Result List
Get a list of Result objects. Results have a 1:1 mapping with Observations.
GET /api/v3/results/?format=api&offset=10200
{ "count": 11555, "next": "https://api.catalogue.ceda.ac.uk/api/v3/results/?format=api&limit=100&offset=10300", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/results/?format=api&limit=100&offset=10100", "results": [ { "ob_id": 39915, "uuid": "8588f5d1ddc8455e8128fc7eb2d15102", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/deposited2023/moasa_clean_air_data/M325", "numberOfFiles": 38, "volume": 53002788, "fileFormat": "NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39914, "uuid": "d5bb9cf66df8410e9e5e5ac889688720", "short_code": "ob", "title": "MOASA flight M325: airborne observations from the Clean Air project over London, UK", "abstract": "In-situ airborne observations collected during flight M325 on 24 March 2022 by the Clean Air instrument suite on board the Met Office Atmospheric Survey Aircraft (MOASA) for the MOASA Clean Air project. \r\n\r\nThis dataset contains in meteorological and situ atmospheric composition measurements (gaseous nitrogen dioxide, ozone, sulphur dioxide and fine mode (PM2.5) aerosol) over London, UK, in a city survey flight pattern as part of the Strategic Priorities Fund (SPF) Clean Air Programme Intense Observational Period. Quicklook plots are also included.\r\n\r\nFull flight track and other MOASA flights can be seen via the CEDA Flight Finder tool linked to on this record." }, "onlineresource_set": [] }, { "ob_id": 39919, "uuid": "830e176a6f454a13af361cb5304a7528", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/deposited2023/moasa_clean_air_data/M324", "numberOfFiles": 38, "volume": 47144590, "fileFormat": "NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39918, "uuid": "93595f4627a1472595c1ebd740af669c", "short_code": "ob", "title": "MOASA flight M324: airborne observations from the Clean Air project over London, UK", "abstract": "In-situ airborne observations collected during flight M324 on 14 March 2022 by the Clean Air instrument suite on board the Met Office Atmospheric Survey Aircraft (MOASA) for the MOASA Clean Air project. \r\n\r\nThis dataset contains in meteorological and situ atmospheric composition measurements (gaseous nitrogen dioxide, ozone, sulphur dioxide and fine mode (PM2.5) aerosol) over London, UK, in a city survey flight pattern as part of the Strategic Priorities Fund (SPF) Clean Air Programme Intense Observational Period. Quicklook plots are also included.\r\n\r\nFull flight track and other MOASA flights can be seen via the CEDA Flight Finder tool linked to on this record." }, "onlineresource_set": [] }, { "ob_id": 39923, "uuid": "217cf43a60244cecaa363c70bd45d344", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/cmip6/data/CMIP6/CFMIP/AS-RCEC/TaiESM1/amip-piForcing", "numberOfFiles": 88, "volume": 62878767318, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39922, "uuid": "52235c515e45499fa0af13803ab3e862", "short_code": "ob", "title": "WCRP CMIP6: Research Center for Environmental Changes (AS-RCEC) TaiESM1 model output for the \"amip-piForcing\" experiment", "abstract": "The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the Research Center for Environmental Changes (AS-RCEC) TaiESM1 model output for the \"AMIP SSTs with pre-industrial anthropogenic and natural forcing\" (amip-piForcing) experiment. These are available at the following frequency: Amon. The runs included the ensemble member: r1i1p1f1.\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." }, "onlineresource_set": [] }, { "ob_id": 39926, "uuid": "0a67da480fde4826b222a1c4a5e694ba", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/cmip6/data/CMIP6/CFMIP/CNRM-CERFACS/CNRM-CM6-1/amip-piForcing", "numberOfFiles": 41, "volume": 46690785668, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39925, "uuid": "b7511d7df153441b92b734da6273d4e9", "short_code": "ob", "title": "WCRP CMIP6: the CNRM-CERFACS team CNRM-CM6-1 model output for the \"amip-piForcing\" experiment", "abstract": "The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the the CNRM-CERFACS team CNRM-CM6-1 model output for the \"AMIP SSTs with pre-industrial anthropogenic and natural forcing\" (amip-piForcing) experiment. These are available at the following frequencies: Amon and fx. 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 CNRM-CERFACS team team consisted of the following agencies: Centre National de Recherches Météorologiques (CNRM) and Centre Européen de Recherche et Formation Avancée en Calcul Scientifique (CERFACS)." }, "onlineresource_set": [] }, { "ob_id": 39929, "uuid": "41bb50286ad0471ea37dc272b1ef3f5c", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/cmip6/data/CMIP6/CFMIP/NASA-GISS/GISS-E2-1-G/amip-piForcing", "numberOfFiles": 33, "volume": 3201440889, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39928, "uuid": "02c0743ee0544ed7a8575f21598cfdf5", "short_code": "ob", "title": "WCRP CMIP6: NASA Goddard Institute for Space Studies (NASA GISS) GISS-E2-1-G model output for the \"amip-piForcing\" experiment", "abstract": "The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the NASA Goddard Institute for Space Studies (NASA GISS) GISS-E2-1-G model output for the \"AMIP SSTs with pre-industrial anthropogenic and natural forcing\" (amip-piForcing) experiment. These are available at the following frequency: Amon. The runs included the ensemble member: r1i1p1f1.\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." }, "onlineresource_set": [] }, { "ob_id": 39932, "uuid": "715dcf447c754b978c37d7dc8e81306b", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/deposited2023/moasa_clean_air_data/M323", "numberOfFiles": 38, "volume": 55672417, "fileFormat": "NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39931, "uuid": "d73f02a3a0934b45bd332f0851953ce7", "short_code": "ob", "title": "MOASA flight M323: airborne observations from the Clean Air project over London, UK", "abstract": "In-situ airborne observations collected during flight M323 on 10 March 2022 by the Clean Air instrument suite on board the Met Office Atmospheric Survey Aircraft (MOASA) for the MOASA Clean Air project. \r\n\r\nThis dataset contains in meteorological and situ atmospheric composition measurements (gaseous nitrogen dioxide, ozone, sulphur dioxide and fine mode (PM2.5) aerosol) over London, UK, in a city survey flight pattern as part of the Strategic Priorities Fund (SPF) Clean Air Programme Intense Observational Period. Quicklook plots are also included.\r\n\r\nFull flight track and other MOASA flights can be seen via the CEDA Flight Finder tool linked to on this record." }, "onlineresource_set": [] }, { "ob_id": 39936, "uuid": "381ecfc0b0fd4081aa16582fc2f9c8b3", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/deposited2023/moasa_clean_air_data/M320", "numberOfFiles": 38, "volume": 89656049, "fileFormat": "NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39935, "uuid": "8795188227b141d4851b43c31f724b57", "short_code": "ob", "title": "MOASA flight M320: airborne observations from the Clean Air project over Manchester, UK", "abstract": "Observations collected during flight M320 on 01 March 2022 by the Clean Air instrument suite on board the Met Office Atmospheric Survey Aircraft (MOASA) for the MOASA Clean Air project. \r\n\r\nThis dataset contains in meteorological and situ atmospheric composition measurements (gaseous nitrogen dioxide, ozone, sulphur dioxide and fine mode (PM2.5) aerosol) over Manchester, UK, in a city survey flight pattern as part of the Strategic Priorities Fund (SPF) Clean Air Programme Intense Observational Period. Quicklook plots are also included.\r\n\r\nFull flight track and other MOASA flights can be seen via the CEDA Flight Finder tool linked to on this record." }, "onlineresource_set": [] }, { "ob_id": 39940, "uuid": "6a54628892a84f299263ec137ec7c416", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/biomass/data/agb/maps/v4.0/", "numberOfFiles": 5182, "volume": 302039423059, "fileFormat": "Data are netCDF and geotiff formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39899, "uuid": "af60720c1e404a9e9d2c145d2b2ead4e", "short_code": "ob", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020, v4", "abstract": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. \r\n\r\nThis release of the data is version 4. Compared to version 3, version 4 consists of an update of the three maps of AGB for the years 2010, 2017 and 2018 and new AGB maps for 2019 and 2020. New AGB change maps have been created for consecutive years (2018-2017, 2019-2018 and 2020-2019) and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.\r\n\r\n\r\n\r\nThe data products consist of two (2) global layers that include estimates of:\r\n1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots\r\n2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)\r\n\r\nIn addition, files describing the AGB change between two consecutive years (i.e., 2018-2017, 2019-2018 and 2020-2010) and over a decade (2020-2010) are provided (labelled as 2018_2017, 2019_2018, 2020_2019 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.\r\n\r\n\r\nData are provided in both netcdf and geotiff format." }, "onlineresource_set": [] }, { "ob_id": 39944, "uuid": "854a4abe400448058821c1b1cab45443", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ocean_colour/data/v6.0-release/geographic/netcdf/rrs", "numberOfFiles": 12575, "volume": 9463744399706, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39942, "uuid": "a0782135bcd04d77a1dae4aa71fba47c", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 6.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection)." }, "onlineresource_set": [] }, { "ob_id": 39946, "uuid": "b30a2bb1366246009f18621e7e673025", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ocean_colour/data/v6.0-release/geographic/netcdf/all_products", "numberOfFiles": 12575, "volume": 24382256018121, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39945, "uuid": "0875b4675f1e46ebadb526e0b95505c5", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products) at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains all their Version 6.0 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Data are also available as monthly climatologies.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)" }, "onlineresource_set": [] }, { "ob_id": 39948, "uuid": "35d4e1dda8634cf8afa87d45d244e005", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ocean_colour/data/v6.0-release/sinusoidal/netcdf/chlor_a", "numberOfFiles": 12576, "volume": 1654621805755, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39947, "uuid": "474ac06235e54e6cb0ec6eed635e1213", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 6.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the chlorophyll-a data are also included in the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)" }, "onlineresource_set": [] }, { "ob_id": 39960, "uuid": "614fe7bcd7214857a5965f5d9cb64998", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ocean_colour/data/v6.0-release/sinusoidal/netcdf/iop", "numberOfFiles": 12575, "volume": 12669516210645, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39959, "uuid": "024292dcda5d42ceb326850f89f8b40d", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 6.0 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the IOP data are also included in the 'All Products' dataset. \r\n\r\nThe inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)" }, "onlineresource_set": [] }, { "ob_id": 39962, "uuid": "81b7ad947dbc4636a20d50567da13a47", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ocean_colour/data/v6.0-release/geographic/netcdf/kd", "numberOfFiles": 12575, "volume": 1519343791261, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39961, "uuid": "8ecae26f390b4938b67a97cbce3ecd8b", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 6.