically reprocessed to take into account latest calibration and processing techniques and released to CEDA.", "removedDataReason": "", "keywords": "ATSR, Average Surface Temperature, AST", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-10-02T09:14:10", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40159, "dataPath": "/neodc/aatsr_multimission/atsr2-v3.0.1/data/at2_ar__2p", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2837886761867, "numberOfFiles": 196349, "fileFormat": "ENVISAT PDS" }, "timePeriod": { "ob_id": 2317, "startTime": "1995-05-31T23:00:00", "endTime": "2008-01-31T00:00:00" }, "resultQuality": { "ob_id": 2169, "explanation": "", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-22" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 40087, "uuid": "303ef938c0aa460eac9bea5e79b9c547", "short_code": "cmppr", "title": "Composite Process for: ATSR-2 Average Surface Temperature (AST) Product (AT2_AR__2P) v3.0.1", "abstract": "This process is comprised of multiple procedures: 1. Acquisition: Acquisition Process for: ATSR-2 Average Surface Temperature (AST) Product (AT2_AR__2P) v3.0.1; \r\n2. Computation: DETAILS NEEDED - COMPUTATION CREATED FOR SATELLITE COMPOSITE. deployed on ERS-2;" }, "imageDetails": [ 99 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2580, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 45, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/Terms-and-Conditions-for-the-use-of-ESA-Data.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 19910, "uuid": "be02159d0d9b4ce49e9c90378206e283", "short_code": "proj", "title": "ATSR-2 Mission", "abstract": "Along-Track Scanning Radiometer (ATSR-2) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [ { "ob_id": 10558, "vocabService": "nerc_skos_vocab", "uri": "http://vocab.nerc.ac.uk/collection/L22/current/TOOL1073/", "resolvedTerm": "Along Track Scanning Radiometer - 2" } ], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40086, "uuid": "943749c71fe9467fbcaeb40310b35049", "short_code": "coll", "title": "ATSR-2 Multimission land and sea surface data, version 3.0.1", "abstract": "Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR). \r\n\r\nThis dataset collection contains version 3 ATSR2 Multimission land and sea surface data. These data result from the 3rd reprocessing second pass and are tagged v3.0.1.\r\n\r\nThe instrument uses thermal channels at 3.7, 10.8, and 12 microns wavelength; and reflected visible/near infra-red channels at 0.555, 0.659, 0.865, and 1.61 microns wavelength. Level 1b products contain gridded brightness temperature and reflectance. Level 2 products contain land and sea-surface temperature, and NDVI at a range of spatial resolutions. The third reprocessing was done to implement updated algorithms, processors, and auxiliary files. The data were acquired by the European Space Agency's (ESA) Envisat satellite, and the NERC Earth Observation Data Centre (NEODC) mirrors the data for UK users." } ], "responsiblepartyinfo_set": [ 195292, 195290, 195291, 195293, 195294, 195295, 195296, 195297 ], "onlineresource_set": [ 83511, 83512 ] }, { "ob_id": 40089, "uuid": "c622adfeb4cc4ae181dc4cca82c2311c", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Cross-Chapter Box 9.1, Figure 1 (v20230523)", "abstract": "Data for Cross-Chapter Box 9.1, Figure 1 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nCross-Chapter Box 9.1, Figure 1 shows observed and simulated regional probability ratio of marine heatwaves (MHWs) for the 1985-2014 period and for the end of the 21st century under two different greenhouse gas emissions scenarios. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels with data provided for all panels in the main directory. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Main assessment timeseries for GMSL change, OHC and ThSL. Timeseries are global integrals over the following vertical layers: 0-300 m; 0-700 m; 0-2000 m; 700-2000 m; > 2000 m; Full-depth.\r\n\r\n\r\nThis dataset are also used in the following figures:\r\na) AR6 FGD assessment timeseries GMSL satellite altimeter: Figure 2.28; \r\nb) AR6 FGD assessment timeseries GMSL tide gauge: Figure 2.28;\r\nc) AR6 FGD assessment timeseries OHC: Figure 3.26, Box 7.2, Figure 1; \r\n\r\nOther figures/tables: Figure 2.26, Table 2.7; Figure 3.26; Box 7.2 Figure 1, Table 9.5; Figure TS.8; Figure TS.13.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a: \r\n Data file: “AR6_FGD_assessment_timeseries_OHC.csv” => column 2 is used to plot the light blue shaded region, column 4 is used to plot the medium blue shaded region, column 6 is used to plot the dark blue shaded region in CCBox9.1 Figure 1 panel a). \r\n\r\n\r\nPanel b: \r\n Data file: “AR6_FGD_assessment_timseries_GMSL_satellite_altimeter.csv” => column 2 is used to plot the dashed black line in CCBox9.1 Figure 1 panel b)\r\n Data file: “AR6_FGD_assesssment_timeseries_GMSL_tide_gauge.csv” => column 2 is used to to plot the dashed black line in CCBox9.1 Figure 1 panel b)\r\n Data file: “AR6_FGD_assessment_timeseries_ThSL.csv” => column 2 is used to plot the light blue shaded region, column 4 is used to plot the medium blue shaded region, column 6 is used to plot the dark blue shaded region in CCBox9.1 Figure 1 panel b).\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 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the code for the figure, archived on github repository for chapter 9.\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to Chapter 2 Figure 2.26 \r\n - Link to Chapter 2 Figure 2.28\r\n - Link to Chapter 3 Figure 3.26\r\n - Link to Chapter 7 Box 7.2, Figure 1\r\n - Link to Technical Summary Figure TS.13\r\n - Link to input data for Cross-Chapter Box 9.1, Figure 1", "creationDate": "2023-05-23T10:59:09.130510", "lastUpdatedDate": "2023-05-23T10:59:09", "latestDataUpdateTime": "2023-05-30T12:30:49", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, global ocean heat content, global thermal expansion, ocean observations, in situ observations", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-07T14:15:23", "doiPublishedTime": "2023-09-26T14:43:27.185621", "removedDataTime": null, "geographicExtent": { "ob_id": 3831, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 244, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 40090, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_ccb9_1_fig1/v20230523", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 27629, "numberOfFiles": 8, "fileFormat": "Data are csv formatted" }, "timePeriod": { "ob_id": 11097, "startTime": "1900-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 4282, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40091, "uuid": "2c4f8aeee2c6459c9545ed207d6e9b7d", "short_code": "comp", "title": "Caption for Cross-Chapter Box 9.1, Figure 1 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global Energy Inventory and Sea Level Budget. (a) Observed changes in the global energy inventory for 1971–2018 (shaded time series) with component contributions as indicated in the figure legend. Earth System Heating for the whole period and associated uncertainty is indicated to the right of the plot (red bar = central estimate; shading =very likely range); (b) Observed changes in components of global mean sea level for 1971–2018 (shaded time series) as indicated in the figure legend. Observed global mean sea level change from tide gauge reconstructions (1971–1993) and satellite altimeter measurements (1993–2018) is shown for comparison (dashed line) as a three-year running mean to reduce sampling noise. Closure of the global sea level budget for the whole period is indicated to the right of the plot (red bar = component sum central estimate; red shading =very likely range; black bar = total sea level central estimate; grey shading =very likely range). Full details of the datasets and methods used are available in Annex I. Further details on energy and sea level components are reported in Table 7.1 and Table 9.5." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12693 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 195308, 195309, 195310, 195311, 195312, 195313, 195314, 195315, 195316, 195317, 195318, 195319, 195320, 195321, 195322, 195323, 195324, 195325, 195326, 195327, 195328, 195329, 195330, 195331, 195332, 195333, 195334, 195335, 195336, 195337 ], "onlineresource_set": [ 83543, 83544, 83545, 83547, 83555, 83562, 83563, 83565, 83546, 83570, 84959 ] }, { "ob_id": 40093, "uuid": "12ce1a305f7649bc85a9b81e782da0c9", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.28 (v20230523)", "abstract": "Data for Figure 2.28 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 2.28 shows changes in global mean sea level.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\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 Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 2.28:\r\n\r\n a) AR6 FGD assessment timeseries GMSL satellite altimeter \r\n b) AR6 FGD assessment timeseries GMSL tide gauge\r\n\r\nThese data files can be found from data for Cross-Chapter Box 9.1, Figure 1. The link to this dataset is provided in the Related Documents section of this catalogue record.\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 Cross-Chapter Box 9.1, Figure 1", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-07-11T16:21:03", "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, Intergovernmental Panel on Climate Change, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 2, global mean sea level", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "Based on point data Other", "status": "ongoing", "dataPublishedTime": "2023-06-16T09:08:20", "doiPublishedTime": "2023-09-26T15:27:49.322446", "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": 66, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "Based on point data" }, "result_field": { "ob_id": 40298, "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig28/v20230523", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 12810, "numberOfFiles": 5, "fileFormat": "Files are CSV formatted" }, "timePeriod": { "ob_id": 11098, "startTime": "1900-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 3770, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-10-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40094, "uuid": "89ce14fbdeee4793b4992ab1674d6f76", "short_code": "comp", "title": "Caption for Figure 28 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 global mean sea level. (a) Reconstruction of sea-level from ice core oxygen isotope analysis for the last 800 kyr. For target paleo periods (CCB2.1) and MIS11 the estimates based upon a broader range of sources are given as box whiskers. Note the much broader axis range (200 m) than for later panels (tenths of metres). (b) Reconstructions for the last 2500 years based upon a range of proxy sources with direct instrumental records superposed since the late 19th century. (c) Tide-gauge and, more latterly, altimeter-based estimates since 1850. The consensus estimate used in various calculations in Chapters 7 and 9 is shown in black. (d) The most recent period of record from tide-gauge and altimeter-based records. 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": [ 12696 ], "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": [ 195342, 195343, 195344, 195345, 195346, 195347, 195348, 198426, 195349 ], "onlineresource_set": [ 83553, 83554, 83551, 83560 ] }, { "ob_id": 40095, "uuid": "3659eca2afe54ab9ae437bf25fec1c2e", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.26 (v20230523)", "abstract": "Data for Figure 2.26 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 2.26 shows changes in ocean heat content (OHC).\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 Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 2.26:\r\n\r\n- AR6 FGD assessment timeseries OHC\r\n\r\nThese data files are from data for Cross-Chapter Box 9.1, Figure 1. The link to this dataset is provided in the Related Documents section of this catalogue record.\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 Cross-Chapter Box 9.1, Figure 1", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-07-11T16:27:25", "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, Intergovernmental Panel on Climate Change, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 2, global ocean heat content, global thermal expansion, ocean observations, in situ observations", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "Based on point data Other", "status": "ongoing", "dataPublishedTime": "2023-06-16T09:04:28", "doiPublishedTime": "2023-09-26T15:18:58.114835", "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": 66, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "Based on point data" }, "result_field": { "ob_id": 40299, "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig26/v20230523", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9908, "numberOfFiles": 4, "fileFormat": "Files are in CSV format" }, "timePeriod": { "ob_id": 11099, "startTime": "1871-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 3770, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-10-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40096, "uuid": "83c9b0009a6b496497d92c138b7b9def", "short_code": "comp", "title": "Caption for Figure 2.26 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Figure 2.26 | Changes in ocean heat content (OHC). Changes are shown over (a) full depth of the ocean from 1871–2019 from a selection of indirect and direct measurement methods. The series from Table 2.7 is shown in solid black in both (a) and (b) (see Table 2.7 caption for details). (b) as (a) but for 0–2000 m depths only and reflecting the broad range of available estimates over this period. For further details see 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": [ 12695 ], "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": [ 195353, 195355, 195356, 195357, 195358, 195359, 195360, 198425, 195354 ], "onlineresource_set": [ 83559, 83557, 83561, 83558 ] }, { "ob_id": 40097, "uuid": "73978fa38e4f40b29d47b45e654d83b9", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.8 v20230523", "abstract": "Data for Figure TS.8 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure TS.13 shows observed, simulated and projected changes compared to the 1995–2014 average in four key indicators of the climate system through to 2100 differentiated by Shared Socio-economic Pathway (SSP) scenario.\r\nThe intent of this figure is to show how future emissions choices impact key, iconic large-scale indicators and to highlight that our collective choices matter. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nArias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 4 subpanels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure TS.8:\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n- Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-08-02T08:54:20.938535", "lastUpdatedDate": "2022-08-02T08:42:14", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, ocean heat content, energy budget, ocean, cryosphere, land", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 11100, "startTime": "1950-01-01T00:00:00", "endTime": "2100-01-01T23:59:59" }, "resultQuality": { "ob_id": 1, "explanation": "See dataset associated documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-08-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 195365, 195366, 195367, 195368, 195369, 195370, 195371, 195364, 195372, 195373 ], "onlineresource_set": [ 83567, 83566, 83568 ] }, { "ob_id": 40099, "uuid": "17f2465ce84a4492b3fa2ba2e558d869", "title": "CALIPSO: Cloud and Aerosol Lidar Level 2 Vertical Feature Mask Version 4-20 Product (CAL_LID_L2_VFM-Standard-V4-20)", "abstract": "CAL_LID_L2_VFM-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Vertical Feature Mask (VFM), Version 4-20 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. 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The main scientific objective of the GOME-2 mission is to measure the global distribution of ozone and several trace gases which play an important role in the ozone chemistry of the Earth's stratosphere and troposphere, for example, NO2, BrO, OClO, and SO2.\r\n\r\nThis dataset contains version 3.00 ozone profiles derived by the Remote Sensing Group (RSG) at the STFC Rutherford Appleton Laboratory, Oxfordshire, UK, as part of the National Centre for Earth Observation (NCEO). These were derived from radiances measured by the GOME-2 on-board Metop-A. The collection also includes total column ozone, column BrO, and column NO2 as well as cloud heights derived from the Along Track Scanning Radiometer (ATSR), which are included to aid interpretation of the ozone profiles.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "", "removedDataReason": "", "keywords": "ozone, tropospheric ozone, satellite, observation, global , ESA CCI, GOME-2, Metop-A", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 11103, "startTime": "2007-01-29T00:00:00", "endTime": "2019-08-31T23:59:59" }, "resultQuality": { "ob_id": 650, "explanation": "Not available at this time.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-01-26" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 40104, "uuid": "632c646669874a7da983ad78d4209eff", "short_code": "cmppr", "title": "Ozone from GOME-2 on Metop-A", "abstract": "Composite process for retrieval of ozone from GOME-2 on Metop-A." }, "imageDetails": [ 46 ], "discoveryKeywords": [], "permissions": [], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 43018, "uuid": "38a7bf730db84cd9951fd4f71f386563", "short_code": "coll", "title": "RAL Ozone collection", "abstract": "A collection of satellite retrieved ozone products produced by the Remote Sensing Group at RAL Space, Oxfordshire, UK, as part of the National Centre for Earth Observation (NCEO). 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Additional funding has been provided from numerous contracts, including from ESA and EUMETSAT studies, the ESA Climate Change Initiative (CCI) and EU Copernicus Climate Change Service (C3S) and the UK Earth Observation Climate Information Service (EOCIS). Input datasets were provided by ESA, EUMETSAT and ECMWF." } ], "responsiblepartyinfo_set": [ 195402, 195405, 195406, 195407, 195408, 195409, 195410, 195404, 195403, 195401 ], "onlineresource_set": [ 92961 ] }, { "ob_id": 40105, "uuid": "91d3ce592aae4215ba57c1f36e6d04c4", "title": "GOME: Vertical Profiles of Ozone and other Trace Gases - Ozone Profiles Version 3.01", "abstract": "The Global Ozone Monitoring Experiment (GOME) was an instrument aboard the European Remote Sensing satellite 2 (ERS-2). 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The main scientific objective of the GOME mission is to measure the global distribution of ozone and several trace gases which play an important role in the ozone chemistry of the Earth's stratosphere and troposphere, for example, NO2, BrO, OClO, and SO2." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 60968, 60970, 60971, 60972, 60973, 60974, 60975, 60976, 60977, 60979, 60980, 60984, 60985, 60987, 60990, 60991, 60992, 60993, 60994, 60995, 60996, 69038, 84388, 84389, 84390, 84391, 84392, 84393, 84394, 84395, 84396, 84397, 84398, 84399, 84400, 84401, 84402, 84403, 84404, 84405, 84406, 84407, 84408, 84409, 84410, 84411, 84412, 84413, 84414, 84415, 84416, 84417, 84418, 84419, 84420, 84421, 84422, 84423, 84424, 84425, 84426, 84427, 84428, 84429, 84430, 84431, 84432, 84433, 84434, 84435, 84436, 84437, 84438, 84439, 84440, 84441, 84442, 84443, 84444, 84445, 84446, 84447, 84448, 84449, 84450, 84451, 84452, 84453 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 43018, "uuid": "38a7bf730db84cd9951fd4f71f386563", "short_code": "coll", "title": "RAL Ozone collection", "abstract": "A collection of satellite retrieved ozone products produced by the Remote Sensing Group at RAL Space, Oxfordshire, UK, as part of the National Centre for Earth Observation (NCEO). Ozone products were derived from radiances measured by the UV-visible Global Ozone Monitoring Experiment (GOME) class of instruments: GOME, SCIAMACHY, OMI, GOME-2(A-C), TROPOMI (Sentinel-5P), Sentinel-4 and 5.\r\n\r\nThe original GOME was an instrument aboard ERS-2 (1995). The main scientific objective of the mission was to measure the global distribution of ozone and several trace gases that play an important role in the ozone chemistry of the Earth's stratosphere and troposphere, for example, NO2, BrO, OClO, and SO2. This has been continued since 1995 with various European follow-on instruments.\r\n\r\nAlgorithm development and data processing was primarily funded by UK National funding, initially through the Data Assimilation Research Centre (DARC) and then NCEO, both under the UK Natural Environment Research Council (NERC). Additional funding has been provided from numerous contracts, including from ESA and EUMETSAT studies, the ESA Climate Change Initiative (CCI) and EU Copernicus Climate Change Service (C3S) and the UK Earth Observation Climate Information Service (EOCIS). Input datasets were provided by ESA, EUMETSAT and ECMWF." } ], "responsiblepartyinfo_set": [ 195424, 195425, 195470, 195471, 195472, 195473, 195474, 195475, 195423, 195421 ], "onlineresource_set": [ 92958, 92959, 92960 ] }, { "ob_id": 40106, "uuid": "7b70b41c59464b94b737fb35f1eac8fe", "title": "Ultrafine and Submicron Particles in the Urban Environment in Thailand: Aerosol particle number concentration on public transport in Bangkok, Thailand", "abstract": "This dataset contains particle number concentration (PNC) measurements made using a hand held sampler on public transport routes in Bangkok, Thailand for the Ultrafine and Submicron Particles in the Urban Environment in Thailand project. PNC was measured using a TSI 3007 hand held particle counter on a defined public transport route, each journey consisted of nine stages:\r\n\r\n1. Walking from the Chulbhorn Research Institute (13.879763°, 100.577923°) to the Lak Si railway station (13.883559°, 100.58\r\n0674°).\r\n2. Travelling on the State Railway of Thailand South from Lak Si railway station to Hua Lumphong railway station (13.739527°\r\n, 100.516820°).\r\n3. Walking from Hua Lumphong railway station to Hua Lumphong MRT undergound station (13.737864°, 100.517173°).\r\n4. Travelling on the MRT underground train East (blue line) from MRT Hua Lumphong station to MRT Sukhumvit Station (13.73857\r\n9°, 100.561544°).\r\n5. Walking from the MRT Sukhumvit underground station to the Asok BTS Skytrain overground station (13.737076°, 100.560393°).\r\n6. Travelling on the BTS Sukhumvit line North from Asok BTS station to Mo Chit BTS station (13.802621°, 100.553829°).\r\n7. Walking from BTS Skytrain overground station to Mo Chit bus stop (13.803730°, 100.554085°)\r\n8. Travelling on the public bus North from the Mo Chit bus stop to the Miracle Grand bus stop (13.876419°, 100.576880°)\r\n9. Walking from the Miracle Grand bus stop back to the starting position at the Chulbhorn Research Institute.\r\n\r\nData was collected at 1 m height from ground at 1 s sampling intervals. The data covered three seasons in Bangkok, hot, cool\r\n and rainy, from May 2018 until November 2018. Measurements were taken by the staff of the Toxicology group in the Chulabhorn Research Institute, Thailand and the Atmospheric Chemistry Research Group in the University of Bristol, UK. NE/P014674/1", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-05-26T16:27:28", "updateFrequency": "", "dataLineage": "Data sent by the project participants to the Centre for Environmental Data Anaylsis (CEDA) for archiving.", "removedDataReason": "", "keywords": "Thailand, NE/P014674/1, aerosol,", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-06T14:26:50", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3833, "bboxName": "Bangkok aerosol on public transport", "eastBoundLongitude": 100.58, "westBoundLongitude": 100.51682, "southBoundLatitude": 13.737864, "northBoundLatitude": 13.883559 }, "verticalExtent": null, "result_field": { "ob_id": 40109, "dataPath": "/badc/deposited2023/bangkok_aerosol_pt", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 17558401, "numberOfFiles": 3, "fileFormat": "BADC-CSV" }, "timePeriod": { "ob_id": 7556, "startTime": "2018-03-05T00:00:00", "endTime": "2018-11-15T23:59:59" }, "resultQuality": { "ob_id": 3340, "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": "2019-10-24" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 40108, "uuid": "8c9b116e69e340de8ce000d26d7244cf", "short_code": "acq", "title": "Acquisition for Bangkok aerosol on public transport", "abstract": "Acquisition for Bangkok aerosol on public transport" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2521, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 28097, "uuid": "d753d76d68e946baa19484b2307b7748", "short_code": "proj", "title": "Ultrafine and Submicron Particles in the Urban Environment in Thailand - Size, Concentration, Composition and Health Impacts", "abstract": "This project was the first to report ultrafine particle (UFP) number concentration and size distributions in the submicron (smaller than 1 micrometre) size range in urban Bangkok, Thailand.\r\n\r\nIt is well known that particulate matter (PM) poses a significant health risk, especially to urban dwellers, with often the poorest in society most affected. Ultrafine particles (size smaller than 100 nanometres), as a component of PM, are increasingly implicated in disease and mortality. However, much of the research available in the literature is based on data from the developed world, especially for ultrafine particles, and without robust data it is not possible to determine trends in this important pollutant for Thailand and Bangkok in particular, and strategies for health protection therefore lack this vital information. In addition, aerosol particles provide the single largest source of uncertainty in most global climate models, and production of primary particles and gas precursors e.g. Volatile Organic Compounds (VOCs) from the transport and industrial sectors contribute significantly to this. Therefore, in order for the impact of these activities on climate to be assessed and reduced, determination of sources and levels of emission of both gases and particles much be undertaken. NE/P014674/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 63217, 63218, 63219, 63220, 63221, 63222, 63223, 63224, 63225, 63226, 63227, 63228, 63229, 63230, 63231, 63232 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 195426, 195427, 195429, 195430, 195431, 195432, 195433, 195434, 195428 ], "onlineresource_set": [] }, { "ob_id": 40110, "uuid": "1e60155294934ffcaf194e555a81294b", "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.26 v20230530", "abstract": "Data for Figure 4.26 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 4.26 shows the projected long-term changes in zonal-mean, zonal wind.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Lee, J.-Y., J. Marotzke, G. Bala, L. Cao, S. Corti, J.P. Dunne, F. Engelbrecht, E. Fischer, J.C. Fyfe, C. Jones, A. Maycock, J. Mutemi, O. Ndiaye, S. Panickal, and T. Zhou, 2021: Future Global Climate: Scenario-Based Projections and Near-Term Information. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 553–672, doi:10.1017/9781009157896.006.\r\n\r\n\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for all panels.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n a) Global projected spatial patterns of multi-model mean change in DJF seasonal mean zonal-mean zonal wind in 2081-2100 relative to 1995-2014 in SSP1‑2.6\r\n b) Global projected spatial patterns of multi-model mean change in JJA seasonal mean zonal-mean zonal wind in 2081-2100 relative to 1995-2014 in SSP1‑2.6\r\n c) Global projected spatial patterns of multi-model mean change in DJF seasonal mean zonal-mean zonal wind in 2081-2100 relative to 1995-2014 in SSP3‑7.0\r\n d) Global projected spatial patterns of multi-model mean change in JJA seasonal mean zonal-mean zonal wind in 2081-2100 relative to 1995-2014 in SSP3‑7.0\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file Data_shown_in_figure_panels_a_and_b.nc (panels a and b) includes the multi-model mean zonal mean zonal wind as a function of latitude and pressure level for SSP1-2.6\r\n Data file Data_shown_in_figure_panels_c_and_d.nc (panels c and d) includes the multi-model mean zonal mean zonal wind as a function of latitude and pressure level for SSP3-7.0\r\n\r\n\r\nDJF stands for December, January, February.\r\nJJA stands for June, July, August.\r\nSSP1-2.6 is based on Shared Socioeconomic Pathway SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP3-7.0 is based on Shared Socioeconomic Pathway SSP3 which is characterized by high challenges to both mitigation and adaptation and RCP7.0, a future pathway with a radiative forcing of 7.0 W/m2 in the year 2100.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure\r\n - Link to the report component containing the figure (Chapter 4)\r\n - Link to the Supplementary Material for Chapter 4, which contains details on the input data used in Table 4.SM.1", "creationDate": "2023-05-31T08:08:32.042900", "lastUpdatedDate": "2023-05-31T08:08:32", "latestDataUpdateTime": "2023-05-31T12:12:50", "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\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Chapter 4, Sixth Assessment Report, Working Group 1, Physical Science Basis, zonal wind", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-07T11:36:00", "doiPublishedTime": "2023-07-03T10:11:11.269652", "removedDataTime": null, "geographicExtent": { "ob_id": 3834, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 245, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 40111, "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig26/v20230530", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2144622, "numberOfFiles": 5, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 11106, "startTime": "1995-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4284, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-31" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40112, "uuid": "a763e258f5a8426d93cf0af3fe5f1acf", "short_code": "comp", "title": "Caption for Figure 4.26 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Long-term change of zonal-mean, zonal wind. Displayed are multi-model mean changes in (left) boreal winter (December–January–February, DJF) and (right) austral winter (June–July–August, JJA) zonal mean, zonal wind (m s–1) in 2081–2100 for (top) SSP1-2.6 and (right) SSP3-7.0 relative to 1995–2014. The 1995–2014 climatology is shown in contours with spacing 10 m s–1. Diagonal lines indicate regions where less than 80% of the models agree on the sign of the change and no overlay where at least 80% of the models agree on the sign of the change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46706, 46708, 50418, 52664, 62360, 65969, 65970 ], "vocabularyKeywords": [], "identifier_set": [ 12556 ], "observationcollection_set": [ { "ob_id": 32719, "uuid": "5b30b3c2146048388ac97e4278cb5128", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information.\r\n\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 4.12\r\n- data for Figure 4.13\r\n- data for Figure 4.19\r\n- data for Figure 4.22\r\n- data for Figure 4.23\r\n- data for Figure 4.24\r\n- data for Figure 4.25\r\n- data for Figure 4.26\r\n- data for Figure 4.31\r\n- data for Figure 4.32\r\n- data for Figure 4.41\r\n- data for Figure 4.42" } ], "responsiblepartyinfo_set": [ 195440, 195441, 195442, 195443, 195444, 195445, 195446, 195447 ], "onlineresource_set": [ 83577, 83578, 83579, 88608 ] }, { "ob_id": 40113, "uuid": "8fa708d0474d4a3caa5c9f645a89d282", "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.31 v20230531", "abstract": "Data for Figure 4.31 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 4.31 shows the projected spatial patterns of change in annual average near-surface temperature (°C) at different levels of global warming\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Lee, J.-Y., J. Marotzke, G. Bala, L. Cao, S. Corti, J.P. Dunne, F. Engelbrecht, E. Fischer, J.C. Fyfe, C. Jones, A. Maycock, J. Mutemi, O. Ndiaye, S. Panickal, and T. Zhou, 2021: Future Global Climate: Scenario-Based Projections and Near-Term Information. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 553–672, doi:10.1017/9781009157896.006.\r\n\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has seven panels, with data provided for the first four panels in separate NetCDF files.\r\n a) Multi-model mean change in annual mean temperature at 1.5°C global warming relative to 1850-1900\r\n b) Multi-model mean change in annual mean temperature at 2°C global warming relative to 1850-1900\r\n c) Multi-model mean change in annual mean temperature at 3°C global warming relative to 1850-1900\r\n d) Multi-model mean change in annual mean temperature at 4°C global warming relative to 1850-1900\r\n \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Multi-model mean change in annual mean temperature at 1.5, 2, 3, and 4°C global warming relative to 1850-1900.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Four data files are given for the four panels a-d\r\n Data_shown_in_figure_panel_a.nc includes the variables tas representing the temperature change shown in panel a\r\n Data_shown_in_figure_panel_b.nc includes the variables tas representing the temperature change shown in panel b\r\n Data_shown_in_figure_panel_c.nc includes the variables tas representing the temperature change shown in panel c\r\n Data_shown_in_figure_panel_d.nc includes the variables tas representing the temperature change shown in panel d\r\n The information of panels e-g can be calculated from the difference of the respective data files a-d\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Time range: This is for the 20-yr time period in which any given model reaches a given warming level.\r\n The respective time periods are documented here: https://github.com/mathause/cmip_warming_levels\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 figure\r\n - Link to the report component containing the figure (Chapter 4)\r\n - Link to the Supplementary Material for Chapter 4, which contains details on the input data used in Table 4.SM.1", "creationDate": "2023-05-31T09:27:05.158697", "lastUpdatedDate": "2023-05-31T09:27:05", "latestDataUpdateTime": "2023-05-31T12:28:31", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Chapter 4, Sixth Assessment Report, Working Group 1, Physical Science Basis, near-surface temperature", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-07T11:46:02", "doiPublishedTime": "2023-07-03T10:16:03.178919", "removedDataTime": null, "geographicExtent": { "ob_id": 3835, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 246, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 40114, "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig31/v20230531", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 16380412, "numberOfFiles": 7, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 11126, "startTime": "1850-01-01T00:00:00", "endTime": "2100-01-01T23:59:59" }, "resultQuality": { "ob_id": 4285, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-31" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40115, "uuid": "31da838d60ee4360bd31c099679f08a6", "short_code": "comp", "title": "Caption for Figure 4.31 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected spatial patterns of change in annual average near-surface temperature (°C) at different levels of global warming. Displayed are (a–d) spatial patterns of change in annual average near-surface temperature at 1.5°C, 2°C, 3°C, and 4°C of global warming relative to the period 1850–1900 and (e–g) spatial patterns of differences in temperature change at 2°C, 3°C, and 4°C of global warming compared to 1.5°C of global warming. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability. That is, where at least 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Values were assessed from a 20-year period at a given warming level, based on model simulations under the Tier-1 SSPs of CMIP6. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46706, 46708, 52664, 52665, 62359, 62360, 62362 ], "vocabularyKeywords": [], "identifier_set": [ 12557 ], "observationcollection_set": [ { "ob_id": 32719, "uuid": "5b30b3c2146048388ac97e4278cb5128", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information.\r\n\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 4.12\r\n- data for Figure 4.13\r\n- data for Figure 4.19\r\n- data for Figure 4.22\r\n- data for Figure 4.23\r\n- data for Figure 4.24\r\n- data for Figure 4.25\r\n- data for Figure 4.26\r\n- data for Figure 4.31\r\n- data for Figure 4.32\r\n- data for Figure 4.41\r\n- data for Figure 4.42" } ], "responsiblepartyinfo_set": [ 195450, 195451, 195452, 195453, 195454, 195455, 195456, 195457 ], "onlineresource_set": [ 83580, 83581, 83582, 88609, 94638 ] }, { "ob_id": 40116, "uuid": "0192ae3037794e0eb93b022c5140f399", "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.32 v20230531", "abstract": "Data for Figure 4.32 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 4.32 shows projected spatial patterns of change in annual average precipitation (expressed as a percentage change) at different levels of global warming.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Lee, J.-Y., J. Marotzke, G. Bala, L. Cao, S. Corti, J.P. Dunne, F. Engelbrecht, E. Fischer, J.C. Fyfe, C. Jones, A. Maycock, J. Mutemi, O. Ndiaye, S. Panickal, and T. Zhou, 2021: Future Global Climate: Scenario-Based Projections and Near-Term Information. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 553–672, doi:10.1017/9781009157896.006.\r\n\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for all panels separate fileds\r\n a) Multi-model mean change in annual mean precipitation at 1.5°C global warming relative to 1850-1900\r\n b) Multi-model mean change in annual mean precipitation at 2°C global warming relative to 1850-1900\r\n c) Multi-model mean change in annual mean precipitation at 3°C global warming relative to 1850-1900\r\n d) Multi-model mean change in annual mean precipitation at 4°C global warming relative to 1850-1900\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Multi-model mean change in annual mean precipitation at 1.5, 2, 3, and 4°C global warming relative to 1850-1900.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Four data files are given for the four panels a-d\r\n Data_shown_in_figure_panel_a.nc includes the variable pr representing the precipitation change shown in panel a\r\n Data_shown_in_figure_panel_b.nc includes the variable pr representing the precipitation change shown in panel b\r\n Data_shown_in_figure_panel_c.nc includes the variable pr representing the precipitation change shown in panel c\r\n Data_shown_in_figure_panel_d.nc includes the variable pr representing the precipitation change shown in panel d\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Temporal range: This is for the 20-yr time period in which any given model reaches a given warming level.