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. It is computed from the Ocean Colour CCI Version 6.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection)." }, "onlineresource_set": [] }, { "ob_id": 39964, "uuid": "9e0f8ce195a2474ab85cc0d8ccb140aa", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ocean_colour/data/v6.0-release/geographic/netcdf/chlor_a", "numberOfFiles": 12575, "volume": 1511785707097, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39963, "uuid": "b0ec72a28b6a4829a33ed9adc215d5bc", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 6.0 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day, monthly and yearly composites) covering the period 1997 - 2022. Note, this chlor_a data is also included in the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)" }, "onlineresource_set": [] }, { "ob_id": 39966, "uuid": "99cde9b54cfa4aa9a42af0c4a1c0e071", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ocean_colour/data/v6.0-release/geographic/netcdf/iop", "numberOfFiles": 12577, "volume": 13747999057840, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39965, "uuid": "90682bac7d0e4e418085f30eba43dfba", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 6.0 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the IOP data is also included in the 'All Products' dataset. \r\n\r\nThe inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)" }, "onlineresource_set": [] }, { "ob_id": 39968, "uuid": "a0cbbfcf4d584ef892ed9e86d14b6d76", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ocean_colour/data/v6.0-release/sinusoidal/netcdf/kd", "numberOfFiles": 12576, "volume": 1662919915746, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39967, "uuid": "3bdb21a4cd004e5f8cc148fea5f1d4e3", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 6.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. It is computed from the Ocean Colour CCI Version 6.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection)." }, "onlineresource_set": [] }, { "ob_id": 39970, "uuid": "414686d08e76411484c1177394c1d83e", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ocean_colour/data/v6.0-release/sinusoidal/netcdf/all_products", "numberOfFiles": 12575, "volume": 22242114092326, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39969, "uuid": "86d360431f3b4184b89cdd1cd707bb33", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products) at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains all their Version 6.0 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. \r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)" }, "onlineresource_set": [] }, { "ob_id": 39974, "uuid": "a0a4a272cbec4cc382f833e6d04f0f5f", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ocean_colour/data/v6.0-release/sinusoidal/netcdf/rrs", "numberOfFiles": 12575, "volume": 8813928738015, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39949, "uuid": "8b9d461f245b4efd8ea9fa080366e3b1", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 6.0 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection)." }, "onlineresource_set": [] }, { "ob_id": 39975, "uuid": "06b722771d3748c88112bbe9f9024fe0", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ocean_colour/data/v6.0-release/climatology/netcdf/monthly/v6.0", "numberOfFiles": 13, "volume": 13145226229, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39971, "uuid": "690fdf8f229c4d04a2aa68de67beb733", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Monthly climatology of global ocean colour data products at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains a monthly climatology of the generated ocean colour products covering the period 1997 - 2022.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided." }, "onlineresource_set": [] }, { "ob_id": 39979, "uuid": "ce87ac52ab3044c48315841fe432b3ad", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/c3s_sst/data/ICDR_v2/AVHRR/L3C/v2.1/", "numberOfFiles": 5833, "volume": 502463249903, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39978, "uuid": "86d55a4576284365906f661aa547e566", "short_code": "ob", "title": "Copernicus Climate Change Service Dataset: Sea Surface Temperature Integrated Climate Data Record (ICDR) from the Advanced Very High Resolution Radiometer (AVHRR), Level 3C (L3C), version 2.1", "abstract": "This dataset provides gridded Sea Surface Temperature data derived from the Advance Very High Resolution Radiometer (AVHRR) series of satellites. Data is available separately for the AVHRR instruments on NOAA-19, METOP-A and METOP-B.\r\n\r\nThis dataset is produced as an Intermediate Climate Data Record 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 catalgoue and through the ESA CCI Open Data Portal." }, "onlineresource_set": [] }, { "ob_id": 39981, "uuid": "80de3c2693474eda923da4b177849c09", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/c3s_sst/data/ICDR_v2/SLSTR/L3C/v2.0/", "numberOfFiles": 5698, "volume": 110445073326, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39980, "uuid": "da2f3536e656437594db6c6de86df26f", "short_code": "ob", "title": "Copernicus Climate Change Service Dataset: Sea Surface Temperature Integrated Climate Data Record (ICDR) from the SLSTR instrument on Sentinel 3, Level 3C (L3C), version 2.0", "abstract": "This dataset provides gridded Sea Surface Temperature data derived from the Sea and Land Surface Temperature Radiometer(SLSTR) on the Sentinel-3 series of satellites. \r\n\r\nThis dataset is produced as an Intermediate Climate Data Record 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." }, "onlineresource_set": [] }, { "ob_id": 39985, "uuid": "ea0d2e80c76d43858a157dbbbe91c38f", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/c3s_sst/data/ICDR_v2/SLSTR/L3C/v2.1/", "numberOfFiles": 7153, "volume": 188135477832, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39984, "uuid": "04f822f5cce4436ab88ac1f9a5109441", "short_code": "ob", "title": "Copernicus Climate Change Service Dataset: Sea Surface Temperature Integrated Climate Data Record (ICDR) from the SLSTR instrument on Sentinel 3, Level 3C (L3C), version 2.1", "abstract": "This dataset provides gridded Sea Surface Temperature data derived from the Sea and Land Surface Temperature Radiometer(SLSTR) on the Sentinel-3 series of satellites. \r\n\r\nThis dataset is produced as an Intermediate Climate Data Record 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." }, "onlineresource_set": [] }, { "ob_id": 39987, "uuid": "59284303125c47798101b72141ccf392", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/c3s_sst/data/ICDR_v2/Analysis/L4/v2.0/", "numberOfFiles": 1828, "volume": 30715456343, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39986, "uuid": "5352ffa477fa489094a7c0a4b32ff677", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 39990, "uuid": "94a3802e5f4c41a7a559ae46d3abb38c", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/c3s_sst/data/ICDR_v2/Analysis/L4/v2.1/", "numberOfFiles": 2192, "volume": 36452399946, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39989, "uuid": "edc03b1f5c4f42dcbe5906dd3b5fd592", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 39994, "uuid": "d430009199c04da18b84c5210fc6c73a", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/esacci/land_surface_temperature/data/GOES_IMAGER/L3U/v1.00/", "numberOfFiles": 41837, "volume": 587554684259, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 39995, "uuid": "66f389fcdb5e48c6ad45e2125d1b60fb", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/land_surface_temperature/data/GOES_IMAGER/L3C/v1.00/", "numberOfFiles": 1537, "volume": 25492049220, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 40003, "uuid": "34e861471e464a0784b47b0b0b3003d4", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/sea_ice/data/sea_ice_concentration/L4/ssmi_ssmis/12.5km/v3.0/", "numberOfFiles": 21809, "volume": 83566854605, "fileFormat": "The data are in NetCDF format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39793, "uuid": "eade27004395466aaa006135e1b2ad1a", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): High(er) Resolution Sea Ice Concentration Climate Data Record Version 3 (SSM/I and SSMIS)", "abstract": "This climate data record of sea ice concentration (SIC) is obtained using passive microwave satellite data from the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) over the polar regions (Arctic and Antarctic). The processing chain features: 1) dynamic tuning of tie-points and algorithms, 2) correction of atmospheric noise using a Radiative Transfer Model, 3) computation of per-pixel uncertainties, 4) an optimal hybrid sea ice concentration algorithm, and 5) pan-sharpening of the SIC fields using the near-90 GHz imagery channels. This dataset was generated by the ESA Climate Change Initiative (CCI+) Sea Ice Phase 1 project. This dataset is an enhanced-resolution version of the EUMETSAT Ocean and Sea Ice Satellite Application Facility Global Sea Ice Concentration Climate Data Record (OSI SAF OSI-450-a CDR) over the period 1991-2020." }, "onlineresource_set": [] }, { "ob_id": 40005, "uuid": "2a3b8d45903e4c68b0d70558877ac195", "short_code": "result", "curationCategory": "", "dataPath": "/badc/icecaps-ace/data/MIXCRA_1_fog/", "numberOfFiles": 62, "volume": 4558239, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40004, "uuid": "0ad1068f45724169afbe541b2525e81c", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40024, "uuid": "4f2c8a76a4684efb810362b5151c0d9e", "short_code": "result", "curationCategory": "C", "dataPath": "/neodc/caliop/data/l2_vfm/v4-21", "numberOfFiles": 15283, "volume": 691238827564, "fileFormat": "Data are HDF4 formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40021, "uuid": "72381743f3294be0b3c00de0bef4c409", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40027, "uuid": "26132770544e466b89cc7c89158f5f5e", "short_code": "result", "curationCategory": "A", "dataPath": "/bodc/BGS220061", "numberOfFiles": 441, "volume": 47869431270, "fileFormat": "Data are CF-Compliant NetCDF formatted and .csv data files", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40026, "uuid": "14dd5580eab9410fb3696340711b1d67", "short_code": "ob", "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)." }, "onlineresource_set": [] }, { "ob_id": 40043, "uuid": "f6cd8d6e7bde4ea9af4f685fd737a7e0", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/land_surface_temperature/data/GOES_IMAGER_ABI/L3U/v1.00/", "numberOfFiles": 41838, "volume": 587554685209, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [ { "ob_id": 39994, "uuid": "d430009199c04da18b84c5210fc6c73a", "short_code": "result", "title": null, "abstract": null } ], "observation": { "ob_id": 37316, "uuid": "1fb8f831da5b45a59213da1d8a4503b8", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Geostationary Operational Environmental Satellite (GOES) level 3U (L3U) product (2009-2020), version 1.00", "abstract": "This dataset contains land surface temperatures (LST) and their uncertainty estimates from the IMAGER onboard the Geostationary Operational Environmental Satellite (GOES-12 and GOES-13) and from the Advanced Baseline Imager (ABI) onboard GOES-16. The surface temperatures are generated every 3 hours for GOES 12 and 13 and every hour for GOES 16. Data are distributed on a regular latitude-longitude grid with a resolution of 0.05ºx0.05º. The coverage is limited to land surfaces within the GOES disk, which encompasses North and South America. \r\n\r\nLSTs are estimated from infrared measurements using a single channel algorithm in the case of GOES 12 and 13, and a split-window algorithm in the case of GOES 16. Observations are only available under clear-sky conditions. Quality of single channel algorithms is generally lower than dual channel ones, users are advised to read the respective Validation Report for more information on expected quality of these LST estimates.