\r\n The respective time periods are documented here: https://github.com/mathause/cmip_warming_levels\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\r\n - Link to the report component containing the figure (Chapter 4)\r\n - Link to the Supplementary Material for Chapter 4, which contains details on the input data used in Table 4.SM.1", "creationDate": "2023-05-31T10:51:40.552824", "lastUpdatedDate": "2023-05-31T10:51:40", "latestDataUpdateTime": "2024-03-09T03:21:50", "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, Chapter 4, Sixth Assessment Report, Working Group 1, Physical Science Basis, precipitation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-07T11:49:49", "doiPublishedTime": "2023-07-03T10:20:52.817317", "removedDataTime": null, "geographicExtent": { "ob_id": 3836, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 247, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 40117, "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig32/v20230531", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 20040868, "numberOfFiles": 7, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 11127, "startTime": "1850-01-01T00:00:00", "endTime": "2100-01-01T23:59:59" }, "resultQuality": { "ob_id": 4286, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-31" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40118, "uuid": "d2ba5efc9ca54d0c9bb1b204502a9061", "short_code": "comp", "title": "Caption for Figure 4.32 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected spatial patterns of change in annual average precipitation (expressed as a percentage change) at different levels of global warming. Displayed are (a–d) spatial patterns of change in annual precipitation at 1.5°C, 2°C, 3°C, and 4°C of global warming relative to the period 1850–1900. No map overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Values were assessed from a 20-year period at a given warming level, based on model simulations under the Tier-1 SSPs of CMIP6. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46706, 46708, 52664, 52665, 62359, 62360, 62361 ], "vocabularyKeywords": [], "identifier_set": [ 12558 ], "observationcollection_set": [ { "ob_id": 32719, "uuid": "5b30b3c2146048388ac97e4278cb5128", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 4: Future global climate: scenario-based projections and near-term information.\r\n\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 4.12\r\n- data for Figure 4.13\r\n- data for Figure 4.19\r\n- data for Figure 4.22\r\n- data for Figure 4.23\r\n- data for Figure 4.24\r\n- data for Figure 4.25\r\n- data for Figure 4.26\r\n- data for Figure 4.31\r\n- data for Figure 4.32\r\n- data for Figure 4.41\r\n- data for Figure 4.42" } ], "responsiblepartyinfo_set": [ 195460, 195461, 195462, 195463, 195464, 195465, 195466, 195467 ], "onlineresource_set": [ 83583, 83584, 83585, 88610, 94637 ] }, { "ob_id": 40123, "uuid": "d15196fa0aec4cf4b489f62f866a1a72", "title": "Temperature-attributable mortality (and hospital admission) time series, UK (1900-2099)", "abstract": "This dataset contains estimates of mortality and number of hospital admissions that can be attributed to temperature, from observations and climate projections, and includes some of the underlying climate data. The data are divided into the subdirectories ‘epi_model’, ‘HadUKgrid’, ‘London’, ‘regimes’, and ‘UKCP18’ as follows:\r\n\r\nepi_model: \r\n- Model fits of exposure-response relationships \r\n\r\nHadUKgrid: \r\n- Temperature-attributable mortality/hospital admission time series for the observed record (1981/1991-2018)\r\n- List of the 10 highest mortality days from 1991 to 2018 based on UK-total temperature-related mortality\r\n\r\nLondon: \r\n- Average daily temperature by London boroughs simulated with an urban model, October 2015 to 2019\r\n- Attributable hospital admission by London boroughs based on the above temperature time series\r\n\r\nregimes: \r\n- Weather regime and pattern classification for the observed record (1850/1979-2019)\r\n\r\nUKCP18: \r\n- Attributable mortality time series for UKCP18 climate projections (1900-2099)\r\n\r\nFurther details including file contents and methods can be found in the README.txt files for each dataset. This dataset was produced for the UK Climate Resilience Programme - Addressing the resilience needs of the UK health sector: climate service pilots.", "creationDate": "2023-06-02T11:41:48.777176", "lastUpdatedDate": "2023-06-05T14:44:04", "latestDataUpdateTime": "2023-07-25T17:05:47", "updateFrequency": "notPlanned", "dataLineage": "Mortality data was obtained from the Office for National Statistics, from the Northern Ireland Statistics and Research Agency and from the National Records of Scotland. NHS England and NHS Wales Informatics Service supplied the hospital admissions data to the Met Office who generated the necessary statistics. ERA5 reanalyses were obtained from Copernicus/ECMWF; HadUK-Grid, UKCP18 and weather regime data from the Met Office. All of these were analysed by the University of Reading who supplied the resulting data to the Met Office for archiving on the MEDMI (Medical and Environmental Data Mashup Infrastructure) platform at the University of Exeter, and at the Centre for Environmental Data Analysis (CEDA)", "removedDataReason": "", "keywords": "mortality, temperature, hospital admission, exposure-response", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-07-27T09:43:45", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3854, "bboxName": "", "eastBoundLongitude": 1.8, "westBoundLongitude": -8.7, "southBoundLatitude": 49.9, "northBoundLatitude": 60.9 }, "verticalExtent": null, "result_field": { "ob_id": 40335, "dataPath": "/badc/deposited2023/SPF_CRHealth", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 103780556354, "numberOfFiles": 3724, "fileFormat": "Data are provided in Comma Separated Values file (.csv) and R data file (*.Rds ) format." }, "timePeriod": { "ob_id": 11116, "startTime": "1900-01-01T00:00:00", "endTime": "2099-12-31T23:59:59" }, "resultQuality": { "ob_id": 4299, "explanation": "", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-06-02" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40328, "uuid": "64e362ad3e4040d7a2772d16cc2d04ab", "short_code": "comp", "title": "Distributed Lag Non-linear Model (DLNM)", "abstract": "Statistical regression using distributed lag non-linear model is fully described in Gasparrini, Armstrong and Kenward, 2010, and Vicedo-Cabrera, Sera and Gasparrini, 2019.\r\nComputation details for Temperature-attributable mortality (and hospital admission) time series, UK (1900-2099) dataset.\r\nModel setup: natural cubic splines in all dimensions, 3 log-spaced knots in lag dimension, 8 degrees of freedom per year in long-term trend, confounding by day of week.\r\n\r\n- Mortality: temperature knots at 0.1, 0.75, 0.9 quantiles, 21 lag days.\r\n\r\n- Hospital admission: temperature knots at 0.4, 0.9 quantiles, 28 lag days." }, "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": 40124, "uuid": "d40144aa6d0e4a448cd9ffe7d3171976", "short_code": "proj", "title": "UK Climate Resilience Programme - Addressing the resilience needs of the UK health sector: climate service pilots", "abstract": "This project is part of the The UK Climate Resilience Programme - a four-year Strategic Priorities Fund (SPF) interdisciplinary research programme led jointly by UK Research and Innovation (UKRI) and the Met Office.\r\n\r\nThe aim of this project is to develop new datasets of regional mortality attributed to non-optimal temperatures based on the new HadUK-Grid dataset. These datasets and statistical models have been supplied to the Met Office and are available through the MEDMI portal. Combining up-to-date mortality, hospital admissions and meteorological data have produced the best, current estimate of how climate variability has affected mortality in the past. This data can be used by many other projects and end-users to anticipate the impact of climate variability on the health and social care sectors.\r\n\r\nThe same statistical models have been applied to the UKCP18 climate projections for the UK. The datasets produced from this analysis, including alternative versions which explore the impact of uncertainty on future projections, are also available from the Met Office to help prepared end-users in the health and social care sectors to adapt to climate change. A key from this analysis is that temperature related mortality in the UK is strongly linked to changes in global mean temperature. When global mean warming exceeds two degrees above pre-industrial temperatures, the number of deaths due to hot weather accelerates rapidly without significant adaptation.\r\n\r\nA greater understanding of the link between recurrent, typical weather patterns in the North Atlantic and the UK and mortality was developed. This approach could allow new application of longer range forecasts for the health sector to be developed." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 83041, 83042, 83043, 83044, 83045, 83046, 83047, 83048, 83049, 83050, 83051 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 195485, 195486, 195487, 195488, 195489, 195490, 195491, 196621, 195492, 195493, 195494 ], "onlineresource_set": [ 83610, 83611, 83612, 83613, 83614, 83615, 83616, 83617, 83618, 83619, 83620, 83609 ] }, { "ob_id": 40125, "uuid": "b39898e76ab7434a9a20a6dc4ab721f0", "title": "HadUK-Grid Climate Observations by Administrative Regions over the UK, v1.2.0.ceda (1836-2022)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK administrative regions consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2022 but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-10-31T02:07:09", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office for archiving in the Centre for Environmental Data Analysis (CEDA) archives.", "removedDataReason": "", "keywords": "Met Office, UKCP18, BEIS, Defra, land surface, climate observations, hadobs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2023-07-24T15:31:23", "doiPublishedTime": "2023-08-30T15:41:11", "removedDataTime": null, "geographicExtent": { "ob_id": 2307, "bboxName": "", "eastBoundLongitude": 1.76, "westBoundLongitude": -8.18, "southBoundLatitude": 49.86, "northBoundLatitude": 60.86 }, "verticalExtent": null, "result_field": { "ob_id": 40136, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.2.0.ceda/region", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 19033620, "numberOfFiles": 163, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 11118, "startTime": "1836-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3946, "explanation": "Data quality control details for the HadUK-Grid version 1.0 datasets is available in section 2.2. of Hollis et al. (2019). See linked documentation for further details.", "passesTest": true, "resultTitle": "HadUK-Grid v1.1 Data Quality Statement", "date": "2022-05-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26870, "uuid": "b1b352825f5548a8bf0639afe335f5ae", "short_code": "comp", "title": "HadUK-Grid gridded climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography." }, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 50511, 50517, 51196, 51197, 54990, 54991, 54994, 54997, 61135, 62352, 62353, 62354, 62355, 62356, 62358, 64067 ], "vocabularyKeywords": [], "identifier_set": [ 12676 ], "observationcollection_set": [ { "ob_id": 26862, "uuid": "4dc8450d889a491ebb20e724debe2dfb", "short_code": "coll", "title": "HadUK-Grid gridded and regional average climate observations for the UK", "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised." } ], "responsiblepartyinfo_set": [ 195496, 195497, 195498, 195499, 195500, 195501, 195502, 195503, 195505, 195506, 195504, 195507, 195508, 195509 ], "onlineresource_set": [ 83623, 83626, 83627, 83624, 83625 ] }, { "ob_id": 40126, "uuid": "3d30627eee5a48be844c32723b7b6be8", "title": "HadUK-Grid Climate Observations by UK countries, v1.2.0.ceda (1836-2022)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK countries consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2022, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-10-31T02:07:09", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office for archiving in the Centre for Environmental Data Analysis (CEDA) archives.", "removedDataReason": "", "keywords": "Met Office, UKCP18, BEIS, Defra, land surface, climate observations, hadobs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2023-07-24T15:38:25", "doiPublishedTime": "2023-08-30T15:44:42", "removedDataTime": null, "geographicExtent": { "ob_id": 2309, "bboxName": "", "eastBoundLongitude": 1.76, "westBoundLongitude": -8.18, "southBoundLatitude": 49.16, "northBoundLatitude": 60.86 }, "verticalExtent": null, "result_field": { "ob_id": 40135, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.2.0.ceda/country", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 12682969, "numberOfFiles": 163, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 11119, "startTime": "1836-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3946, "explanation": "Data quality control details for the HadUK-Grid version 1.0 datasets is available in section 2.2. of Hollis et al. (2019). See linked documentation for further details.", "passesTest": true, "resultTitle": "HadUK-Grid v1.1 Data Quality Statement", "date": "2022-05-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26870, "uuid": "b1b352825f5548a8bf0639afe335f5ae", "short_code": "comp", "title": "HadUK-Grid gridded climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography." }, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 50511, 50516, 50517, 51193, 51196, 51197, 54991, 54992, 60895, 61135, 62352, 62353, 62354 ], "vocabularyKeywords": [], "identifier_set": [ 12681 ], "observationcollection_set": [ { "ob_id": 26862, "uuid": "4dc8450d889a491ebb20e724debe2dfb", "short_code": "coll", "title": "HadUK-Grid gridded and regional average climate observations for the UK", "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised." } ], "responsiblepartyinfo_set": [ 195517, 195510, 195511, 195512, 195513, 195514, 195515, 195516, 195518, 195519, 195520, 195521, 195522, 195523 ], "onlineresource_set": [ 83628, 83629, 83631, 83630, 83632 ] }, { "ob_id": 40127, "uuid": "e6822428e4124c5986b689a37fda10bc", "title": "HadUK-Grid Climate Observations by UK river basins, v1.2.0.ceda (1836-2022)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK river basins consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2022, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-10-31T02:07:10", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office for archiving in the Centre for Environmental Data Analysis (CEDA) archives.", "removedDataReason": "", "keywords": "Met Office, UKCP18, BEIS, Defra, land surface, climate observations, hadobs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2023-07-24T15:38:34", "doiPublishedTime": "2023-08-30T15:41:44", "removedDataTime": null, "geographicExtent": { "ob_id": 2308, "bboxName": "", "eastBoundLongitude": 1.76, "westBoundLongitude": -8.84, "southBoundLatitude": 49.86, "northBoundLatitude": 60.86 }, "verticalExtent": null, "result_field": { "ob_id": 40133, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.2.0.ceda/river", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 24727519, "numberOfFiles": 163, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 11120, "startTime": "1836-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3946, "explanation": "Data quality control details for the HadUK-Grid version 1.0 datasets is available in section 2.2. of Hollis et al. (2019). See linked documentation for further details.", "passesTest": true, "resultTitle": "HadUK-Grid v1.1 Data Quality Statement", "date": "2022-05-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26870, "uuid": "b1b352825f5548a8bf0639afe335f5ae", "short_code": "comp", "title": "HadUK-Grid gridded climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography." }, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 50511, 50517, 51195, 51197, 54992, 54994, 54997, 62352, 62353, 62354, 62355, 62356, 62357, 64067 ], "vocabularyKeywords": [], "identifier_set": [ 12677 ], "observationcollection_set": [ { "ob_id": 26862, "uuid": "4dc8450d889a491ebb20e724debe2dfb", "short_code": "coll", "title": "HadUK-Grid gridded and regional average climate observations for the UK", "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised." } ], "responsiblepartyinfo_set": [ 195527, 195528, 195524, 195525, 195526, 195529, 195530, 195531, 195532, 195533, 195534, 195535, 195536, 195537 ], "onlineresource_set": [ 83633, 83635, 83634, 83636, 83637 ] }, { "ob_id": 40128, "uuid": "640d33e0cf99477990f7fee35a101850", "title": "HadUK-Grid Gridded Climate Observations on a 12km grid over the UK, v1.2.0.ceda (1836-2022)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 12 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from climate projections. The dataset spans the period from 1836 to 2022, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation). \r\n\r\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp- spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-10-31T02:07:05", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office for archiving in the Centre for Environmental Data Analysis (CEDA) archives.", "removedDataReason": "", "keywords": "Met Office, UKCP18, BEIS, Defra, land surface, climate observations, hadobs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2023-07-24T15:38:41", "doiPublishedTime": "2023-08-30T15:46:49", "removedDataTime": null, "geographicExtent": { "ob_id": 2305, "bboxName": "HadUK-Grid area", "eastBoundLongitude": 4.59, "westBoundLongitude": -12.61, "southBoundLatitude": 48.83, "northBoundLatitude": 60.57 }, "verticalExtent": null, "result_field": { "ob_id": 40139, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.2.0.ceda/12km", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2828287916, "numberOfFiles": 6436, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 11118, "startTime": "1836-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3946, "explanation": "Data quality control details for the HadUK-Grid version 1.0 datasets is available in section 2.2. of Hollis et al. (2019). See linked documentation for further details.", "passesTest": true, "resultTitle": "HadUK-Grid v1.1 Data Quality Statement", "date": "2022-05-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26870, "uuid": "b1b352825f5548a8bf0639afe335f5ae", "short_code": "comp", "title": "HadUK-Grid gridded climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography." }, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 11484, 11485, 11486, 51186, 51187, 51188, 51189, 51193, 51195, 51196, 54990, 54991, 62352, 62353, 62354 ], "vocabularyKeywords": [], "identifier_set": [ 12683 ], "observationcollection_set": [ { "ob_id": 26862, "uuid": "4dc8450d889a491ebb20e724debe2dfb", "short_code": "coll", "title": "HadUK-Grid gridded and regional average climate observations for the UK", "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised." } ], "responsiblepartyinfo_set": [ 195538, 195539, 195540, 195541, 195542, 195543, 195544, 195545, 195546, 195547, 195548, 195549, 195550, 195551 ], "onlineresource_set": [ 83640, 83638, 83641, 83639, 83642 ] }, { "ob_id": 40129, "uuid": "46f8c1377f8849eeb8570b8ac9b26d86", "title": "HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.2.0.ceda (1836-2022)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The datasets cover the UK at 1 km x 1 km resolution. These 1 km x 1 km data have been used to provide a range of other resolutions and across countries, administrative regions and river basins to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2022, but the start time is dependent on climate variable and temporal resolution. \r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-06-06T13:18:25", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office for archiving in the Centre for Environmental Data Analysis (CEDA) archives.", "removedDataReason": "", "keywords": "Met Office, UKCP18, BEIS, Defra, land surface, climate observations, hadobs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2023-07-24T15:38:51", "doiPublishedTime": "2023-08-30T15:45:15", "removedDataTime": null, "geographicExtent": { "ob_id": 2305, "bboxName": "HadUK-Grid area", "eastBoundLongitude": 4.59, "westBoundLongitude": -12.61, "southBoundLatitude": 48.83, "northBoundLatitude": 60.57 }, "verticalExtent": null, "result_field": { "ob_id": 40140, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.2.0.ceda/1km", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 321018964336, "numberOfFiles": 6435, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 11118, "startTime": "1836-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3946, "explanation": "Data quality control details for the HadUK-Grid version 1.0 datasets is available in section 2.2. of Hollis et al. (2019). See linked documentation for further details.", "passesTest": true, "resultTitle": "HadUK-Grid v1.1 Data Quality Statement", "date": "2022-05-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26870, "uuid": "b1b352825f5548a8bf0639afe335f5ae", "short_code": "comp", "title": "HadUK-Grid gridded climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography." }, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 11484, 11485, 11486, 50516, 50517, 51186, 51187, 51188, 51189, 51195, 54990, 54997, 62352, 62353, 62354, 62355, 62356 ], "vocabularyKeywords": [], "identifier_set": [ 12682 ], "observationcollection_set": [ { "ob_id": 26862, "uuid": "4dc8450d889a491ebb20e724debe2dfb", "short_code": "coll", "title": "HadUK-Grid gridded and regional average climate observations for the UK", "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised." } ], "responsiblepartyinfo_set": [ 195552, 195553, 195554, 195555, 195556, 195557, 195558, 195559, 195560, 195561, 195562, 195563, 195564, 195565 ], "onlineresource_set": [ 83643, 83647, 83646, 83644, 83645, 87894 ] }, { "ob_id": 40130, "uuid": "0545f37fb7124df381d42468eb63c144", "title": "HadUK-Grid Gridded Climate Observations on a 25km grid over the UK, v1.2.0.ceda (1836-2022)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 25 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2022, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-10-31T02:06:03", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office for archiving in the Centre for Environmental Data Analysis (CEDA) archives.", "removedDataReason": "", "keywords": "Met Office, UKCP18, BEIS, Defra, land surface, climate observations, hadobs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2023-07-24T15:39:01", "doiPublishedTime": "2023-08-30T15:43:54", "removedDataTime": null, "geographicExtent": { "ob_id": 2305, "bboxName": "HadUK-Grid area", "eastBoundLongitude": 4.59, "westBoundLongitude": -12.61, "southBoundLatitude": 48.83, "northBoundLatitude": 60.57 }, "verticalExtent": null, "result_field": { "ob_id": 40138, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.2.0.ceda/25km", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 879358055, "numberOfFiles": 6436, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 11119, "startTime": "1836-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3946, "explanation": "Data quality control details for the HadUK-Grid version 1.0 datasets is available in section 2.2. of Hollis et al. (2019). See linked documentation for further details.", "passesTest": true, "resultTitle": "HadUK-Grid v1.1 Data Quality Statement", "date": "2022-05-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26870, "uuid": "b1b352825f5548a8bf0639afe335f5ae", "short_code": "comp", "title": "HadUK-Grid gridded climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography." }, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 11484, 11485, 11486, 50516, 50517, 51186, 51187, 51188, 51189, 51195, 51196, 51197, 54991, 62352, 62353, 62354 ], "vocabularyKeywords": [], "identifier_set": [ 12680 ], "observationcollection_set": [ { "ob_id": 26862, "uuid": "4dc8450d889a491ebb20e724debe2dfb", "short_code": "coll", "title": "HadUK-Grid gridded and regional average climate observations for the UK", "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised." } ], "responsiblepartyinfo_set": [ 195566, 195567, 195568, 195569, 195570, 195571, 195572, 195573, 195574, 195575, 195576, 195577, 195578, 195579 ], "onlineresource_set": [ 83648, 83649, 83650, 83651, 83652 ] }, { "ob_id": 40131, "uuid": "adf1a6cf830b4f5385c5d73609df8423", "title": "HadUK-Grid Gridded Climate Observations on a 5km grid over the UK, v1.2.0.ceda (1836-2022)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 5 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2022, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-10-31T02:06:02", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office for archiving in the Centre for Environmental Data Analysis (CEDA) archives.", "removedDataReason": "", "keywords": "Met Office, UKCP18, BEIS, Defra, land surface, climate observations, hadobs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2023-07-24T15:39:09", "doiPublishedTime": "2023-08-30T15:43:21", "removedDataTime": null, "geographicExtent": { "ob_id": 2305, "bboxName": "HadUK-Grid area", "eastBoundLongitude": 4.59, "westBoundLongitude": -12.61, "southBoundLatitude": 48.83, "northBoundLatitude": 60.57 }, "verticalExtent": null, "result_field": { "ob_id": 40141, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.2.0.ceda/5km", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 14542746499, "numberOfFiles": 6436, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 11119, "startTime": "1836-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3946, "explanation": "Data quality control details for the HadUK-Grid version 1.0 datasets is available in section 2.2. of Hollis et al. (2019). See linked documentation for further details.", "passesTest": true, "resultTitle": "HadUK-Grid v1.1 Data Quality Statement", "date": "2022-05-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26870, "uuid": "b1b352825f5548a8bf0639afe335f5ae", "short_code": "comp", "title": "HadUK-Grid gridded climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography." }, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 11484, 11485, 11486, 50516, 50517, 51186, 51187, 51188, 51189, 51193, 51196, 51197, 54992, 54997, 62352, 62353, 62354, 62355, 62356 ], "vocabularyKeywords": [], "identifier_set": [ 12679 ], "observationcollection_set": [ { "ob_id": 26862, "uuid": "4dc8450d889a491ebb20e724debe2dfb", "short_code": "coll", "title": "HadUK-Grid gridded and regional average climate observations for the UK", "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised." } ], "responsiblepartyinfo_set": [ 195580, 195581, 195582, 195583, 195584, 195585, 195586, 195587, 195588, 195589, 195590, 195591, 195592, 195593 ], "onlineresource_set": [ 83653, 83655, 83656, 83654, 83657 ] }, { "ob_id": 40132, "uuid": "22df6602b5064b1686dda7e9455f86fc", "title": "HadUK-Grid Gridded Climate Observations on a 60km grid over the UK, v1.2.0.ceda (1836-2022)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 60 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2022, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-10-31T02:05:50", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office for archiving in the Centre for Environmental Data Analysis (CEDA) archives.", "removedDataReason": "", "keywords": "Met Office, UKCP18, BEIS, Defra, land surface, climate observations, hadobs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2023-07-24T15:39:17", "doiPublishedTime": "2023-08-30T15:42:56", "removedDataTime": null, "geographicExtent": { "ob_id": 2305, "bboxName": "HadUK-Grid area", "eastBoundLongitude": 4.59, "westBoundLongitude": -12.61, "southBoundLatitude": 48.83, "northBoundLatitude": 60.57 }, "verticalExtent": null, "result_field": { "ob_id": 40137, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.2.0.ceda/60km", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 332739715, "numberOfFiles": 6435, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 11120, "startTime": "1836-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3946, "explanation": "Data quality control details for the HadUK-Grid version 1.0 datasets is available in section 2.2. of Hollis et al. (2019). See linked documentation for further details.", "passesTest": true, "resultTitle": "HadUK-Grid v1.1 Data Quality Statement", "date": "2022-05-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26870, "uuid": "b1b352825f5548a8bf0639afe335f5ae", "short_code": "comp", "title": "HadUK-Grid gridded climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography." }, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 11484, 11485, 11486, 50516, 50517, 51186, 51187, 51188, 51189, 51195, 51196, 54990, 54997, 62352, 62353, 62354, 62355, 62356 ], "vocabularyKeywords": [], "identifier_set": [ 12678 ], "observationcollection_set": [ { "ob_id": 26862, "uuid": "4dc8450d889a491ebb20e724debe2dfb", "short_code": "coll", "title": "HadUK-Grid gridded and regional average climate observations for the UK", "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised." } ], "responsiblepartyinfo_set": [ 195594, 195595, 195596, 195597, 195598, 195599, 195600, 195601, 195602, 195603, 195604, 195605, 195606, 195607 ], "onlineresource_set": [ 83661, 83659, 83660, 83658, 83662 ] }, { "ob_id": 40143, "uuid": "0481959c92944c41983dd024172ef84d", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Box TS.13, Figure 1 (v20230606)", "abstract": "Data for Box TS.13, Figure 1 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nBox TS.13, Figure 1 shows global and regional monsoons: past trends and projected changes\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three panels with data provided for panels b and c.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains\r\n \r\n - processed data for the figure from observation (CRU, GPCC & APHRO)\r\n - CMIP6 (DAMIP and future projection;SSP2-4.5)\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nDAMIP is the Detection and Attribution Model Intercomparison Project\r\nSSP2-4.5 is based on Shared Socioeconomic Pathway SSP2 with medium challenges to climate change mitigation and adaptation and RCP4.5, a future pathway with a radiative forcing of 4.5 W/m2 in the year 2100.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Percentile values for box and whisker plots and observed trends; for regional and global monsoon areas.\r\n \r\n - Data file: BoxTS.13.Fig1b_data.nc relates to panel b, showing historical trend in monsoon precipitation\r\n - Data file: BoxTS.13.Fig1c_data.nc relates to panel c, showing projected future change in monsoon precipitation (SSP2-4.45)\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The data can be used directly to plot the figure.\r\n\r\n For more details on the datasets used, please refer to the data table of Chapter 8 linked in the Related Documents.\r\n\r\n This dataset is also used in Figure 8.11 and Figure 8.22 , Chapter 8, AR6.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to the Supplementary Material for Chapter 8, which contains details on the input data used in Table 8.SM.1", "creationDate": "2023-06-06T14:54:16.816881", "lastUpdatedDate": "2023-06-06T14:54:16", "latestDataUpdateTime": "2024-03-09T01:44:21", "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": "Regional monsoon precipitation changes from observations and model attribution; Projected regional monsoons precipitation changes, IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-06-07T14:43:37", "doiPublishedTime": "2023-09-26T13:58:51.480987", "removedDataTime": null, "geographicExtent": { "ob_id": 3855, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 248, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 40144, "dataPath": "/badc/ar6_wg1/data/TS/BOX_ts13_fig1/v20230606", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 49925, "numberOfFiles": 6, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 11121, "startTime": "1850-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4304, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-06-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40145, "uuid": "276c038366ab4c9d9a822dc8cfa815a4", "short_code": "comp", "title": "Caption for Box TS.13, Figure 1 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global and regional monsoons: past trends and projected changes. The intent of this figure is to show changes in precipitation over regional monsoon domains in terms of observed past trends, how greenhouse gases and aerosols relate to these changes, and in terms of future projections in one intermediate emissions scenario in the near, medium and long term. (a) Global (black contour) and regional monsoons (colour shaded) domains. The global monsoon (GM) is defined as the area with local summer-minus-winter precipitation rate exceeding 2.5 mm day–1 (see Annex V). The regional monsoon domains are defined based on published literature and expert judgement (see Annex V) and accounting for the fact that the climatological summer monsoon rainy season varies across the individual regions. Assessed regional monsoons are South and South East Asia (SAsiaM, Jun–July–August–September), East Asia (EAsiaM, June–July–August), West Africa (WAfriM, June–July–August–September), North America (NAmerM, July–August–-September), South America (SAmerM, December–January–February), Australia and Maritime Continent Monsoon (AusMCM, December–January–February). Equatorial South America (EqSAmer) and South Africa (SAfri)regions are also shown, as they receive unimodal summer seasonal rainfall although their qualification as monsoons is subject to discussion. (b) Global and regional monsoons precipitation trends based on DAMIP CMIP6 simulations with both natural and anthropogenic (ALL), greenhouse gas only (GHG), aerosols only (AER) and natural only (NAT) radiative forcing. Weighted ensemble means are based on nine Coupled model Intercomparison Project Phase 6 (CMIP6) models contributing to the MIP (with at least three members). Observed trends computed from CRU, GPCP and APHRO (only forSAsiaM and EAsiaM) datasets are shown as well. (c) Percentage change in projected seasonal mean precipitation over global and regional monsoons domain in the near term (2021–2040), mid-term (2041–2060), and long term (2081–2100) under SSP2-4.5 based on 24 CMIP6 models. {Figures 8.11 and 8.22}" }, "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": [ 62348, 62349, 62350, 62351, 64066 ], "vocabularyKeywords": [], "identifier_set": [ 12691 ], "observationcollection_set": [ { "ob_id": 39213, "uuid": "7409c7ea8a5045f8ab08e44db9cccb1a", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Technical Summary", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Technical Summary.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure TS.1\r\n- data for Figure TS.9\r\n- input data for Figure TS.12 \r\n- data for Figure TS.13\r\n- data for Figure TS.15\r\n- data for Figure TS.17\r\n- data for Figure TS.19\r\n- data for Figure TS.22\r\n- input data for Figure TS.24\r\n- data for Figure TS.25\r\n- data for Box TS.2, Figure 1\r\n- data for Box TS.2, Figure 2\r\n- data for Box TS.4, Figure 1\r\n- input data for Box Ts.4, Figure 1\r\n- input data for Box TS.5, Figure 1\r\n- data for Box TS.6, Figure 1\r\n- data for Box TS.7, Figure 1\r\n- data for Box TS.13, Figure 1\r\n- data for Cross-Section Box TS.1, Figure 1" } ], "responsiblepartyinfo_set": [ 195610, 195611, 195612, 195613, 195614, 195615, 195616, 195617 ], "onlineresource_set": [ 83664, 83665, 83666 ] }, { "ob_id": 40147, "uuid": "fab3d1d8abce46f6a53270b0b48a9312", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): A combined high resolution global TCWV product from microwave and near infrared imagers - COMBI, v3.1", "abstract": "This global total column water vapour (TCWV) data record, provided by the Satellite Application Facility on Climate Monitoring (CM-SAF), combines microwave and near-infrared imager based TCWV over the ice-free ocean as well as over land, coastal ocean and sea-ice, respectively. This dataset is held externally on the CM-SAF and is catalogued here as the scientific research towards the data was also funded by the ESA Climate Change Initiative. \r\n\r\n The data record relies on microwave observations from SSM/I, SSMIS, AMSR-E and TMI, partly based on a fundamental climate data record (Fennig et al., 2020; Fennig et al., 2017) and on near-infrared observations from MERIS (3rd reprocessing), MODIS-Terra (collection 6.1) and OLCI (1st reprocessing). Details of the retrieval are described in Andersson et al. (2010) and ATBD HOAPS for the microwave imagers as well as in Lindstrot et al. (2012), Diedrich et al. (2015) and ABTD NIR Level 2 for the near-infrared imagers. The water vapour of the atmosphere is vertically integrated over the full column and given in units of kg/m². The microwave and near-infrared data streams are processed independently and combined afterwards by not changing the individual TCWV values and their uncertainties. The combined data record has a spatial resolution of 0.5°x0.5° and 0.05°x0.05°, with the near-infrared based data being averaged and the microwave-based data being oversampled to match the lower and higher spatial resolution, respectively. The product is available as daily and monthly means and covers the period July 2002 – December 2017.\r\n\r\nThis version of the data is version 3.1.", "creationDate": "2023-06-06T16:44:13.909334", "lastUpdatedDate": "2023-06-06T16:31:57", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "The combined MW and NIR product was initiated and funded by the ESA Water_Vapour_cci project. The NIR retrieval was developed by Spectral Earth. The MW data was processed by EUMETSAT CM SAF. The NIR data was processed and combined with the MW data by Brockmann Consult. NIR data is owned by Brockmann Consult and Spectral Earth. The MW data and the combined MW and NIR product is owned by EUMETSAT CM SAF.\r\n\r\nThis record has been added to the CEDA catalogue in the context of the ESA Climate Change Initiative Open Data Portal, and points directly to the data held by the CM-SAF.", "removedDataReason": "", "keywords": "CM-SAF, ESA Climate Change Initiative", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-09-12T09:00:13", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40146, "dataPath": "https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=COMBI_V001", "oldDataPath": [], "storageLocation": "external", "storageStatus": "online", "volume": 0, "numberOfFiles": 0, "fileFormat": "Data are in NetCDF-4 format" }, "timePeriod": { "ob_id": 11122, "startTime": "2002-07-01T00:00:00", "endTime": "2017-12-31T23:59:59" }, "resultQuality": { "ob_id": 4305, "explanation": "For information on the data quality see the related documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-06-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 40150, "uuid": "d8dd8e01fb404c02bc88321c4061e53a", "short_code": "cmppr", "title": "Composite process for: ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): A combined high resolution global TCWV product from microwave and near infrared imagers - COMBI, v3.1", "abstract": "The dataset has been derived from microwave observations from SSM/I, SSMIS, AMSR-E and TMI, partly based on a fundamental climate data record (Fennig et al., 2020; Fennig et al., 2017) and from near-infrared observations from MERIS (3rd reprocessing), MODIS-Terra (collection 6.1) and OLCI (1st reprocessing). Details of the retrieval are described in Andersson et al. (2010) and ATBD HOAPS for the microwave imagers as well as in Lindstrot et al. (2012), Diedrich et al. (2015) and ABTD NIR Level 2 for the near-infrared imagers. The water vapour of the atmosphere is vertically integrated over the full column and given in units of kg/m². The microwave and near-infrared data streams are processed independently and combined afterwards by not changing the individual TCWV values and their uncertainties." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2672, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 98, "licenceURL": "https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 32934, "uuid": "24186d13405b4000aef0ba577b9031aa", "short_code": "proj", "title": "ESA Water Vapour Climate Change Initiative Project", "abstract": "The Water Vapour Climate Change Initiatve Project (Water_Vapour_cci) is part of the European Space Agency's Climate Change Initiative Programme. The project aims to generate new global high-quality climate data records of both total column and vertically resolved water vapour." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12528 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 195643, 195620, 195624, 195625, 195626, 195628, 195623, 195622, 195621, 195627, 195629, 195630, 195631, 195632, 195633, 195634, 195635, 195636, 195637, 195638 ], "onlineresource_set": [ 83667, 83668, 83669, 83670, 83671 ] }, { "ob_id": 40152, "uuid": "86162ca42a6a4ebc8779bcddc817a7b3", "title": "ATSR-2: Gridded Brightness Temperature/Reflectance (GBTR) Product (AT2_TOA_1P), v3.0.1", "abstract": "Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).\r\n\r\nThis dataset contains the Along-Track Scanning Radiometer on ESA ERS-2 satellite (ATSR-2) Gridded Brightness Temperature/Reflectance (GBTR) Product. These data are the Level 1B product that consists of Top of Atmosphere (TOA) radiance measurements and brightness temperatures at full resolution for both the nadir and forward views. The product was calibrated for instrumental and atmospheric effects and re-sampled to a fixed grid aligned to the sub-satellite track. This product were derived from the Level 0 product and auxiliary data, and serves as the input data for all Level 2 products. The third reprocessing was done to implement the updated algorithms, processors, and auxiliary files.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2017-12-14T12:13:18.054208", "updateFrequency": "notPlanned", "dataLineage": "The data were acquired by the European Space Agency's (ESA) Envisat satellite, and the NERC Earth Observation Data Centre (NEODC) mirrors the data for UK users. This is data from the 3rd reprocessing and thus supercedes previous reprocessings", "removedDataReason": "", "keywords": "ATSR, Gridded Brightness Temperature/Reflectance, GBTR", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-10-02T09:04:48", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40153, "dataPath": "/neodc/aatsr_multimission/atsr2-v3.0.1/data/at2_toa_1p", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 37062402384071, "numberOfFiles": 309956, "fileFormat": "ENVISAT PDS format" }, "timePeriod": { "ob_id": 2324, "startTime": "1995-05-31T23:00:00", "endTime": "2008-01-31T00:00:00" }, "resultQuality": { "ob_id": 2175, "explanation": "", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-22" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 40162, "uuid": "423b72cfd35f480dabb2df754d5de7be", "short_code": "cmppr", "title": "Composite Process for: ATSR-2 Gridded Brightness Temperature/Reflectnace (GBTR) Product (AT2_TOA_1P) v3.0.1", "abstract": "This process is comprised of multiple procedures: 1. Acquisition: Acquisition Process for: ATSR-2 Gridded Brightness Temperature/Reflectnace (GBTR) Product (AT2_TOA_1P) v3.0.1; \r\n2. Computation: DETAILS NEEDED - COMPUTATION CREATED FOR SATELLITE COMPOSITE. deployed on ERS-2;" }, "imageDetails": [ 99 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2580, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 45, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/Terms-and-Conditions-for-the-use-of-ESA-Data.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 19910, "uuid": "be02159d0d9b4ce49e9c90378206e283", "short_code": "proj", "title": "ATSR-2 Mission", "abstract": "Along-Track Scanning Radiometer (ATSR-2) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40086, "uuid": "943749c71fe9467fbcaeb40310b35049", "short_code": "coll", "title": "ATSR-2 Multimission land and sea surface data, version 3.0.1", "abstract": "Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR). \r\n\r\nThis dataset collection contains version 3 ATSR2 Multimission land and sea surface data. These data result from the 3rd reprocessing second pass and are tagged v3.0.1.\r\n\r\nThe instrument uses thermal channels at 3.7, 10.8, and 12 microns wavelength; and reflected visible/near infra-red channels at 0.555, 0.659, 0.865, and 1.61 microns wavelength. Level 1b products contain gridded brightness temperature and reflectance. Level 2 products contain land and sea-surface temperature, and NDVI at a range of spatial resolutions. The third reprocessing was done to implement updated algorithms, processors, and auxiliary files. The data were acquired by the European Space Agency's (ESA) Envisat satellite, and the NERC Earth Observation Data Centre (NEODC) mirrors the data for UK users." } ], "responsiblepartyinfo_set": [ 195649, 195645, 195646, 195647, 195650, 195648, 195652, 195651 ], "onlineresource_set": [ 83672, 83673 ] }, { "ob_id": 40160, "uuid": "4371686200b444ffa6abf675334fd932", "title": "ATSR-2: Gridded Surface Temperature (GST) Product (AT2_NR__2P), v3.0.1", "abstract": "Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).\r\n\r\nThis dataset contains the Along-Track Scanning Radiometer on ESA ERS-2 satellite (ATSR-2) Gridded Surface Temperature (GST) Product. These data are the Level 2 full spatial resolution (approximately 1 km by 1 km) geophysical product derived from Level 1B product and auxiliary data. The contents of the pixel fields, which are a mixture of Top of Atmosphere (TOA) and surface brightness temperature/radiance, are switch-able depending on the surface type. The third reprocessing was done to implement updated algorithms, processors, and auxiliary files.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2017-12-14T16:33:26.006058", "updateFrequency": "notPlanned", "dataLineage": "The data were acquired by the European Space Agency's (ESA) Envisat satellite, and the NERC Earth Observation Data Centre (NEODC) mirrors the data for UK users.", "removedDataReason": "", "keywords": "ATSR, Gridded Surface Temperature", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-10-02T09:02:07", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40161, "dataPath": "/neodc/aatsr_multimission/atsr2-v3.0.1/data/at2_nr__2p", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6774709114693, "numberOfFiles": 202409, "fileFormat": "ENVISAT PDS" }, "timePeriod": { "ob_id": 2322, "startTime": "1995-05-31T23:00:00", "endTime": "2008-01-31T00:00:00" }, "resultQuality": { "ob_id": 2173, "explanation": "", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-22" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 40164, "uuid": "f66c66f319674feba2a5dd00fe2c61a1", "short_code": "cmppr", "title": "Composite Process for: ATSR-2 Gridded Surface Temperature (GST) Product (AT2_NR__2P) v3.0.1", "abstract": "This process is comprised of multiple procedures: 1. Acquisition: Acquisition Process for: ATSR-2 Gridded Surface Temperature (GST) Product (AT2_NR__2P) v3; \r\n2. Computation: DETAILS NEEDED - COMPUTATION CREATED FOR SATELLITE COMPOSITE. deployed on ERS-2;" }, "imageDetails": [ 99 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2580, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 45, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/Terms-and-Conditions-for-the-use-of-ESA-Data.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 19910, "uuid": "be02159d0d9b4ce49e9c90378206e283", "short_code": "proj", "title": "ATSR-2 Mission", "abstract": "Along-Track Scanning Radiometer (ATSR-2) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40086, "uuid": "943749c71fe9467fbcaeb40310b35049", "short_code": "coll", "title": "ATSR-2 Multimission land and sea surface data, version 3.0.1", "abstract": "Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR). \r\n\r\nThis dataset collection contains version 3 ATSR2 Multimission land and sea surface data. These data result from the 3rd reprocessing second pass and are tagged v3.0.1.\r\n\r\nThe instrument uses thermal channels at 3.7, 10.8, and 12 microns wavelength; and reflected visible/near infra-red channels at 0.555, 0.659, 0.865, and 1.61 microns wavelength. Level 1b products contain gridded brightness temperature and reflectance. Level 2 products contain land and sea-surface temperature, and NDVI at a range of spatial resolutions. The third reprocessing was done to implement updated algorithms, processors, and auxiliary files. The data were acquired by the European Space Agency's (ESA) Envisat satellite, and the NERC Earth Observation Data Centre (NEODC) mirrors the data for UK users." } ], "responsiblepartyinfo_set": [ 195830, 195831, 195832, 195833, 195834, 195835, 195836, 195837 ], "onlineresource_set": [ 83677, 83678 ] }, { "ob_id": 40166, "uuid": "8956cf9e31334914ab4991796f0f645a", "title": "HadISDH.land: gridded global monthly land surface humidity data version 4.5.1.2022f", "abstract": "This is the HadISDH.land 4.5.1.2022f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.land is a near-global gridded monthly mean land surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from weather stations. The observations have been quality controlled and homogenised. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2022. \r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2022. Users are advised to read the update document in the Docs section for full details on all changes from the previous release.\r\n\r\nAs in previous years, the annual scrape of NOAAs Integrated Surface Dataset for HadISD.3.3.0.2022f, which is the basis of HadISDH.land, has pulled through some historical changes to stations. This, and the additional year of data, results in small changes to station selection. The homogeneity adjustments differ slightly due to sensitivity to the addition and loss of stations, historical changes to stations previously included and the additional 12 months of data.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E.,\r\nJones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and\r\ntemperature record for climate monitoring, Clim. Past, 10, 1983-2006,\r\ndoi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more\r\ndetail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de\r\nPodesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface\r\nspecific humidity product for climate monitoring. Climate of the Past, 9, 657-677,\r\ndoi:10.5194/cp-9-657-2013.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-06-12T11:32:37", "updateFrequency": "notPlanned", "dataLineage": "HadISDH.land is a global land surface (~2 m) humidity dataset and is produced by the Met Office Hadley Centre in collaboration with Maynooth University, NOAA NCEI, NPL and CRU. It is based on the quality controlled sub-daily HadISD from the Met Office Hadley Centre which is in turn based on the ISD dataset from NOAA's NCEI. It is passed to CEDA for archiving and distribution.", "removedDataReason": "", "keywords": "HadISDH, humidity, surface, land, gridded, station, specific humidity, temperature, dew point temperature, wet bulb temperature, dew point temperature, vapour pressure, in-situ", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2023-06-13T14:04:20", "doiPublishedTime": "2023-06-13T14:06:42", "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": 40167, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadISDH/mon/HadISDHTable/r1/v4-5-1-2022f/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 14474148, "numberOfFiles": 8, "fileFormat": "Files are NetCDF formatted." }, "timePeriod": { "ob_id": 11130, "startTime": "1973-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3043, "explanation": "Uncertainty estimates are provided as part of the dataset both at the station and gridbox level, this includes information covering station uncertainty (climatological, homogenisation and measurement uncertainty), gridbox spatial and temporal sampling uncertainty and combined station and sampling uncertainty. See dataset associated documentation for full details.", "passesTest": true, "resultTitle": "HadISDH Data Quality Statement", "date": "2016-06-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 13526, "uuid": "02d903686bc9471a866e6b0d7c19f727", "short_code": "comp", "title": "HadISDH.land: gridded global land surface humidity dataset produced by the Met Office Hadley Centre", "abstract": "HadISDH.land utilises simultaneous subdaily temperature and dew point temperature data from over 3000 quality controlled HadISD stations that have sufficiently long records. All humidity variables are calculated at hourly resolution and monthly means are created. \r\n\r\nMonthly means are homogenised to detect and adjust for features within the data that do not appear to be of climate origin. While unlikely to be perfect, this process does help remove large errors from the data an improve robustness of long-term climate monitoring. The NCEI's Pairwise Homogenisation Algorithm has been used directly on DPD and T. An indirect PHA method (ID PHA) is used whereby changepoints detected in DPD and T are used to make adjustments to q, e, Tw and RH. Changepoints from DPD are also applied to T. Td is derived from homogenised T and DPD. See Docs 'HadISDH.land process diagram'.\r\n\r\nStation measurement, climatological and homogeneity adjustment uncertainties are estimated for each month. Climatological averages are calculated (the climatological period is dependent on product version) and monthly mean climate anomalies obtained. These anomalies (in addition to climatological mean and standard deviation, actual values and uncertainty components) are then averaged over 5° by 5° gridboxes centred on -177.5°W and -87.5°S to 177.5°E and 87.5°N. Given the uneven distribution of stations over time and space, sampling uncertainty is estimated for each gridbox month.\r\n\r\nFor greater detail please see:\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E., Jones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and temperature record for climate monitoring, Clim. Past, 10, 1983-2006, doi:10.5194/cp-10-1983-2014, 2014. \r\n\r\nand\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface specific humidity product for climate monitoring. Climate of the Past, 9, 657-677, doi:10.5194/cp-9-657-2013.\r\n\r\nDocs contains links to both these publications" }, "procedureCompositeProcess": null, "imageDetails": [ 157 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2561, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 32, "licenceURL": "http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/", "licenceClassifications": [ { "ob_id": 6, "classification": "personal" }, { "ob_id": 4, "classification": "academic" }, { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 56886, 56887, 56888, 56889, 56890, 56891, 56892, 56893, 56894, 56895, 60438, 66182, 66183, 66184, 66185, 66186, 66187, 66188, 66189, 66190, 66191, 66192 ], "vocabularyKeywords": [], "identifier_set": [ 12529 ], "observationcollection_set": [ { "ob_id": 13522, "uuid": "251474c7b09449d8b9e7aeaf1461858f", "short_code": "coll", "title": "HadISDH: global surface humidity data", "abstract": "HadISDH (Integrated Surface Database Humidity) is a monthly 5° by 5° gridded global surface humidity climate monitoring dataset created from in-situ sub-daily synoptic data. The data have been quality controlled and homogenised (land), bias adjusted (marine) and buddy checked (marine). \r\n\r\nMonthly mean climate anomalies are provided alongside uncertainty estimates, actual values, climatological means and standard deviations for specific humidity, relative humidity, vapour pressure, dew point temperature, wet bulb temperature, dew point depression in addition to the simultaneously observed temperature." } ], "responsiblepartyinfo_set": [ 195841, 195842, 195843, 195844, 195845, 195846, 195847, 195848, 195849, 195850, 195851, 195852, 195853, 195854, 195855, 195856, 195857 ], "onlineresource_set": [ 83687, 83682, 83681, 83685, 83686, 83679, 83680, 83683, 83684, 83688, 83701, 83702, 87625, 87626 ] }, { "ob_id": 40168, "uuid": "2d1613955e1b4cd1b156e5f3edbd7e66", "title": "HadISDH.extremes: gridded global monthly land surface wet bulb and dry bulb temperature extremes index data version 1.0.0.2022f", "abstract": "This is the HadISDH.extremes 1.0.0.2022f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.extremes is a near-global gridded monthly land surface extremes index climate monitoring product. It is created from in situ sub-daily observations of wet bulb (converted from dew point temperature) and dry bulb temperature from weather stations. The observations have been quality controlled at the hourly level with strict temporal completeness thresholds applied at daily, monthly, annual, climatological and whole period scales to minimise biases. Gridbox months are assessed for inhomogeneity and scores provided (see Homogeneity Score Document in Docs). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2022.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for 27 different heat extremes indices based on the ET-SCI (Expert Team on Sector-Specific Climate Indices; https://public.wmo.int/en/events/meetings/expert-team-sector-specific-climate-indices-et-sci) framework. These indices capture a range of moderate to severe extremes. They utilise the daily maximum and minimum values of sub-daily dry bulb and wet bulb temperature observations. Note that these will most likely underestimate the true extremes even when hourly data are available. The data are designed for assessing large scale features over long time scales, ideally using the anomaly fields as these are less affected by sampling biases. Users are advised to cross-compare with national datasets other supporting evidence when assessing small scale localised features.\r\n\r\nThis version is the first with annual updates envisaged. An update record will be maintained in the Docs section.\r\n\r\nHadISD.3.3.0.2022f is the basis of HadISDH.extremes.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K, in press: HadISDH.extremes Part 1: a gridded wet bulb temperature extremes index product for climate monitoring. Advances in Atmospheric Sciences, doi: 10.1007/s00376-023-2347-8. http://www.iapjournals.ac.cn/aas/en/article/doi/10.1007/s00376-023-2347-8\r\n\r\nWillett, K. in press: HadISDH.extremes Part 2: exploring humid heat extremes using wet bulb temperature indices. Advances in Atmospheric Sciences, doi: 10.1007/s00376-023-2348-7. http://www.iapjournals.ac.cn/aas/en/article/doi/10.1007/s00376-023-2348-7\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-06-07T01:47:40", "updateFrequency": "notPlanned", "dataLineage": "HadISDH.extremes is a global land surface (~2 m) humid and dry heat extremes index dataset and is produced by the Met Office Hadley Centre through the Met Office Climate Science for Service Partnership (CSSP) China project. It is based on the quality controlled sub-daily HadISD from the Met Office Hadley Centre which is in turn based on the ISD dataset from NOAA's NCEI. It is passed to the BADC for archiving and distribution.", "removedDataReason": "", "keywords": "HadISDH, humidity, surface, land, gridded, station, heat, extremes index, air temperature, wet bulb temperature, in-situ", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2023-06-13T14:14:02", "doiPublishedTime": "2023-06-13T14:14:48", "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": 40169, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadISDH-extremes/mon/HadISDHTable/r1/v1-0-0-2022f/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 219377292, "numberOfFiles": 32, "fileFormat": "Data are NetCDF formatted" }, "timePeriod": { "ob_id": 11131, "startTime": "1973-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 4310, "explanation": "The sub-daily observations have been quality controlled and data completeness thresholds are applied at the daily, monthly, annual, climatological and whole record level. Each gridbox month is given a score relating to likely inhomogeneity which can be used to filter the dataset to reduce the influence of large inhomogeneity.", "passesTest": true, "resultTitle": "HadISDH.extremes QC statement", "date": "2023-06-12" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40170, "uuid": "62c89c91587c4bc9adcf20b2fe7677fd", "short_code": "comp", "title": "HadISDH.extremes: gridded global monthly land surface wet bulb and dry bulb temperature extremes index dataset produced by the Met Office Hadley Centre", "abstract": "HadISDH.extremes utilises simultaneous sub-daily dry bulb and wet bulb temperature (calculated from dry bulb and dew point temperature) data from over 4000 quality controlled HadISD stations that have sufficiently long records. After checking for sufficient completeness at the daily, monthly, annual, climatological and whole record scale, monthly indices are created from the maximum and minimum of the available daily values. Note that these likely underestimate the true extremes. Climatological averages are calculated over 1991-2020 and monthly climate anomalies obtained. These anomalies (in addition to climatological mean and standard deviation, actual values) are then averaged over 5° by 5° gridboxes centred on -177.5°W and -87.5°S to 177.5°E and 87.5°N. Each gridbox month has an associated homogeneity score obtained from the homogenisation information from HadISDH.landT and HadISDH.landTw. Users can filter the data to remove those gridboxes likely affected by large inhomogeneity. While unlikely to be perfect, this process does help remove large errors from the data an improve robustness of long-term climate monitoring. For greater detail please see: \r\n\r\n\r\n\r\nWillett, K, 2023: HadISDH.extremes Part 1: a gridded wet bulb temperature extremes index product for climate monitoring. Advances in Atmospheric Sciences, 40, 1952–1967, doi: 10.1007/s00376-023-2347-8. https://link.springer.com/article/10.1007/s00376-023-2347-8. \r\n\r\nWillett, K. 2023: HadISDH.extremes Part 2: exploring humid heat extremes using wet bulb temperature indices. Advances in Atmospheric Sciences, 40, 1968–1985, doi: 10.1007/s00376-023-2348-7. https://link.springer.com/article/10.1007/s00376-023-2348-7.\r\n\r\nSee the documentation links in the online resources section of this record for links to both these publications." }, "procedureCompositeProcess": null, "imageDetails": [ 157 ], "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": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 56886, 56887, 56888, 56889, 56891, 56892, 56893, 56894, 56895, 60438, 66085, 66086, 66087, 66088, 66089, 66090, 66091, 66092, 66093, 66094, 66095, 66096, 66097, 66098, 66099, 66100, 66101, 66102, 66103, 66104, 66105, 66106, 66107, 66108, 66109, 66110, 66111, 66112, 66113, 66114, 66115, 66116, 66117, 66118, 66119, 66120, 66121, 66122, 66123, 66124, 66125, 66126, 66127, 66128, 66129, 66130, 66131, 66132, 66133, 66134, 66135, 66136, 66137, 66138, 66139, 66140, 66141, 66142, 66143, 66144, 66145, 66146, 66147, 66148, 66149, 66150, 66151, 66152, 66153, 66154, 66155, 66156, 66157, 66158, 66159, 66160, 66161, 66162, 66163, 66164, 66165, 66166, 66168, 66169, 66170, 66171, 66172, 66173, 66174, 66175, 66176, 66177, 66178, 66179, 66180, 66181, 73614, 73616, 73617, 73618, 73619, 73620, 73621, 73622, 73624, 73625, 73626, 73627, 73628, 73629, 73630, 73631, 73632, 73633, 73635, 73636, 73637, 73638, 73639, 73640, 73642, 79999, 80000, 80001, 80002 ], "vocabularyKeywords": [], "identifier_set": [ 12530 ], "observationcollection_set": [ { "ob_id": 13522, "uuid": "251474c7b09449d8b9e7aeaf1461858f", "short_code": "coll", "title": "HadISDH: global surface humidity data", "abstract": "HadISDH (Integrated Surface Database Humidity) is a monthly 5° by 5° gridded global surface humidity climate monitoring dataset created from in-situ sub-daily synoptic data. The data have been quality controlled and homogenised (land), bias adjusted (marine) and buddy checked (marine). \r\n\r\nMonthly mean climate anomalies are provided alongside uncertainty estimates, actual values, climatological means and standard deviations for specific humidity, relative humidity, vapour pressure, dew point temperature, wet bulb temperature, dew point depression in addition to the simultaneously observed temperature." } ], "responsiblepartyinfo_set": [ 195858, 195859, 195860, 195861, 195862, 195863, 195864, 195865, 195866, 195868 ], "onlineresource_set": [ 83692, 83693, 83694, 83695, 83696, 83697, 83691, 83698, 83699, 83700, 87609, 87610 ] }, { "ob_id": 40174, "uuid": "b52a6ea9d67845cf9e37c63a7c2266ee", "title": "sdsdf ssdf sdf sdgdsfg dsfgdfsg dfg d", "abstract": "dfg dsfgsdf gdsfgdfsg dsfg dfsgdf gdfg dfg dfg fdg dfgdfg dfgg dfdf g", "creationDate": "2023-06-15T08:57:12.293267", "lastUpdatedDate": "2023-06-15T08:57:12", "latestDataUpdateTime": "2023-06-15T08:57:12", "updateFrequency": "notPlanned", "dataLineage": "fdg dfsg dfsg fdg fdsgdfsgdfg dfgdfg f", "removedDataReason": "", "keywords": "", "publicationState": "preview", "nonGeographicFlag": true, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": null, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 11132, "startTime": "2023-06-15T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4311, "explanation": "df dgdf gdfsg sdfgdfs gdfsgdfgsdfg", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-06-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 195886, 195887, 195888, 195889, 195890, 195891, 195892, 195893 ], "onlineresource_set": [] }, { "ob_id": 40175, "uuid": "372375fff81e44428ed62dacd562a5f2", "title": "BICEP : Monthly global dissolved organic carbon (DOC), between 2010-2018 at 9km resolution (derived from the Ocean Colour Climate Change Initiative v4.2 dataset)", "abstract": "This global dissolved organic carbon (DOC) dataset contains monthly DOC concentrations between 2010-2018 at 9km resolution. By using in-situ data set from Hansell et al. 2021 a random forest regression model for near surface ocean DOC is trained. The model uses Ocean colour Earth observation reflectance, primary production, SST, salinity and geographical information as predictors. The model has been used to produce monthly global marine DOC for years 2010-2019 using global monthly version of the predictors, namely Ocean Colour CCI , PP, Salinity CCI and SST. The work has been done as a part of ESA funded BICEP project (2020-2023).\r\n\r\nThe ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies. This dataset contains their Version 5.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Note, the chlorophyll-a data are also included in the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", "creationDate": "2023-06-15T13:58:20.065076", "lastUpdatedDate": "2023-06-15T13:51:17", "latestDataUpdateTime": "2023-06-15T13:51:17", "updateFrequency": "", "dataLineage": "Data were produced in an ESA funded project led 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.", "removedDataReason": "", "keywords": "", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3857, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 11133, "startTime": "2010-01-01T00:00:00", "endTime": "2018-12-31T23:59:59" }, "resultQuality": { "ob_id": 4312, "explanation": "The random forest model for global DOC produces estimates that are with good agreement with the available in-situ data in open water (>300 km from shore), where the relative uncertainty is on the average smaller that 10%. However, as the estimates have not been fully validated, this data set is provided as a proof-of-concept, only.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-06-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "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": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 195894, 195895, 195896, 195897, 195898, 195899, 195900, 195903, 195901, 195902 ], "onlineresource_set": [ 83712, 83713, 83714, 83715, 83716 ] }, { "ob_id": 40176, "uuid": "c3866a255e15470f9ed4a566ad0053ff", "title": "BICEP/NCEO: Monthly global particulate inorganic carbon (PIC) for between 1997-2021 at 9 km spatial resolution (derived from the Ocean Colour Climate Change Initiative version 5.0 dataset)", "abstract": "The BICEP/NCEO: Monthly global particulate inorganic carbon (PIC) between 1997-2021 at 9km spatial resolution.\r\n\r\n\r\nParticulate inorganic carbon (PIC) data were generated using a random forest approach that incorporates the following key input variables: remote sensing reflectances (Rrs) at 560 and 665 nm, chlorophyll-a concentration, colour index, and maximum waterclass values. The Rrs(560), Rrs(665), and chlorophyll-a concentration data were obtained directly from the Ocean Colour Climate Change Initiative (OC-CCI) version 5.0. The colour index values were estimated using Mitchell et al. (2017) algorithm: Rrs(560) minus Rrs(665). The maximum waterclass values were estimated using fourteen optical waterclasses obtained from the OC-CCI version 5.0. The PIC data are provided as netCDF files containing global, month PIC concentration at 9 km spatial resolution (1997-2021). For more details on the algorithm and its validation, please see the BICEP algorithm theoretical basline document (https://bicep-project.org/Home).\r\n\r\nA related dataset based on the ESA Ocean Colour Climate Change Initiative v5.0 data is also available (see link in the related records section).", "creationDate": "2023-06-15T15:33:28.726302", "lastUpdatedDate": "2023-06-15T15:24:17", "latestDataUpdateTime": "2023-06-15T15:24:17", "updateFrequency": "", "dataLineage": "The particulate inorganic carbon 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": "", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3858, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 11134, "startTime": "1997-09-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 4313, "explanation": "BICEP/NCEO: We developed a random forest machine learning approach to estimate global particulate inorganic concentration (PIC) concentrations from satellite OC-CCI version 5.0 data. A large set of in situ PIC data were used to validate the random forest PIC approach. The proposed random forest PIC approach was also compared with existing candidate PIC algorithm (Mitchell et al. 2017). Our results show that the random forest method can retrieve PIC concentrations relatively well across different water types. For more detail of the random forest PIC approach, please see the please see the BICEP report (https://bicep-project.org/Home)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-06-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40177, "uuid": "027a8c62ddbf42c681c7cd73b08313ad", "short_code": "comp", "title": "Computation for BICEP/NCEO: Monthly global particulate inorganic carbon (PIC) for between 1997-2021 at 9 km spatial resolution (derived from the Ocean Colour Climate Change Initiative version 5.0 dataset)", "abstract": "Computation of the particulate inorganic carbon (PIC) were generated using a random forest approach that incorporates the following key input variables: remote sensing reflectances (Rrs) at 560 and 665 nm, chlorophyll-a concentration, colour index, and maximum waterclass values. The Rrs(560), Rrs(665), and chlorophyll-a concentration data were obtained directly from the Ocean Colour Climate Change Initiative (OC-CCI) version 5.0. The colour index values were estimated using Mitchell et al. (2017) algorithm: Rrs(560) minus Rrs(665). The maximum waterclass values were estimated using fourteen optical waterclasses obtained from the OC-CCI version 5.0. The PIC data are provided as netCDF files containing global, month PIC concentration at 9 km spatial resolution (1997-2021). For more details on the algorithm and its validation, please see the BICEP algorithm theoretical basline document (https://bicep-project.org/Home)" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "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": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." }, { "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": [ 195904, 195905, 195906, 195907, 195908, 195909, 195910, 195919, 195911, 195912, 195913, 195914, 195915, 195916, 195917, 195918 ], "onlineresource_set": [ 83717 ] }, { "ob_id": 40191, "uuid": "9443bf96bdc044b9b8a43280ba0d662b", "title": "CCMI-2022: refD2 data produced by the MIROC-ES2H model from MIROC", "abstract": "This dataset contains model data for CCMI-2022 experiment refD2 produced by the MIROC-ES2H model which is based on a global climate model MIROC (Model for Interdisciplinary Research on Climate). This has been cooperatively developed by JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan).\r\n\r\nThe refD2 experiment is the baseline projection for updated projections of ozone recovery. Specified forcings largely following the same specifications as for the SSP2-4.5 scenario of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), with the exception of the near-surface mixing ratio of Ozone Depleting Substances which follow the baseline projection from WMO (2018).\r\n\r\nSSP2-4.5 is a Shared Socio-economic Pathway scenario that follows socio-economic storyline SSP2 with intermediate mitigation and adaptation challenges and climate forcing pathway RCP4.5 which leads to a radiative forcing of 4.5 Wm-2 by the year 2100.\r\n\r\nWMO-2018 refers to the Scientific Assessment of Ozone Depletion: 2018.\r\n\r\nThe CCMI-2022 Chemistry-climate model initiative is a set of model experiments focused on the stratosphere, with the goals of providing updated projections towards the future evolution of the ozone layer and improving our understanding of chemistry-climate interactions from models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Review of the global models used within phase 1 of the Chemistry-Climate Model Initiative (CCMI)\r\n- A new set of Chemistry-Climate Model Initiative (CCMI) Community Simulations to Update the Assessment of Models and Support Upcoming Ozone Assessment Activities, David Plummer and Tatsuya Nagashima and Simone Tilmes and Alex Archibald and Gabriel Chiodo and Suvarna Fadnavis and Hella Garny and Beatrice Josse and Joowan Kim and Jean-Francois Lamarque and Olaf Morgenstern and Lee Murray and Clara Orbe and Amos Tai and Martyn Chipperfield and Bernd Funke and Martin Juckes and Doug Kinnison and Markus Kunze and Beiping Luo and Katja Matthes and Paul A. Newman and Charlotte Pascoe and Thomas Peter (2021), SPARC Newsletter, volume 57, pp 22-30", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-11-29T15:24:04", "latestDataUpdateTime": "2024-03-09T03:01:58", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by scientists from a collaboration of JAMSTEC, AORI, NIES, and R-CCS and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CCMI-2022, refD2, SSP245, Hindcast, Scenario, MIROC-ES2H, MIROC, JAMSTEC, AORI, NIES, R-CCS, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "250 km", "status": "completed", "dataPublishedTime": "2023-08-11T11:31:01", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40192, "dataPath": "/badc/ccmi/data/post-cmip6/ccmi-2022/MIROC/MIROC-ES2H/refD2", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 826096862967, "numberOfFiles": 15538, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 9071, "startTime": "1960-01-01T00:00:00", "endTime": "2101-01-01T00:00:00" }, "resultQuality": { "ob_id": 3727, "explanation": "Data are as given by the data provider, ceda-cc quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CCMI-2022 Data and Metadata Quality Statement", "date": "2021-08-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40193, "uuid": "4b08c47fd0314dc78648c54ee515401b", "short_code": "comp", "title": "MIROC-ES2H model based on a global climate model MIROC (Model for Interdisciplinary Research on Climate) which has been cooperatively developed by JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan).", "abstract": "MIROC-ES2H model based on a global climate model MIROC (Model for Interdisciplinary Research on Climate) which has been cooperatively developed by JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan)." }, "procedureCompositeProcess": null, "imageDetails": [ 146 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2544, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "ccmi-2022", "label": "restricted: ccmi-2022 group", "licence": { "ob_id": 21, "licenceURL": "https://artefacts.ceda.ac.uk/licences/rugl_versions/rugl_v1-0.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32805, "uuid": "92dddf542adc44b5898f535be4179705", "short_code": "proj", "title": "CCMI-2022 Chemistry-climate model initiative, phase 2", "abstract": "CCMI-2022 Chemistry-climate model initiative, phase 2 is a World Climate Research Programme (WCRP) Stratosphere-Troposphere Processes and their Role in Climate (SPARC) project to study the evolution of the ozone layer using chemistry-climate model simulations. 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APARC (Atmospheric Processes And their Role in Climate) is a core project of the World Climate Research Programme (WCRP). IGAC is the International Global Atmospheric Chemistry which currently operates under the umbrella of Future Earth." } ], "responsiblepartyinfo_set": [ 195972, 195973, 195974, 195975, 195976, 195977, 195978, 195980, 196637 ], "onlineresource_set": [ 83727, 83728 ] }, { "ob_id": 40194, "uuid": "6fdda887181143f9a7d265883bc00b63", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): control data produced by the GEM-NEMO model at ECCC", "abstract": "This dataset contains model data for SNAPSI experiment 'control' produced by the seasonal prediction research team at ECCC (Environment and Climate Change Canada). It is generated with the coupled climate model GEM-NEMO. \r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe control experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, the stratospheric zonal mean temperatures and zonal winds are nudged towards the time-evolving climatological state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- Smith, G. C., Bélanger, J.-M., Roy, F., Pellerin, P., Ritchie, H., Onu, K., Roch, M., Zadra, A., Colan, D. S., Winter, B., Fontecilla, J.-S., and Deacu., D.: Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill, Mon. Wea. Rev., 146, 1157–1180, https://doi.org/10.1175/MWR-D-17-0157.1, 2018\r\n- Lin, H., Merryfield, W. J., Muncaster, R., Smith, G. C., Markovic, M., Dupont, F., Roy, F., Lemieux, J.-F., Dirkson, A., Kharin, V. V., Lee, W.-S., Charron, M., and Erfani, A.: The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2), Weather and Forecasting, 35, 1317–1343, https://doi.org/10.1175/WAF-D-19-0259.1, 2020", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Data were produced by scientists at Environment and Climate Change Canada (ECCC) and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "control, GEM-NEMO, ECCC, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40196, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/ECCC/GEM-NEMO/control/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2425478883770, "numberOfFiles": 6001, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40195, "uuid": "2c1df78e252d4953a3c32534b7d8776d", "short_code": "comp", "title": "GEM-NEMO model", "abstract": "This data was produced by the GEM-NEMO model run by scientists at Environment and Climate Change Canada (ECCC) for the SNAPSI project." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50419, 50426, 50427, 50429, 50431, 50475, 50481, 50496, 50498, 50549, 50566, 50579, 50597, 50609, 52192, 52193, 52755, 54228, 60438, 62560, 62561, 64078, 66076, 66082, 66083 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40201, "uuid": "0760dcb81380402f8e7a1dcb20d1eec9", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the GEM-NEMO model at ECCC", "abstract": "The GEM-NEMO model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by the modelling team at Environment and Climate Change Canada (ECCC).\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\nModel reference publications:\r\n- Smith, G. C., Bélanger, J.-M., Roy, F., Pellerin, P., Ritchie, H., Onu, K., Roch, M., Zadra, A., Colan, D. S., Winter, B., Fontecilla, J.-S., and Deacu., D.: Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill, Mon. Wea. Rev., 146, 1157–1180, https://doi.org/10.1175/MWR-D-17-0157.1, 2018\r\n- Lin, H., Merryfield, W. J., Muncaster, R., Smith, G. C., Markovic, M., Dupont, F., Roy, F., Lemieux, J.-F., Dirkson, A., Kharin, V. V., Lee, W.-S., Charron, M., and Erfani, A.: The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2), Weather and Forecasting, 35, 1317–1343, https://doi.org/10.1175/WAF-D-19-0259.1, 2020" } ], "responsiblepartyinfo_set": [ 195981, 195983, 195984, 195985, 195986, 195987, 195988, 195982 ], "onlineresource_set": [ 83732, 83733, 83730, 83731 ] }, { "ob_id": 40197, "uuid": "5e924de0f26e48c194568075338ad3ff", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): nudged data produced by the GEM-NEMO model at ECCC", "abstract": "This dataset contains model data for SNAPSI experiment 'nudged' produced by the seasonal prediction research team at ECCC (Environment and Climate Change Canada). It is generated with the coupled climate model GEM-NEMO. \r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe nudged experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, the stratospheric zonal mean temperatures and zonal winds are nudged towards the observed time-evolving state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- Smith, G. C., Bélanger, J.-M., Roy, F., Pellerin, P., Ritchie, H., Onu, K., Roch, M., Zadra, A., Colan, D. S., Winter, B., Fontecilla, J.-S., and Deacu., D.: Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill, Mon. Wea. Rev., 146, 1157–1180, https://doi.org/10.1175/MWR-D-17-0157.1, 2018\r\n- Lin, H., Merryfield, W. J., Muncaster, R., Smith, G. C., Markovic, M., Dupont, F., Roy, F., Lemieux, J.-F., Dirkson, A., Kharin, V. V., Lee, W.-S., Charron, M., and Erfani, A.: The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2), Weather and Forecasting, 35, 1317–1343, https://doi.org/10.1175/WAF-D-19-0259.1, 2020", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Data were produced by scientists at Environment and Climate Change Canada (ECCC) and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "nudged, GEM-NEMO, ECCC, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40198, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/ECCC/GEM-NEMO/nudged/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2435915825720, "numberOfFiles": 6001, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40195, "uuid": "2c1df78e252d4953a3c32534b7d8776d", "short_code": "comp", "title": "GEM-NEMO model", "abstract": "This data was produced by the GEM-NEMO model run by scientists at Environment and Climate Change Canada (ECCC) for the SNAPSI project." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50419, 50426, 50429, 50431, 50475, 50481, 50496, 50498, 50549, 50566, 50579, 50597, 50609, 52192, 52193, 52755, 54228, 60438, 62560, 62561, 64078, 66076, 66082, 66083 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40201, "uuid": "0760dcb81380402f8e7a1dcb20d1eec9", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the GEM-NEMO model at ECCC", "abstract": "The GEM-NEMO model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by the modelling team at Environment and Climate Change Canada (ECCC).\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\nModel reference publications:\r\n- Smith, G. C., Bélanger, J.-M., Roy, F., Pellerin, P., Ritchie, H., Onu, K., Roch, M., Zadra, A., Colan, D. S., Winter, B., Fontecilla, J.-S., and Deacu., D.: Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill, Mon. Wea. Rev., 146, 1157–1180, https://doi.org/10.1175/MWR-D-17-0157.1, 2018\r\n- Lin, H., Merryfield, W. J., Muncaster, R., Smith, G. C., Markovic, M., Dupont, F., Roy, F., Lemieux, J.-F., Dirkson, A., Kharin, V. V., Lee, W.-S., Charron, M., and Erfani, A.: The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2), Weather and Forecasting, 35, 1317–1343, https://doi.org/10.1175/WAF-D-19-0259.1, 2020" } ], "responsiblepartyinfo_set": [ 195990, 195991, 195992, 195993, 195994, 195995, 195996, 195997 ], "onlineresource_set": [ 83734, 83737, 83735, 83736 ] }, { "ob_id": 40199, "uuid": "f7812032a3f94c89bba253b94eed465d", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): free data produced by the GEM-NEMO model at ECCC", "abstract": "This dataset contains model data for SNAPSI experiment 'free' produced by the seasonal prediction research team at ECCC (Environment and Climate Change Canada). 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The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- Smith, G. C., Bélanger, J.-M., Roy, F., Pellerin, P., Ritchie, H., Onu, K., Roch, M., Zadra, A., Colan, D. S., Winter, B., Fontecilla, J.-S., and Deacu., D.: Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill, Mon. Wea. Rev., 146, 1157–1180, https://doi.org/10.1175/MWR-D-17-0157.1, 2018\r\n- Lin, H., Merryfield, W. J., Muncaster, R., Smith, G. C., Markovic, M., Dupont, F., Roy, F., Lemieux, J.-F., Dirkson, A., Kharin, V. V., Lee, W.-S., Charron, M., and Erfani, A.: The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2), Weather and Forecasting, 35, 1317–1343, https://doi.org/10.1175/WAF-D-19-0259.1, 2020", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2023-02-02T19:40:24", "updateFrequency": "", "dataLineage": "Data were produced by scientists at Environment and Climate Change Canada (ECCC) and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "free, GEM-NEMO, ECCC, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2024-09-26T08:21:22", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40200, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/ECCC/GEM-NEMO/free/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2418887568527, "numberOfFiles": 5401, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40195, "uuid": "2c1df78e252d4953a3c32534b7d8776d", "short_code": "comp", "title": "GEM-NEMO model", "abstract": "This data was produced by the GEM-NEMO model run by scientists at Environment and Climate Change Canada (ECCC) for the SNAPSI project." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50419, 50426, 50427, 50429, 50431, 50481, 50496, 50498, 50549, 50566, 50579, 50597, 50609, 52192, 52193, 52755, 54228, 60438, 62560, 62561, 64078, 66083 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40201, "uuid": "0760dcb81380402f8e7a1dcb20d1eec9", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the GEM-NEMO model at ECCC", "abstract": "The GEM-NEMO model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by the modelling team at Environment and Climate Change Canada (ECCC).\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\nModel reference publications:\r\n- Smith, G. C., Bélanger, J.-M., Roy, F., Pellerin, P., Ritchie, H., Onu, K., Roch, M., Zadra, A., Colan, D. S., Winter, B., Fontecilla, J.-S., and Deacu., D.: Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill, Mon. Wea. Rev., 146, 1157–1180, https://doi.org/10.1175/MWR-D-17-0157.1, 2018\r\n- Lin, H., Merryfield, W. J., Muncaster, R., Smith, G. C., Markovic, M., Dupont, F., Roy, F., Lemieux, J.-F., Dirkson, A., Kharin, V. V., Lee, W.-S., Charron, M., and Erfani, A.: The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2), Weather and Forecasting, 35, 1317–1343, https://doi.org/10.1175/WAF-D-19-0259.1, 2020" } ], "responsiblepartyinfo_set": [ 196003, 196004, 196005, 195998, 195999, 196000, 196001, 196002, 205492 ], "onlineresource_set": [ 83740, 83738, 83739, 83741 ] }, { "ob_id": 40202, "uuid": "5dd9097ff0cf49d2ab84c357ca3a4f4c", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): nudged data produced by the GLOBO model at CNR-ISAC", "abstract": "This dataset contains model data for SNAPSI experiment 'nudged' produced by scientists at CNR-ISAC (Institute of Atmospheric Sciences and Climate, Bologna, Italy). It is generated with the coupled climate model GLOBO. \r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe nudged experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, the stratospheric zonal mean temperatures and zonal winds are nudged towards the observed time-evolving state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- Malguzzi, P., Buzzi, A., and Drofa, O.: The Meteorological Global Model GLOBO at the ISAC-CNR of Italy Assessment of 1.5 Yr of experimental Use for Medium-Range Weather Forecasts, Weather Forecast., 26, 1045–1055, https://doi.org/10.1175/WAF-D-11-00027.1, 2011\r\n- Mastrangelo, D. and Malguzzi, P.: Verification of Two Years of CNR-ISAC Subseasonal Forecasts, Weather and Forecasting, 34, 331–344, https://doi.org/10.1175/WAF-D-18-0091.1, 2019", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2024-09-26T02:40:11", "updateFrequency": "", "dataLineage": "Data were produced by scientists at the Institute of Atmospheric Sciences and Climate, Bologna, Italy (CNR-ISAC) and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "nudged, GLOBO, CNR-ISAC, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1x1 degree", "status": "ongoing", "dataPublishedTime": "2024-09-25T13:09:23", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40203, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/CNR-ISAC/GLOBO/nudged/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2463609722283, "numberOfFiles": 6901, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40204, "uuid": "d46157a80d9d4f7b8cfc8d0897e56c30", "short_code": "comp", "title": "GLOBO model run by scientists at CNR-ISAC", "abstract": "This data was produced by the GLOBO model run by scientists at CNR-ISAC (Institute of Atmospheric Sciences and Climate, Bologna, Italy) for the SNAPSI project." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 9045, 50415, 50417, 50418, 50419, 50426, 50427, 50429, 50431, 50445, 50475, 50481, 50496, 50498, 50549, 50566, 50577, 50579, 50582, 50590, 50597, 50609, 52755, 54228, 60438, 62560, 62561, 64078, 66076, 66082 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40209, "uuid": "181b2e501be0452984371d1c77fdab2a", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the GLOBO model at CNR-ISAC", "abstract": "The GLOBO model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by the modelling team at the Institute of Atmospheric Sciences and Climate, Bologna, Italy (CNR-ISAC).\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- Malguzzi, P., Buzzi, A., and Drofa, O.: The Meteorological Global Model GLOBO at the ISAC-CNR of Italy Assessment of 1.5 Yr of experimental Use for Medium-Range Weather Forecasts, Weather Forecast., 26, 1045–1055, https://doi.org/10.1175/WAF-D-11-00027.1, 2011\r\n- Mastrangelo, D. and Malguzzi, P.: Verification of Two Years of CNR-ISAC Subseasonal Forecasts, Weather and Forecasting, 34, 331–344, https://doi.org/10.1175/WAF-D-18-0091.1, 2019" } ], "responsiblepartyinfo_set": [ 196014, 196015, 196016, 196017, 196018, 196019, 196020, 196021 ], "onlineresource_set": [ 83746, 83747, 83748, 83749 ] }, { "ob_id": 40205, "uuid": "5521afe5d13e451eb2b09469e3920a61", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): control data produced by the GLOBO model at CNR-ISAC", "abstract": "This dataset contains model data for SNAPSI experiment 'control' produced by scientists at CNR-ISAC (Institute of Atmospheric Sciences and Climate, Bologna, Italy). It is generated with the coupled climate model GLOBO. \r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe control experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, the stratospheric zonal mean temperatures and zonal winds are nudged towards the time-evolving climatological state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- Malguzzi, P., Buzzi, A., and Drofa, O.: The Meteorological Global Model GLOBO at the ISAC-CNR of Italy Assessment of 1.5 Yr of experimental Use for Medium-Range Weather Forecasts, Weather Forecast., 26, 1045–1055, https://doi.org/10.1175/WAF-D-11-00027.1, 2011\r\n- Mastrangelo, D. and Malguzzi, P.: Verification of Two Years of CNR-ISAC Subseasonal Forecasts, Weather and Forecasting, 34, 331–344, https://doi.org/10.1175/WAF-D-18-0091.1, 2019", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2025-06-03T16:25:21", "updateFrequency": "", "dataLineage": "Data were produced by scientists at the Institute of Atmospheric Sciences and Climate, Bologna, Italy (CNR-ISAC) and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "control, GLOBO, CNR-ISAC, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1x1 degree", "status": "ongoing", "dataPublishedTime": "2024-09-25T11:54:46", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40206, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/CNR-ISAC/GLOBO/control", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2463665849695, "numberOfFiles": 6951, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40204, "uuid": "d46157a80d9d4f7b8cfc8d0897e56c30", "short_code": "comp", "title": "GLOBO model run by scientists at CNR-ISAC", "abstract": "This data was produced by the GLOBO model run by scientists at CNR-ISAC (Institute of Atmospheric Sciences and Climate, Bologna, Italy) for the SNAPSI project." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 9045, 50415, 50417, 50418, 50419, 50426, 50427, 50429, 50431, 50445, 50475, 50481, 50496, 50498, 50549, 50566, 50577, 50579, 50582, 50590, 50597, 50609, 52755, 54228, 60438, 62560, 62561, 64078, 66076, 66082 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40209, "uuid": "181b2e501be0452984371d1c77fdab2a", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the GLOBO model at CNR-ISAC", "abstract": "The GLOBO model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by the modelling team at the Institute of Atmospheric Sciences and Climate, Bologna, Italy (CNR-ISAC).\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- Malguzzi, P., Buzzi, A., and Drofa, O.: The Meteorological Global Model GLOBO at the ISAC-CNR of Italy Assessment of 1.5 Yr of experimental Use for Medium-Range Weather Forecasts, Weather Forecast., 26, 1045–1055, https://doi.org/10.1175/WAF-D-11-00027.1, 2011\r\n- Mastrangelo, D. and Malguzzi, P.: Verification of Two Years of CNR-ISAC Subseasonal Forecasts, Weather and Forecasting, 34, 331–344, https://doi.org/10.1175/WAF-D-18-0091.1, 2019" } ], "responsiblepartyinfo_set": [ 196028, 196029, 196030, 196023, 196024, 196025, 196026, 196027 ], "onlineresource_set": [ 83751, 83752, 83753, 83750 ] }, { "ob_id": 40207, "uuid": "d1683a835200480bb1c7227d1dd1c884", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): free data produced by the GLOBO model at CNR-ISAC", "abstract": "This dataset contains model data for SNAPSI experiment 'free' produced by scientists at CNR-ISAC (Institute of Atmospheric Sciences and Climate, Bologna, Italy). It is generated with the coupled climate model GLOBO. \r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe free experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- Malguzzi, P., Buzzi, A., and Drofa, O.: The Meteorological Global Model GLOBO at the ISAC-CNR of Italy Assessment of 1.5 Yr of experimental Use for Medium-Range Weather Forecasts, Weather Forecast., 26, 1045–1055, https://doi.org/10.1175/WAF-D-11-00027.1, 2011\r\n- Mastrangelo, D. and Malguzzi, P.: Verification of Two Years of CNR-ISAC Subseasonal Forecasts, Weather and Forecasting, 34, 331–344, https://doi.org/10.1175/WAF-D-18-0091.1, 2019", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2024-09-26T08:53:53", "updateFrequency": "", "dataLineage": "Data were produced by scientists at the Institute of Atmospheric Sciences and Climate, Bologna, Italy (CNR-ISAC) and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "free, GLOBO, CNR-ISAC, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1x1 degree", "status": "ongoing", "dataPublishedTime": "2024-09-25T13:18:25", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40208, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/CNR-ISAC/GLOBO/free", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2443061019375, "numberOfFiles": 6301, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40204, "uuid": "d46157a80d9d4f7b8cfc8d0897e56c30", "short_code": "comp", "title": "GLOBO model run by scientists at CNR-ISAC", "abstract": "This data was produced by the GLOBO model run by scientists at CNR-ISAC (Institute of Atmospheric Sciences and Climate, Bologna, Italy) for the SNAPSI project." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 9045, 50415, 50417, 50418, 50419, 50426, 50427, 50429, 50431, 50445, 50475, 50481, 50496, 50498, 50549, 50566, 50577, 50579, 50582, 50590, 50597, 50609, 52755, 54228, 60438, 62560, 62561, 64078 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40209, "uuid": "181b2e501be0452984371d1c77fdab2a", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the GLOBO model at CNR-ISAC", "abstract": "The GLOBO model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by the modelling team at the Institute of Atmospheric Sciences and Climate, Bologna, Italy (CNR-ISAC).\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- Malguzzi, P., Buzzi, A., and Drofa, O.: The Meteorological Global Model GLOBO at the ISAC-CNR of Italy Assessment of 1.5 Yr of experimental Use for Medium-Range Weather Forecasts, Weather Forecast., 26, 1045–1055, https://doi.org/10.1175/WAF-D-11-00027.1, 2011\r\n- Mastrangelo, D. and Malguzzi, P.: Verification of Two Years of CNR-ISAC Subseasonal Forecasts, Weather and Forecasting, 34, 331–344, https://doi.org/10.1175/WAF-D-18-0091.1, 2019" } ], "responsiblepartyinfo_set": [ 196031, 196032, 196033, 196034, 196035, 196036, 196037, 196038 ], "onlineresource_set": [ 83756, 83757, 83754, 83755 ] }, { "ob_id": 40210, "uuid": "87792f7c7fa343168fa47aa3040d1584", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): control data produced by the GRIMs model at SNU", "abstract": "This dataset contains model data for SNAPSI experiment 'control' produced by scientists at SNU (Seoul National University). The dataset contains data from the Global/Regional Integrated Model System (GRIMs) at T126 horizontal and L64 vertical resolutions. The GRIMs model is an atmospheric general circulation model (AGCM) using Optimum Interpolation Sea Surface Temperature (OISST) dataset as ocean boundary conditions and climatological ozone. All data in this dataset are regridded to 1.5x1.5 degree resolution.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe control experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, the stratospheric zonal mean temperatures and zonal winds are nudged towards the time-evolving climatological state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publication:\r\nKoo, MS., Song, K., Kim, JE.E. et al. The Global/Regional Integrated Model System (GRIMs): an Update and Seasonal Evaluation. Asia-Pac J Atmos Sci 59, 113–132 (2023). https://doi.org/10.1007/s13143-022-00297-y", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2023-01-25T00:09:45", "updateFrequency": "", "dataLineage": "Data were generated using the Global/Regional Integrated Model System (GRIMs) at T126 horizontal and L64 vertical resolutions. Simulation data have been converted to CF-netCDF by the SNU team using CMOR, then published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "control, GRIMs, SNU, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1.5x1.5 degree", "status": "ongoing", "dataPublishedTime": "2024-09-30T15:40:46", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40211, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/SNU/GRIMs/control", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 891297668875, "numberOfFiles": 8101, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40212, "uuid": "b09b68bead9e4278aed895e7ff907e84", "short_code": "comp", "title": "Global/Regional Integrated Model System (GRIMs) deployed on KISTI NURION", "abstract": "Global/Regional Integrated Model System (GRIMs) deployed on KISTI NURION. 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Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 50415, 50417, 50418, 50419, 50426, 50427, 50429, 50431, 50445, 50475, 50481, 50496, 50498, 50549, 50566, 50579, 50587, 50588, 50597, 52755, 54228, 60438, 62560, 62561, 64080, 66075, 66076, 66077, 66082, 71619, 71634, 74366 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40217, "uuid": "8210180b4c664012831f8a66c934c004", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the GRIMs model at SNU", "abstract": "The GRIMs model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at Seoul National University (SNU). \r\n\r\nThese datasets contain data from the Global/Regional Integrated Model System (GRIMs) at T126 horizontal and L64 vertical resolutions. The GRIMs model is an atmospheric general circulation model (AGCM) using Optimum Interpolation Sea Surface Temperature (OISST) dataset as ocean boundary conditions and climatological ozone. All data in this dataset are regridded to 1.5x1.5 degree resolution.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publication:\r\nKoo, MS., Song, K., Kim, JE.E. et al. The Global/Regional Integrated Model System (GRIMs): an Update and Seasonal Evaluation. Asia-Pac J Atmos Sci 59, 113–132 (2023). https://doi.org/10.1007/s13143-022-00297-y" } ], "responsiblepartyinfo_set": [ 196047, 196048, 196049, 196050, 196051, 196052, 196053, 196055, 196056 ], "onlineresource_set": [ 83762, 83763, 88091 ] }, { "ob_id": 40213, "uuid": "fd604dd31ffc4d12bd90c17b43ba12a6", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): nudged data produced by the GRIMs model at SNU", "abstract": "This dataset contains model data for SNAPSI experiment 'nudged' produced by scientists at SNU (Seoul National University). The dataset contains data from the Global/Regional Integrated Model System (GRIMs) at T126 horizontal and L64 vertical resolutions. The GRIMs model is an atmospheric general circulation model (AGCM) using Optimum Interpolation Sea Surface Temperature (OISST) dataset as ocean boundary conditions and climatological ozone. All data in this dataset are regridded to 1.5x1.5 degree resolution.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe nudged experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, the stratospheric zonal mean temperatures and zonal winds are nudged towards the observed time-evolving state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publication:\r\nKoo, MS., Song, K., Kim, JE.E. et al. The Global/Regional Integrated Model System (GRIMs): an Update and Seasonal Evaluation. 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Simulation data have been converted to CF-netCDF by the SNU team using CMOR, then published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "nudged, GRIMs, SNU, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1.5x1.5 degree", "status": "ongoing", "dataPublishedTime": "2024-09-30T15:46:55", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40214, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/SNU/GRIMs/nudged", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 891216116243, "numberOfFiles": 8101, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40212, "uuid": "b09b68bead9e4278aed895e7ff907e84", "short_code": "comp", "title": "Global/Regional Integrated Model System (GRIMs) deployed on KISTI NURION", "abstract": "Global/Regional Integrated Model System (GRIMs) deployed on KISTI NURION. 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All data in this dataset are regridded to 1.5x1.5 degree resolution.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publication:\r\nKoo, MS., Song, K., Kim, JE.E. et al. The Global/Regional Integrated Model System (GRIMs): an Update and Seasonal Evaluation. Asia-Pac J Atmos Sci 59, 113–132 (2023). https://doi.org/10.1007/s13143-022-00297-y" } ], "responsiblepartyinfo_set": [ 196065, 196058, 196059, 196060, 196061, 196062, 196063, 196064, 196066 ], "onlineresource_set": [ 83766, 83767, 88093 ] }, { "ob_id": 40215, "uuid": "a53e90273cc24d8ca7845367bf30085b", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): free data produced by the GRIMs model at SNU", "abstract": "This dataset contains model data for SNAPSI experiment 'free' produced by scientists at SNU (Seoul National University). The dataset contains data from the Global/Regional Integrated Model System (GRIMs) at T126 horizontal and L64 vertical resolutions. The GRIMs model is an atmospheric general circulation model (AGCM) using Optimum Interpolation Sea Surface Temperature (OISST) dataset as ocean boundary conditions and climatological ozone. All data in this dataset are regridded to 1.5x1.5 degree resolution.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe free experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publication:\r\nKoo, MS., Song, K., Kim, JE.E. et al. The Global/Regional Integrated Model System (GRIMs): an Update and Seasonal Evaluation. Asia-Pac J Atmos Sci 59, 113–132 (2023). https://doi.org/10.1007/s13143-022-00297-y", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2023-02-02T10:40:03", "updateFrequency": "", "dataLineage": "Data were generated using the Global/Regional Integrated Model System (GRIMs) at T126 horizontal and L64 vertical resolutions. Simulation data have been converted to CF-netCDF by the SNU team using CMOR, then published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "free, GRIMs, SNU, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1.5x1.5 degree", "status": "ongoing", "dataPublishedTime": "2024-09-30T15:49:07", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40216, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/SNU/GRIMs/free", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 889851981949, "numberOfFiles": 7501, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40212, "uuid": "b09b68bead9e4278aed895e7ff907e84", "short_code": "comp", "title": "Global/Regional Integrated Model System (GRIMs) deployed on KISTI NURION", "abstract": "Global/Regional Integrated Model System (GRIMs) deployed on KISTI NURION. The GRIMs model is an atmospheric general circulation model (AGCM) using Optimum Interpolation Sea Surface Temperature (OISST) dataset as ocean boundary conditions and climatological ozone. All data in this dataset are regridded to 1.5x1.5 degree resolution." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 50415, 50417, 50418, 50419, 50426, 50427, 50429, 50431, 50445, 50475, 50481, 50496, 50498, 50549, 50566, 50579, 50587, 50588, 50597, 52755, 54228, 60438, 62560, 62561, 64080, 66075, 66077, 71619, 71634, 74366 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40217, "uuid": "8210180b4c664012831f8a66c934c004", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the GRIMs model at SNU", "abstract": "The GRIMs model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at Seoul National University (SNU). \r\n\r\nThese datasets contain data from the Global/Regional Integrated Model System (GRIMs) at T126 horizontal and L64 vertical resolutions. The GRIMs model is an atmospheric general circulation model (AGCM) using Optimum Interpolation Sea Surface Temperature (OISST) dataset as ocean boundary conditions and climatological ozone. All data in this dataset are regridded to 1.5x1.5 degree resolution.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publication:\r\nKoo, MS., Song, K., Kim, JE.E. et al. The Global/Regional Integrated Model System (GRIMs): an Update and Seasonal Evaluation. Asia-Pac J Atmos Sci 59, 113–132 (2023). https://doi.org/10.1007/s13143-022-00297-y" } ], "responsiblepartyinfo_set": [ 196070, 196067, 196073, 196071, 196068, 196069, 196072, 196074, 196075 ], "onlineresource_set": [ 83768, 83769, 88094 ] }, { "ob_id": 40218, "uuid": "9243d4cabb934d648f70f5c8b32b8abf", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): control data produced by the GloSea6-GC32 model at KMA", "abstract": "This dataset contains model data for SNAPSI experiment 'control' produced by scientists at KMA (Korea Meteorological Administration). The dataset contains data from the Global Seasonal Forecasting System version 6 (GloSea6) at N216 (432x324) horizontal and L85 vertical resolutions. The GloSea6 model is a coupled general circulation model (CGCM) consisting of UM11.5, NEMO3.6, CICE5.1.2, JULES5.6 for atmosphere, ocean, sea ice, and land models, respectively. All data in this dataset are regridded to 1.5x1.5 degree resolution.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe control experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, the stratospheric zonal mean temperatures and zonal winds are nudged towards the time-evolving climatological state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P., and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. J. R. Meteorol. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2014\r\n- Williams, K. D., Harris, C. M., Bodas-Salcedo, A., Camp, J., Comer, R. E., Copsey, D., Fereday, D., Graham, T., Hill, R., Hinton, T., Hyder, P., Ineson, S., Masato, G., Milton, S. F., Roberts, M. J., Rowell, D. P., Sanchez, C., Shelly, A., Sinha, B., Walters, D. N., West, A., Woollings, T., and Xavier, P. K.: The Met Office Global Coupled model 2.0 (GC2) configuration, Geosci. Model Dev., 8, 1509–1524, https://doi.org/10.5194/gmd-8-1509-2015, 2015\r\n- Walters, D., Boutle, I., Brooks, M., Melvin, T., Stratton, R., Vosper, S., Wells, H., Williams, K., Wood, N., Allen, T., Bushell, A., Copsey, D., 675 Earnshaw, P., Edwards, J., Gross, M., Hardiman, S., Harris, C., Heming, J., Klingaman, N., Levine, R., Manners, J., Martin, G., Milton, S., Mittermaier, M., Morcrette, C., Riddick, T., , Roberts, M., Sanchez, C., Selwood, P., Stirling, A., Smith, C., Suri, D., Tennant, W., Vidale, P. L., Wilkinson, J., Willett, M., Woolnough, S., and Xavier, P.: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations, Geosci. Model Dev., 10, 1487–1520, https://doi.org/10.5194/gmd-10-1487-2017, 2017", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2023-02-22T16:54:11", "updateFrequency": "", "dataLineage": "Data were generated using the Global Seasonal Forecasting System version 6 (GloSea6) at N216 horizontal and L85 vertical resolutions. Model output data has been converted to CF-netCDF using CDO, then published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "control, GloSea6-GC32, KMA, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1.5x1.5 degree", "status": "ongoing", "dataPublishedTime": "2024-09-25T14:03:00", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40219, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/KMA/GloSea6-GC32/control", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 898263021827, "numberOfFiles": 4201, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40220, "uuid": "02e928af2efb42d6856750b518bd6ea4", "short_code": "comp", "title": "Global Seasonal Forecasting System version 6 (GloSea6) deployed on the Korea Meteorological Administration's 5th supercomputer.", "abstract": "Global Seasonal Forecasting System version 6 (GloSea6) deployed on KMA's 5th supercomputer at N216 (432x324) horizontal and L85 vertical resolutions. The GloSea6 model is a coupled Global Climate Model (CGCM) consisting of UM11.5, NEMO3.6, CICE5.1.2, JULES5.6 for atmosphere, ocean, sea ice, and land models, respectively. All data in this dataset are regridded to 1.5x1.5 degree resolution." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 50415, 50417, 50418, 50475, 50481, 50496, 50498, 52755, 60438 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40225, "uuid": "81516f7545ef4ba1b39ec87ed5d0e5f1", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the GloSea6-GC32 model at KMA", "abstract": "The GloSea6-GC32 model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at Korea Meteorological Administration (KMA). \r\n\r\nThese datasets contains data from the Global Seasonal Forecasting System version 6 (GloSea6) of Korea Meteorological Administration at N216 (432x324) horizontal and L85 vertical resolutions. The GloSea6 model is a coupled general circulation model (CGCM) consisting of UM11.5, NEMO3.6, CICE5.1.2, JULES5.6 for atmosphere, ocean, sea ice, and land models, respectively. All data in this dataset are regridded to 1.5x1.5 degree resolution.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P., and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. J. R. Meteorol. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2014\r\n- Williams, K. D., Harris, C. M., Bodas-Salcedo, A., Camp, J., Comer, R. E., Copsey, D., Fereday, D., Graham, T., Hill, R., Hinton, T., Hyder, P., Ineson, S., Masato, G., Milton, S. F., Roberts, M. J., Rowell, D. P., Sanchez, C., Shelly, A., Sinha, B., Walters, D. N., West, A., Woollings, T., and Xavier, P. K.: The Met Office Global Coupled model 2.0 (GC2) configuration, Geosci. Model Dev., 8, 1509–1524, https://doi.org/10.5194/gmd-8-1509-2015, 2015\r\n- Walters, D., Boutle, I., Brooks, M., Melvin, T., Stratton, R., Vosper, S., Wells, H., Williams, K., Wood, N., Allen, T., Bushell, A., Copsey, D., 675 Earnshaw, P., Edwards, J., Gross, M., Hardiman, S., Harris, C., Heming, J., Klingaman, N., Levine, R., Manners, J., Martin, G., Milton, S., Mittermaier, M., Morcrette, C., Riddick, T., , Roberts, M., Sanchez, C., Selwood, P., Stirling, A., Smith, C., Suri, D., Tennant, W., Vidale, P. L., Wilkinson, J., Willett, M., Woolnough, S., and Xavier, P.: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations, Geosci. Model Dev., 10, 1487–1520, https://doi.org/10.5194/gmd-10-1487-2017, 2017" } ], "responsiblepartyinfo_set": [ 196086, 196085, 196087, 196088, 196089, 196090, 196091, 196092, 196093 ], "onlineresource_set": [ 83774, 83775, 83776, 83777, 83778 ] }, { "ob_id": 40221, "uuid": "3a4c89e4107a4fc6a9af1255649c335c", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): nudged data produced by the GloSea6-GC32 model at KMA", "abstract": "This dataset contains model data for SNAPSI experiment 'nudged' produced by scientists at KMA (Korea Meteorological Administration). The dataset contains data from the Global Seasonal Forecasting System version 6 (GloSea6) at N216 (432x324) horizontal and L85 vertical resolutions. The GloSea6 model is a coupled general circulation model (CGCM) consisting of UM11.5, NEMO3.6, CICE5.1.2, JULES5.6 for atmosphere, ocean, sea ice, and land models, respectively. All data in this dataset are regridded to 1.5x1.5 degree resolution.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe nudged experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, the stratospheric zonal mean temperatures and zonal winds are nudged towards the observed time-evolving state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P., and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. J. R. Meteorol. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2014\r\n- Williams, K. D., Harris, C. M., Bodas-Salcedo, A., Camp, J., Comer, R. E., Copsey, D., Fereday, D., Graham, T., Hill, R., Hinton, T., Hyder, P., Ineson, S., Masato, G., Milton, S. F., Roberts, M. J., Rowell, D. P., Sanchez, C., Shelly, A., Sinha, B., Walters, D. N., West, A., Woollings, T., and Xavier, P. K.: The Met Office Global Coupled model 2.0 (GC2) configuration, Geosci. Model Dev., 8, 1509–1524, https://doi.org/10.5194/gmd-8-1509-2015, 2015\r\n- Walters, D., Boutle, I., Brooks, M., Melvin, T., Stratton, R., Vosper, S., Wells, H., Williams, K., Wood, N., Allen, T., Bushell, A., Copsey, D., 675 Earnshaw, P., Edwards, J., Gross, M., Hardiman, S., Harris, C., Heming, J., Klingaman, N., Levine, R., Manners, J., Martin, G., Milton, S., Mittermaier, M., Morcrette, C., Riddick, T., , Roberts, M., Sanchez, C., Selwood, P., Stirling, A., Smith, C., Suri, D., Tennant, W., Vidale, P. L., Wilkinson, J., Willett, M., Woolnough, S., and Xavier, P.: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations, Geosci. Model Dev., 10, 1487–1520, https://doi.org/10.5194/gmd-10-1487-2017, 2017", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2024-09-26T02:41:03", "updateFrequency": "", "dataLineage": "Data were generated using the Global Seasonal Forecasting System version 6 (GloSea6) at N216 horizontal and L85 vertical resolutions. 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The GloSea6 model is a coupled Global Climate Model (CGCM) consisting of UM11.5, NEMO3.6, CICE5.1.2, JULES5.6 for atmosphere, ocean, sea ice, and land models, respectively. 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Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 50415, 50417, 50418, 50419, 50426, 50427, 50429, 50431, 50475, 50481, 50496, 50498, 50549, 50566, 50579, 50597, 52755, 54228, 60438 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40225, "uuid": "81516f7545ef4ba1b39ec87ed5d0e5f1", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the GloSea6-GC32 model at KMA", "abstract": "The GloSea6-GC32 model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at Korea Meteorological Administration (KMA). \r\n\r\nThese datasets contains data from the Global Seasonal Forecasting System version 6 (GloSea6) of Korea Meteorological Administration at N216 (432x324) horizontal and L85 vertical resolutions. The GloSea6 model is a coupled general circulation model (CGCM) consisting of UM11.5, NEMO3.6, CICE5.1.2, JULES5.6 for atmosphere, ocean, sea ice, and land models, respectively. All data in this dataset are regridded to 1.5x1.5 degree resolution.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P., and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. J. R. Meteorol. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2014\r\n- Williams, K. D., Harris, C. M., Bodas-Salcedo, A., Camp, J., Comer, R. E., Copsey, D., Fereday, D., Graham, T., Hill, R., Hinton, T., Hyder, P., Ineson, S., Masato, G., Milton, S. F., Roberts, M. J., Rowell, D. P., Sanchez, C., Shelly, A., Sinha, B., Walters, D. N., West, A., Woollings, T., and Xavier, P. K.: The Met Office Global Coupled model 2.0 (GC2) configuration, Geosci. Model Dev., 8, 1509–1524, https://doi.org/10.5194/gmd-8-1509-2015, 2015\r\n- Walters, D., Boutle, I., Brooks, M., Melvin, T., Stratton, R., Vosper, S., Wells, H., Williams, K., Wood, N., Allen, T., Bushell, A., Copsey, D., 675 Earnshaw, P., Edwards, J., Gross, M., Hardiman, S., Harris, C., Heming, J., Klingaman, N., Levine, R., Manners, J., Martin, G., Milton, S., Mittermaier, M., Morcrette, C., Riddick, T., , Roberts, M., Sanchez, C., Selwood, P., Stirling, A., Smith, C., Suri, D., Tennant, W., Vidale, P. L., Wilkinson, J., Willett, M., Woolnough, S., and Xavier, P.: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations, Geosci. Model Dev., 10, 1487–1520, https://doi.org/10.5194/gmd-10-1487-2017, 2017" } ], "responsiblepartyinfo_set": [ 196095, 196096, 196097, 196098, 196099, 196100, 196101, 196102, 196103 ], "onlineresource_set": [ 83783, 83779, 83781, 83780, 83782 ] }, { "ob_id": 40223, "uuid": "9140c9f6b8b14e4a9bd7c976fe61b467", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): free data produced by the GloSea6-GC32 model at KMA", "abstract": "This dataset contains model data for SNAPSI experiment 'free' produced by scientists at KMA (Korea Meteorological Administration). The dataset contains data from the Global Seasonal Forecasting System version 6 (GloSea6) at N216 (432x324) horizontal and L85 vertical resolutions. The GloSea6 model is a coupled general circulation model (CGCM) consisting of UM11.5, NEMO3.6, CICE5.1.2, JULES5.6 for atmosphere, ocean, sea ice, and land models, respectively. All data in this dataset are regridded to 1.5x1.5 degree resolution.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe free experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P., and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. J. R. Meteorol. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2014\r\n- Williams, K. D., Harris, C. M., Bodas-Salcedo, A., Camp, J., Comer, R. E., Copsey, D., Fereday, D., Graham, T., Hill, R., Hinton, T., Hyder, P., Ineson, S., Masato, G., Milton, S. F., Roberts, M. J., Rowell, D. P., Sanchez, C., Shelly, A., Sinha, B., Walters, D. N., West, A., Woollings, T., and Xavier, P. K.: The Met Office Global Coupled model 2.0 (GC2) configuration, Geosci. Model Dev., 8, 1509–1524, https://doi.org/10.5194/gmd-8-1509-2015, 2015\r\n- Walters, D., Boutle, I., Brooks, M., Melvin, T., Stratton, R., Vosper, S., Wells, H., Williams, K., Wood, N., Allen, T., Bushell, A., Copsey, D., 675 Earnshaw, P., Edwards, J., Gross, M., Hardiman, S., Harris, C., Heming, J., Klingaman, N., Levine, R., Manners, J., Martin, G., Milton, S., Mittermaier, M., Morcrette, C., Riddick, T., , Roberts, M., Sanchez, C., Selwood, P., Stirling, A., Smith, C., Suri, D., Tennant, W., Vidale, P. L., Wilkinson, J., Willett, M., Woolnough, S., and Xavier, P.: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations, Geosci. Model Dev., 10, 1487–1520, https://doi.org/10.5194/gmd-10-1487-2017, 2017", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2023-02-22T13:11:19", "updateFrequency": "", "dataLineage": "Data were generated using the Global Seasonal Forecasting System version 6 (GloSea6) at N216 horizontal and L85 vertical resolutions. Model output data has been converted to CF-netCDF using CDO, then published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "free, GloSea6-GC32, KMA, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1.5x1.5 degree", "status": "ongoing", "dataPublishedTime": "2024-09-25T14:07:04", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40224, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/KMA/GloSea6-GC32/free", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 897233655510, "numberOfFiles": 4201, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40220, "uuid": "02e928af2efb42d6856750b518bd6ea4", "short_code": "comp", "title": "Global Seasonal Forecasting System version 6 (GloSea6) deployed on the Korea Meteorological Administration's 5th supercomputer.", "abstract": "Global Seasonal Forecasting System version 6 (GloSea6) deployed on KMA's 5th supercomputer at N216 (432x324) horizontal and L85 vertical resolutions. The GloSea6 model is a coupled Global Climate Model (CGCM) consisting of UM11.5, NEMO3.6, CICE5.1.2, JULES5.6 for atmosphere, ocean, sea ice, and land models, respectively. 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Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 50415, 50417, 50418, 50419, 50431, 50498, 50549, 50566, 50579, 54228, 60438 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40225, "uuid": "81516f7545ef4ba1b39ec87ed5d0e5f1", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the GloSea6-GC32 model at KMA", "abstract": "The GloSea6-GC32 model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at Korea Meteorological Administration (KMA). \r\n\r\nThese datasets contains data from the Global Seasonal Forecasting System version 6 (GloSea6) of Korea Meteorological Administration at N216 (432x324) horizontal and L85 vertical resolutions. The GloSea6 model is a coupled general circulation model (CGCM) consisting of UM11.5, NEMO3.6, CICE5.1.2, JULES5.6 for atmosphere, ocean, sea ice, and land models, respectively. All data in this dataset are regridded to 1.5x1.5 degree resolution.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P., and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. J. R. Meteorol. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2014\r\n- Williams, K. D., Harris, C. M., Bodas-Salcedo, A., Camp, J., Comer, R. E., Copsey, D., Fereday, D., Graham, T., Hill, R., Hinton, T., Hyder, P., Ineson, S., Masato, G., Milton, S. F., Roberts, M. J., Rowell, D. P., Sanchez, C., Shelly, A., Sinha, B., Walters, D. N., West, A., Woollings, T., and Xavier, P. K.: The Met Office Global Coupled model 2.0 (GC2) configuration, Geosci. Model Dev., 8, 1509–1524, https://doi.org/10.5194/gmd-8-1509-2015, 2015\r\n- Walters, D., Boutle, I., Brooks, M., Melvin, T., Stratton, R., Vosper, S., Wells, H., Williams, K., Wood, N., Allen, T., Bushell, A., Copsey, D., 675 Earnshaw, P., Edwards, J., Gross, M., Hardiman, S., Harris, C., Heming, J., Klingaman, N., Levine, R., Manners, J., Martin, G., Milton, S., Mittermaier, M., Morcrette, C., Riddick, T., , Roberts, M., Sanchez, C., Selwood, P., Stirling, A., Smith, C., Suri, D., Tennant, W., Vidale, P. L., Wilkinson, J., Willett, M., Woolnough, S., and Xavier, P.: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations, Geosci. Model Dev., 10, 1487–1520, https://doi.org/10.5194/gmd-10-1487-2017, 2017" } ], "responsiblepartyinfo_set": [ 196104, 196105, 196106, 196107, 196108, 196109, 196110, 196111, 196112 ], "onlineresource_set": [ 83785, 83784, 83786, 83787, 83788 ] }, { "ob_id": 40226, "uuid": "95697468005740fa96f08d223c407a18", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): control data produced by the GloSea6 model at UKMO", "abstract": "This dataset contains model data for SNAPSI experiment 'control' produced by scientists at UKMO (UK Met Office, Exeter, UK). It is generated with the coupled climate ensemble prediction system GloSea6. \r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe control experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, the stratospheric zonal mean temperatures and zonal winds are nudged towards the time-evolving climatological state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P., and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. 605 J. R. Meteorol. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2014", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2024-10-18T02:17:28", "updateFrequency": "", "dataLineage": "Data were produced by scientists at the Met Office, Exeter, UK (UKMO) and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "control, GloSea6, UKMO, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "100 km", "status": "ongoing", "dataPublishedTime": "2024-10-17T08:25:25", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40228, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/UKMO/GloSea6/control", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5817347629423, "numberOfFiles": 8716, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40227, "uuid": "c94db763fc6d4c8daca317928af8ad73", "short_code": "comp", "title": "GloSea6 model run by scientists at UKMO", "abstract": "This data was produced by the GloSea6 model run by scientists at the UK Met Office for the SNAPSI project. GloSea6 is an ensemble prediction system built around the high resolution version of the Met Office climate prediction model: HadGEM3 family." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 50415, 50417, 50418, 50431, 50445, 50481, 50496, 50498, 50590, 50597, 50605, 51211, 52755, 60438 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40379, "uuid": "8fc98ddf822f464dbd8a9e89b2da063c", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the GloSea6 model at UKMO", "abstract": "The GloSea6 model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at the UK Met Office (UKMO). \r\n\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publication:\r\nMacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P., and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. 605 J. R. Meteorol. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2014" } ], "responsiblepartyinfo_set": [ 196129, 196122, 196123, 196124, 196125, 196126, 196127, 196128 ], "onlineresource_set": [ 83794, 83795, 83798, 88131 ] }, { "ob_id": 40229, "uuid": "da04b3aeaf684d57b1ddb63cd9b2ebb0", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): nudged data produced by the GloSea6 model at UKMO", "abstract": "This dataset contains model data for SNAPSI experiment 'nudged' produced by scientists at UKMO (UK Met Office, Exeter, UK). It is generated with the coupled climate ensemble prediction system GloSea6. \r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe nudged experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, the stratospheric zonal mean temperatures and zonal winds are nudged towards the observed time-evolving state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P., and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. 605 J. R. Meteorol. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2014", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2024-10-18T02:17:29", "updateFrequency": "", "dataLineage": "Data were produced by scientists at the Met Office, Exeter, UK (UKMO) and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "nudged, GloSea6, UKMO, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "100 km", "status": "ongoing", "dataPublishedTime": "2024-10-17T08:27:58", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40230, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/UKMO/GloSea6/nudged/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5805759715552, "numberOfFiles": 8701, "fileFormat": "Filesare Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40227, "uuid": "c94db763fc6d4c8daca317928af8ad73", "short_code": "comp", "title": "GloSea6 model run by scientists at UKMO", "abstract": "This data was produced by the GloSea6 model run by scientists at the UK Met Office for the SNAPSI project. GloSea6 is an ensemble prediction system built around the high resolution version of the Met Office climate prediction model: HadGEM3 family." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 9045, 50415, 50417, 50418, 50426, 50427, 50429, 50431, 50445, 50475, 50481, 50496, 50498, 50549, 50577, 50579, 50582, 50587, 50588, 50590, 50597, 50605, 50609, 51210, 51211, 52755, 53111, 54228, 54350, 54366, 54378, 60438, 62560, 62561, 64078 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40379, "uuid": "8fc98ddf822f464dbd8a9e89b2da063c", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the GloSea6 model at UKMO", "abstract": "The GloSea6 model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at the UK Met Office (UKMO). \r\n\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publication:\r\nMacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P., and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. 605 J. R. Meteorol. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2014" } ], "responsiblepartyinfo_set": [ 196131, 196132, 196133, 196134, 196135, 196136, 196137, 196138 ], "onlineresource_set": [ 83800, 83799, 83801, 88133 ] }, { "ob_id": 40231, "uuid": "e49233d13cc244aaab12843c66c51e79", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): free data produced by the GloSea6 model at UKMO", "abstract": "This dataset contains model data for SNAPSI experiment 'free' produced by scientists at UKMO (UK Met Office, Exeter, UK). It is generated with the coupled climate ensemble prediction system GloSea6. \r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe free experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P., and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. 605 J. R. Meteorol. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2014", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2024-10-18T02:17:30", "updateFrequency": "", "dataLineage": "Data were produced by scientists at the Met Office, Exeter, UK (UKMO) and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "free, GloSea6, UKMO, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "100 km", "status": "ongoing", "dataPublishedTime": "2024-10-17T08:29:11", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40232, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/UKMO/GloSea6/free", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5803474903490, "numberOfFiles": 8701, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40227, "uuid": "c94db763fc6d4c8daca317928af8ad73", "short_code": "comp", "title": "GloSea6 model run by scientists at UKMO", "abstract": "This data was produced by the GloSea6 model run by scientists at the UK Met Office for the SNAPSI project. GloSea6 is an ensemble prediction system built around the high resolution version of the Met Office climate prediction model: HadGEM3 family." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 9045, 50415, 50417, 50418, 50419, 50426, 50427, 50429, 50431, 50445, 50475, 50481, 50496, 50498, 50549, 50577, 50579, 50582, 50587, 50588, 50590, 50597, 50605, 50609, 51210, 51211, 52755, 53111, 54228, 54350, 54366, 54378, 60438, 62560, 62561, 64078 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40379, "uuid": "8fc98ddf822f464dbd8a9e89b2da063c", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the GloSea6 model at UKMO", "abstract": "The GloSea6 model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at the UK Met Office (UKMO). \r\n\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publication:\r\nMacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P., and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. 605 J. R. Meteorol. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2014" } ], "responsiblepartyinfo_set": [ 196141, 196142, 196143, 196144, 196145, 196146, 196139, 196140 ], "onlineresource_set": [ 83802, 83804, 83803, 88134 ] }, { "ob_id": 40233, "uuid": "1ddb8b0ca0ae4638b872ad3d60d30933", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): nudged-full data produced by the GloSea6 model at UKMO", "abstract": "This dataset contains model data for SNAPSI experiment 'nudged-full' produced by scientists at UKMO (UK Met Office, Exeter, UK). It is generated with the coupled climate ensemble prediction system GloSea6. \r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe nudged-full experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, stratospheric temperatures and horizontal winds are nudged towards the observed time-evolving state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P., and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. 605 J. R. Meteorol. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2014", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2023-05-25T12:12:20", "updateFrequency": "", "dataLineage": "Data were produced by scientists at the Met Office, Exeter, UK (UKMO) and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "nudged-full, GloSea6, UKMO, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "100 km", "status": "ongoing", "dataPublishedTime": "2024-10-17T08:26:39", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40234, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/UKMO/GloSea6/nudged-full", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5752858210467, "numberOfFiles": 8701, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40227, "uuid": "c94db763fc6d4c8daca317928af8ad73", "short_code": "comp", "title": "GloSea6 model run by scientists at UKMO", "abstract": "This data was produced by the GloSea6 model run by scientists at the UK Met Office for the SNAPSI project. GloSea6 is an ensemble prediction system built around the high resolution version of the Met Office climate prediction model: HadGEM3 family." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 50415, 50417, 50418, 50419, 50427, 50429, 50445, 50475, 50498, 50588, 50590, 50597, 51210, 52755, 60438 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40379, "uuid": "8fc98ddf822f464dbd8a9e89b2da063c", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the GloSea6 model at UKMO", "abstract": "The GloSea6 model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at the UK Met Office (UKMO). \r\n\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publication:\r\nMacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P., and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. 605 J. R. Meteorol. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2014" } ], "responsiblepartyinfo_set": [ 196154, 196147, 196148, 196149, 196150, 196151, 196152, 196153 ], "onlineresource_set": [ 83806, 83805, 83807, 88132 ] }, { "ob_id": 40263, "uuid": "ae1df3ef736f4248927984b7aa079d2e", "title": "Quantification of Utility of Atmospheric Network Technologies: (QUANT): Low-cost air quality measurements from 52 commerical devices at three UK urban monitoring sites.", "abstract": "This dataset contains air quality measurements from 52 commercial low-cost devices at three UK urban monitoring sites over a period of 3 years. This data was collected as part of the Quantification of Utility of Atmospheric Network Technologies: (QUANT) project, specifically Work Package 1 which had the objective to deliver 'Transparent assessment of commercial low-cost sensor devices in multiple UK urban environments'. The three sites are the Manchester Air Quality Supersite (MAQS), London Air Quality Supersite (LAQS), and the Fishergate Automatic Urban and Rural Network (AURN) monitoring site in York. MAQS and LAQS are urban background locations, while Fishergate is a roadside site. The devices report a range of species, with every device measuring either NO2 or PM2.5, with the vast majority recording both. The data was collected over the period 2019-12-10 to 2022-10-31, with half the devices being deployed throughout this entire period and the other half starting in July 2021 as part of a side-study referred to as the Wider Participation Study.\r\n\r\nQUANT was funded as part of the NERC led UKRI Strategic Priorities Fund Clean Air program (grant no. NE/T00195X/1), with support from Defra.\"", "creationDate": "2023-07-03T16:04:35.400670", "lastUpdatedDate": "2023-07-03T16:04:35", "latestDataUpdateTime": "2024-02-20T17:16:18", "updateFrequency": "notPlanned", "dataLineage": "Data were collected and prepared for archiving by the instrument scientists before upload to the Centre for Environmental Data Analysis (CEDA) for long term archiving.", "removedDataReason": "", "keywords": "", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-07-04T09:38:16", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3865, "bboxName": "", "eastBoundLongitude": -0.037389, "westBoundLongitude": -2.214417, "southBoundLatitude": 51.449694, "northBoundLatitude": 53.951917 }, "verticalExtent": null, "result_field": { "ob_id": 40264, "dataPath": "/badc/deposited2023/QUANT", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7693944780, "numberOfFiles": 270, "fileFormat": "NetCDF, CSV" }, "timePeriod": { "ob_id": 11143, "startTime": "2019-12-10T00:00:00", "endTime": "2022-10-31T23:59:59" }, "resultQuality": { "ob_id": 4328, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-07-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 40267, "uuid": "0f681d275cf1425c98943ff3d58f4915", "short_code": "acq", "title": "Acquisition for QUANT", "abstract": "Air quality measurements from 52 commercial low-cost devices at three UK urban monitoring sites over a period of 3 years for the QUANT project" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2521, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 39559, "uuid": "221b1cd13a354434b1e9d22774306078", "short_code": "proj", "title": "Clean Air Programme", "abstract": "The aim of the Clean Air programme is to bring together the UK’s world-class research base and support high-quality multi- and interdisciplinary research and innovation to develop practical solutions for today’s air quality issues and equip the UK to proactively tackle future air quality challenges, in order to protect health and support clean growth.\r\n\r\nThe funding is part of the Strategic Priorities Fund (SPF), delivered by UKRI to drive an increase in high quality multi- and interdisciplinary research and innovation. It will ensure that UKRI’s investment links up effectively with government research priorities and opportunities.\r\n\r\nThe vision of the Clean Air programme is a coordinated landscape of research and innovation co-designed with users to develop robust solutions that reduce emissions and impacts of atmospheric pollution." }, { "ob_id": 40265, "uuid": "9c84439f1bb44192821b0a64aa63b208", "short_code": "proj", "title": "Quantification of Utility of Atmospheric Network Technologies: (QUANT)", "abstract": "Low-cost air pollution sensors have a potentially vital role to play in tackling air pollution in the UK, and globally. The high time resolution and ability to create dense networks of these devices offers a paradigm shift in the way we measure key pollutants, evaluate health impacts of air pollution exposure and assess potential solutions. The QUANT project is assessing and enabling the use of low-cost sensors for UK clean air challenges. This is being achieved through the delivery of a real-world open and traceable assessment of commercial low-cost sensor devices, in a range of UK urban environments, and the development of novel data methods that enhance the information provided by these devices. QUANT is generating new data, using existing and developmental sensor technologies, and novel data methods in order to provide air pollution source information, and is also using data from existing UK sensor networks to demonstrate the retrieval of information currently not accessible to UK air pollution networks. This work has been supported by grants: NE/T00195X/1, NE/T001879/1, NE/T001860/1, NE/T001968/1, NE/T001801/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 55387, 66799, 66800, 66801, 66802, 66803, 66804, 66805, 66806, 66807, 66808, 66809, 66810, 66811, 66812, 66813, 66814, 66815, 66816, 66817, 66818, 66819, 66820, 66821, 66822, 66823, 66824, 69859, 92052, 92053, 92054, 92055, 92056, 92057, 92058, 92059 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 196235, 196236, 196237, 196238, 196239, 196240, 196244, 196245, 196246, 196247 ], "onlineresource_set": [ 83905, 83906 ] }, { "ob_id": 40271, "uuid": "aed8e269513f446fb1b5d2512bb387ad", "title": "CRU JRA v2.4: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901 - Dec.2022.", "abstract": "The CRU JRA V2.4 dataset is a 6-hourly, land surface, gridded time series of ten meteorological variables produced by the Climatic Research Unit (CRU) at the University of East Anglia (UEA), and is intended to be used to drive models. The variables are provided on a 0.5 degree latitude x 0.5 degree longitude grid, the grid is near global but excludes Antarctica (this is the same as the CRU TS grid, though the set of variables is different). The data are available at a 6 hourly time-step from January 1901 to December 2022.\r\n\r\nThe dataset is constructed by regridding data from the Japanese Reanalysis data (JRA) produced by the Japanese Meteorological Agency (JMA), adjusting where possible to align with the CRU TS 4.07 data (see the Process section and the ReadMe file for full details).\r\n\r\nThe CRU JRA data consists of the following ten meteorological variables: 2-metre temperature, 2-metre maximum and minimum temperature, total precipitation, specific humidity, downward solar radiation flux, downward long wave radiation flux, pressure and the zonal and meridional components of wind speed (see the ReadMe file for further details).\r\n\r\nThe CRU JRA dataset is intended to be a replacement of the CRU NCEP forcing dataset. The CRU JRA dataset follows the style of Nicolas Viovy's original CRU NCEP dataset rather than that which is available from UCAR. A link to the CRU NCEP documentation for comparison is provided in the documentation section. \r\nThis version of CRUJRA, v2.4 (1901-2022) is, where possible, adjusted to align with CRU TS monthly means or totals. A consequence of this is that, if CRU TS changes, then CRUJRA changes.\r\n\r\nFor this version, and version 4.07 of CRU TS, the CLD (cloud cover, %) variable is now actualised (converted from gridded anomalies) using the original CLD climatology and not the revised climatology introduced last year. This change/reversion is summarised here: https://crudata.uea.ac.uk/cru/data/hrg/cru_cl_1.1/Read_Me_CRU_CL_CLD_Reversion.txt\r\n\r\nSince CLD is used to align DSWRF, CRUJRA DSWRF will now be 'closer to' version 2.2 and earlier and should be used in preference to v2.3.\r\n\r\nIf this dataset is used in addition to citing the dataset as per the data citation string users must also cite the following:\r\n\r\nHarris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS\r\nmonthly high-resolution gridded multivariate climate dataset.\r\nSci Data 7, 109 (2020). https://doi.org/10.1038/s41597-020-0453-3\r\n\r\nHarris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2014), Updated\r\nhigh-resolution grids of monthly climatic observations - the CRU TS3.10\r\nDataset. International Journal of Climatology 34, 623-642.\r\n\r\nKobayashi, S., et. al., The JRA-55 Reanalysis: General Specifications and\r\nBasic Characteristics. J. Met. Soc. Jap., 93(1), 5-48\r\nhttps://dx.doi.org/10.2151/jmsj.2015-001", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-07-04T18:18:06", "updateFrequency": "notPlanned", "dataLineage": "The CRU JRA data are produced by the Climatic Research Unit (CRU) at the University of East Anglia and are passed to the Centre for Environmental Data Analysis (CEDA) for long-term archival and distribution.", "removedDataReason": "", "keywords": "CRU, JRA, CRUJRA, atmosphere, earth science, climate", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "0.5x0.5 degree grid", "status": "ongoing", "dataPublishedTime": "2023-07-21T14:37:25", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 513, "bboxName": "CRU High Resolution Grid", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -60.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40272, "dataPath": "/badc/cru/data/cru_jra/cru_jra_2.4/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 413450903926, "numberOfFiles": 1221, "fileFormat": "The data are provided as gzipped NetCDF files, with one file per variable, per year." }, "timePeriod": { "ob_id": 11146, "startTime": "1901-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3075, "explanation": "The data are quality controlled by the Climatic Research Unit (CRU) at the University of East Anglia. Details are given in the paper Harries et al. 2014 and the release notes, links to both can be found in the documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2017-02-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40325, "uuid": "c800f7a0b13f495aaf4f0f7f767f156f", "short_code": "comp", "title": "Climatic Research Unit (CRU) procedure to produce the CRU JRA v2.4 data.", "abstract": "The CRU JRA (Japanese reanalysis) data is a replacement to the CRU NCEP dataset, CRU JRA data follows the style of Nicolas Viovy's original dataset rather than that which is available from UCAR.\r\n\r\nThe CRU JRA dataset is based on the JRA-55 reanalysis dataset and aligned where appropriate with the CRU TS dataset version 4.07 (1901-2022).\r\n\r\nAll JRA variables are regridded from their native TL319 Gaussian grid to the CRU regular 0.5° x 0.5° grid, using the g2fsh spherical harmonics routine from NCL (NCAR Command Language), based on the 'Spherepack' code. The exception is precipitation, which is regridded using ESMF 'nearest neighbour': all other algorithms tried exhibited unwanted artifacts.\r\n\r\nThe JRA-55 reanalysis dataset starts in 1958. The years 1901-1957 are constructed using randomly-selected years between 1958 and 1967. Where alignment with CRU TS occurs, the relevant CRU TS data is used.\r\n\r\nOf the ten variables listed above, the last four do not have analogs in the CRU TS dataset. These are simply regridded, masked for land only, and output as CRUJRA. The other six are aligned with CRU TS as follows:\r\n\r\nTMP is aligned with CRU TS TMP. A monthly mean for the JRA data is\r\ncalculated and compared with the equivalent CRU TS mean. The difference\r\nbetween the means is added to every JRA value.\r\n\r\n---\r\n\r\nTMAX and TMIN are aligned with CRUJRA TMP and CRU TS DTR. Firstly, at\r\neach time step, the TMAX-TMP-TMIN triplets are checked and adjusted so\r\nthat TMAX is always >= TMP, and TMIN is always <= TMP. This triplet\r\nalignment is prioritised above DTR alignment. Secondly, monthly JRA DTR\r\nis calculated by first establishing the daily maxima and minima (max and\r\nmin of the subdaily values in TMAX and TMIN respectively), then monthly\r\nmaxima and minima, (means of the daily DTR values), giving JRA monthly\r\nDTR. This is compared with CRU TS DTR and the fractional difference\r\n(factor) calculated as (CRU TS DTR) / (JRA monthly DTR). This factor is\r\nthen used to adjust the DTR of each pair of subdaily TMAX and TMIN\r\nvalues, though not if the triplet alignment would be broken.\r\n\r\n---\r\n\r\nPRE is aligned with CRU TS PRE and WET (rain day counts). Firstly, the\r\nmonthly total precipitation is calculated for JRA and compared to CRU TS\r\nPRE; an adjustment factor is acquired (crupre/jrapre) and all values\r\nadjusted. Precipitation amounts are now aligned at a monthly level, and\r\nthis alignment is prioritised above WET alignment. Secondly, the number\r\nof rain days is calculated for JRA: a day is declared wet if the total\r\nprecipitation is equal to, or exceeds, 0.1mm (the same threshold as CRU\r\nTS WET). If JRA has more wet days than CRU TS, then the driest of those\r\nare reduced to a random amount below 0.1 (an adjustment factor is\r\ncalculated and applied to each time step, to preserve the subdaily\r\ndistribution). If JRA has fewer wet days than CRU TS, then sufficient\r\ndry days are set to a random amount equal to or closely above 0.1mm,\r\nagain using an adjustment factor to preserve the subdaily distribution. \r\nWhere wet day alignment threatens precipitation alignment, the process\r\nis abandoned and the cell/month reverts to the previously-aligned\r\nprecip version. Exception handling is very complicated and cannot be\r\nsummarised here.\r\n\r\n---\r\n\r\nSPFH is aligned with CRU TS VAP. VAP is converted to SPFH, and JRA mean\r\nmonthly SPFH is calculated. The fractional difference (factor) is\r\ncalculated as (CRU TS SPFH) / (JRA monthly SPFH), this factor is then\r\napplied to the JRA subdaily humidity values.\r\n\r\n---\r\n\r\nDSWRF is aligned with CRU TS CLD. CLD is converted to shortwave\r\nradiation, and JRA mean monthly DSWRF is calculated. The fractional\r\ndifference (factor) is calculated as (CRU TS SWR) / (JRA monthly DSWRF),\r\nthis factor is then applied to the JRA subdaily radiation values.\r\n\r\n---\r\n\r\nWhere appropriate, CRUJRA values are kept within physically-appropriate\r\nconstraints (such as negative precipitation), which could result from\r\nregridding as well as adjustments." }, "procedureCompositeProcess": null, "imageDetails": [ 103 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 6672, "uuid": "b6c783922d1ce68c4293d90caede5bb9", "short_code": "proj", "title": "UEA Climatic Research Unit (CRU) Gridded Datasets production project", "abstract": "The Climatic Research Unit at the University of East Anglia is producing various resolution gridded datasets.\r\nSome of those datasets are stored at CEDA-BADC." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26851, "uuid": "863a47a6d8414b6982e1396c69a9efe8", "short_code": "coll", "title": "CRU JRA: Collection of CRU JRA forcing datasets of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data.", "abstract": "This is a collection of the University of East Anglia Climatic Research Unit (CRU) Japanese Reanalysis (JRA) data. The CRU JRA data are 6-hourly, land surface, gridded time series of ten meteorological variables produced by the Climatic Research Unit (CRU) at the University of East Anglia (UEA), and is intended to be used to drive models.\r\n\r\nThe dataset is constructed by combining data from the Japanese Reanalysis data produced by the Japanese Meteorological Agency (JMA) and adjusted where possible to align with the CRU TS data (these 'ten meteorological variables' are not the same ten available from CRU TS).\r\n\r\nThe CRU JRA dataset is intended to be a replacement of the CRUNCEP forcing dataset. The CRU JRA dataset follows the style of Nicolas Viovy's original CRUNCEP dataset rather than that which is available from UCAR." } ], "responsiblepartyinfo_set": [ 196279, 196280, 196281, 196282, 196283, 196284, 196285, 196286, 196287, 196288, 196289, 196290, 196291 ], "onlineresource_set": [ 83823, 83829, 83824, 83825, 83826, 83830, 83827, 83828 ] }, { "ob_id": 40288, "uuid": "07954daa05574788b3f9c5303c80af42", "title": "WCRP CMIP6: the MIROC team MIROC-ES2L model output for the \"esm-pi-cdr-pulse\" experiment", "abstract": "The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the the MIROC team MIROC-ES2L model output for the \"pulse removal of 100 Gt carbon from pre-industrial atmosphere\" (esm-pi-cdr-pulse) experiment. These are available at the following frequencies: Amon, Lmon and Omon. The runs included the ensemble member: r1i1p1f2.\n\nCMIP6 was a global climate model intercomparison project, coordinated by PCMDI (Program For Climate Model Diagnosis and Intercomparison) on behalf of the WCRP and provided input for the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6).\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.\n\nThe the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).", "creationDate": "2023-07-11T11:41:15.839662", "lastUpdatedDate": "2023-07-11T11:41:15.839678", "latestDataUpdateTime": "2024-09-11T13:14:01", "updateFrequency": "asNeeded", "dataLineage": "Data were produced and verified by the MIROC team scientists before publication via the Earth Systems Grid Federation (ESGF) and a copy obtained by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CMIP6, WCRP, climate change, MIROC, MIROC-ES2L, esm-pi-cdr-pulse, Amon, Lmon, Omon", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-07-11T11:41:15.959183", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40289, "dataPath": "/badc/cmip6/data/CMIP6/CDRMIP/MIROC/MIROC-ES2L/esm-pi-cdr-pulse", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1020190784, "numberOfFiles": 9, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 11147, "startTime": "1860-01-16T12:00:00", "endTime": "2100-12-16T12:00:00" }, "resultQuality": { "ob_id": 3341, "explanation": "The CMIP6 data are copied to CEDA from international distributors. CEDA perform no quality control on these data. Where any CMIP6 data are identified as having a quality issue this is recorded in the CMIP6 errata service: https://errata.es-doc.org/static/index.html", "passesTest": true, "resultTitle": "CMIP6 Quality Statement - replica data", "date": "2017-02-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40290, "uuid": "1145230fd72547d68b168b72ee9359c2", "short_code": "comp", "title": "the MIROC team running: experiment esm-pi-cdr-pulse using the MIROC-ES2L model.", "abstract": "The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).the MIROC team running the \"pulse removal of 100 Gt carbon from pre-industrial atmosphere\" (esm-pi-cdr-pulse) experiment using the MIROC-ES2L model. See linked documentation for available information for each component." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2520, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 1, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/CMIP6_Terms_of_Use.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 28348, "uuid": "cbc2dea90a524ca98be60d1712bc7e78", "short_code": "proj", "title": "WCRP CMIP6: the MIROC team contribution", "abstract": "World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 contribution to the project by the the MIROC team The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI) team." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 27828, 27829, 27830, 27831, 50415, 50417, 50496, 50498, 50542, 50543, 50577, 50606, 50607, 60438, 62715, 62741 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 28736, "uuid": "a31ca90e3d524c99aef5d8223135f397", "short_code": "coll", "title": "WCRP CMIP6: the MIROC team MIROC-ES2L model output collection", "abstract": "The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6): Collection of simulations from the the MIROC team MIROC-ES2L model.\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record." } ], "responsiblepartyinfo_set": [ 196324, 196325, 196326, 196327, 196328, 196329, 196330, 196333, 196335, 196331, 196334, 196336, 196332 ], "onlineresource_set": [ 83845, 83846, 83848, 83850, 83851, 83852 ] }, { "ob_id": 40291, "uuid": "40c6b352d1d04007831b4df511217fdb", "title": "WCRP CMIP6: the MIROC team MIROC-ES2H model output for the \"abrupt-4xCO2\" experiment", "abstract": "The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the the MIROC team MIROC-ES2H model output for the \"abrupt quadrupling of CO2\" (abrupt-4xCO2) experiment. These are available at the following frequency: Amon. The runs included the ensemble members: r1i1p1f2, r1i1p2f2, r1i1p3f2 and r1i1p4f2.\n\nCMIP6 was a global climate model intercomparison project, coordinated by PCMDI (Program For Climate Model Diagnosis and Intercomparison) on behalf of the WCRP and provided input for the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6).\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.\n\nThe the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).", "creationDate": "2023-07-11T11:42:42.699208", "lastUpdatedDate": "2023-07-11T11:42:42.699226", "latestDataUpdateTime": "2023-06-12T13:47:08", "updateFrequency": "asNeeded", "dataLineage": "Data were produced and verified by the MIROC team scientists before publication via the Earth Systems Grid Federation (ESGF) and a copy obtained by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CMIP6, WCRP, climate change, MIROC, MIROC-ES2H, abrupt-4xCO2, Amon", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-07-11T11:42:42.834502", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40292, "dataPath": "/badc/cmip6/data/CMIP6/CMIP/MIROC/MIROC-ES2H/abrupt-4xCO2", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 833392973, "numberOfFiles": 21, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 11148, "startTime": "1850-01-16T12:00:00", "endTime": "1989-12-16T12:00:00" }, "resultQuality": { "ob_id": 3341, "explanation": "The CMIP6 data are copied to CEDA from international distributors. CEDA perform no quality control on these data. Where any CMIP6 data are identified as having a quality issue this is recorded in the CMIP6 errata service: https://errata.es-doc.org/static/index.html", "passesTest": true, "resultTitle": "CMIP6 Quality Statement - replica data", "date": "2017-02-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40293, "uuid": "b9a384476daa4c9daf9d01440ea2ff37", "short_code": "comp", "title": "the MIROC team running: experiment abrupt-4xCO2 using the MIROC-ES2H model.", "abstract": "The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).the MIROC team running the \"abrupt quadrupling of CO2\" (abrupt-4xCO2) experiment using the MIROC-ES2H model. 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These are available at the following frequency: Amon. The runs included the ensemble members: r1i1p1f2, r1i1p2f2, r1i1p3f2 and r1i1p4f2.\n\nCMIP6 was a global climate model intercomparison project, coordinated by PCMDI (Program For Climate Model Diagnosis and Intercomparison) on behalf of the WCRP and provided input for the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6).\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.\n\nThe the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).", "creationDate": "2023-07-11T11:44:03.446489", "lastUpdatedDate": "2023-07-11T11:44:03.446515", "latestDataUpdateTime": "2023-06-12T13:27:49", "updateFrequency": "asNeeded", "dataLineage": "Data were produced and verified by the MIROC team scientists before publication via the Earth Systems Grid Federation (ESGF) and a copy obtained by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CMIP6, WCRP, climate change, MIROC, MIROC-ES2H, piControl, Amon", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-07-11T11:44:03.733412", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40295, "dataPath": "/badc/cmip6/data/CMIP6/CMIP/MIROC/MIROC-ES2H/piControl", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2458871418, "numberOfFiles": 30, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 11149, "startTime": "1850-01-16T12:00:00", "endTime": "2269-12-16T12:00:00" }, "resultQuality": { "ob_id": 3341, "explanation": "The CMIP6 data are copied to CEDA from international distributors. CEDA perform no quality control on these data. Where any CMIP6 data are identified as having a quality issue this is recorded in the CMIP6 errata service: https://errata.es-doc.org/static/index.html", "passesTest": true, "resultTitle": "CMIP6 Quality Statement - replica data", "date": "2017-02-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40296, "uuid": "fc2b087ef820440184a41a6c66ea79b1", "short_code": "comp", "title": "the MIROC team running: experiment piControl using the MIROC-ES2H model.", "abstract": "The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).the MIROC team running the \"pre-industrial control\" (piControl) experiment using the MIROC-ES2H model. See linked documentation for available information for each component." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2520, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 1, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/CMIP6_Terms_of_Use.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 28348, "uuid": "cbc2dea90a524ca98be60d1712bc7e78", "short_code": "proj", "title": "WCRP CMIP6: the MIROC team contribution", "abstract": "World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 contribution to the project by the the MIROC team The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI) team." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 50415, 50417, 50496, 50498, 50587, 50588, 50591, 50596, 60438 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 33859, "uuid": "3030e41d2a5b4d0cb2492c007df4ea03", "short_code": "coll", "title": "WCRP CMIP6: the MIROC team MIROC-ES2H model output collection", "abstract": "The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6): Collection of simulations from the the MIROC team MIROC-ES2H model.\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record." } ], "responsiblepartyinfo_set": [ 196366, 196367, 196368, 196369, 196370, 196371, 196372, 196375, 196377, 196373, 196376, 196378, 196374 ], "onlineresource_set": [ 83874, 83871, 83872, 83876, 83878, 83880, 83882, 83883, 83884 ] }, { "ob_id": 40297, "uuid": "63ce273377414fdfadae3cdd242e2f90", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Along-Track Scanning Radiometer (ATSR) Level 3 Collated (L3C) Climate Data Record, version 3.0", "abstract": "This v2.1 SST_cci Along-Track Scanning Radiometer (ATSR) Level 3 Collated (L3C) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the ATSR series of satellite instruments. It covers the period between 11/1991 and 04/2012. This L3C product provides these SST data on a 0.05 regular latitude-longitude grid and collated to include all orbits for a day (separated into daytime and nighttime files).\r\n\r\nThe dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThis CDR Version 2.1 product supercedes the CDR v2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .\r\n\r\nWhen citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI SST project team and supplied to the Centre for Environmental Data Analysis (CEDA) for archiving in the context of the ESA CCI Open Data Portal project and the National Centre for Earth Observation (NCEO).", "removedDataReason": "", "keywords": "SST, ESA Climate Change Initiative, CCI", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.05 degree", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 7135, "startTime": "1991-11-01T00:00:00", "endTime": "2012-04-08T22:59:59" }, "resultQuality": { "ob_id": 3153, "explanation": "As provided by the CCI SST project", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-07-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 27432, "uuid": "d75a78a832c74476a4739c6dff0991c1", "short_code": "cmppr", "title": "CCI SST retrieval process from the (A)ATSR series of instruments", "abstract": "The ESA Climate Change Initiative Sea Surface Temperature (SST) product has retrieved sea surface temperature from the (A)ATSR series of satellite instruments." }, "imageDetails": [ 137 ], "discoveryKeywords": [], "permissions": [], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12726 ], "observationcollection_set": [ { "ob_id": 11005, "uuid": "1dc189bbf94209b48ed446c0e9a078af", "short_code": "coll", "title": "Collection of Sea Surface Temperature (SST) Data of the Global Oceans as part of the ESA Climate Change Initiative (CCI)", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (ESA SST_cci) datasets accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThe latest version (v2.1) of the data are described in the data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . To comply with the attribution aspect, please cite the above reference and the dataset citation given on the relevant dataset page." }, { "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": [ 196387, 196388, 196389, 196390, 196391, 196392, 196393, 196394, 196397, 196395, 196398, 196396, 196399 ], "onlineresource_set": [ 83886, 83887, 83888, 83889, 83885, 83890 ] }, { "ob_id": 40300, "uuid": "5fda109ab71947b6b7724077bf7eb753", "title": "CRU TS4.07: Climatic Research Unit (CRU) Time-Series (TS) version 4.07 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2022)", "abstract": "The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.07 data are month-by-month variations in climate over the period 1901-2022, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia and funded by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre.\r\n\r\nThe CRU TS4.07 variables are cloud cover, diurnal temperature range, frost day frequency, wet day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2022.\r\n\r\nThe CRU TS4.07 data were produced using angular-distance weighting (ADW) interpolation. All versions prior to 4.00 used triangulation routines in IDL. Please see the release notes for full details of this version update. \r\n\r\nThe CRU TS4.07 data are monthly gridded fields based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. The NetCDF versions contain an additional integer variable, ’stn’, which provides, for each datum in the main variable, a count (between 0 and 8) of the number of stations used in that interpolation. The missing value code for 'stn' is -999.\r\n\r\nAll CRU TS output files are actual values - NOT anomalies.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-07-05T14:02:35", "updateFrequency": "notPlanned", "dataLineage": "The CRU TS data are produced by the Climatic Research Unit (CRU) at the University of East Anglia and are passed to the Centre for Environmental Data Analysis (CEDA) for long-term archival and distribution. Previous releases of the CRU TS data include:\r\n\r\nCRU TS 4.07 was provided to CEDA for archival in June 2023.\r\n\r\nCRU TS 4.06 was provided to CEDA for archival in May 2022.\r\n\r\nCRU TS 4.05 was provided to CEDA for archival in June 2021.\r\n\r\nCRU TS 4.04 was provided to CEDA for archival in April 2020.\r\n\r\nCRU TS 4.03 was provided to CEDA for archival in May 2019. \r\n\r\nCRU TS 4.02 was provided to CEDA for archival in December 2018. \r\n\r\nCRU TS 4.01 was provided to CEDA for archival in September 2017. \r\n\r\nCRU TS 4.00 was provided to CEDA for archival in March 2017. \r\n\r\nCRU TS 3.24.01 was provided to CEDA for archival in January 2017. This is the latest version available and is a replacement for the withdrawn dataset 3.24, it supersedes all previous data versions (which are available to allow user comparisons)\r\n\r\nCRU TS 3.24 was provided to CEDA for archival in July 2016. This is the latest version available, superseding all previous data versions (which are available to allow user comparisons), v3.24 has been withdrawn.\r\n\r\nCRU TS 3.23 was provided to CEDA in October 2015 by CRU. This is the latest version available, superseding all previous data versions (which are available to allow user comparisons).\r\n\r\nCRU TS 3.22 was provided to CEDA for archival in July 2014 by CRU.\r\n\r\nCRU TS 3.21 was provided to CEDA for archival in July 2013 by CRU.\r\n\r\nCRU TS 3.20 was produced in December 2012.\r\nIn March 2013, CRU TS observation databases for TMP and PRE variables were provided by CRU. Others are in preparation. In July 2013, two errors were found in the PRE and WET variables of CRU TS v3.20. These have been repaired in CRU TS v3.21. Details of the errors found are available in the Release Notes in the archive.\r\n\r\nCRU TS 3.10.01 In July 2012, systematic errors were discovered in the CRUTS v3.10 process. The effect was, in some cases, to reduce the gridded values for PRE and therefore WET. Values of FRS were found to be unrealistic in some areas due to the algorithms used for synthetic generation. The files (pre, frs and wet) were immediately removed from BADC. The corrected run for precipitation, based on the v3.10 precipitation station data, was generated as a direct replacement and given the version number 3.10.01. There were no corrected runs produced for wet and frs.\r\n\r\nCRU TS 3.00 data files acquired directly from CRU in 2007. CRU provided the BADC with software to generate the CRU datasets in 2010, and this was used to produce CRU TS 3.10 at the BADC in early 2011.", "removedDataReason": "", "keywords": "CRU, CRU TS, 4.07, CRU TS4.07, CRU TS 4, CRU TS 4.07,atmosphere, earth science, climate,", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "0.5x0.5 degree grid", "status": "ongoing", "dataPublishedTime": "2023-11-07T16:19:49", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 513, "bboxName": "CRU High Resolution Grid", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -60.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40301, "dataPath": "/badc/cru/data/cru_ts/cru_ts_4.07", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6954893371, "numberOfFiles": 409, "fileFormat": "Data are provided in ASCII and NetCDF formats." }, "timePeriod": { "ob_id": 11150, "startTime": "1901-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3416, "explanation": "The data are quality controlled by the Climatic Research Unit (CRU) at the University of East Anglia. Details are given in the paper Harris et al. 2020 and the release notes, links to both can be found in the documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-05-21" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 20388, "uuid": "81842aa686174647ae132a4c841d73b6", "short_code": "comp", "title": "UEA Climatic Research Unit (CRU) high resolution gridding software deployed on UEA CRU computer system for v4.00", "abstract": "This computation involved: UEA Climate Research Unit (CRU) High Resolution gridding software deployed on UEA Climate Research Unit (CRU) computer system. For details about the production of CRU TS and CRU CY datasets, please refer to Harris et al. (2020) - see Details/Docs tab, moderated by the Release Notes for v4.00 (which outline the new gridding process)" }, "procedureCompositeProcess": null, "imageDetails": [ 103 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 6672, "uuid": "b6c783922d1ce68c4293d90caede5bb9", "short_code": "proj", "title": "UEA Climatic Research Unit (CRU) Gridded Datasets production project", "abstract": "The Climatic Research Unit at the University of East Anglia is producing various resolution gridded datasets.\r\nSome of those datasets are stored at CEDA-BADC." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 27513, "uuid": "3587430e588b491e8a795664466a27d1", "short_code": "coll", "title": "Climatic Research Unit (CRU): Time-series (TS) datasets of variations in climate with variations in other phenomena v4", "abstract": "Time-series (TS) datasets are month-by-month variation in climate over the last century or so as produced by the Climatic Research Unit (CRU) at the University of East Anglia. These are calculated on high-resolution (0.5x0.5 degree) grids, which are based on an archive of monthly mean temperatures provided by more than 4000 weather stations distributed around the world. They allow variations in climate to be studied, and include variables such as cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum temperature, vapour pressure, potential evapo-transpiration and wet day frequency.\r\n\r\nThe CRU TS data are monthly gridded fields based on daily values -hence the ASCII and netcdf files both contain monthly mean values for the various parameters." } ], "responsiblepartyinfo_set": [ 196400, 196401, 196402, 196403, 196404, 196405, 196406, 196407, 196409, 196410, 196408, 196412, 196411, 196413, 196414 ], "onlineresource_set": [ 83891, 83893, 83894, 83895, 83898, 83899, 83900, 83901, 83902, 83896, 83892, 83897 ] }, { "ob_id": 40302, "uuid": "abe7ca8911a94147888b2859501d4caa", "title": "Synergistic CloudSat-CALIPSO-MODIS retrievals of Cloud-Aerosol-Precipitation (CCM-CAP)", "abstract": "The dataset contains cloud, microphysical and aerosol data retrievals based on observations from the A-train of satellites. Combined observations from CloudSat, CALIOP, and MODIS are used together with data from ECMWF and the liDAR-raDAR (DARDAR) cloud classification to simultaneously retrieve cloud, precipitation and aerosol properties. The dataset includes Particle Size Distribution (PSD) parameters for ice, rain and liquid, further microphysical parameters derived from these PSDs, and aerosol properties.\r\n\r\nThe dataset covers the period that include observations from CloudSat, CALIPSO and MODIS together with the DARDAR cloud classification. Files each include a single granule of retrievals together with errors and metadata of retrieval quality.", "creationDate": "2023-07-12T16:02:10.478365", "lastUpdatedDate": "2023-07-12T16:02:10", "latestDataUpdateTime": "2023-07-12T16:02:10", "updateFrequency": "notPlanned", "dataLineage": "CCM-CAP uses the CAPTIVATE (cloud, aerosols and precipitation from multiple instruments using a variational technique) algorithm developed to provide unified and synergistic optimal estimation retrieval product (ACM-CAP; Mason et al. 2023) from the EarthCARE (Earth Cloud, Aerosol and Radiation Explorer) satellite. \r\n\r\nThe inputs to CCM-CAP are the DARDAR-MASK product (Delanoe and Hogan, 2010; Ceccaldi et al., 2012), supplemented by official CloudSat and CALIPSO products (2B-TB94, MODIS-AUX, and CALIOP VFM). All versions of input files are indicated in the metadata.", "removedDataReason": "", "keywords": "Cloud,Aerosols,Precipitation,Remote Sensing,Optimal estimation", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3868, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": 180.0, "southBoundLatitude": 82.0, "northBoundLatitude": 82.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 11151, "startTime": "2006-01-01T00:00:00", "endTime": "2011-12-31T00:00:00" }, "resultQuality": { "ob_id": 4331, "explanation": "The product follows the ESA standards for the EarthCARE ACM-CAP product. It is a netCDF4/HDF5 file and all metadata and variable names follow the conventions set for that product.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-07-12" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 196415, 196416, 196417, 196418, 196419, 196420 ], "onlineresource_set": [] }, { "ob_id": 40303, "uuid": "6bdd4ff1d50b455cb89b70ad074cb27d", "title": "Radiative flux and heating rates derived from synergistic CloudSat-CALIPSO-MODIS retrievals (CCM-RAD)", "abstract": "This dataset contains radiative data retrievals based on observations from the A-train of satellites. The offline version of the ECMWF radiative transfer scheme (ecRad) has been used to estimate the profiles of broadband radiative fluxes and heating rates at the 1km along-track and 60m vertical resolution of the CCM-CAP (attached to this dataset) product; the top-of-atmosphere radiative fluxes have been compared to CERES broadband flux measurements along the CloudSat track for a simple radiative closure assessment.\r\n\r\nThe dataset covers the period that include observations from CloudSat, CALIPSO and MODIS together with the DARDAR cloud classification. Files each include a single granule of retrievals together with errors and metadata of retrieval quality.", "creationDate": "2023-07-14T14:20:33.889469", "lastUpdatedDate": "2023-07-14T14:20:33", "latestDataUpdateTime": "2023-07-14T14:20:33", "updateFrequency": "notPlanned", "dataLineage": "CCM-RAD is derived from the CCM-CAP retrieval product to facilitate a simple radiative closure assessment against CERES measurements.", "removedDataReason": "", "keywords": "Radiation,Cloud,Aerosol", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3869, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": 180.0, "southBoundLatitude": 82.0, "northBoundLatitude": 82.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 11152, "startTime": "2006-01-01T00:00:00", "endTime": "2011-12-31T00:00:00" }, "resultQuality": { "ob_id": 4332, "explanation": "", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-07-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 196421, 196422, 196423, 196424, 196425, 196426 ], "onlineresource_set": [] }, { "ob_id": 40312, "uuid": "8b92e02868d5430aa63f6b16d4801455", "title": "Non-methane hydrocarbon data from the Manchester Air Quality site, 2022 onwards", "abstract": "A long term dataset of volatile organic carbons (VOCs) measured by a thermal desorption unit coupled to a gas chromatograph fitted with two flame ionisation detectors at the Manchester Air Quality Supersite from 2022 onwards, for the Integrated Research Observation System for Clean Air (OSCA) project.", "creationDate": "2022-08-23T16:27:35.658595", "lastUpdatedDate": "2022-08-23T16:27:35", "latestDataUpdateTime": "2023-07-19T17:03:29", "updateFrequency": "monthly", "dataLineage": "Data is transferred directly from the instrument to the computer database, before being transferred to the general database, where it is processed, and flagged where data is suspect. Once complete, data are uploaded to CEDA for archiving.", "removedDataReason": "", "keywords": "OSCA, air quality, supersite, maqs, OSCA-AQ", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-07-25T12:32:05", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3611, "bboxName": "", "eastBoundLongitude": -2.214244, "westBoundLongitude": -2.214244, "southBoundLatitude": 53.456636, "northBoundLatitude": 53.456636 }, "verticalExtent": null, "result_field": { "ob_id": 40315, "dataPath": "/badc/osca/data/manchester/OSCA_MAQS_VOC_TDGCFID", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3224784, "numberOfFiles": 2, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 11161, "startTime": "2022-01-01T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4341, "explanation": "QA / QC done as part of processing routine and flagged accordingly. Instrument checks performed weekly. Data is c alibrated to the Global Atmosphere Watch (GAW) scale for VOCs through traceable gas standards. The instrument is calibrated at least monthly", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-07-19" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 40314, "uuid": "145a6ccb5476499cb4f771395416c692", "short_code": "acq", "title": "Acquisition for Non-methane hydrocarbon data from the Manchester Air Quality site, 2022", "abstract": "A thermal desorption unit coupled to a gas chromatograph fitted with two flame ionisation detectors (TDGCFID) was used at the Manchester Air Quality Supersite during 2022 for the Integrated Research Observation System for Clean Air (OSCA) project." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37963, "uuid": "ab605b618884401c91afd0274c92144e", "short_code": "proj", "title": "Integrated Research Observation System for Clean Air (OSCA)", "abstract": "The Integrated Research Observation System for Clean Air (OSCA) is a multidisciplinary research project, combining atmospheric observations, laboratory studies, data processing development and integrated scientific synthesis to deliver improved understanding of urban air pollution in the UK, and enable delivery of key objectives of the Clean Air: Analysis and Solutions Programme. OSCA exploits recent significant UK investment in air pollution measurement infrastructure - the air pollution supersites in London, Birmingham and Manchester - and other new UKRI-funded capability developed for field, modelling and laboratory studies of air pollution processes. \r\n\r\nThe project brings together a research team spanning 5 UK HEIs and 2 NERC centres across disciplines of atmospheric science, engineering, mathematics, chemistry, physics and computer science and includes investigators with direct involvement in the provision of science advice in support of policy development - through established links with (e.g.) Department for Environment, Food and Rural Affairs (Defra), Department for Transport (DfT), Department of Health (DoH), regional policymakers, and international bodies. OSCA will provide scientific insights that will inform implementation of the new UK Clean Air Strategy, contribute to development and evaluation of regional air quality policy measures, and enable the development and optimisation of emission abatement measures, for the protection of human health. The project provides a definitive assessment of the current state of UK urban air quality, and of trends in air pollutants - both those expected in response to policy and changing behaviour, and unanticipated consequences of these - and provides data and infrastructure to underpin other proposed projects in the Clean Air Programme. NE/T001984/1, \tNE/T001917/1, NE/T001909/2, NE/T001798/1, NE/T001925/1, NE/T001798/2, NE/T001976/1, NE/T001909/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 86022, 86023, 86024, 86025, 86026, 86027, 86028, 86029, 86030, 86031, 86032, 86033, 86034, 86035, 86036, 86037, 86038, 86039, 86040, 86041, 86042, 86043, 86044, 86045, 86046, 86047, 86048, 86049, 86050, 86051, 86052, 86053, 86054, 86055, 86056, 86057, 86058, 86059, 86060, 86061, 86062, 86063, 86064, 86065, 86066, 86067, 86068, 86069, 86070, 86071, 86072, 86073, 86074, 86075, 86076, 86077, 86078, 86079, 86080, 86081, 86082, 86083, 86084, 86085, 86086, 86087, 86088, 86089, 86090, 86091, 86092, 86093, 86094, 86095, 86096, 86097, 86098, 86099, 86100, 86101, 86102, 86103, 86104, 86105, 86106, 86107, 86108, 86109, 86110, 86111, 86112, 86113, 86114, 86115, 86116, 86117, 86118, 86119, 86120, 86121, 86122, 86123, 86124, 86125, 86126, 86127, 86128, 86129, 86130, 86131, 86132, 86133, 86134, 86135, 86136, 86137, 86138 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 37907, "uuid": "65b50d3348cb4745bb7acfcf6f2057b8", "short_code": "coll", "title": "Integrated Research Observation System for Clean Air (OCSA): Birmingham, Manchester and London air quality supersites data collection", "abstract": "A collection of ground-based observation datasets made at 3 air quality supersites in Manchester, Birmingham and London for the Integrated Research Observation System for Clean Air (OSCA) project which is part of the Strategic Priorities Fund (SPF) Clean Air Programme. \r\n\r\nMeasurements were made of a broad range of atmospheric components by similar comprehensive instrument suites at the 3 sites on a longterm basis beginning at the end of 2018. Additional instrumentation was temporarily added for intensive operation periods in June 2021 and January 2022.\r\n\r\nThis project was a collaboration between University of Manchester, University of Birmingham, Kings College London, University of Cambridge, University of York, Centre for Ecology and Hydrology, National Centre for Atmospheric Science (NCAS) and University of Edinburgh." }, { "ob_id": 45670, "uuid": "824c0c058de442ab885431eba938dc03", "short_code": "coll", "title": "NCAS archive of Air quality measurements from Manchester, Birmingham and London air quality supersites", "abstract": "This collection contains maeasurements of air quality parameters from a suite of instruments deployed at 3 urban supesites located in Manchester, Birmingham and London Honor Oak Park.\r\n\r\nMore details to follow." } ], "responsiblepartyinfo_set": [ 196479, 196480, 196481, 196482, 196476, 196477, 196478, 196510, 196511, 196475 ], "onlineresource_set": [ 83907 ] }, { "ob_id": 40322, "uuid": "005f2e0aebc24ed98a9772a0ba3798e2", "title": "ForestScan Project: Multiple Unpiloted Aerial Vehicle LiDAR Scanning (UAV-LS) data acquisitions of FBRMS-01: Paracou, French Guiana, plots 4, 5, 6, 8, IRD-CNES and Flux-Tower area, October 2019", "abstract": "This dataset contains complete merged point clouds (.laz) per acquisition flight, derived raster products in .tif georeferenced format, Digital surface models (.DSM), Canopy height models (.CHM) and Digital terrain models (.DTM). The data is from multiple drone flights over different plots in Paracou French Guiana. All products are the following projection EPSG:2972 RGFG95 and UTM (Universal Transverse Mercator) zone 22N. Different scanning scenarios should allow for sensitivity analyses with respect to:\r\n\r\n(i) scanning altitude (above the LiDAR (Light Detection and Ranging) derived DTM, either in terrain follow mode (AGL) or at constant altitude (AMSL)\r\n(ii) scanning pattern main orientation\r\n\r\nOver Plot 6 (P6): two different scan altitudes are available (80m AGL and 145m AMSL ~ 125 AGL), as well as different flight pattern main directions (75°, 345°, 120°, 165°)\r\n\r\nOver Plots 4 and 5 (P4 & P5): altitude = 110m AMSL ~ 90m AGL, flight directions = 345°\r\n\r\nOver PCNES & Tower: altitude = 105m AMSL ~ 80m AGL and 80m AGL, flight directions = 0° and 90° \r\n\r\nOver P8: altitude = 105m AMSL ~ 80m AGL, flight directions = 75° and 345° \r\n\r\nInformation on the individual drone flights and directory location can be found below in the following format: \r\n\r\nCountry\\Zone\\Pilot\\Scanner\\Date\\Flight\\Flight (delivery)\\Freq mirror rot (Hz)\\Interline(m)\\Direction (°)\\Speed(m/s)\\Alt(m) \r\ndirectory location- XX/XX\r\n\r\n\r\nFrench Guiana\\Paracou \\P6\\NB\\VX20021\\18/10/2019\\YS-20191018-124006\\V1\\20\\20\\345\\5t80 AGL \r\ndirectory location - CIRAD_Plot_P6_80mAGL/V1\r\n\r\nFrench Guiana\\Paracou\\P6\\NB\\VX20021\\18/10/2019\\tYS-20191018-131043\\V2\\20\\20\\345\\5\\80 AGL \r\ndirectory location - CIRAD_Plot_P6_80mAGL/V2\r\n\r\nFrench Guiana\\Paracou\\P6\\NB\\VX20021\\18/10/2019\\YS-20191018-183057\\V3\\20\\20\\120\\t5\\80 AGL \r\ndirectory location - CIRAD_Plot_P6_80mAGL/V3\r\n\r\nFrench Guiana\\Paracou\\P6\\NB\\VX20021\\18/10/2019\\YS-20191018-185416\\V4\\20\\20\\120\\5\\80 AGL \r\ndirectory location - CIRAD_Plot_P6_80mAGL/V4\r\n\r\nFrench Guiana\\Paracou\\P6\\NB\\VX20021\\19/10/2019\\YS-20191019-190345\\V5\\20\\20\\75\\5\\80 AGL \r\ndirectory location - CIRAD_Plot_P6_80mAGL/ V5\r\n\r\nFrench Guiana\\Paracou\\P6\\NB\\VX20021\\18/10/2019\\YS-20191018-200932\\V1\\20\\20\\165\\5\\145 amsl \r\ndirectory location - CIRAD_Plot_P6_145_amsl/V1\r\n\r\nFrench Guiana\\Paracou\\P6\\NB\\VX20021\\19/10/2019\\YS-20191019-115917\\V2\\20\\20\\75\\5\\145 amsl \r\ndirectory location - CIRAD_Plot_P6_145_amsl/V2\r\n\r\nFrench Guiana\\Paracou\\P4&5\\NB\\VX20021\\19/10/2019\\YS-20191019-172347\\20\\50\\345\\5\\100 amsl \r\ndirectory location - CIRAD_Plot_4_5/\r\n\r\nFrench Guiana\\Paracou\\Tower\\NB\\VX20021\\19/10/2019\\YS-20191019-162557\\V1\\20\\50\\0\\5\\80 AGL \r\ndirectory location - IRD_CNES_Plot1_and_Flux_Tower_area/V1\r\n\r\nFrench Guiana\\Paracou\\Tower\\NB\\VX20021\\19/10/2019\\YS-20191019-181021\\V2\\20\\50\\90\\5\\105 amsl \r\ndirectory location - IRD_CNES_Plot1_and_Flux_Tower_area/V2\r\n\r\nFrench Guiana\\Paracou\\P8\\NB\\VX20021\\20/10/2019\\YS-20191020-113907\\20\\50\\75&345\\5\\105 amsl \r\ndirectory location - CIRAD_Plot_P8\r\n\r\nThe data was gathered to support the systematic collection and understanding of reference data for biomass product validation. The CEOS Good Practices Guideline can be found in the documentation section.", "creationDate": "2023-07-20T10:53:25.507487", "lastUpdatedDate": "2023-07-20T10:46:25", "latestDataUpdateTime": "2025-03-29T01:54:34", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). Revised metadata was provided by the UCL project team", "removedDataReason": "", "keywords": "ForestScan project, GEO-TREES, BIOMASS mission, European Space Agency (ESA), Earth Observation (EO) calibration/validation, Unpiloted Aerial Vehicle LiDAR Scanning (UAV-LS), Aerial LiDAR Scanning (ALS), Digital twins, Forest structureracou, Forest", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-02-24T10:41:35", "doiPublishedTime": "2025-03-28T16:52:47.233213", "removedDataTime": null, "geographicExtent": { "ob_id": 3953, "bboxName": "UAV Paracou", "eastBoundLongitude": -52.032647, "westBoundLongitude": -52.9211533, "southBoundLatitude": 5.262807, "northBoundLatitude": 5.280352 }, "verticalExtent": null, "result_field": { "ob_id": 40628, "dataPath": "/neodc/forestscan/data/french_guiana/paracou/UAV_Paracou_2023_MultiplePlots/AMAP_Paracou_drone/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3964810390, "numberOfFiles": 165, "fileFormat": "Complete merged point clouds (.laz) per acquisition flight and derived raster products in .tif georeferenced format (Digital surface model - DSM; Canopy height model - CHM; Digital terrain model - DTM)" }, "timePeriod": { "ob_id": 11275, "startTime": "2019-10-19T00:00:00", "endTime": "2019-10-20T00:00:00" }, "resultQuality": { "ob_id": 4410, "explanation": "Data was validated by the ForestScan project team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-08-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 40637, "uuid": "0114f101e5a84fb0b1cf00d4438b65c8", "short_code": "cmppr", "title": "Forest Scan UAV Paracou 2019", "abstract": "Forest Scan UAV Paracou 2019" }, "imageDetails": [ 111 ], "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": 40337, "uuid": "3da9f666e3ea4bb3af9cca064f05d3af", "short_code": "proj", "title": "ForestScan Project: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data", "abstract": "The ForestScan project was conceived to evaluate new technologies for characterizing forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). It is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and is part of GEO-TREES, an international consortium dedicated to establishing a global network of Forest Biomass Reference Measurement Sites (FBRMS) to support EO and encourage investment in relevant field-based observations and science. ForestScan is the first demonstration of what can be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of E-derived AGB estimates.\r\n\r\nWe present data from the ForestScan project, a unique multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unpiloted aerial vehicle LiDAR scanning (UAV-LS), airborne LiDAR scanning (ALS), and in-situ tree census and ancillary data. These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for three FBRMS: FBRMS-01: Paracou, French Guiana; FBRMS-02: Lopé, Gabon; and FBRMS-03: Kabili-Sepilok, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection. We also provide detailed data collection protocols and recommendations for TLS, UAV-LS, and plot census measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates. We outline the requirements and challenges for field data collection for each data type and discuss the practical considerations for establishing new FBRMS or upgrading existing sites to FBRMS standard, including insights into the associated costs and benefits.\r\n\r\nThe ForestScan Project was funded under ESA contract: 4000126857/20/NL/AI" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13275 ], "observationcollection_set": [ { "ob_id": 40623, "uuid": "88a8620229014e0ebacf0606b302112d", "short_code": "coll", "title": "ForestScan Collection", "abstract": "This collection is part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates. The collection contains a multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unoccupied aerial vehicle LiDAR scanning (UAV-LS), airborne laser scanning (ALS), and in-situ tree census and ancillary data. ForestScan was conceived to evaluate new technologies for characterising forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for the first three Forest Biomass Research Monitoring Sites in Paracou Research Station in French Guiana; Station d'Etudes des Gorilles et Chimpanzes, Lopé National Park in Gabon; and Kabili-Sepilok Forest Reserve, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, unmanned aerial vehicle-based laser scanning (UAV-LS) and airborne laser scanning (ALS) where possible, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection.\r\n\r\nWe also provide detailed field data collection protocols for TLS, UAV-LS, and ALS measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates.\r\n\r\nThe ForestScan project is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and GEO-TREES, a new Group on Earth Observations (GEO) initiative aimed at establishing a network of FBRM sites. ForestScan is the first demonstration of what could be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of EO-derived AGB estimates" }, { "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": [ 196544, 196545, 196546, 196547, 196548, 196549, 197704, 197705, 197706 ], "onlineresource_set": [ 88336, 88433, 94258 ] }, { "ob_id": 40331, "uuid": "d3018891eac34460a7723811a2b69580", "title": "Daily Light Detection and Ranging (LiDAR) scans of an eroding soft cliff at Happisburgh, UK (April-September 2019)", "abstract": "This dataset contains 169 point-cloud elevation and colour intensity data collected daily along a 450 metre coastal stretch at Happisburgh, UK, over a time span of 6 months (April 6, 2019 to September 30, 2019). Also included are subsets of these point-clouds, named slices and grids. Scans were taken approximately daily, and on some days only one scanner was run resulting in half-size scans. A single FARO S350 LiDAR scanner was placed at two fixed locations on the beach, spaced 178 metres alongshore and between 30 to 40 metres from the 10 metre high cliff. The duration of the scanning at each location was around 30 minutes. This data was collected to better understand the dynamic of beach-cliff and shore platform interaction along soft cliffed coasts. ScanLAB Projects Ltd were responsible for the collection of the data under the United Kingdom Research and Innovation (UKRI) Innovate UK funded project “Multiscale 3D Scanning with Framerate for TV and Immersive Applications”. The data are restricted to non-commercial use.", "creationDate": "2023-07-25T14:03:02.444219", "lastUpdatedDate": "2023-07-25T14:03:02", "latestDataUpdateTime": "2025-01-18T03:20:16", "updateFrequency": "notPlanned", "dataLineage": "The raw data collected was further filtered and co-registered during post processing using FARO scene software. The global position of each TLS was recorded using a Leica GS15 at the end of the 9 month capture period. Then the exported scan data was transformed spatially to the GPS position and orientation recorded on-site, using ScanLABs proprietary processing software. All elevations are shown relative to Ordnance Datum Newlyn. The data are archived on the British Oceanographic Data Centre (BODC)'s space at the Centre for Environmental Data Analysis (CEDA) and assigned a DOI. No quality control procedures were applied by BODC.", "removedDataReason": "", "keywords": "coast, cliff, beach, shore, platform, Happisburgh, LiDAR, meteorology, oceanography", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-08-11T08:58:21", "doiPublishedTime": "2023-10-11T12:31:26", "removedDataTime": null, "geographicExtent": { "ob_id": 3892, "bboxName": "", "eastBoundLongitude": 1.540195603, "westBoundLongitude": 1.531080727, "southBoundLatitude": 52.82323871, "northBoundLatitude": 52.8288520697982 }, "verticalExtent": null, "result_field": { "ob_id": 40332, "dataPath": "/bodc/BGS230127", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4921100010842, "numberOfFiles": 10860, "fileFormat": ".xyz" }, "timePeriod": { "ob_id": 11194, "startTime": "2019-04-06T00:00:00", "endTime": "2019-09-30T23:59:59" }, "resultQuality": { "ob_id": 3897, "explanation": "The data are provided as-is with no quality control undertaken by the British Oceanographic Data Centre (BODC). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "BODC Data Quality Statement", "date": "2022-03-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 40333, "uuid": "baba71debc674432b017a1e85f8c9568", "short_code": "acq", "title": "Acquisition for: Daily Light Detection and Ranging (LiDAR) scans of an eroding soft cliff at Happisburgh, UK (April-September 2019)", "abstract": "A single FARO S350 LiDAR scanner was placed at two fixed locations on the beach, spaced 178 m alongshore and between 30 to 40 m from the 10m high cliff. One location at the concrete foundation of an old staircase (30 m from the cliff), now removed, and the other being at a partially buried concrete pill box, previously on top of the cliff, but now on the beach and around 40 m from the cliff due to cliff recession. These two locations were accessible during low tide and their positions were found to be sufficiently stable during the observation period. The system used to anchor the TLS to each location differed at each one and were designed to ensure fast and accurate positioning of the TLS. At the pill box, a stainless-steel element was attached to the concrete providing a stable anchoring point, and at the staircase the TLS was attached to a tripod, whose leg lengths were fixed and equal throughout the whole observation period. The TLS were aligned with the horizon to assist during the co-registration process. The duration of the scanning at each location was around 30 minutes, and the TLS was moved from one location to the other consecutively. The TLS had a maximum usable target distance of 350m and point distance of 1.5mm at 10m. The raw data collected was further filtered and co-registered during post processing using FARO scene software. The global position of each TLS was recorded using a Leica GS15 at the end of the 9 month capture period. Then the exported scan data was transformed spatially to the GPS position and orientation recorded on-site, using ScanLABs proprietary processing software. All elevations are shown relative to Ordnance Datum Newlyn." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2651, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "scanlab", "label": "restricted: scanlab group", "licence": { "ob_id": 95, "licenceURL": "https://artefacts.ceda.ac.uk/licences/cuncgl_versions/cuncgl_v1-0.pdf", "licenceClassifications": [ { "ob_id": 6, "classification": "personal" }, { "ob_id": 4, "classification": "academic" }, { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 40348, "uuid": "d3181552932446dcbd9846789d7c5c35", "short_code": "proj", "title": "Multiscale 3D Scanning with Framerate for TV and Immersive Applications", "abstract": "To tell a truly immersive story captured real world content must be compelling, accurate and believable. It must also be spatial and digitally manipulable. Existing techniques to capture live action in 3D over time are prohibitively expensive and limited in range to human scale events. 3D scanning techniques and the resultant 'pointclouds' created are uniquely positioned to fulfil these needs, capturing photoreal content in a spatial manner across scales. ScanLAB are a pioneering creative technology practice and global leaders in pointcloud visualisation for offline and realtime rendered static content (i.e. data without a framerate). This project will enable ScanLAB to create a fundamentally new tool set to capture and render pointcloud data with a framerate at multiple scales." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12716 ], "observationcollection_set": [ { "ob_id": 40060, "uuid": "2c6f3201f01d4346a97ff8f08a8c15c9", "short_code": "coll", "title": "LiDAR (Light Detection And Ranging) images and model output from cliffs at Happisburgh, Norfolk, UK, 2019, from BLUE-coast and ScanLAB projects.", "abstract": "A colour LiDAR (Light Detection And Ranging) dataset was obtained at the cliffs at Happisburgh, Norfolk, UK, over a period of 9 months (April 6, 2019 to December 23, 2019). The scans were taken daily for 90% of the study period using a FARO S350 TLS (Terrestrial LiDAR Scanner). Scans were carried out from two locations consecutively, positioned at around 40 m from the cliffs. The full scans are also split into smaller subsets: \"slices\", 1 m wide bands oriented perpendicular to the shoreline, and \"grids\", smaller areas of the beach, to assist analysis. The numerical model SWAN (Simulated Waves Nearshore) (v41.31a), run in non-stationary mode, was used to simulate hourly sea states at the study site to aid in the context of environmental conditions. Wind parameters from the ERA5 reanalysis and bathymetry from the OceanWise 1 arc second digital elevation model (DEM) were used to force the SWAN model, and obtained wave parameters in 4x6 km rectangular grid around the scanning site, with a 10m interval, and a 26x26 km square grid encompassing the smaller grid, with a 100 m interval. The LiDAR scans were also projected into both colour and intensity images, viewing the shoreline from above. This research was funded by the UK Natural Environment Research Council (NE/M004996/1; BLUE-coast project). The on-location LiDAR Scanning and Technical R&D operated by ScanLAB Projects Ltd was funded by Innovate UK's Audience of the Future Program (Multiscale 3D Scanning with Framerate for TV and Immersive Applications project). The first 6 months of LiDAR scans (April to September 2019) were funded by Innovate UK, and this project was continued by the NERC BLUE-coast funding for the last 3 months (October to December 2019)." } ], "responsiblepartyinfo_set": [ 196587, 196590, 196584, 196586, 196585, 196598, 196639, 196589, 196588, 196592, 196593, 196594, 196595, 196596, 196597, 196591 ], "onlineresource_set": [ 83926 ] }, { "ob_id": 40346, "uuid": "def64ef885684e199f03a4c50bc2f8dc", "title": "CRU CY4.07: Climatic Research Unit year-by-year variation of selected climate variables by country version 4.07 (Jan. 1901 - Dec. 2022)", "abstract": "The Climatic Research Unit (CRU) Country (CY) data version 4.07 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency: including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure, potential evapotranspiration and wet day frequency. This version uses the updated set of country definitions, please see the appropriate Release Notes.\r\n\r\nThis dataset was produced in 2023 by CRU at the University of East Anglia and extends the CRU CY4.06 data to include 2022. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY4.07 is derived directly from the CRU time series (TS) 4.06 dataset. CRU CY version 4.07 spans the period 1901-2022 for 292 countries.\r\n\r\nTo understand the CRU CY4.07 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.06. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.07 release notes listed in the online documentation on this record.\r\n\r\nCRU CY data are available for download to all CEDA users.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-07-26T01:45:39", "updateFrequency": "notPlanned", "dataLineage": "The Climatic Research Unit (CRU) CY data are derived directly from the CRU TS data, and version numbering is matched between the two datasets. The CRU CY data are produced by the CRU unit at the University of East Anglia and passed to the Centre for Environmental Data Analysis (CEDA) for long-term archival and distribution. Previous releases of CRU CY include:\r\nCRU CY 4.07 data were passed to CEDA for archival and distribution by CRU in May 2023.\r\n\r\nCRU CY 4.06 data were passed to CEDA for archival and distribution by CRU in May 2022.\r\n\r\nCRU CY 4.05 data were passed to CEDA for archival and distribution by CRU in June 2021.\r\n\r\nCRU CY 4.04 data were passed to CEDA for archival and distribution by CRU in October 2020.\r\n\r\nCRU CY 4.03 data were passed to CEDA for archival and distribution by CRU in May 2019.\r\n\r\nCRU CY 4.02 data were passed to CEDA for archival and distribution by CRU in November 2018.\r\n\r\nCRU CY 4.01 data were passed to CEDA for archival and distribution by CRU in September 2017.\r\n\r\nCRU CY 4.00 data were passed to CEDA for archival and distribution by CRU in March 2017.\r\n\r\nCRU CY 3.24.01 data files supplied to CEDA for long term archival by CRU in January 2017.\r\n\r\nThe CRU CY 3.24 data were withdrawn by CRU and CEDA in January 2017 due to known issues with the data.\r\n\r\nCRU CY 3.24 data files supplied to CEDA for long term archival by CRU in October 2016.", "removedDataReason": "", "keywords": "CRU, CRU CY, CY, climate", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "0.5x0.5 degree grid", "status": "ongoing", "dataPublishedTime": "2024-07-25T11:12:53", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 513, "bboxName": "CRU High Resolution Grid", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -60.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40347, "dataPath": "/badc/cru/data/cru_cy/cru_cy_4.07/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 51821666, "numberOfFiles": 2924, "fileFormat": "The CRU CY data are provided as text files with the extension \".per\", most text editors will open these files. See the linked file formats guide for more information." }, "timePeriod": { "ob_id": 11200, "startTime": "1901-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3080, "explanation": "CRU CY data are quality controlled by the Climatic Research Unit (CRU) at the University of East Anglia. Details are given in the paper Harris et al. 2014 and the release notes, links to both can be found in the documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2017-04-07" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 20388, "uuid": "81842aa686174647ae132a4c841d73b6", "short_code": "comp", "title": "UEA Climatic Research Unit (CRU) high resolution gridding software deployed on UEA CRU computer system for v4.00", "abstract": "This computation involved: UEA Climate Research Unit (CRU) High Resolution gridding software deployed on UEA Climate Research Unit (CRU) computer system. For details about the production of CRU TS and CRU CY datasets, please refer to Harris et al. (2020) - see Details/Docs tab, moderated by the Release Notes for v4.00 (which outline the new gridding process)" }, "procedureCompositeProcess": null, "imageDetails": [ 103 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 6672, "uuid": "b6c783922d1ce68c4293d90caede5bb9", "short_code": "proj", "title": "UEA Climatic Research Unit (CRU) Gridded Datasets production project", "abstract": "The Climatic Research Unit at the University of East Anglia is producing various resolution gridded datasets.\r\nSome of those datasets are stored at CEDA-BADC." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 27835, "uuid": "a5fc25a8153148b9872f24ab889f64a9", "short_code": "coll", "title": "Climatic Research Unit (CRU): Year-by-Year Variation of Selected Climate Variables by CountrY (CY) v4", "abstract": "The CRU CY datasets consists of country averages at a monthly, seasonal and annual frequency, for ten climate variables in 289 countries. Spatial averages are calculated using area-weighted means. Variables include cloud cover (cld), diurnal temperature range (dtr), frost day frequency (frs), precipitation (pre), daily mean temperature (tmp), monthly average daily maximum (tmx) and minimum (tmn) temperature, vapour pressure (vap), Potential Evapo-transpiration (pet) and wet day frequency (wet). The CRU CY datasets produced by the Climatic Research Unit (CRU) at the University of East Anglia.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY is derived directly from the CRU TS dataset and version numbering is matched between the two datasets. Thus, the first official version of CRU CY is v3.21, as it is based on CRU TS v3.21 (1901-2012) and the latest version of CRU-CY is v4.03, as it is based on CRU TS v4.03. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nTo understand the CRU-CY dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS. It is therefore recommended that all users read the paper referenced below (Harris et al, 2014)." } ], "responsiblepartyinfo_set": [ 196622, 196623, 196624, 196625, 196626, 196627, 196628, 196629, 196630, 196631, 196632, 196633, 196634, 196635, 196636 ], "onlineresource_set": [ 83921, 83922, 83924, 83923, 83920, 83918, 83919, 83925 ] }, { "ob_id": 40351, "uuid": "af0ea05fec104b418d4dc43f230f35e5", "title": "CCMI-2022: senD2-sai data produced by the MIROC-ES2H model from MIROC", "abstract": "This dataset contains model data for CCMI-2022 experiment senD2-sai produced by the MIROC-ES2H model which is based on a global climate model MIROC (Model for Interdisciplinary Research on Climate). This has been cooperatively developed by JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan).\r\n\r\nThe senD2-sai simulation is based on the refD2 experiment but with a modified specified stratospheric aerosol distribution reflecting increased stratospheric aerosol amounts from stratospheric aerosol injection (SAI). Sea ice and sea surface temperatures (SSTs) are specified to follow a repeating annual cycle taken from those used by the same model for their refD2 experiment over 2020 - 2030, the period when SAI is assumed to have been initiated.\r\n\r\nThe refD2 experiment is the baseline projection for updated projections of ozone recovery. Specified forcings largely following the same specifications as for the SSP2-4.5 scenario of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), with the exception of the near-surface mixing ratio of Ozone Depleting Substances which follow the baseline projection from WMO (2018).\r\n\r\nSSP2-4.5 is a Shared Socio-economic Pathway scenario that follows socio-economic storyline SSP2 with intermediate mitigation and adaptation challenges and climate forcing pathway RCP4.5 which leads to a radiative forcing of 4.5 Wm-2 by the year 2100.\r\n\r\nWMO-2018 refers to the Scientific Assessment of Ozone Depletion: 2018.\r\n\r\nThe CCMI-2022 Chemistry-climate model initiative is a set of model experiments focused on the stratosphere, with the goals of providing updated projections towards the future evolution of the ozone layer and improving our understanding of chemistry-climate interactions from models.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Review of the global models used within phase 1 of the Chemistry-Climate Model Initiative (CCMI)\r\n- A new set of Chemistry-Climate Model Initiative (CCMI) Community Simulations to Update the Assessment of Models and Support Upcoming Ozone Assessment Activities, David Plummer and Tatsuya Nagashima and Simone Tilmes and Alex Archibald and Gabriel Chiodo and Suvarna Fadnavis and Hella Garny and Beatrice Josse and Joowan Kim and Jean-Francois Lamarque and Olaf Morgenstern and Lee Murray and Clara Orbe and Amos Tai and Martyn Chipperfield and Bernd Funke and Martin Juckes and Doug Kinnison and Markus Kunze and Beiping Luo and Katja Matthes and Paul A. Newman and Charlotte Pascoe and Thomas Peter (2021), SPARC Newsletter, volume 57, pp 22-30", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-11-29T15:24:04", "latestDataUpdateTime": "2023-08-02T09:12:10", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by scientists from a collaboration of JAMSTEC, AORI, NIES, and R-CCS and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CCMI-2022, senD2-sai, refD2, SSP245, Hindcast, Scenario, MIROC-ES2H, MIROC, JAMSTEC, AORI, NIES, R-CCS, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "250 km", "status": "completed", "dataPublishedTime": "2023-08-11T11:34:33", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40352, "dataPath": "/badc/ccmi/data/post-cmip6/ccmi-2022/MIROC/MIROC-ES2H/senD2-sai/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 445630412659, "numberOfFiles": 8518, "fileFormat": "Data are Net-CDF formatted" }, "timePeriod": { "ob_id": 11211, "startTime": "2025-01-01T00:00:00", "endTime": "2100-12-31T00:00:00" }, "resultQuality": { "ob_id": 3727, "explanation": "Data are as given by the data provider, ceda-cc quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CCMI-2022 Data and Metadata Quality Statement", "date": "2021-08-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40193, "uuid": "4b08c47fd0314dc78648c54ee515401b", "short_code": "comp", "title": "MIROC-ES2H model based on a global climate model MIROC (Model for Interdisciplinary Research on Climate) which has been cooperatively developed by JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan).", "abstract": "MIROC-ES2H model based on a global climate model MIROC (Model for Interdisciplinary Research on Climate) which has been cooperatively developed by JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan)." }, "procedureCompositeProcess": null, "imageDetails": [ 146 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2544, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "ccmi-2022", "label": "restricted: ccmi-2022 group", "licence": { "ob_id": 21, "licenceURL": "https://artefacts.ceda.ac.uk/licences/rugl_versions/rugl_v1-0.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32805, "uuid": "92dddf542adc44b5898f535be4179705", "short_code": "proj", "title": "CCMI-2022 Chemistry-climate model initiative, phase 2", "abstract": "CCMI-2022 Chemistry-climate model initiative, phase 2 is a World Climate Research Programme (WCRP) Stratosphere-Troposphere Processes and their Role in Climate (SPARC) project to study the evolution of the ozone layer using chemistry-climate model simulations. CCMI-2022 data will support the World Meteorologcial Organisation (WMO)/ United Nations Environment Programme (UNEP) Scientific Assessment of Ozone Depletion Report 2022." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 50415, 50417, 50418, 50419, 50420, 50421, 50422, 50423, 50424, 50425, 50426, 50427, 50429, 50430, 50431, 50435, 50437, 50439, 50440, 50441, 50444, 50445, 50450, 50453, 50454, 50456, 50458, 50460, 50461, 50462, 50463, 50464, 50465, 50466, 50467, 50469, 50470, 50473, 50475, 50478, 50480, 50482, 50483, 50486, 50487, 50489, 50491, 50492, 50493, 50494, 50495, 50496, 50498, 50501, 50502, 50504, 50506, 50508, 50566, 50576, 50590, 50591, 50598, 50603, 50608, 53111, 60438, 60452, 61535, 61536, 66084, 71572, 71613, 71634, 71691, 71925 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40190, "uuid": "a918c09740b345a089fd47c4c48526b4", "short_code": "coll", "title": "CCMI-2022 data produced by the MIROC-ES2H model from MIROC", "abstract": "The MIROC-ES2H model contribution to CCMI-2022 set of experiments defined by the APARC- and IGAC-supported Chemistry-Climate Model Initiative.\r\n\r\nThe CCMI-2022 set of model experiments focus on the stratosphere, with the goals of providing updated projections of the future evolution of ozone and improving our understanding of chemistry-climate interactions and how they are represented in models.\r\n\r\nThe MIROC-ES2H chemistry-climate model is based on a global climate model MIROC (Model for Interdisciplinary Research on Climate) which has been cooperatively developed by JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) and configured to follow forcings as laid out in the CCMI2022 founding document (Plummer et al., 2021).\r\n\r\nAPARC (formerly SPARC) and IGAC projects coordinate international research in atmospheric chemistry. APARC (Atmospheric Processes And their Role in Climate) is a core project of the World Climate Research Programme (WCRP). IGAC is the International Global Atmospheric Chemistry which currently operates under the umbrella of Future Earth." } ], "responsiblepartyinfo_set": [ 196663, 196664, 196665, 196666, 196661, 196662, 196667, 196668, 196669 ], "onlineresource_set": [ 83936, 83937 ] }, { "ob_id": 40354, "uuid": "80567d38de3f4b038ee6e6e53ed1af8a", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2022), version 3.0", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme.\r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the masking effect of the forest canopy. The SCFG is given in percentage (%) per pixel. \r\n\r\nThe global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. \r\n\r\nThe SCFG time series provides daily products for the period 2000 – 2022. \r\n\r\nThe SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. \r\n\r\nThe retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO (ENVironmental Earth Observation IT GmbH). For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The Snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nThe main differences of the snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of spatially variable background reflectance and forest reflectance maps instead of global constant values for snow free land and forest, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the update of the global forest canopy transmissivity based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) to assure in forested areas consistency of the SCFG and the SCFV CRDP v3.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/e955813b0e1a4eb7af971f923010b4a3) using the same retrieval approach.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. Salt lakes are masked based on a manual delineation from MODIS data. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.\r\n\r\nCompared to the SCFG CRDP v2.0 (https://catalogue.ceda.ac.uk/uuid/8847a05eeda646a29da58b42bdf2a87c/) the following improvements were applied for the generation of the SCFG CRDP v3.0: \r\n1) the pre-classification module to identify snow free areas has been relaxed to consider more pixels for the SCFG retrieval; \r\n2) the SCFG retrieval has been improved adapting the spectral reflectance value for wet snow;\r\n3) the uncertainty estimation of the SCFG has been updated to account for the changes in the retrieval algorithm;\r\n4) salt lakes retrieved by manual delineation from Terra MODIS data are masked in the SCFG CRDP v3.0 and a new class for salt lakes is added in the coding;\r\n5) the time series, starting in February 2000, was extended from December 2020 to December 2022;\r\n6) two additional layers are provided for each daily product: \r\n•\tthe sensor zenith angle in degree per pixel;\r\n\tthe image acquisition time per pixel referring to the scanline time of the MODIS granule used for the classification of the pixel. \r\n\r\nThe SCFG product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\nENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development.\r\n\r\nThere are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2022. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFG products are available but have data gaps.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-07-11T13:03:52", "updateFrequency": "notPlanned", "dataLineage": "The snow_cci SCFG products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe snow_cci SCF processing chain for MODIS includes the masking of clouds, the identification of certainly snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFG products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. Salt lakes are masked based on a manual delineation of such areas from Terra MODIS data. All SCFG products are prepared according to the CCI data standards.\r\n\r\nAn automated and a manual quality check was performed on the full time series.\r\n\r\nWe acknowledge Norsk Regnesentral (Norwegian Computing Center, NR) for downloading the MODIS data from NASA, and UNINETT Sigma2 AS (Sigma2, The Norwegian e-infrastructure for Research & Education) for providing the processing infrastructure for the CRDP generation from MODIS.", "removedDataReason": "", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-10-15T12:46:03", "doiPublishedTime": "2024-10-15T16:45:25.166384", "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 43047, "dataPath": "/neodc/esacci/snow/data/scfg/MODIS/v3.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 861587059895, "numberOfFiles": 8278, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 11202, "startTime": "2000-02-24T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3837, "explanation": "The unbiased root mean square error of snow cover fraction adapted from the approach of Salminen et al. (2018) is added as uncertainty layer in each product. The MODIS based SCFG products are matching the CCI data standards version 2.3, released in July 2021. For more information on data quality, see the Snow_cci documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43064, "uuid": "1dbc814b8c6f430abd16e2a3f5b55aac", "short_code": "cmppr", "title": "Composite process for the ESA Snow Climate Change Initiative SCFG MODIS v3.0 product", "abstract": "The snow_cci SCFG products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nAlgorithm improvements for v3.0 SCF MODIS products are as follows: \r\n• Improved pre-classification of snow-free areas (updated NDSI basemap) \r\n• Improved SCF retrieval (update of snow reflectance parameter based on statistical analysis)\r\n• Salt lakes added as additional static mask \r\n• Updated uncertainty estimation accounting for changes in \r\nalgorithm\r\n\r\nAdditional variables in v3.0 SCF MODIS products are as follows: \r\n• Sensor zenith angle in degrees per pixel \r\n• Image acquisition time (scanline time per MODIS granule) \r\n\r\nExtension of time series (start in 2000): \r\n• Extended from 2020 to 2022" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2571, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 38, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30229, "uuid": "93cf539bc3004cc8b98006e69078d86b", "short_code": "proj", "title": "ESA Snow Climate Change Initiative (snow_cci)", "abstract": "The overarching goal of snow_cci is the generation of homogeneous, well calibrated, long-term time series of key snow cover parameters (snow area extent and snow mass) from multi-sensor satellite data for climate applications. A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 32072, 61130, 61131, 61132, 62644, 62645, 74106, 74107 ], "vocabularyKeywords": [], "identifier_set": [ 13199 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 196676, 196673, 196670, 196674, 196677, 196675, 196678, 203906, 196671, 196672, 196681, 196679, 196682, 205570 ], "onlineresource_set": [ 83946, 83947, 83948, 83941, 83942, 83943, 83949, 83944, 83939, 83945, 83940, 83938, 86601, 87912 ] }, { "ob_id": 40355, "uuid": "e955813b0e1a4eb7af971f923010b4a3", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2022), version 3.0", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from the MODIS satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. \r\n\r\nThe SCFV time series provides daily products for the period 2000 – 2022. \r\n\r\nThe SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. \r\n\r\nThe retrieval method of the Snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO (ENVironmental Earth Observation IT GmbH). For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the Snow_cci SCFV retrieval method is applied. \r\n\r\nThe main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the adaptation of the retrieval method using of a spatially variable ground reflectance instead of global constant values for snow free land, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data to assure in forested areas consistency of the SCFV and the SCFG CRDP v3.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/80567d38de3f4b038ee6e6e53ed1af8a) using the same retrieval approach.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. Salt lakes are masked based on a manual delineation from MODIS data. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.\r\n\r\nCompared to the SCFV CRDP v2.0 (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b/) the following improvements were applied for the generation of the SCFV CRDP v3.0: \r\n1) the pre-classification module to identify snow free areas has been relaxed to consider more pixels for the SCFG retrieval; \r\n2) the SCFG retrieval has been improved adapting the spectral reflectance value for wet snow;\r\n3) the uncertainty estimation of the SCFG has been updated to account for the changes in the retrieval algorithm;\r\n4) salt lakes retrieved by manual delineation from Terra MODIS data are masked in the SCFG CRDP v3.0 and a new class for salt lakes is added in the coding;\r\n5) the time series, starting in February 2000, was extended from December 2020 to December 2022;\r\n6) two additional layers are provided for each daily product: \r\n•\tthe sensor zenith angle in degree per pixel;\r\n•\tthe image acquisition time per pixel referring to the scanline time of the MODIS granule used for the classification of the pixel.\r\n\r\nThe SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nENVEO is responsible for the SCFV product development and generation from MODIS data, SYKE supported the development.\r\n\r\nThere are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2022. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFV products are available but have data gaps.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-07-09T13:44:38", "updateFrequency": "notPlanned", "dataLineage": "The snow_cci SCFV products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe snow_cci SCF processing chain for MODIS includes the masking of clouds, the identification of certainly snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFV products based on the Land Cover CCI map of the year 2000. Salt lakes are masked based on a manual delineation of such areas from Terra MODIS data. All SCFV products are prepared according to the CCI data standards.\r\n\r\nAn automated and a manual quality check was performed on the full time series.\r\n\r\nWe acknowledge Norsk Regnesentral (Norwegian Computing Center, NR) for downloading the MODIS data from NASA, and UNINETT Sigma2 AS (Sigma2, The Norwegian e-infrastructure for Research & Education) for providing the processing infrastructure for the CRDP generation from MODIS.", "removedDataReason": "", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-10-15T12:46:50", "doiPublishedTime": "2024-10-15T16:45:55.913766", "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 43046, "dataPath": "/neodc/esacci/snow/data/scfv/MODIS/v3.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 864551742525, "numberOfFiles": 8278, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 11203, "startTime": "2000-02-24T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3646, "explanation": "The unbiased root mean square error of snow cover fraction adapted from the approach of Salminen et al. (2018) is added as an uncertainty layer in each product. The MODIS based SCFV products match the CCI data standards version 2.3, released in July 2021. For further information on the data quality, see the Snow_cci documentation..", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-04-26" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43063, "uuid": "0b4638c1733e4520a1d29c7c6b84088c", "short_code": "cmppr", "title": "Composite process for the ESA Snow Climate Change Initiative SCFV MODIS v3.0 product", "abstract": "The snow_cci SCFV products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nAlgorithm improvements for v3.0 SCF MODIS products are as follows: \r\n• Improved pre-classification of snow-free areas (updated NDSI basemap) \r\n• Improved SCF retrieval (update of snow reflectance parameter based on statistical analysis)\r\n• Salt lakes added as additional static mask \r\n• Updated uncertainty estimation accounting for changes in \r\nalgorithm\r\n\r\nAdditional variables in v3.0 SCF MODIS products are as follows: \r\n• Sensor zenith angle in degrees per pixel \r\n• Image acquisition time (scanline time per MODIS granule) \r\n\r\nExtension of time series (start in 2000): \r\n• Extended from 2020 to 2022" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2571, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 38, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30229, "uuid": "93cf539bc3004cc8b98006e69078d86b", "short_code": "proj", "title": "ESA Snow Climate Change Initiative (snow_cci)", "abstract": "The overarching goal of snow_cci is the generation of homogeneous, well calibrated, long-term time series of key snow cover parameters (snow area extent and snow mass) from multi-sensor satellite data for climate applications. A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 32072, 61130, 61131, 62645, 63201, 63202, 74106, 74107 ], "vocabularyKeywords": [], "identifier_set": [ 13200 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 196684, 196685, 196686, 196687, 196688, 196690, 203905, 196691, 196689, 196683, 196694, 196692, 196695, 205569 ], "onlineresource_set": [ 83952, 83957, 83953, 83954, 83955, 83958, 83956, 83960, 83950, 83959, 83951, 86600, 87911 ] }, { "ob_id": 40356, "uuid": "56ff07acabab42888afe2d20b488ec49", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1979 - 2022), version 3.0", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. \r\n\r\nThe global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFG time series provides daily products for the period 1979-2022. \r\n\r\nThe product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. \r\n\r\nThe retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. \r\n\r\nThe following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground.\r\n\r\nThe SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFG product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nThe SCFG AVHRR product comprises a few data gaps in 1979 – 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March – 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-08-13T13:40:10", "updateFrequency": "notPlanned", "dataLineage": "The snow_cci SCFG product based on AVHRR was developed and processed at the University of Bern in the frame of ESA CCI+ Snow project. The AVHRR baseline FCDR was pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project (Devasthale et al. 2017, Stengel et al. 2020). \r\nThe final product is quality checked.\r\n\r\nData were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-10-15T12:45:43", "doiPublishedTime": "2024-10-15T16:44:54.095097", "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 43049, "dataPath": "/neodc/esacci/snow/data/scfg/AVHRR_SINGLE/v3.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 613701421439, "numberOfFiles": 40711, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 11204, "startTime": "1979-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3893, "explanation": "The unbiased root mean square error per-pixel is added as uncertainty layer in the product. The AVHRR based SCFG product matches the CCI data standards version 2.3, released in July 2021. For more information on data quality see the Snow_cci documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-02-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43066, "uuid": "8db8987749c442fdae7dd77dc390c685", "short_code": "cmppr", "title": "Composite process for the ESA Snow Climate Change Initiative SCFG AVHRR v3.0 product", "abstract": "The ESA Snow Climate Change Initiative SCFG AVHRR v2.0 product is based on an AVHRR baseline FCDR pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project.\r\n\r\nInput data for v3.0 SCF AVHRR products are as follows: \r\n• EUMETSAT FDR replaces ESA CCI cloud products \r\n(AVHRR global composites and cloud) \r\no Morning and afternoon orbits. \r\no MetOp-Satellites are included \r\no CLARA A3 replaces ESA CCI cloud mask \r\n\r\nAlgorithm improvements for v3.0 SCF AVHRR products are as follows: \r\n• Improved pre-classification of snow-free areas: \r\no Considering the additional orbits \r\n• Improved SCF retrieval: \r\no Update of ref_reflectance values (snow, forest, \r\nground) to consider SZA and VZA changes applying LUT. \r\n• Updated uncertainty estimation accounting for the morning \r\nand afternoon orbits \r\n• Updated post-classification to remove erroneous snow \r\npixels in desert areas. \r\n\r\nAdditional variables for v3.0 SCF AVHRR products are as follows: \r\n• Sensor zenith angle in degrees per pixel \r\n• Image acquisition time (scanline time per AVHRR swath) \r\n\r\nExtension of time series (start in 1979): \r\n• Extended from 2019 to 2022" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2571, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 38, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30229, "uuid": "93cf539bc3004cc8b98006e69078d86b", "short_code": "proj", "title": "ESA Snow Climate Change Initiative (snow_cci)", "abstract": "The overarching goal of snow_cci is the generation of homogeneous, well calibrated, long-term time series of key snow cover parameters (snow area extent and snow mass) from multi-sensor satellite data for climate applications. A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 32072, 61130, 61131, 61132, 62644, 62645, 74106, 74107 ], "vocabularyKeywords": [], "identifier_set": [ 13198 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 196696, 196698, 196699, 196700, 196701, 196702, 196703, 205571, 196697, 203902, 196704, 196705, 196706, 196708, 196709, 196710 ], "onlineresource_set": [ 83967, 83968, 83969, 83962, 83963, 83964, 83966, 83970, 83971, 83972, 86598, 86599, 83965, 83961, 87910 ] }, { "ob_id": 40357, "uuid": "7491427f8c3442ce825ba5472c224322", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1979 - 2022), version 3.0", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFV time series provides daily products for the period 1979-2022. \r\n\r\nThe product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. \r\n\r\nThe retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.630 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. \r\n\r\nThe following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water; permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map.\r\n\r\nThe SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFV product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation. \r\n\r\nThe SCFV AVHRR product comprises a few data gaps in 1979 – 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March – 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-07-22T16:58:01", "updateFrequency": "notPlanned", "dataLineage": "The snow_cci SCFV product based on AVHRR was developed and processed at the University of Bern in the frame of ESA CCI+ Snow project. The AVHRR baseline FCDR was pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project (Devasthale et al. 2017, Stengel et al. 2020). \r\n\r\nThe final product is quality checked.", "removedDataReason": "", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-10-15T12:46:58", "doiPublishedTime": "2024-10-15T16:44:43.283849", "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 43048, "dataPath": "/neodc/esacci/snow/data/scfv/AVHRR_SINGLE/v3.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 616674519406, "numberOfFiles": 40711, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 11205, "startTime": "1979-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3843, "explanation": "The unbiased root mean square error per-pixel is added as an uncertainty layer in the product. The AVHRR based SCFV product matches the CCI data standards version 2.3, released in July 2021. For more information on data quality, see the Snow_cci documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43065, "uuid": "9bb1fb55b04049869b1e357fdf4f924e", "short_code": "cmppr", "title": "Composite process for the ESA Snow Climate Change Initiative SCFV AVHRR v3.0 product", "abstract": "The ESA Snow Climate Change Initiative SCFG AVHRR v2.0 product is based on an AVHRR baseline FCDR pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project.\r\n\r\nInput data for v3.0 SCF AVHRR products are as follows: \r\n• EUMETSAT FDR replaces ESA CCI cloud products \r\n(AVHRR global composites and cloud) \r\no Morning and afternoon orbits. \r\no MetOp-Satellites are included \r\no CLARA A3 replaces ESA CCI cloud mask \r\n\r\nAlgorithm improvements for v3.0 SCF AVHRR products are as follows: \r\n• Improved pre-classification of snow-free areas: \r\no Considering the additional orbits \r\n• Improved SCF retrieval: \r\no Update of ref_reflectance values (snow, forest, ground) to consider SZA and VZA changes applying LUT. \r\n• Updated uncertainty estimation accounting for the morning \r\nand afternoon orbits \r\n• Updated post-classification to remove erroneous snow \r\npixels in desert areas. \r\n\r\nAdditional variables for v3.0 SCF AVHRR products are as follows: \r\n• Sensor zenith angle in degrees per pixel \r\n• Image acquisition time (scanline time per AVHRR swath) \r\n\r\nExtension of time series (start in 1979): \r\n• Extended from 2019 to 2022" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2571, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 38, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30229, "uuid": "93cf539bc3004cc8b98006e69078d86b", "short_code": "proj", "title": "ESA Snow Climate Change Initiative (snow_cci)", "abstract": "The overarching goal of snow_cci is the generation of homogeneous, well calibrated, long-term time series of key snow cover parameters (snow area extent and snow mass) from multi-sensor satellite data for climate applications. A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 32072, 61130, 61131, 62645, 63201, 63202, 74106, 74107 ], "vocabularyKeywords": [], "identifier_set": [ 13197 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 196711, 196712, 196713, 196714, 196715, 196716, 196717, 205572, 196718, 203904, 196719, 196720, 196721, 196723, 196724, 196725 ], "onlineresource_set": [ 83980, 83981, 83974, 83975, 83982, 83976, 83977, 83979, 83983, 83984, 83978, 86596, 86597, 83973, 87909, 92707 ] }, { "ob_id": 40358, "uuid": "b06c4c5ea7694d30b33e1db04f0ecb6a", "title": "ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 - 2022), version 3.0", "abstract": "This dataset contains v3.0 of the Daily Snow Water Equivalent (SWE) product from the ESA Climate Change Initiative (CCI) Snow project, at 0.1 degree resolution.\r\n\r\nSnow water equivalent (SWE) indicates the amount of accumulated snow on land surfaces, in other words the amount of water contained within the snowpack. The SWE product time series covers the period from 1979/01 to 2022/12. Northern Hemisphere SWE products are available at daily temporal resolution with alpine areas masked. \r\n\r\nThe product is based on data from the Scanning Multichannel Microwave Radiometer (SMMR) operated on National Aeronautics and Space Administration’s (NASA) Nimbus-7 satellite, the Special Sensor Microwave / Imager (SSM/I) and the Special Sensor Microwave Imager / Sounder (SSMI/S) carried onboard the Defense Meteorological Satellite Program (DMSP) 5D- and F-series satellites. The satellite bands provide spatial resolutions between 15 and 69 km. The retrieval methodology combines satellite passive microwave radiometer (PMR) measurements with ground-based synoptic weather station observations by Bayesian non-linear iterative assimilation. A background snow-depth field from re-gridded surface snow-depth observations and a passive microwave emission model are required components of the retrieval scheme.\r\n\r\nThe dataset is aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modelling, and aspects of hydrology and meteorology.\r\n\r\nThe Finnish Meteorological Institute is responsible for the SWE product development and generation. \r\n\r\nFor the period from 1979 to May 1987, the products are available every second day. From October 1987 till December 2022, the products are available daily. Products are only generated for the Northern Hemisphere winter seasons, usually from beginning of October till the middle of May. A limited number of SWE products are available for days in June and September; products are not available for the months July and August as there is usually no snow information reported on synoptic weather stations, which is required as input for the SWE retrieval. Because of known limitations in alpine terrain, a complex-terrain mask is applied based on the sub-grid variability in elevation determined from a high-resolution digital elevation model. All land ice and large lakes are also masked; retrievals are not produced for coastal regions of Greenland.\r\n\r\nPassive microwave radiometer data are obtained from the recalibrated enhanced resolution CETB ESDR dataset (MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) https://nsidc.org/pmesdr/data-sets/) Spatially and temporally varying snow density fields are implemented into the SWE retrieval, dry snow detection algorithm has been updated and snow masking in post-production has been improved. The time series has been extended from snow_cci version 2 by two years with data from 2020 to 2022 added.