\r\n\r\nThe dataset was produced by the Portuguese Institute for Sea and Atmosphere (IPMA) as part of the ESA Land Surface Temperature Climate Change Initiative. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty." }, "onlineresource_set": [] }, { "ob_id": 40047, "uuid": "202f1b2f357a44cda117b65ea67c7994", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/land_surface_temperature/data/GOES_IMAGER_ABI/L3C/v1.00/monthly/", "numberOfFiles": 1538, "volume": 25492050178, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [ { "ob_id": 39995, "uuid": "66f389fcdb5e48c6ad45e2125d1b60fb", "short_code": "result", "title": null, "abstract": null } ], "observation": { "ob_id": 38240, "uuid": "c01275d56cba4970bda78ca79b5b1273", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Geostationary Operational Environmental Satellite (GOES) level 3C (L3C) product (2009-2020), version 1.00", "abstract": "This dataset contains monthly averaged land surface temperatures (LST) and their uncertainty estimates from the IMAGER onboard the Geostationary Operational Environmental Satellite (GOES-12 and GOES-13) and from the Advanced Baseline Imager (ABI) onboard GOES-16. The original surface temperatures are generated every 3 hours for GOES 12 and 13 and every hour for GOES 16, and in the L3C dataset a monthly average at each time step is provided. Data are distributed on a regular latitude-longitude grid with a resolution of 0.05ºx0.05º. The coverage is limited to land surfaces within the GOES disk, which encompasses North and South America. \r\n\r\nLSTs are estimated from infrared measurements using a single channel algorithm in the case of GOES 12 and 13, and a split-window algorithm in the case of GOES 16. Observations are only available under clear-sky conditions. Quality of single channel algorithms is generally lower than dual channel ones, users are advised to read the respective Validation Report for more information on expected quality of these LST estimates.\r\n\r\nThe dataset was produced by the Portuguese Institute for Sea and Atmosphere (IPMA) as part of the ESA Land Surface Temperature Climate Change Initiative. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty." }, "onlineresource_set": [] }, { "ob_id": 40048, "uuid": "b4062f14681a4d5fb4cf2ea9d80d320e", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig37/v20211215/", "numberOfFiles": 8, "volume": 27263, "fileFormat": "Net-CDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 33451, "uuid": "691c673c0d204911893659e10d4ddcba", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.37 (v20211215)", "abstract": "Data for Figure 2.37 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.37 shows indices of interannual climate variability from 1950-2019 based upon several sea surface temperature data products\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has five panels, with data provided for all panels in one single directory.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - IOB.nc contains IOB index from COBE, ERSST, HADI, KAPL and OISST (yearly data, 1950-2019).\r\n - IOD.nc contains IOD index from COBE, ERSST, HADI, KAPL and OISST (yearly data, 1950-2019).\r\n - NINO34.nc contains Nino 3.4 index from COBE, ERSST, HADI, KAPL and OISST (yearly data, 1950-2019).\r\n - AMM.nc contains AMM index from COBE, ERSST, HADI, KAPL and OISST (yearly data, 1950-2019).\r\n - AZM.nc contains AZM index from COBE, ERSST, HADI, KAPL and OISST (yearly data, 1950-2019).\r\n \r\n Data acronyms:\r\n COBE [Objective Analyses of Sea-Surface Temperature and Marine Meteorological Variables for the 20th Century using ICOADS (International Comprehensive Ocean-Atmosphere Data Set) and the Kobe Collection].\r\n ERSST [NOAA Extended Reconstructed Sea Surface Temperature].\r\n HADI [Hadley Centre Sea Ice and Sea Surface Temperature data set].\r\n KAPL [Kaplan Extended SST].\r\n OISST [NOAA Optimum Interpolation Sea Surface Temperature].\r\n IOB [Indian Ocean Basin]\r\n IOD [Indian Ocean Dipole]\r\n AMM [Atlantic Meridional Mode]\r\n AZM [Atlantic Zonal Mode]\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n First panel:\r\n - Data file: IOB.nc\r\n \r\n Second panel:\r\n - Data file: IOD.nc\r\n \r\n Third panel:\r\n - Data file: Nino34.nc\r\n \r\n Fourth panel:\r\n - Data file: AMM.nc\r\n \r\n Fifth panel:\r\n - Data file: AZM.nc\r\n \r\n In all the cases:\r\n - Blue line corresponds to COBE data set\r\n - Red correspinds to ERSST data set\r\n - Skyblue corresponds to HADI data set\r\n - Green corresponds to KAPL data set\r\n - Yellow corresponds to OISST data set\r\n For all the nc files the sources are arranged in columns 1 to 5, respectively.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1" }, "onlineresource_set": [] }, { "ob_id": 40050, "uuid": "ecf4d420e15a43a4bf9ace1ad9563bed", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/cmip6/data/CMIP6/CFMIP/NCC/NorESM2-LM/amip-p4K", "numberOfFiles": 71, "volume": 572477439, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40049, "uuid": "9be6a7a9b2c4461e8977e2a18bf9c0c6", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40052, "uuid": "19b9871d95a64bcbab02dc28bbbc3a79", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ar6_wg1/data/ch_07/ch7_fig17/v20221017", "numberOfFiles": 6, "volume": 19825, "fileFormat": "CSV", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 37806, "uuid": "b9303c07edb24582b45088795f347ca9", "short_code": "ob", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 7.17 (v20220721)", "abstract": "Data for Figure 7.17 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.17 shows time evolution of the effective radiative forcing (ERF) to the CO2 concentration increased by 1% per year until year 70 (equal to the time of doubling) and kept fixed afterwards, and surface temperature response to the CO2 forcing calculated using the emulator.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- (a) Idealized radiative forcing. Time evolution of the effective radiative forcing (ERF) to the CO2 concentration increased by 1% per year until year 70 (equal to the time of doubling) and kept fixed afterwards (white line). The likely and very likely ranges of ERF indicated by light and dark orange have been assessed in Section 7.3.2.1. \r\n\r\n- (b) Emulated temperature response. Surface temperature response to the CO2 forcing calculated using the emulator with a given value of ECS, considering uncertainty in ΔF2×CO2, α, and κ associated with the ocean heat uptake and efficacy (white line). The likely and very likely ranges are indicated by cyan and blue, respectively. For comparison, the temperature response to abrupt doubling of the CO2 concentration is displayed by a grey curve. \r\n\r\nThe mean, likely and very likely ranges of ECS and TCR are shown at the right (the values of TCR also presented in panel b). \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 7.17:\r\n \r\n - Data file: 'Figure_17_values_panel_a.csv'\r\n - Data file: 'Figure_17_values_panel_b.csv'\r\n - Data file: 'Figure_17_values_panel_b_side.csv'\r\n\r\nERF stands for Effective Radiative Forcing.\r\nTCR stands for Transient Climate Response.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures for Chapter 7 are produced by the Jupyter Notebooks that live inside the notebooks directory of the Chapter 7 GitHub repository. \r\nThe data provided for this figure is the output plotted data as provided by the author.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n- Link to the code for the figure, archived on Zenodo\r\n - Link to the Chapter 7 GitHub repository" }, "onlineresource_set": [] }, { "ob_id": 40054, "uuid": "a176ce9f5afd4c10b2ff0a57ebd55113", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig22/v20220712", "numberOfFiles": 110, "volume": 21000000, "fileFormat": "NetCDF, txt", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 40055, "uuid": "dd076c309f7248d095b259a53898d762", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig22/v20220712", "numberOfFiles": 110, "volume": 21000000, "fileFormat": "NetCDF, txt", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 40056, "uuid": "cdfaf786153b4a15ab09c58547258d5e", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig22/v20220712", "numberOfFiles": 110, "volume": 21000000, "fileFormat": "NetCDF, txt", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 40057, "uuid": "55bd0985ddc6421ebbfcd6ee369fabe2", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig22/v20230206", "numberOfFiles": 112, "volume": 5230594, "fileFormat": "NetCDF, txt", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40053, "uuid": "503edf9eb68040c4a439fed88b81c8c9", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40058, "uuid": "f15e7368fba14a9ea2b778dd774bdb0a", "short_code": "result", "curationCategory": "", "dataPath": "/badc/aphh/data/delhi/promote/UKESM1-climate-response-NO3/", "numberOfFiles": 90, "volume": 47413422337, "fileFormat": "Netcdf", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39991, "uuid": "b70e6ae10a9f463d88819eb981cd4d0f", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40059, "uuid": "e2dd07967a9849129b50403be9436a73", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/ch_10/ch10_fig18/v20220622", "numberOfFiles": 12, "volume": 92078, "fileFormat": "csv", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 34629, "uuid": "567ca2ab6d6043479a1eaec678bfe91a", "short_code": "ob", "title": "Chapter 10 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 10.18 (v20220622)", "abstract": "Data for Figure 10.18 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 10.18 shows historical and projected rainfall and Southern Annular Mode (SAM) over the Cape Town region.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nDoblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1363–1512, doi:10.1017/9781009157896.012.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 4 subpanels. Data for all subpanels is provided.