\r\n\r\nThe ESA CCI phased product development framework allowed for a systematic analysis of these changes in the snow density parameterization, snow dry detection and snow masking that occurred between v2 and v3 using a series of step-wise developmental datasets. In comparison with in-situ snow courses, the correlation and RMSE of v3 improved 0.014 and 0.6 mm, respectively, relative to v2. The timing of peak snow mass is shifted two weeks later compared to v1 and reduction in peak snow mass presented in v2 is removed in v3.\r\n\r\nThis dataset has been deprecated due to data errors in the v3.0 product.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-01-10T01:57:25", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) as part of the CCI Knowledge Exchange project.", "removedDataReason": "", "keywords": "ESA, CCI, Snow, SWE", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "deprecated", "dataPublishedTime": "2024-11-18T13:57:37", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 43050, "dataPath": "/neodc/esacci/snow/data/swe/MERGED/v3.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 12968625940, "numberOfFiles": 8899, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 11866, "startTime": "1979-01-01T00:00:00", "endTime": "2023-12-31T23:59:59" }, "resultQuality": { "ob_id": 3892, "explanation": "For information on data quality see the Snow_cci documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-02-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41960, "uuid": "99b57d420ca946449495a56a359297e8", "short_code": "cmppr", "title": "Composite process for: ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 – 2022), version 3.0", "abstract": "The product is based on data from the Scanning Multichannel Microwave Radiometer (SMMR) operated on National Aeronautics and Space Administration’s (NASA) Nimbus-7 satellite, the Special Sensor Microwave / Imager (SSM/I) and the Special Sensor Microwave Imager / Sounder (SSMI/S) carried onboard the Defense Meteorological Satellite Program (DMSP) 5D- and F-series satellites. The satellite bands provide spatial resolutions between 15 and 69 km. The retrieval methodology combines satellite passive microwave radiometer (PMR) measurements with ground-based synoptic weather station observations by Bayesian non-linear iterative assimilation. A background snow-depth field from re-gridded surface snow-depth observations and a passive microwave emission model are required components of the retrieval scheme." }, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2571, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 38, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30229, "uuid": "93cf539bc3004cc8b98006e69078d86b", "short_code": "proj", "title": "ESA Snow Climate Change Initiative (snow_cci)", "abstract": "The overarching goal of snow_cci is the generation of homogeneous, well calibrated, long-term time series of key snow cover parameters (snow area extent and snow mass) from multi-sensor satellite data for climate applications. A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 62645, 73925, 73926, 73927, 73928, 73929 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 196727, 196728, 196729, 196730, 196731, 196732, 196733, 196726, 196734, 196735, 196736, 196737, 196738, 196739, 196740, 196741, 196742 ], "onlineresource_set": [ 83985, 83992, 83986, 83987, 83988, 83989, 83990, 83991, 87903, 87904 ] }, { "ob_id": 40359, "uuid": "f5dce1f7bec2447093cf460a4d3ba2c2", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from SLSTR (2020 - 2022), version 1.0", "abstract": "This dataset provides daily Snow Cover Fraction Viewable from above (SCFV) derived from Sentinel-3A&B SLSTR observations, produced within the ESA Climate Change Initiative Snow project.\r\n\r\nSCFV expresses the proportion of land area within each about 1 km x 1 km pixel that is covered by snow. SCFV represents snow viewable from above, whether on the forest canopy or on the ground in clear-cut or non-forested areas. The SCFV is given in percentage (%) per pixel. The SCFV product is available at about 1 km pixel size for global land areas except the Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. The SCFV time series spans 01 September 2020 to 31 December 2022. The time series is extended within the Copernicus Climate Change Service (C3S) for Cryosphere from 1 January 2023 onwards.\r\n\r\nThe SCFV product is based on Sea and Land Surface Temperature Radiometer (SLSTR) data on-board the Sentinel-3A and Sentinel-3B satellites. For the SCFV product generation from SLSTR, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm (SCDA) (Metsämäki et al., 2015). For all remaining pixels, the snow_cci SCFV retrieval method is applied, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach that first identifies pixels which are assessed as snow free, followed by SCFV retrieval for remaining pixels. Permanent snow/ice and water bodies are masked using the Land Cover CCI 2000 dataset, supplemented by a manually mapped salt-lake mask. Per-pixel uncertainty is provided in the ancillary variable as an unbiased Root Mean Square Error (RMSE) for all observed land pixels.\r\n\r\nThe retrieval approach used for the SLSTR based SCFV CRDP (Climate Research Data Package) v1.0 is the same as the one used for the SCFV CRDP v4.0 from Moderate resolution Imaging Spectroradiometer (MODIS) on board of the Terra satellite, covering the period 2000 – 2023 (https://catalogue.ceda.ac.uk/uuid/bc13bb02a958449aac139853c4638f32/).\r\nThe SCFV product is aimed to support cryosphere and climate research applications, including variability and trend analyses, climate modelling and studies in hydrology, meteorology, and ecology. \r\n\r\nENVEO leads the SCFV product development and product generation from SLSTR data, with contributions on the product development from Syke.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "The snow_cci SCFV products from SLSTR are based on the Sentinel-3A&B SLSTR Level-1B product (SL_1_RBT), providing radiances and brightness temperatures for each pixel in a regular image grid for each view and SLSTR channel. The nadir view observations from Non-Time Critical (NTC) data products of baseline collection 4 are used as input, provided by Copernicus and ESA as frames for every 3 minutes.\r\n\r\nThe snow_cci SCF processing chain for SLSTR includes the masking of clouds, the pre-classification of largely snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Permanent snow and ice areas as well as water bodies are masked in the SCFV products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. Salt lakes are masked based on a manual delineation of such areas from Terra MODIS data. The same water, permanent snow and ice area and salt lake mask as for the Terra MODIS based SCFV CRDP v4.0 (https://catalogue.ceda.ac.uk/uuid/bc13bb02a958449aac139853c4638f32/) is used to ensure consistency between the SCFV products across the different sensors and time series. \r\n\r\nSCFV products from individual frames are merged into daily global SCFV products.\r\n\r\nAll SCFV products are prepared according to the CCI data standards.\r\n\r\nAn automated and a manual quality check was performed on the full time series.", "removedDataReason": "", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-12-03T18:00:30", "doiPublishedTime": "2025-12-03T18:05:03", "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 45123, "dataPath": "/neodc/esacci/snow/data/scfv/SLSTR/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 87304149114, "numberOfFiles": 853, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 12820, "startTime": "2020-09-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 4838, "explanation": "The unbiased root mean square error of snow cover fraction adapted from the approach of Salminen et al. (2018) is added as uncertainty layer in each product. The SLSTR based SCFV products are matching the CCI data standards version 2.3, released in July 2021. For more information on data quality, see the Snow_cci documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-11-26" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 45117, "uuid": "f19c7812de3f4b019249e275b368ce9b", "short_code": "cmppr", "title": "Composite process for the ESA Snow Climate Change Initiative SCFV SLSTR v1.0 product", "abstract": "See the snow_cci documentation for further information on the SLSTR SCFV v1.0 product." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2571, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 38, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30229, "uuid": "93cf539bc3004cc8b98006e69078d86b", "short_code": "proj", "title": "ESA Snow Climate Change Initiative (snow_cci)", "abstract": "The overarching goal of snow_cci is the generation of homogeneous, well calibrated, long-term time series of key snow cover parameters (snow area extent and snow mass) from multi-sensor satellite data for climate applications. A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 32072, 61130, 61131, 62645, 63201, 63202, 74106, 74107 ], "vocabularyKeywords": [], "identifier_set": [ 13662 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 196744, 196745, 196746, 196747, 196748, 196749, 196750, 196751, 196743, 196753, 196754, 215971, 196752, 215972, 215973, 215974, 215975, 196755 ], "onlineresource_set": [ 95092, 83994, 84000, 84001, 84003, 83996, 83998, 84002, 94496, 94508 ] }, { "ob_id": 40360, "uuid": "38a71d034b5c4097821de29ee3bc2498", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from SLSTR (2020 - 2022), version 1.0", "abstract": "This dataset provides daily Snow Cover Fraction on Ground (SCFG) derived from Sentinel-3A&B SLSTR observations, produced within the ESA Climate Change Initiative Snow project.\r\n\r\nSCFG expresses the proportion of land area within each about 1 km x 1 km pixel that is covered by snow. In forested areas, the masking effect of the forest canopy is corrected to estimate the SCFG. The SCFG is given in percentage (%) per pixel. The SCFG product is available at about 1 km pixel size for global land areas except the Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. The SCFG time series spans 01 September 2020 to 31 December 2022. The time series is extended within the Copernicus Climate Change Service (C3S) for Cryosphere from 1 January 2023 onwards. \r\n\r\nThe SCFG product is based on Sea and Land Surface Temperature Radiometer (SLSTR) data on-board the Sentinel-3A and Sentinel-3B satellites. For the SCFG product generation from SLSTR, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm (SCDA) (Metsämäki et al., 2015). For all remaining pixels, the snow_cci SCFG retrieval method is applied, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach that first identifies pixels which are assessed as snow free, followed by SCFG retrieval for remaining pixels. Permanent snow/ice and water bodies are masked using the Land Cover CCI 2000 dataset, supplemented by a manually mapped salt-lake mask. Per-pixel uncertainty is provided in the ancillary variable as an unbiased Root Mean Square Error (RMSE) for all observed land pixels.\r\n\r\nThe retrieval approach used for the SLSTR based SCFG CRDP (Climate Research Data Package) v1.0 is the same as the one used for the SCFG CRDP v4.0 from Moderate resolution Imaging Spectroradiometer (MODIS) on board of the Terra satellite, covering the period 2000 – 2023 (https://catalogue.ceda.ac.uk/uuid/ 375ffdb8f0a445e380b4b9548655f5f9).\r\nThe SCFG product is aimed to support cryosphere and climate research applications, including variability and trend analyses, climate modelling and studies in hydrology, meteorology, and ecology.\r\n\r\nENVEO leads the SCFG product development and product generation from SLSTR data, with contributions on the product development from Syke.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "The snow_cci SCFG products from SLSTR are based on the Sentinel-3A&B SLSTR Level-1B product (SL_1_RBT), providing radiances and brightness temperatures for each pixel in a regular image grid for each view and SLSTR channel. The nadir view observations from Non-Time Critical (NTC) data products of baseline collection 4 are used as input, provided by Copernicus and ESA as frames for every 3 minutes.\r\n\r\nThe snow_cci SCF processing chain for SLSTR includes the masking of clouds, the pre-classification of largely snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Permanent snow and ice areas as well as water bodies are masked in the SCFG products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. Salt lakes are masked based on a manual delineation of such areas from Terra MODIS data. The same water, permanent snow and ice area and salt lake mask as for the Terra MODIS based SCFG CRDP v4.0 (https://catalogue.ceda.ac.uk/uuid/375ffdb8f0a445e380b4b9548655f5f9/) is used to ensure consistency between the SCFG products across the different sensors and time series. \r\n\r\nSCFG products from individual frames are merged into daily global SCFG products.\r\n\r\nAll SCFG products are prepared according to the CCI data standards.\r\n\r\nAn automated and a manual quality check was performed on the full time series.", "removedDataReason": "", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-12-03T18:05:29", "doiPublishedTime": "2025-12-03T18:06:53", "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 45122, "dataPath": "/neodc/esacci/snow/data/scfg/SLSTR/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 86888387272, "numberOfFiles": 853, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 12819, "startTime": "2020-09-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 4837, "explanation": "The unbiased root mean square error of snow cover fraction adapted from the approach of Salminen et al. 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A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 32072, 61130, 61131, 61132, 62644, 62645, 74106, 74107 ], "vocabularyKeywords": [], "identifier_set": [ 13663 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 196757, 196758, 196759, 196760, 196761, 196762, 196763, 196764, 196756, 196766, 196767, 215966, 196765, 215967, 215968, 215969, 215970, 196768 ], "onlineresource_set": [ 95091, 84005, 84007, 84008, 84014, 84006, 84010, 94494, 94507, 84012 ] }, { "ob_id": 40361, "uuid": "4610c48742a14980afdb4ff5b2f733d4", "title": "CCMI-2022: refD1 data produced by the CESM2-WACCM model at NCAR", "abstract": "This dataset contains model data for CCMI-2022 experiment refD1 produced by the CESM2-WACCM model run by the modelling team at the NCAR (National Center for Atmospheric Research), in the US.\r\n\r\nThe refD1 experiment is a hindcast of the atmospheric state, using a prescribed evolution of sea surface temperature and sea ice from observations along with forcings for the extra-terrestrial solar flux, long-lived greenhouse gases and ozone depleting substances, stratospheric aerosols and an imposed quasi-biennial oscillation that approximate the observed variations over the historical period to the fullest extent possible.\r\n\r\nThe CCMI-2022 Chemistry-climate model initiative is a set of model experiments focused on the stratosphere, with the goals of providing updated projections towards the future evolution of the ozone layer and improving our understanding of chemistry-climate interactions from models.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Review of the global models used within phase 1 of the Chemistry-Climate Model Initiative (CCMI)\r\n- A new set of Chemistry-Climate Model Initiative (CCMI) Community Simulations to Update the Assessment of Models and Support Upcoming Ozone Assessment Activities, David Plummer and Tatsuya Nagashima and Simone Tilmes and Alex Archibald and Gabriel Chiodo and Suvarna Fadnavis and Hella Garny and Beatrice Josse and Joowan Kim and Jean-Francois Lamarque and Olaf Morgenstern and Lee Murray and Clara Orbe and Amos Tai and Martyn Chipperfield and Bernd Funke and Martin Juckes and Doug Kinnison and Markus Kunze and Beiping Luo and Katja Matthes and Paul A. 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The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- ECMWF IFS model reference publication", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2024-10-01T02:05:34", "updateFrequency": "", "dataLineage": "The data is based on pressure-level output from the IFS model provided by Inna Polichtchouk. It has been lightly processed to conform to the SNAPSI data request. 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Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 9045, 50415, 50417, 50418, 50419, 50426, 50427, 50428, 50429, 50431, 50445, 50475, 50481, 50496, 50498, 50549, 50566, 50577, 50579, 50582, 50587, 50588, 50590, 50597, 50605, 50609, 50615, 52755, 53111, 54228, 54350, 60438, 62560, 62561, 64078, 66075, 66077, 71619, 71634 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40380, "uuid": "d160e81ccf9842d0b1c0a25b56a5ddfa", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the IFS model at ECMWF", "abstract": "The IFS model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at the European Centre for Medium-Range Weather Forecasts (ECMWF). \r\n\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- ECMWF IFS model reference publication" } ], "responsiblepartyinfo_set": [ 196808, 196805, 196806, 196807, 196812, 196809, 196810, 196811, 196818, 196819 ], "onlineresource_set": [ 84024, 84025, 84026 ] }, { "ob_id": 40372, "uuid": "a597a291cb00455fa1184f7a701dbc2e", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): free data produced by the IFS model at ECMWF", "abstract": "This dataset contains model data for SNAPSI experiment 'free' produced by scientists at ECMWF (European Centre for Medium-Range Weather Forecasts, United Kingdom). This dataset contains all ensemble members by the ECMWF IFS model.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe free experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- ECMWF IFS model reference publication", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2024-10-01T02:05:33", "updateFrequency": "", "dataLineage": "The data is based on pressure-level output from the IFS model provided by Inna Polichtchouk. It has been lightly processed to conform to the SNAPSI data request. The data has been published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "free, IFS, ECMWF, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "50 km", "status": "ongoing", "dataPublishedTime": "2024-09-30T16:05:48", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 249, "highestLevelBound": 40.0, "lowestLevelBound": 100000.0, "units": "Pa" }, "result_field": { "ob_id": 40373, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/ECMWF/IFS/free", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1795903845015, "numberOfFiles": 9793, "fileFormat": "Data are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40371, "uuid": "2c99b8a0b15746e9bc97a7f6a331c3c7", "short_code": "comp", "title": "ECMWF IFS Model", "abstract": "The underlying data was produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) IFS model for the SNAPSI project." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 9045, 50415, 50417, 50418, 50419, 50426, 50427, 50428, 50429, 50431, 50445, 50475, 50481, 50496, 50498, 50549, 50566, 50577, 50579, 50582, 50587, 50588, 50590, 50597, 50605, 50609, 50615, 52755, 53111, 54228, 54350, 60438, 62560, 62561, 64078, 66075, 66077, 71619, 71634 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40380, "uuid": "d160e81ccf9842d0b1c0a25b56a5ddfa", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the IFS model at ECMWF", "abstract": "The IFS model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at the European Centre for Medium-Range Weather Forecasts (ECMWF). \r\n\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- ECMWF IFS model reference publication" } ], "responsiblepartyinfo_set": [ 196824, 196820, 196821, 196822, 196825, 196823, 196826, 196827, 196828, 196829 ], "onlineresource_set": [ 84029, 84030, 84031 ] }, { "ob_id": 40374, "uuid": "d1a2fb1e6a57477d853b9d12a3ca42c8", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): nudged-full data produced by the IFS model at ECMWF", "abstract": "This dataset contains model data for SNAPSI experiment 'nudged-full' produced by scientists at ECMWF (European Centre for Medium-Range Weather Forecasts, United Kingdom). This dataset contains all ensemble members by the ECMWF IFS model.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe nudged-full experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, stratospheric temperatures and horizontal winds are nudged towards the observed time-evolving state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- ECMWF IFS model reference publication", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2023-09-26T09:00:41", "updateFrequency": "", "dataLineage": "The data is based on pressure-level output from the IFS model provided by Inna Polichtchouk. It has been lightly processed to conform to the SNAPSI data request. The data has been published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "nudged-full, IFS, ECMWF, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "50 km", "status": "ongoing", "dataPublishedTime": "2024-09-30T16:06:30", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 249, "highestLevelBound": 40.0, "lowestLevelBound": 100000.0, "units": "Pa" }, "result_field": { "ob_id": 40375, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/ECMWF/IFS/nudged-full", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1779515504075, "numberOfFiles": 9793, "fileFormat": "Data are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40371, "uuid": "2c99b8a0b15746e9bc97a7f6a331c3c7", "short_code": "comp", "title": "ECMWF IFS Model", "abstract": "The underlying data was produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) IFS model for the SNAPSI project." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 9045, 50415, 50417, 50418, 50419, 50426, 50427, 50428, 50429, 50431, 50445, 50475, 50481, 50496, 50498, 50549, 50566, 50577, 50579, 50582, 50587, 50588, 50590, 50597, 50605, 50609, 50615, 52755, 53111, 54228, 54350, 60438, 62560, 62561, 64078, 66075, 66077, 71619, 71634 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40380, "uuid": "d160e81ccf9842d0b1c0a25b56a5ddfa", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the IFS model at ECMWF", "abstract": "The IFS model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at the European Centre for Medium-Range Weather Forecasts (ECMWF). \r\n\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- ECMWF IFS model reference publication" } ], "responsiblepartyinfo_set": [ 196830, 196831, 196832, 196833, 196834, 196835, 196836, 196837, 196838, 196839 ], "onlineresource_set": [ 84033, 84034, 84032 ] }, { "ob_id": 40376, "uuid": "c2576bf741044871826aeea94b8ccbc1", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): free data produced by the SPEAR model at NOAA-GFDL", "abstract": "This dataset contains model data for SNAPSI experiment 'free' produced by scientists at NOAA-GFDL (National Oceanic and Atmospheric Administration- Geophysical Fluid Dynamics Laboratory) in Princeton, USA. This dataset contains all ensemble members by the GFDL SPEAR model.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe free experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- GFDL SPEAR reference publication: Delworth et al. (2020)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2023-09-25T14:14:47", "updateFrequency": "", "dataLineage": "The data is based on pressure-level output from the SPEAR model provided by Baoqiang Xiang. It has been lightly processed to conform to the SNAPSI data request. The data has been published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "free, SPEAR, NOAA-GFDL, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "50 km", "status": "ongoing", "dataPublishedTime": "2024-09-30T16:10:42", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 249, "highestLevelBound": 40.0, "lowestLevelBound": 100000.0, "units": "Pa" }, "result_field": { "ob_id": 40377, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/NOAA-GFDL/SPEAR/free", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1676538038900, "numberOfFiles": 10801, "fileFormat": "Data are Net-CDF formatted" }, "timePeriod": { "ob_id": 11209, "startTime": "2018-02-08T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40378, "uuid": "96543a9250f54e409edeb6225cd28a6d", "short_code": "comp", "title": "NOAA-GFDL SPEAR Model", "abstract": "The underlying data was produced by the National Oceanic and Atmospheric Administration- Geophysical Fluid Dynamics Laboratory (NOAA-GFDL) SPEAR model for the SNAPSI project." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 9045, 50415, 50417, 50418, 50419, 50426, 50427, 50428, 50429, 50431, 50445, 50475, 50481, 50496, 50498, 50549, 50566, 50577, 50579, 50582, 50587, 50588, 50590, 50597, 50605, 50609, 50615, 52755, 53111, 54228, 60438, 62560, 62561, 64078, 64080, 66076, 66077, 66082, 71614, 71619, 71634, 74366, 74367 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40381, "uuid": "300077b625e94c02b7d93e6521b4451d", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the SPEAR model at NOAA-GFDL", "abstract": "The SPEAR model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at the National Oceanic and Atmospheric Administration- Geophysical Fluid Dynamics Laboratory.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- GFDL SPEAR reference publication: Delworth et al. (2020)" } ], "responsiblepartyinfo_set": [ 196840, 196841, 196842, 196843, 196844, 196845, 196846, 196847, 196848 ], "onlineresource_set": [ 84035, 84036, 84037 ] }, { "ob_id": 40384, "uuid": "a9746a66ed654c26983eb7529a592275", "title": "Surface level turbulence derived from MASIN flight measurements and the MetUM model for the Iceland Greenland Sea's Project (IGP)", "abstract": "This dataset contains surface layer turbulent fluxes and meteorological variables derived from Meteorological Airborne Science Instrumentation (MASIN) instrumentation measurements onboard the British Antarctic Survey (BAS) Twin Otter aircraft combined with the Met Office Unified Model (MetUM) 2.2 km quasi-operational forecast model output. These data were based on measurements made during the February-March 2018 field campaign of the Iceland Greenland Sea's Project (IGP) which included the UK component- Atmospheric Forcing of the Iceland Sea (AFIS) project- funded by NERC (NE/N009754/1).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-08-09T16:16:57", "updateFrequency": "notPlanned", "dataLineage": "These data were produced by the project team and the output data were then supplied to CEDA for archiving.", "removedDataReason": "", "keywords": "IGP, AFIS, MetUM, MASIN, turbulence", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-01-16T15:58:19", "doiPublishedTime": "2024-01-18T09:54:41.531559", "removedDataTime": null, "geographicExtent": { "ob_id": 2948, "bboxName": "", "eastBoundLongitude": -9.0, "westBoundLongitude": -25.0, "southBoundLatitude": 64.0, "northBoundLatitude": 72.0 }, "verticalExtent": null, "result_field": { "ob_id": 40383, "dataPath": "/badc/igp/data/turbulence_SL", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 365828, "numberOfFiles": 3, "fileFormat": "ASCII" }, "timePeriod": { "ob_id": 11210, "startTime": "2018-02-28T00:00:00", "endTime": "2018-03-19T23:59:59" }, "resultQuality": { "ob_id": 3959, "explanation": "No quality information available. Data are as provided by the project team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40382, "uuid": "a7dbc7a3bfcf4f6b84155a6b15a72805", "short_code": "comp", "title": "Run-mean computation from MASIN and the MetUM 2.2 km from the Iceland Greenland Sea's Project (IGP) field campaign", "abstract": "Each long-run leg is divided into 150-second (~9 km) sections (\"runs\"), over which the flight data is averaged and fluxes are derived. The distance 9 km was chosen as analysis from the Aerosol Cloud Coupling and Climate Interactions in the Arctic (ACCACIA) project showed this length to be optimal for sampling turbulence over sea ice (i.e. the minimum length required to sample the dominant turbulent length scales - the larger the run length the more likely the calculated fluxes are to be influenced by mesoscale circulations and broad changes in surface characteristics, and the fewer data points will be got)" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2543, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 24899, "uuid": "2780d047461c42f0a12534ccf42f487a", "short_code": "proj", "title": "Iceland Greenland seas Project (IGP) including the Atmospheric Forcing of the Iceland Sea (AFIS)", "abstract": "The Iceland Greenland seas Project (IGP) is an international project involving the UK, US a Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1)\r\n\r\nThe Iceland Sea - to the north and east of Iceland - is arguably the least studied of the North Atlantic's subpolar seas. However new discoveries are forcing a redesign of our conceptual model of the North Atlantic's ocean circulation which places the Iceland Sea at the heart of this system and suggests that it requires urgent scientific focus. The recently discovered North Icelandic Jet is thought to be one of two pathways for dense water to pass through the Denmark Strait - the stretch of ocean between Iceland and Greenland - which is the main route for dense waters from the north to enter the Atlantic. Its discovery suggests a new paradigm for where dense water entering the North Atlantic originates. However at present the source of the North Icelandic Jet remains unknown. It is hypothesized that relatively warm Atlantic-origin water is modified into denser water in the Iceland Sea, although it is unclear precisely where, when or how this happens. \r\n\r\nThis project examined the wintertime atmosphere-ocean processes in the Iceland Sea by characterising its atmospheric forcing, i.e. observing the spatial structure and variability of surface heat, moisture and momentum fluxes in the region and the weather systems that dictate these fluxes. In situ observations of air-sea interaction processes from several platforms (an aircraft; and via project partners an unmanned airborne vehicle, a meteorological buoy and a research vessel) were made and used to evaluate meteorological analyses and reanalyses from operational weather forecasting centres. \r\n\r\nNumerical modelling experiments investigated the dynamics of selected weather systems which strongly influenced the region, but appear not to be well represented; for example, the boundary layers that develop over transitions between sea ice and the open ocean during cold-air outbreaks; or the jets and wakes that occur downstream of Iceland. The unique observations were used to improve model representation of these systems.\r\n\r\nThe project also carried out new high-resolution climate simulations. A series of experiments covered recent past and likely future situations; as well as some idealised situations such as no wintertime sea ice in the Iceland Sea region. This was done using a state-of-the-art atmospheric model with high resolution over the Iceland Sea to investigate changes in the atmospheric circulation and surface fluxes. \r\n\r\nFinally, in collaboration with the international partners, the project analysed new ocean observations and establish which weather systems are important for changing ocean properties in this region. The project used a range of ocean and atmospheric models to establish how current and future ocean circulation pathways function. In short, the project determined the role that atmosphere-ocean processes in the Iceland Sea play in creating the dense waters that flow through Denmark Strait and feed into the lower limb of the AMOC.\r\n\r\nThe subpolar region of the North Atlantic is crucial for the global climate system. It is where coupled atmosphere-ocean processes, on a variety of spatial scales, require an integrated approach for their improved understanding and prediction. This region has enhanced 'communication' between the atmosphere and ocean. Here large surface fluxes of heat and moisture make the surface waters colder, saltier and denser resulting in a convective overturning that contributes to the lower limb of the Atlantic Meridional Overturning Circulation (AMOC). The AMOC is an ocean circulation that carries warm water from the tropics northward with a return flow of cold water southwards at depth; it is instrumental in keeping Europe's climate relatively mild." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 70873, 70874, 70875, 70876, 70877, 70891, 70904, 70905, 70906, 70907, 70918, 70919, 70921, 70922, 70923, 70924, 70931, 70937, 70941, 70961, 70962, 70963, 70964, 70965, 70966, 70967, 70968, 70969, 70970, 70971, 70972, 70973, 70974, 70975, 70976, 70977, 70978, 70979, 70980, 70981, 70982, 70983, 70984, 70985, 70986, 70987, 70988, 70989, 70990, 70991, 70992, 70993, 70994, 70995, 70996, 70997, 70998, 70999, 71000, 71001, 71002, 71003, 71004, 71005, 71006, 71007, 71008, 71009, 71010, 71011, 71012, 71013, 71014, 71015, 71016, 71017, 71018, 71019, 71020, 71021, 71022, 71023, 71024, 71025, 71026, 71027, 71028 ], "vocabularyKeywords": [], "identifier_set": [ 12802 ], "observationcollection_set": [ { "ob_id": 27445, "uuid": "b3e807b8df824a8ca83468ce2e5b54e5", "short_code": "coll", "title": "In situ observations of air-sea interaction processes from the Iceland Greenland seas Project (IGP)", "abstract": "This collection contains a range of in situ observations of meteorological and air-sea interaction processes from a range of instruments on several platforms (buoy, ship , radiosonde) from the Iceland Greenland seas Project (IGP). \r\n\r\n\r\nThe Iceland Greenland seas Project (IGP) was an international project involving the UK, US and Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1)" } ], "responsiblepartyinfo_set": [ 196884, 196885, 196886, 196887, 196888, 196889, 196890, 196891, 196894, 200976 ], "onlineresource_set": [ 84054, 85809 ] }, { "ob_id": 40385, "uuid": "7ff1c538597b4e7f9aa09f52ce5df79c", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): control data produced by the CESM2-CAM6 model at NCAR", "abstract": "This dataset contains model data for SNAPSI experiment 'control' produced by scientists at NCAR (National Center for Atmospheric Research, USA). The data is generated with the coupled climate model CESM2-CAM6.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe control experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, the stratospheric zonal mean temperatures and zonal winds are nudged towards the time-evolving climatological state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- Danabasoglu et al. (2020)\r\n- Richter et al. (2021)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2025-01-10T01:48:33", "updateFrequency": "", "dataLineage": "Data were produced by scientists at the National Center for Atmospheric Research, USA (NCAR) and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "control, CESM2-CAM6, NCAR, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1x1 degree", "status": "ongoing", "dataPublishedTime": "2024-09-26T08:31:39", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40386, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/NCAR/CESM2-CAM6/control", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1498946911030, "numberOfFiles": 11323, "fileFormat": "Data are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40387, "uuid": "c90708df097c4e0aa97655e275647317", "short_code": "comp", "title": "CESM2-CAM6 model deployed at NCAR", "abstract": "This data was produced by the CESM2-CAM6 model run by scientists at NCAR for the SNAPSI project." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50418, 50419, 50426, 50427, 50429, 50431, 50445, 50475, 50481, 50496, 50498, 50549, 50559, 50566, 50582, 50587, 50588, 50590, 50597, 50605, 50609, 50615, 51210, 51211, 53111, 54228, 54366, 54378, 54963, 62560, 62561, 63011, 64078, 64080, 66075, 66077, 71613, 71619, 71634, 74366, 75565, 75568, 84646, 84647 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40392, "uuid": "ce8fb52804934952a00ef7d3d223d305", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the CESM2-CAM6 model at NCAR", "abstract": "The CESM2-CAM6 model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at the National Center for Atmospheric Research, USA (NCAR). \r\n\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- Danabasoglu et al. (2020)\r\n- Richter et al. (2021)" } ], "responsiblepartyinfo_set": [ 196896, 196897, 196898, 196899, 196900, 196901, 196902, 196895, 205493 ], "onlineresource_set": [ 84063, 84064, 84066, 84065 ] }, { "ob_id": 40388, "uuid": "99fed4ea9ce24e6490b8d7009346e362", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): nudged data produced by the CESM2-CAM6 model at NCAR", "abstract": "This dataset contains model data for SNAPSI experiment 'control' produced by scientists at NCAR (National Center for Atmospheric Research, USA). The data is generated with the coupled climate model CESM2-CAM6.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe nudged experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, the stratospheric zonal mean temperatures and zonal winds are nudged towards the observed time-evolving state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- Danabasoglu et al. (2020)\r\n- Richter et al. (2021)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2025-01-10T01:48:34", "updateFrequency": "", "dataLineage": "Data were produced by scientists at the National Center for Atmospheric Research, USA (NCAR) and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "nudged, CESM2-CAM6, NCAR, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1x1 degree", "status": "ongoing", "dataPublishedTime": "2024-09-26T08:32:48", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40389, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/NCAR/CESM2-CAM6/nudged", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1457290059101, "numberOfFiles": 11323, "fileFormat": "Data are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40387, "uuid": "c90708df097c4e0aa97655e275647317", "short_code": "comp", "title": "CESM2-CAM6 model deployed at NCAR", "abstract": "This data was produced by the CESM2-CAM6 model run by scientists at NCAR for the SNAPSI project." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50418, 50419, 50426, 50427, 50429, 50431, 50445, 50475, 50481, 50496, 50498, 50549, 50559, 50566, 50582, 50587, 50588, 50590, 50597, 50605, 50609, 50615, 51210, 51211, 53111, 54228, 54366, 54378, 54963, 62560, 62561, 63011, 64078, 64080, 66075, 66077, 71613, 71619, 71634, 74366, 75565, 75568, 84646, 84647 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40392, "uuid": "ce8fb52804934952a00ef7d3d223d305", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the CESM2-CAM6 model at NCAR", "abstract": "The CESM2-CAM6 model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at the National Center for Atmospheric Research, USA (NCAR). \r\n\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- Danabasoglu et al. (2020)\r\n- Richter et al. (2021)" } ], "responsiblepartyinfo_set": [ 196904, 196905, 196906, 196907, 196908, 196909, 196910, 196911, 205494 ], "onlineresource_set": [ 84067, 84069, 84070, 84068 ] }, { "ob_id": 40390, "uuid": "382452ccb0fe44a0a2345b91ae0dc8f3", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): free data produced by the CESM2-CAM6 model at NCAR", "abstract": "This dataset contains model data for SNAPSI experiment 'control' produced by scientists at NCAR (National Center for Atmospheric Research, USA). The data is generated with the coupled climate model CESM2-CAM6.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe free experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n\r\nModel reference publications:\r\n- Danabasoglu et al. (2020)\r\n- Richter et al. 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