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The data is for the Cape Town region:\r\n \r\n - Observed yearly accumulation of rainfall, 1933-2014 mean, 2015, 2016, 2017\r\n - Observed annual precipitation cycle, 1933-2014 mean, 2015-2017 mean, 2015, 2016, 2017\r\n - Rainfall anomalies 1930-2100 with respect to 1980–2010 means\r\n - SAM index (calculated from sea-level pressure) 1930-2100\r\n - Precipitation trends 1933-2017, 1979-2017 and 2018-2100\r\n - SAM index trends 1933-2017, 1979-2017 and 2018-2100\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel (a):\r\nStation data of yearly accumulation of rainfall over the Cape Town region, 1933-2014 (grey lines), 2015 (orange line), 2016 (red line), 2017 (purple line):\r\n - Data file: \r\nFig_10_18_panel-a.csv\r\n \r\n Panel (b):\r\nStation data of annual precipitation cycle over the Cape Town region, 1933-2014 mean (black line), 2015-2017 mean (grey line), 2015 (orange bars), 2016 (red bars), 2017 (purple bars):\r\n - Data file: \r\nFig_10_18_panel-b.csv\r\n \r\n Panel (c):\r\nSAM index (top part) and rainfall (bottom part) anomalies over the Cape Town region between 1930 and 2100 of observed and reanalysis data (SAM: station based (black line), NCEP/NCAR (light blue line), ERA20C (dark red line) and 20CR (yellow line); rainfall: station based (black line), GPCC (green line) and CRU TS (olive line)) and model data (SAM and rainfall: CMIP5 (blue shading), CMIP6 (red shading), MIROC6 (orange shading); Rainfall: CORDEX (cyan shading), 6 CCAM (purple shading)):\r\n - Data files: \r\nFig_10_18_panel-c_timeseries_precipitation.csv, \r\nFig_10_18_panel-c_timeseries_SAM_index.csv\r\n \r\n Panel (d):\r\nSAM index (top part) and rainfall (bottom part) Theil-Sen trends between 1933-2017, 1979-2017 and 2018-2100 over the Cape Town region (31°S‒35°S, 18°W‒20.5°W) of observed and reanalysis data (SAM: station based (black), NCEP/NCAR (light blue), ERA20C (dark red) and 20CR (yellow); rainfall: station based (black), GPCC (green) and CRU TS (olive)) and model data (CMIP5 (blue), CMIP6 (red), MIROC6 (orange)):\r\n - Data files: \r\nFig_10_18_panel-d_trends_precipitation_1933-2017.csv, \r\nFig_10_18_panel-d_trends_precipitation_2018-2100.csv, \r\nFig_10_18_panel-d_trends_SAM_index_1979-2017.csv, \r\nFig_10_18_panel-d_trends_precipitation_1979-2017.csv, \r\nFig_10_18_panel-d_trends_SAM_index_1933-2017.csv, \r\nFig_10_18_panel-d_trends_SAM_index_2018-2100.csv\r\n\r\n\r\nAcronyms: \r\nCMIP - Coupled Model Intercomparison Project, \r\nNCEP - National Centers for Environmental Prediction, \r\nNCAR - National Center for Atmospheric Research, \r\nCRU TS - Climatic Research Unit (CRU) Time-series (TS), \r\nCORDEX - Coordinated Regional Climate Downscaling Experiment, \r\nCCAM - Conformal Cubic Atmospheric Model, \r\nMIROC6 - Model for Interdisciplinary Research on Climate, \r\nGPCC- Global Precipitation Climatology Centre, \r\nOLS - ordinary least squares regression. \r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The code for ESMValTool is provided.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 10)\r\n - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11\r\n - Link to the code for the figure, archived on Zenodo." }, "onlineresource_set": [] }, { "ob_id": 40061, "uuid": "1e1d572eda994a06b1df97b1a5a9c5cb", "short_code": "result", "curationCategory": "", "dataPath": "/badc/deposited2023/marine-nwsclim/NWSPPE", "numberOfFiles": 6385, "volume": 88644184799, "fileFormat": "Data are provided in NetCDF format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40013, "uuid": "edf66239c70c426e9e9f19da1ac8ba87", "short_code": "ob", "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)." }, "onlineresource_set": [] }, { "ob_id": 40062, "uuid": "49718dac3dc44666b5cb836f7370aa82", "short_code": "result", "curationCategory": "", "dataPath": "/badc/deposited2023/marine-nwsclim/PDCtrl", "numberOfFiles": 605, "volume": 12930509896, "fileFormat": "Data are provided in NetCDF format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40015, "uuid": "66e39885a60e4b6386752b1a295f268a", "short_code": "ob", "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)." }, "onlineresource_set": [] }, { "ob_id": 40063, "uuid": "bcc63f444d594a828f53faca6c067223", "short_code": "result", "curationCategory": "", "dataPath": "/badc/deposited2023/marine-nwsclim/EnsStats", "numberOfFiles": 154, "volume": 1296272351, "fileFormat": "Data are provided in NetCDF format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40016, "uuid": "bd375134bd8c4990a1e9eb6d199cc723", "short_code": "ob", "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)." }, "onlineresource_set": [] }, { "ob_id": 40083, "uuid": "0f7ff26b137541b4a7115da0a6bde7d7", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/hydro-jules/data/Global_drought_indices/", "numberOfFiles": 389, "volume": 1668153770350, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40082, "uuid": "ac43da11867243a1bb414e1637802dec", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40088, "uuid": "0978db70bc5541c48f9bbc02951f4a0a", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ar6_wg1/data/ch_07/ch7_fig11/v20220721", "numberOfFiles": 4, "volume": 6794, "fileFormat": "CSV", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 37812, "uuid": "a95ffffa5a734724b9cf307411208569", "short_code": "ob", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 7.11 (v20220721)", "abstract": "Data for Figure 7.11 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.11 shows the temperature dependence of the feedback parameter, α (W m–2 °C–1), as a function of global mean surface air temperature anomaly relative to pre-industrial, for ESM simulations and paleoclimate proxies. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 1 panel, with data provided for the panel.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Feedback parameter α as a function of mean GSAT anomaly relative to pre-industrial for ESM simulations (red circles and lines)\r\n- Feedback parameter α as a function of mean GSAT anomaly relative to pre-industrial for paleoclimate proxies (grey squares and lines)\r\n\r\nReferences for ESM simulations: Caballero and Huber, 2013; Jonko et al., 2013; Meraner et al., 2013; Good et al., 2015; Duan et al., 2019; Mauritsen et al., 2019; Stolpe et al., 2019; Zhu et al., 2019a.\r\n\r\nReferences for paleoclimate proxies: von der Heydt et al., 2014; Anagnostou et al., 2016, 2020; Friedrich et al., 2016; Royer, 2016; Shaffer et al., 2016; Köhler et al., 2017; Snyder, 2019; Stap et al., 2019). \r\n\r\nFor the ESM simulations, the value on the x-axis refers to the average of the temperature before and after the system has equilibrated to a forcing (in most cases a CO2 doubling), and is expressed as an anomaly relative to an associated pre-industrial global mean temperature from that model. The light blue shaded square extends across the assessed range of α (Table 7.10) on the y-axis, and on the x-axis extends across the approximate temperature range over which the assessment of α is based (taken as from zero to the assessed central value of ECS; see Table 7.13). \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 7.11:\r\n \r\n - Data file: Figure7_11.csv\r\n\r\nESM stands for Earth System Model.\r\nGSAT stands for Global Surface Air Temperature.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThe data provided is the output data of the figure which can be used to reproduce the figure. Plotting scripts for reproducing this figure are linked in the Related Documents section of this catalogue record.\r\nThe original script for plotting this figure can be found in the Chapter 7 GitHub repository also linked but requires IDL.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n - Link to the code for Chapter 7, archived on Zenodo\r\n - Link to scripts used to reproduce figure from data\r\n - Link to the Chapter 7 GitHub repository" }, "onlineresource_set": [] }, { "ob_id": 40090, "uuid": "ea604d1f0d3b4a46a950b0e9cfb55e25", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_ccb9_1_fig1/v20230523", "numberOfFiles": 8, "volume": 27629, "fileFormat": "Data are csv formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40089, "uuid": "c622adfeb4cc4ae181dc4cca82c2311c", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40092, "uuid": "adc03b70b1694830847e9aad84713e4d", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ar6_wg1/data/ch_07/inputdata_ch7_fig13/v20230118", "numberOfFiles": 81, "volume": 3794647, "fileFormat": "NetCDF, CSV", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 37810, "uuid": "4dbd3ccb85d747188586735133f1d3d9", "short_code": "ob", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 7.13 (v20220118)", "abstract": "Input Data for Figure 7.13 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.13 shows polar amplification in paleo proxies and models of the Early Eocene Climatic Optimum (EECO), the Mid-Pliocene Warm Period (MPWP) and the Last Glacial Maximum (LGM). \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 12 subpanels, with input data provided for panels a-l.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\nTemperature anomalies compared with pre-industrial (equivalent to CMIP6 simulation ‘piControl’) for:\r\n - the high-CO2 EECO and MPWP time periods\r\n - the low-CO2 LGM (expressed as pre-industrial minus LGM)\r\n\r\n(a), (b) and (c) Modelled near-surface air temperature anomalies for ensemble-mean simulations of the (a) EECO (Lunt et al., 2021); (b) MPWP (Haywood et al., 2020; Zhang et al., 2021); and (c) LGM (Kageyama et al., 2021; Zhu et al., 2021). Also shown are proxy near-surface air temperature anomalies (coloured circles). \r\n\r\n(d), (e) and (f) Proxy near-surface air temperature anomalies (grey circles), including published uncertainties (grey vertical bars), model ensemble mean zonal mean anomaly (solid red line) for the same model ensembles as in (a–c), light-red lines show the modelled temperature anomaly for the individual models that make up each ensemble (LGM, N=9; MPWP, N=17; EECO, N=5). \r\n\r\n(g), (h) and (i) Proxy sea surface temperature (SST) anomalies, including published uncertainties (grey vertical bars), model ensemble mean zonal mean anomaly (solid red line) for the same model ensembles as in (j-l), light-red lines show the modelled temperature anomaly for the individual models that make up each ensemble (LGM, N=9; MPWP, N=17; EECO, N=5).\r\n\r\n(j), (k) and (l) Modelled sea surface temperature (SST) for ensemble-mean simulations of the (a) EECO (Lunt et al., 2021); (b) MPWP (Haywood et al., 2020; Zhang et al., 2021); and (c) LGM (Kageyama et al., 2021; Zhu et al., 2021). \r\n\r\nBlack dashed lines show the average of the proxy values in each latitude band: 90°S–30°S, 30°S–30°N, and 30°N–90°N. \r\nRed dashed lines show the same banded average in the model ensemble mean, calculated from the same locations as the proxies. \r\nBlack and red dashed lines are only shown if there are five or more proxy points in that band. \r\n\r\nMean differences between the 90°S/N to 30°S/N and 30°S to 30°N bands are quantified for the models and proxies in each plot. \r\n\r\nFor the EECO maps – (a) and (j) – the anomalies are relative to the zonal mean of the pre-industrial, due to the different continental configuration. Proxy datasets are: (a) and (d) Hollis et al. (2019); (b) and (e) Salzmann et al. (2013); Vieira et al. (2018), (c) and (f) Cleator et al. (2020) at the sites defined in Bartlein et al. (2011); (g) and (j) Hollis et al. (2019); (h) and (k) McClymont et al. (2020); (i) and (l) Tierney et al. (2020b). Where there are multiple proxy estimations at a single site, a mean is taken. \r\n\r\nModel ensembles are:\r\n(a), (d), (g) and (j) DeepMIP (only model simulations carried out with a mantle-frame paleogeography, and carried out under CO2 concentrations within the range assessed in Table 2.2, are shown);\r\n(b), (e), (h) and (k) PlioMIP;\r\n(c), (f), (i) and (l) PMIP4. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 7.13:\r\n \r\nObserved data:\r\n - Data file: Figure7_13_obs.csv \r\n\r\nModel data:\r\n- model data in net-CDF files is contained in the directory 'Figure_7_13_mod' in separate directories for Eocene '/eocene', Mid-pliocene '/pliocene' and Last Glacial Maximum '/lgm' periods \r\n\r\nlandsea mask data:\r\n - Data file: Plio_enh_topo_v1.0_regrid.nc\r\n - Data file: peltier_ice4g_orog_21_regrid.nc\r\n - Data file: herold_etal_eocene_topo_1x1.nc\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nDeepMIP is The Deep-Time Model Intercomparison Project.\r\nPlioMIP is the Pliocene Model Intercomparison Project\r\nPMIP4 is the Paleoclimate Modelling Intercomparison Project phase 4.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThe data provided is the input data of plotting scripts which can be used to reproduce the figure. Plotting scripts for reproducing this figure are linked in the Related Documents section of this catalogue record. The notebook 'ipcc_figure_7.13.ipynb' can be run with the provided data to reproduce the figure, you need to edit the directory paths to match your local directory within the notebook.\r\nThe original script for plotting this figure can be found in the Chapter 7 GitHub repository also linked but requires IDL.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n - Link to the plotting scripts to reproduce the figure \r\n - Link to the Chapter 7 GitHub repository\r\n - Link to the code for Chapter 7, archived on Zenodo" }, "onlineresource_set": [] }, { "ob_id": 40100, "uuid": "6bedcc3d59b94c5eb4389418fdfbf976", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/caliop/data/l2_vfm/v4-20", "numberOfFiles": 72020, "volume": 3311943523102, "fileFormat": "Data are HDF4 formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40099, "uuid": "17f2465ce84a4492b3fa2ba2e558d869", "short_code": "ob", "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. Data generation and distribution of this V4.20 product ended on July 1, 2020 to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020 to current. \r\n\r\nCALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES (Centre National D’Etudes Spatiales)." }, "onlineresource_set": [] }, { "ob_id": 40109, "uuid": "fab99941b3f24e3bb594f96bfdb1ca65", "short_code": "result", "curationCategory": "", "dataPath": "/badc/deposited2023/bangkok_aerosol_pt", "numberOfFiles": 3, "volume": 17558401, "fileFormat": "BADC-CSV", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40106, "uuid": "7b70b41c59464b94b737fb35f1eac8fe", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40111, "uuid": "94538ecd821b443abacc214f3aeca1a8", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig26/v20230530", "numberOfFiles": 5, "volume": 2144622, "fileFormat": "Data are netCDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40110, "uuid": "1e60155294934ffcaf194e555a81294b", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40114, "uuid": "4b4ea10c9bd94f4497133e8bd90b3a75", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig31/v20230531", "numberOfFiles": 7, "volume": 16380412, "fileFormat": "Data are netCDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40113, "uuid": "8fa708d0474d4a3caa5c9f645a89d282", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40117, "uuid": "7f403ed5258a42b78cf9c1e1cf0ea1f7", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig32/v20230531", "numberOfFiles": 7, "volume": 20040868, "fileFormat": "Data are netCDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40116, "uuid": "0192ae3037794e0eb93b022c5140f399", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40119, "uuid": "15227961c9624fa4b5af5e29e69da0d7", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ar6_wg1/data/ch_12/inputdata_ch12_fig10/v20220804", "numberOfFiles": 80, "volume": 15117492, "fileFormat": "net-CDF, json, csv, txt", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 37858, "uuid": "b6a36a7fe12644bfa28bc4ec8bfcb028", "short_code": "ob", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 12.10 (v20220804)", "abstract": "Input Data for Figure 12.10 from Chapter 12 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 12.10 shows projected changes in selected climatic impact-driver indices for North-America.\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 Ranasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment. 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. 1767–1926, doi:10.1017/9781009157896.014.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with general data provided in the central directory and specific data in 3 folders (Q100_CMIP5, Q100_CMIP6, Q1000_CORDEX-core).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n - spatial field over North-America of mean change in 1-in-100 year river discharge per unit catchment area (Q100, m3 s-1 km-2) from CORDEX models for 2041-2060 relative to 1995-2014 for RCP8.5\r\n - spatial field of changes of number of days per year with snow water equivalent over 100mm (SWE100) from CORDEX-core models for 2041-2060 relative to 1995-2014 for RCP8.5; the grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n - the associated mask showing the areas with more than 80% of model agreement in the sign of change\r\n - regional averages in North-America of Q100 (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for:\r\n - CMIP6 historical, ssp126 and ssp585\r\n - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n - regional averages of number of days per year with snow water equivalent over 100mm (SWE100) in North-America for:\r\n - CMIP6 historical, ssp126 and ssp585\r\n - CMIP5 and CORDEX-core historical, RCP2.6 and RCP8.5\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n The grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n\r\nCAR, SCA, NWN, NEN, WNA, CNA, ENA and NCA are domains used in the model. \r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 12.9:\r\n \r\nPanel a:\r\n - Q100_map_panel_a_NAM_divdra.nc: Field (colors plotted on the map) of changes of 1-in-100yr river discharge per unit catchment area between 2041-2060 (mid-term) and 1995-2014 (recent past) for CORDEX RCP8.5; the file contains the data for the regions from the NAM CORDEX domain\r\n - Q100_map_panel_a_CAM_for_NAM_divdra.nc: same as above for the CAM CORDEX domain\r\n\r\n Panel b:\r\n - SWE_panel_b_RCP85_2041-2060_minus_1995-2014.nc: spatial field (colors) of changes of number of days per year with snow water equivalent over 100mm (SWE100) from CORDEX-core NAM-22 models for 2041-2060 relative to 1995-2014 for RCP8; the grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero\r\n - mask_80perc-agreement_SWE_panel_b_RCP85_2041-2060_minus_1995-2014.nc: spatial mask (for hatching) showing where at least 80% of the models agree in terms of sign of change (negative change, positive change or zero change); values are: 1 where true, 0 where false\r\n \r\nPanel c:\r\n - txt files containing the median and 5th/95th percentiles of each ensemble of the 1-in-100yr river discharge per unit catchment area (Q100) regional averages of time slices: Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.txt, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${scenario}: the name of the scenario : ssp126, ssp585, rcp26, rcp85\r\n - ${period}: the explicit period used to compute the temporal average: 1995-2014 (recent past), 2041-2060 (mid-term) and 2081-2099 (long term)\r\n - ${CORDEX_domain}: the CORDEX domain\r\n - txt files containing the Q100 regional averages of global warming levels: Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${GWL}: the Global Warming Level: 1.5, 2 and 4\r\n - ${CORDEX_domain}: the CORDEX domain\r\n\r\nPanel d:\r\n- CMIP5_NORTH-AMERICA_snw_mask14_AR6_regional_averages.json: regional averages for the CMIP5 multimodel ensemble of number of days per year with snow water equivalent over 100mm (SWE100) in North-America for recent past (1995-2014), mid-term (2041-2060) long-term (2081-2100) for RCP2.6 and RCP8.5, and for three global warming levels: 1.5, 2 and 4; the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars) - grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n- CMIP6_NORTH-AMERICA_snw_mask14_AR6_regional_averages.json: same as previous file for CMIP6 (ssp126 instead of RCP2.6 and ssp585 instead of RCP8.5) - grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n- NAM-22_CORDEX_NORTH-AMERICA_snw_mask14_AR6_regional_averages.json: same as previous file for the CORDEX-core NAM-22 multimodel ensemble - grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n\r\n- NAM-22_CORDEX_NORTH-AMERICA_snw_mask30_AR6_regional_averages.json: same as previous file for the CORDEX-core NAM-22 multimodel ensemble, but grid points with less than 30 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n- CMIP5_NORTH-AMERICA_snw_mask30_AR6_regional_averages.json: regional averages for the CMIP5 multimodel ensemble of number of days per year with snow water equivalent over 100mm (SWE100) in North-America for recent past (1995-2014), mid-term (2041-2060) long-term (2081-2100) for RCP2.6 and RCP8.5, and for three global warming levels: 1.5, 2 and 4; the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars) - grid points with less than 30 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n- CMIP6_NORTH-AMERICA_snw_mask30_AR6_regional_averages.json: regional averages for the CMIP5 multimodel ensemble of number of days per year with snow water equivalent over 100mm (SWE100) in North-America for recent past (1995-2014), mid-term (2041-2060) long-term (2081-2100) for RCP2.6 and RCP8.5, and for three global warming levels: 1.5, 2 and 4; the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars) - grid points with less than 30 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP. \r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project. \r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project. \r\n\r\nSSP stands for Shared Socioeconomic Pathway. SSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6. \r\n\r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\n\r\nRCP stands for Representative Concentration Pathway. \r\n\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\n\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n For panel a, the plotting script (see data tables and code on Github) draws the rivers and uses a subroutine to identify the rivers to plot them individually with lines; plotting the Q100 netcdf file will produce dots (and not rivers).\r\n\r\n\r\nFor panel c, the recent past values are plotted as absolute values (left column on each regional subpanel) and the future changes are plotted as differences against the recent past values (differences are computed when plotting the values).\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 12)\r\n - Link to the Supplementary Material for Chapter 12, which contains details on the input data used in Table 12.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the Chapter 12 GitHub repository." }, "onlineresource_set": [] }, { "ob_id": 40120, "uuid": "e0139a98d87346d8b25e1d01a56438b1", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ar6_wg1/data/ch_07/ch7_fig19/v20230118", "numberOfFiles": 5, "volume": 9063, "fileFormat": "CSV", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 37823, "uuid": "9ce84c3a242e4b999c24dc1647c89794", "short_code": "ob", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 7.19 (v20220721)", "abstract": "Data for Figure 7.19 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.19 shows global mean temperature anomaly in models and observations from five time periods. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 5 subpanels, with data provided for panels a-e.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Global mean temperature anomaly in:\r\n(a) Historical (CMIP6 models); \r\n(b) post-1975 (CMIP6 models); \r\n(c) Last Glacial Maximum (LGM; Cross-Chapter Box 2.1; PMIP4 models; Kageyama et al., 2021; Zhu et al., 2021); \r\n(d) mid-Pliocene Warm Period (MPWP; Cross-Chapter Box 2.4; PlioMIP models; Haywood et al., 2020; Zhang et al., 2021); \r\n(e) Early Eocene Climatic Optimum (EECO; Cross-Chapter Box 2.1; DeepMIP models; Zhu et al., 2020; Lunt et al., 2021). \r\n\r\nGrey circles show models with ECS in the assessed very likely range; models in red have an ECS greater than the assessed very likely range (>5°C); models in blue have an ECS lower than the assessed very likely range (<2°C). Black ranges show the assessed temperature anomaly derived from observations (Section 2.3). The historical anomaly in models and observations is calculated as the difference between 2005–2014 and 1850–1900, and the post-1975 anomaly is calculated as the difference between 2005–2014 and 1975–1984. \r\n\r\nFor the LGM, MPWP and EECO, temperature anomalies are compared with pre-industrial (equivalent to CMIP6 simulation ‘piControl’). All model simulations of the MPWP and LGM were carried out with atmospheric CO2 concentrations of 400 and 190 ppm respectively. However, CO2 during the EECO is relatively more uncertain, and model simulations were carried out at either 1120ppm or 1680 ppm (except for the one high-ECS EECO simulation which was carried out at 840 ppm; Zhu et al., 2020). The one low-ECS EECO simulation was carried out at 1680 ppm. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\nECS stands for Equilibrium Climate Sensitivity.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 7.19:\r\n \r\n - Data file: Figure7_19_mod.csv\r\n - Data file: Figure7_19_obs.csv\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThe data provided is the output data of the figure which can be used to reproduce the figure. Link to the plotting script for reproducing this figure 'ipcc_figure_7.19.ipynb' can be found in the Related Documents section of this catalogue record.\r\nThe original script for plotting this figure can be found in the Chapter 7 GitHub repository also linked but requires IDL.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n- Link to the code for Chapter 7, archived on Zenodo\r\n- Link to scripts used to reproduce figure from data\r\n- Link to the Chapter 7 GitHub repository" }, "onlineresource_set": [] }, { "ob_id": 40133, "uuid": "0c8909adb61046f3a9137d3fbb3bb15f", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.2.0.ceda/river", "numberOfFiles": 163, "volume": 24727519, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40127, "uuid": "e6822428e4124c5986b689a37fda10bc", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40135, "uuid": "d3d7bbd419a440e78a4b09e6cf1cdc85", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.2.0.ceda/country", "numberOfFiles": 163, "volume": 12682969, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40126, "uuid": "3d30627eee5a48be844c32723b7b6be8", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40136, "uuid": "1ab77d6750174254b6d70480a99eb8b8", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.2.0.ceda/region", "numberOfFiles": 163, "volume": 19033620, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40125, "uuid": "b39898e76ab7434a9a20a6dc4ab721f0", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40137, "uuid": "349145df74a348be9daf6935f3e2952b", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.2.0.ceda/60km", "numberOfFiles": 6435, "volume": 332739715, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40132, "uuid": "22df6602b5064b1686dda7e9455f86fc", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40138, "uuid": "6419b282326c420fb6a55de59499d00c", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.2.0.ceda/25km", "numberOfFiles": 6436, "volume": 879358055, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40130, "uuid": "0545f37fb7124df381d42468eb63c144", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40139, "uuid": "3ccfb73952a543069774a67a5aef15cd", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.2.0.ceda/12km", "numberOfFiles": 6436, "volume": 2828287916, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40128, "uuid": "640d33e0cf99477990f7fee35a101850", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40140, "uuid": "d16ebf03f4b84acc95eb34b89653e971", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.2.0.ceda/1km", "numberOfFiles": 6435, "volume": 321018964336, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40129, "uuid": "46f8c1377f8849eeb8570b8ac9b26d86", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40141, "uuid": "f304d8b0e31e4f4fa0fb740e16559029", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.2.0.ceda/5km", "numberOfFiles": 6436, "volume": 14542746499, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40131, "uuid": "adf1a6cf830b4f5385c5d73609df8423", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40144, "uuid": "8faec35322d74a94849039b88e529bde", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/TS/BOX_ts13_fig1/v20230606", "numberOfFiles": 6, "volume": 49925, "fileFormat": "Data are netCDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40143, "uuid": "0481959c92944c41983dd024172ef84d", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40146, "uuid": "076eb7d9f3f5474d81f6ef56d96b5f9e", "short_code": "result", "curationCategory": "", "dataPath": "https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=COMBI_V001", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are in NetCDF-4 format", "storageStatus": "online", "storageLocation": "external", "oldDataPath": [], "observation": { "ob_id": 40147, "uuid": "fab3d1d8abce46f6a53270b0b48a9312", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40153, "uuid": "202f774a607349b2b35b71294d6ca769", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/aatsr_multimission/atsr2-v3.0.1/data/at2_toa_1p", "numberOfFiles": 309956, "volume": 37062402384071, "fileFormat": "ENVISAT PDS format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40152, "uuid": "86162ca42a6a4ebc8779bcddc817a7b3", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40155, "uuid": "c939e4540e524f2f82293f70958eb925", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ar6_wg1/data/TS/inputdata_ts_13/v20221111/", "numberOfFiles": 9, "volume": 165019, "fileFormat": "CSV", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 38908, "uuid": "f3b6afe197d24d7eb58ed2364ac0f18e", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure TS.13 v20221111", "abstract": "Input data for Figure TS.13 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 estimates of the net cumulative energy change for the period 1971–2018 associated with observations of changes in the Global Energy Inventory, Integrated Radiative Forcing and Integrated Radiative Response.\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\nFigure subpanels\r\n---------------------------------------------------\r\nThe figure has 6 subpanels, with input data provided for panels a-f.\r\n\r\n---------------------------------------------------\r\nList of data provided\r\n---------------------------------------------------\r\nThis dataset contains:\r\n\r\n- Estimates of the net cumulative energy change (ZJ = 1021 Joules) for the period 1971–2018 associated with:\r\n(a) observations of changes in the Global Energy Inventory\r\n(b) Integrated Radiative Forcing;\r\n(c) Integrated Radiative Response.\r\n\r\nBlack dotted lines indicate the central estimate with likely and very likely ranges as indicated in the legend. The grey dotted lines indicate the energy change associated with an estimated pre-industrial Earth energy imbalance of 0.2 W m–2 (a), and an illustration of an assumed pattern effect of –0.5 W m–2 °C–1 (c).\r\n\r\nBackground grey lines indicate equivalent heating rates in W m–2 per unit area of Earth’s surface.\r\nPanels (d) and (e) show the breakdown of components, as indicated in the legend, for the global energy inventory and integrated radiative forcing, respectively. Panel (f) shows the global energy budget assessed for the period 1971–2018, that is, the consistency between the change in the global energy inventory relative to pre-industrial and the implied energy change from integrated radiative forcing plus integrated radiative response under a number of different assumptions, as indicated in the legend, including assumptions of correlated and uncorrelated uncertainties in forcing plus response.\r\n\r\nShading represents the very likely range for observed energy change relative to pre-industrial levels and likely range for all other quantities.\r\nForcing and response time series are expressed relative to a baseline period of 1850–1900.\r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\n---------------------------------------------------\r\nData provided in relation to figure\r\n---------------------------------------------------\r\nData provided in relation to Figure TS.13:\r\n\r\n- Data file: AR6_ERF_1750-2019.csv\r\n- Data file: AR6_energy_GMSL_timeseries_FGD_1971to2018_corrigendum.csv\r\n- Data file: Box7.2_ERF_ZJ_percentiles_FGD_1971to2018.csv\r\n- Data file: Box7.2_Response_ZJ_percentiles_FGD_1971to2018.csv\r\n- Data file: Box7.2_ERFResp_uncorrelated_ZJ_percentiles_FGD_1971to2018.csv\r\n- Data file: Box7.2_ERFResp_correlated_ZJ_percentiles_FGD_1971to2018.csv\r\n\r\nData files are converted to csv from pickle format for archival. A link to the original files on GitHub is provided in the 'Related Documents' section.\r\n\r\n---------------------------------------------------\r\nNotes on reproducing the figure from the provided data\r\n---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. Also listed on the 'master' GitHub page linked in the documentation of this catalogue record are external GitHub repositories and locations within the contributed directory where code for figures have been supplied by other authors. These are provided \"as-is\" and are not guaranteed to be reproducible within this environment. For external GitHub locations, check out the relevant repository READMEs.\r\n\r\nThe data provided here is converted from pickle files which are used in the plotting script. The link to the original pickle files on GitHub is provided. To reproduce the figure from the input data provided, you will need to edit the filepaths within the notebook based on your local directory structure.\r\n\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n - Link to the notebook to plot the figure on the Chapter 7 GitHub repository\r\n - Link to the original pickle files on GitHub\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to Cross-Chapter Box 9.1, Figure 1" }, "onlineresource_set": [] }, { "ob_id": 40159, "uuid": "c2c76886a1104aaf98397f6b8a32abb9", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/aatsr_multimission/atsr2-v3.0.1/data/at2_ar__2p", "numberOfFiles": 196349, "volume": 2837886761867, "fileFormat": "ENVISAT PDS", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40085, "uuid": "4f3d4c97d9be45419679fc498e7f6501", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40161, "uuid": "bbfd12dfae4245ad91f1d035a0f14057", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/aatsr_multimission/atsr2-v3.0.1/data/at2_nr__2p", "numberOfFiles": 202409, "volume": 6774709114693, "fileFormat": "ENVISAT PDS", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40160, "uuid": "4371686200b444ffa6abf675334fd932", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40165, "uuid": "53af74998c624a499383caace84301eb", "short_code": "result", "curationCategory": "", "dataPath": "/badc/deposited2023/indoor_air_study_phaseII", "numberOfFiles": 2, "volume": 169667, "fileFormat": "Data are BADC-CSV formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40101, "uuid": "a7b8c82ff43d4c9aaa7ddf32f535ab37", "short_code": "ob", "title": "Indoor Air Study - Phase II: Volatile Organic Compounds (VOC) concentrations", "abstract": "The effects of using a commercial diffuser indoors were evaluated using a study group of 60 homes in Ashford, UK. Air samples were taken over 3 day periods with the diffuser switched on and in a parallel set of control homes where it was off. At least four measurements were taken in each home using vacuum-release into 6 L silica-coated canisters and with >40 VOCs (Volatile Organic Compounds) quantified using a custom thermal desorber unit (TDU), Agilent 7890A gas chromatograph (GC) fitted with flame ionisation detectors (FID) and an Agilent 5977A quadrupole mass spectrometer (QMS). Occupants self-reported their use of other VOC-containing products. \r\n\r\nData collected was used to understand better how the use of a plug-in diffuser increases population exposure to indoor VOCs. \r\n\r\nPlease see https://doi.org/10.1039/d2em00444e for publication." }, "onlineresource_set": [] }, { "ob_id": 40167, "uuid": "a7d9455537174650b25269756b55b40c", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadISDH/mon/HadISDHTable/r1/v4-5-1-2022f/", "numberOfFiles": 8, "volume": 14474148, "fileFormat": "Files are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40166, "uuid": "8956cf9e31334914ab4991796f0f645a", "short_code": "ob", "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." }, "onlineresource_set": [] }, { "ob_id": 40169, "uuid": "e7c9ff800ac840d0b513a99570f5f899", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadISDH-extremes/mon/HadISDHTable/r1/v1-0-0-2022f/", "numberOfFiles": 32, "volume": 219377292, "fileFormat": "Data are NetCDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40168, "uuid": "2d1613955e1b4cd1b156e5f3edbd7e66", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40192, "uuid": "2451556da4aa43b799314b7fc6bd3d57", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ccmi/data/post-cmip6/ccmi-2022/MIROC/MIROC-ES2H/refD2", "numberOfFiles": 15538, "volume": 826096862967, "fileFormat": "Files are Net-CDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40191, "uuid": "9443bf96bdc044b9b8a43280ba0d662b", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40196, "uuid": "f50dda0721814ba39a35efd14c76a85d", "short_code": "result", "curationCategory": "", "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/ECCC/GEM-NEMO/control/", "numberOfFiles": 6001, "volume": 2425478883770, "fileFormat": "Files are Net-CDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40194, "uuid": "6fdda887181143f9a7d265883bc00b63", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40198, "uuid": "94e6f1e024ab4a61a201e7e43da68444", "short_code": "result", "curationCategory": "", "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/ECCC/GEM-NEMO/nudged/", "numberOfFiles": 6001, "volume": 2435915825720, "fileFormat": "Files are Net-CDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40197, "uuid": "5e924de0f26e48c194568075338ad3ff", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40200, "uuid": "b82027013fce43d0adc4ad22f629d2bf", "short_code": "result", "curationCategory": "", "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/ECCC/GEM-NEMO/free/", "numberOfFiles": 5401, "volume": 2418887568527, "fileFormat": "Files are Net-CDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40199, "uuid": "f7812032a3f94c89bba253b94eed465d", "short_code": "ob", "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). 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 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- 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" }, "onlineresource_set": [] }, { "ob_id": 40203, "uuid": "dac9f07b3cc645828e79a4d1fe5473f4", "short_code": "result", "curationCategory": "", "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/CNR-ISAC/GLOBO/nudged/", "numberOfFiles": 6901, "volume": 2463609722283, "fileFormat": "Files are Net-CDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40202, "uuid": "5dd9097ff0cf49d2ab84c357ca3a4f4c", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40206, "uuid": "97327f7f4c8944778774a502a57f1899", "short_code": "result", "curationCategory": "", "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/CNR-ISAC/GLOBO/control", "numberOfFiles": 6951, "volume": 2463665849695, "fileFormat": "Files are Net-CDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40205, "uuid": "5521afe5d13e451eb2b09469e3920a61", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40208, "uuid": "46aa56792fc4415bb27bac5308a22b88", "short_code": "result", "curationCategory": "", "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/CNR-ISAC/GLOBO/free", "numberOfFiles": 6301, "volume": 2443061019375, "fileFormat": "Files are Net-CDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40207, "uuid": "d1683a835200480bb1c7227d1dd1c884", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40211, "uuid": "28214292e4ef4687a5b8cd669583731d", "short_code": "result", "curationCategory": "", "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/SNU/GRIMs/control", "numberOfFiles": 8101, "volume": 891297668875, "fileFormat": "Files are Net-CDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40210, "uuid": "87792f7c7fa343168fa47aa3040d1584", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40214, "uuid": "298c41dfa2b14ea08018b88661bfa52a", "short_code": "result", "curationCategory": "", "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/SNU/GRIMs/nudged", "numberOfFiles": 8101, "volume": 891216116243, "fileFormat": "Files are Net-CDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40213, "uuid": "fd604dd31ffc4d12bd90c17b43ba12a6", "short_code": "ob", "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. Asia-Pac J Atmos Sci 59, 113–132 (2023). https://doi.org/10.1007/s13143-022-00297-y" }, "onlineresource_set": [] }, { "ob_id": 40216, "uuid": "a0ebc2b1d0b742dbb05bc92db3caeeb2", "short_code": "result", "curationCategory": "", "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/SNU/GRIMs/free", "numberOfFiles": 7501, "volume": 889851981949, "fileFormat": "Files are Net-CDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40215, "uuid": "a53e90273cc24d8ca7845367bf30085b", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40219, "uuid": "4ced46faabf24de39924e158217abce4", "short_code": "result", "curationCategory": "", "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/KMA/GloSea6-GC32/control", "numberOfFiles": 4201, "volume": 898263021827, "fileFormat": "Files are Net-CDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40218, "uuid": "9243d4cabb934d648f70f5c8b32b8abf", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40222, "uuid": "cc53adbb9b894f7aa30f7e98cbe05477", "short_code": "result", "curationCategory": "", "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/KMA/GloSea6-GC32/nudged", "numberOfFiles": 4229, "volume": 903653988150, "fileFormat": "Files are Net-CDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40221, "uuid": "3a4c89e4107a4fc6a9af1255649c335c", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40224, "uuid": "ae765d2a29d744db8d4e64fa452d0394", "short_code": "result", "curationCategory": "", "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/KMA/GloSea6-GC32/free", "numberOfFiles": 4201, "volume": 897233655510, "fileFormat": "Files are Net-CDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40223, "uuid": "9140c9f6b8b14e4a9bd7c976fe61b467", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40228, "uuid": "fe7d2d2e01104643bfd559b578cdf1d0", "short_code": "result", "curationCategory": "", "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/UKMO/GloSea6/control", "numberOfFiles": 8716, "volume": 5817347629423, "fileFormat": "Files are Net-CDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40226, "uuid": "95697468005740fa96f08d223c407a18", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40230, "uuid": "baf22693932a43aba4913f3ca00beba3", "short_code": "result", "curationCategory": "", "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/UKMO/GloSea6/nudged/", "numberOfFiles": 8701, "volume": 5805759715552, "fileFormat": "Filesare Net-CDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40229, "uuid": "da04b3aeaf684d57b1ddb63cd9b2ebb0", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40232, "uuid": "5cdb58e4e2ec4c0e862787c917a7da8e", "short_code": "result", "curationCategory": "", "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/UKMO/GloSea6/free", "numberOfFiles": 8701, "volume": 5803474903490, "fileFormat": "Files are Net-CDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40231, "uuid": "e49233d13cc244aaab12843c66c51e79", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40234, "uuid": "b617b69d0be64bb6909c9da54968d8f2", "short_code": "result", "curationCategory": "", "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/UKMO/GloSea6/nudged-full", "numberOfFiles": 8701, "volume": 5752858210467, "fileFormat": "Files are Net-CDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40233, "uuid": "1ddb8b0ca0ae4638b872ad3d60d30933", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40264, "uuid": "799d3d553f614b13b5e9f5c6ef2f19da", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/deposited2023/QUANT", "numberOfFiles": 270, "volume": 7693944780, "fileFormat": "NetCDF, CSV", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40263, "uuid": "ae1df3ef736f4248927984b7aa079d2e", "short_code": "ob", "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.\"" }, "onlineresource_set": [] }, { "ob_id": 40269, "uuid": "3cc87fffdff14163af62d5d67bd17318", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/sea_level/data/Vertical_Land_Motions_TUM/maps/v1/", "numberOfFiles": 2, "volume": 639063, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39804, "uuid": "91d1fbc572224b9c86f6c14ba9109479", "short_code": "ob", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Regional coastline profile of Vertical Land Motions in Europe and SE Asia/Oceania, v1", "abstract": "This dataset contains a regional coastline profile of Vertical Land Motions in Europe and SE Asia/Oceania produced as part of the ESA Climate Change Initiative Sea Level project.\r\n\r\nVertical Land Motions have been estimated as the difference between the altimeter coastal sea level v1.1 dataset (available from https://catalogue.ceda.ac.uk/uuid/222cf11f49a94d2da8a6da239df2efc4 ) and tide gauge measurements from the Permanent Service for Mean Sea Level (PMSML) network. Spatial interpolation has allowed the production of a regularly spaced coastline profile of vertical land movements together with their uncertainties.\r\n\r\nThe altimeter input data are from the Jason-1, Jason-2 and Jason-3 missions during the period Jan. 2002 - May 2018." }, "onlineresource_set": [] }, { "ob_id": 40272, "uuid": "96a4e6b2cece4e94aa0417f8176301b1", "short_code": "result", "curationCategory": "", "dataPath": "/badc/cru/data/cru_jra/cru_jra_2.4/", "numberOfFiles": 1221, "volume": 413450903926, "fileFormat": "The data are provided as gzipped NetCDF files, with one file per variable, per year.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40271, "uuid": "aed8e269513f446fb1b5d2512bb387ad", "short_code": "ob", "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" }, "onlineresource_set": [] }, { "ob_id": 40274, "uuid": "f3f3839c993349af80eed097d30bfe3b", "short_code": "result", "curationCategory": "", "dataPath": "https://github.com/BrodiePearson/IPCC_AR6_Chapter9_Figures/tree/main/Plotting_code_and_data/Fig9_10_AMOC", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data written into code on the Chapter 9 GitHub repository", "storageStatus": "online", "storageLocation": "external", "oldDataPath": [], "observation": { "ob_id": 37729, "uuid": "260df0db210143dcbecf3182e24817a3", "short_code": "ob", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.10 (v20220712)", "abstract": "Data for Figure 9.10 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 9.10 shows Atlantic Meridional Overturning Circulation (AMOC) strength in simulations and sensitivity to resolution and forcing. \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 4 subpanels, with data for all panels contained in the code archived on Zenodo which is linked in the documentation. Data and code can also be found on the GitHub repository for chapter 9 linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- AMOC magnitude (units: Sverdrup (Sv) = 109 kg s–1) in PMIP experiments (Top left). \r\n- Time series of AMOC from CMIP5 and CMIP6 based on (Menary et al., 2020b) (Top right). \r\n- Percent change in AMOC strength per year at different resolutions over the 1950–2050 period with colours for model families (Roberts et al., 2020) (Bottom left).\r\n- A compilation of percentage changes in the simulated AMOC after applying an additional freshwater flux in the subpolar North Atlantic at the surface for a limited time (de Vries and Weber, 2005; Stouffer et al., 2006; Yin and Stouffer, 2007; Jackson, 2013; Liu and Liu, 2013; Jackson and Wood, 2018; Haskins et al., 2019) (Bottom right). \r\n\r\nSymbols indicate whether the AMOC recovers within 200 years (circles), is starting to recover (upwards arrow), or does not recover within 200 years (downwards arrow). Symbol size indicates rate of freshwater input. \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\nData provided for all panels in the code archived on Zenodo which is linked in the Related Documents section of this catalogue record. Data and code can also be found on the GitHub repository for chapter 9 also linked here.\r\n\r\nAMOC is the Atlantic Meridional Overturning Circulation.\r\nPMIP is the Paleoclimate Modelling Intercomparison Project.\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\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nAll panels were plotted using standard matplotlib software - 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 dedicated GitHub repository for figure." }, "onlineresource_set": [] }, { "ob_id": 40275, "uuid": "c424a674fbf84854aad24a77efdc621a", "short_code": "result", "curationCategory": "", "dataPath": "https://github.com/IPCC-WG1/Chapter-7/blob/main/notebooks/020_chapter7_fig7.16.ipynb", "numberOfFiles": 0, "volume": 0, "fileFormat": "Jupyter notebook on the Chapter 7 GitHub repository", "storageStatus": "online", "storageLocation": "external", "oldDataPath": [], "observation": { "ob_id": 37807, "uuid": "6fcc9d2c792243c1bb99de9c3cfdef2f", "short_code": "ob", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 7.16 (v20220721)", "abstract": "Data for Figure 7.16 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 7.16 shows probability distributions of ERF to CO2 doubling and the net climate feedback, derived from process-based assessments in Sections 7.3.2 and 7.4.2. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 4 subpanels, with data written into the plotting script in the master GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Probability distributions of ERF to CO2 doubling and ECS distribution quantile (ΔF2×CO2; top) \r\n- Net climate feedback (climate feedback parameter vs. effective radiative forcing from 2xCO2, bottom left) and probability density (α; bottom right)\r\n\r\nCentral panel shows the joint probability density function calculated on a two-dimensional plane of ΔF2×CO2 and α (red), on which the 90% range shown by an ellipse is imposed to the background theoretical values of ECS (colour shading). The white dot, and thick and thin curves inside the ellipse represent the mean, likely and very likely ranges of ECS. \r\n\r\nAn alternative estimation of the ECS range (pink) is calculated by assuming that ΔF2×CO2 and α have a covariance. The assumption about the co-dependence between ΔF2×CO2 and α does not alter the mean estimate of ECS but affects its uncertainty. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n No data is provided for Figure 7.16 as the data is written in to the notebook used to plot the figure. This notebook is linked in the Related Documents section of this catalogue record.\r\n\r\nERF stands for Effective Radiative Forcing.\r\nECS stands for Equilibrium Climate Sensitivity.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. Also listed on the 'master' GitHub page linked in the documentation of this catalogue record are external GitHub repositories and locations within the contributed directory where code for figures have been supplied by other authors. These are provided \"as-is\" and are not guaranteed to be reproducible within this environment. For external GitHub locations, check out the relevant repository READMEs.\r\n\r\nWithin the processing chain, every notebook is prefixed by a number. To reproduce all results in the chapter, the notebooks should be run in numerical order, because some later things depend on earlier things (historical temperature attribution requires a constrained ensemble of the two layer climate model, which relies on the generation of the radiative forcing time series). This being said, most notebooks should run standalone, as input data is provided where the datasets are small enough (see the 'master;' GitHub page for these).\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the notebook on the Chapter 7 GitHub repository used to plot figure" }, "onlineresource_set": [] }, { "ob_id": 40276, "uuid": "eb5900b1246b447f98fccab9830bed31", "short_code": "result", "curationCategory": "", "dataPath": "https://github.com/IPCC-WG1/Atlas/tree/main/datasets-aggregated-regionally/data", "numberOfFiles": 0, "volume": 0, "fileFormat": "Files are CSV formatted on the Atlas GitHub repository", "storageStatus": "online", "storageLocation": "external", "oldDataPath": [], "observation": { "ob_id": 38856, "uuid": "789ad030299342ea99534edfb62450d9", "short_code": "ob", "title": "Atlas of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure Atlas.2 (v20221104)", "abstract": "Data for Figure Atlas.2 from Atlas of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure Atlas.2 shows WGI reference regions used in the (a) AR5 and (b) AR6 reports.\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 citations:\r\nFor the report component from which the figure originates: \r\nGutiérrez, J.M., R.G. Jones, G.T. Narisma, L.M. Alves, M. Amjad, I.V. Gorodetskaya, M. Grose, N.A.B. Klutse, S. Krakovska, J. Li, D. Martínez-Castro, L.O. Mearns, S.H. Mernild, T. Ngo-Duc, B. van den Hurk, and J.-H. Yoon, 2021: Atlas. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1927–2058, doi:10.1017/9781009157896.021\r\n\r\nIturbide, M. et al., 2021: Repository supporting the implementation of FAIR principles in the IPCC-WG1 Interactive Atlas. Zenodo. Retrieved from: http://doi.org/10.5281/zenodo.5171760\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, with data provided for both panels in the master GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nThis dataset contains the corner coordinates defining each reference region for the second panel of the figure, which contain coordinate information at a 0.44º resolution.\r\nThe repository directory 'reference-regions' contains data provided for the reference regions as polygons in different formats (CSV with coordinates, R data, shapefile and geojson) together with R and Python notebooks illustrating the use of these regions with worked examples.\r\n\r\nData for reference regions for AR5 can be found here: https://catalogue.ceda.ac.uk/uuid/a3b6d7f93e5c4ea986f3622eeee2b96f\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\nAR5 and AR6 refer to the 5th and 6th Annual Report of the IPCC.\r\nWGI stands for Working Group I\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures produced by the Jupyter Notebooks live inside the notebooks directory. The notebooks describe step by step the basic process followed to generate some key figures of the AR6 WGI Atlas and some products underpinning the Interactive Atlas, such as reference regions, global warming levels, aggregated datasets. They include comments and hints to extend the analysis, thus promoting reusability of the results. These notebooks are provided as guidance for practitioners, more user friendly than the code provided as scripts in the reproducibility folder. \r\n\r\nSome of the notebooks require access to large data volumes out of this repository. To speed up the execution of the notebook, in addition to the full code to access the data, we provide a data loading shortcut, by storing intermediate results in the auxiliary-material folder in this repository. To test other parameter settings, the full data access instructions should be followed, which can take long waiting times.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Atlas)\r\n - Link to the Supplementary Material for Atlas, which contains details on the input data used in Table Atlas.SM.15.\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the necessary notebooks for reproducing the figure from GitHub.\r\n - Link to IPCC AR5 reference regions dataset" }, "onlineresource_set": [] }, { "ob_id": 40277, "uuid": "a5827def45d0435fa0d033ca721f4a3a", "short_code": "result", "curationCategory": "", "dataPath": "https://github.com/IPCC-WG1/Atlas/tree/main/datasets-aggregated-regionally/data", "numberOfFiles": 0, "volume": 0, "fileFormat": "Files are CSV formatted on the Atlas GitHub repository", "storageStatus": "online", "storageLocation": "external", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 40278, "uuid": "4252729469674ee69971b32f8e119b9e", "short_code": "result", "curationCategory": "", "dataPath": "https://github.com/IPCC-WG1/Atlas/tree/main/datasets-aggregated-regionally/data", "numberOfFiles": 0, "volume": 0, "fileFormat": "Files are CSV formatted on the Atlas GitHub repository", "storageStatus": "online", "storageLocation": "external", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 40279, "uuid": "6af728246fb245eba2d0f3c184e811a2", "short_code": "result", "curationCategory": "", "dataPath": "https://github.com/IPCC-WG1/Atlas/tree/main/datasets-aggregated-regionally/data", "numberOfFiles": 0, "volume": 0, "fileFormat": "Files are CSV formatted on the Atlas GitHub repository", "storageStatus": "online", "storageLocation": "external", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 40280, "uuid": "ee45c80122474417b16ac44537c6b406", "short_code": "result", "curationCategory": "", "dataPath": "https://github.com/IPCC-WG1/Atlas/tree/main/datasets-aggregated-regionally/data", "numberOfFiles": 0, "volume": 0, "fileFormat": "Files are CSV formatted on the Atlas GitHub repository", "storageStatus": "online", "storageLocation": "external", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 40281, "uuid": "22e686d3fe7a4c4c9aca999f36fc10b4", "short_code": "result", "curationCategory": "", "dataPath": "https://github.com/IPCC-WG1/Atlas/tree/main/datasets-aggregated-regionally/data", "numberOfFiles": 0, "volume": 0, "fileFormat": "Files are CSV formatted on the Atlas GitHub repository", "storageStatus": "online", "storageLocation": "external", "oldDataPath": [], "observation": null, "onlineresource_set": [] } ] }