Related Observation Info List
Get a list of RelatedObservationInfo objects.
GET /api/v3/relatedobservationinfos/?format=api&offset=600
{ "count": 1153, "next": "https://api.catalogue.ceda.ac.uk/api/v3/relatedobservationinfos/?format=api&limit=100&offset=700", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/relatedobservationinfos/?format=api&limit=100&offset=500", "results": [ { "ob_id": 635, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 34606, "uuid": "fc3310aaa2fe4a26a29a63642e1164f2", "short_code": "ob", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour daily gridded data over land at 0.05 degree resolution, version 3.2", "abstract": "This dataset consists of daily total column water vapour (TCWV) over land, at a 0.05 degree resolution, observed by various satellite instruments. It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1 (TCWV-land (CDR-1).\r\n\r\nThis version of the data is v3.2. This is an updated dataset, which fixes an issue with the filtering of the v3.1 data." }, "objectObservation": { "ob_id": 32068, "uuid": "5b8217eb6bc242229035d846671c62fa", "short_code": "ob", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour daily gridded data over land at 0.05 degree resolution, version 3.1", "abstract": "This dataset consists of daily total column water vapour (TCWV) over land, at a 0.05 degree resolution, observed by various satellite instruments. It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1 (TCWV-land (CDR-1).\r\n\r\nThis version of the data is v3.1." } }, { "ob_id": 636, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 34639, "uuid": "8194b416cbee482b89e0dfbe17c5786c", "short_code": "ob", "title": "CHESS-SCAPE: Future projections of meteorological variables at 1 km resolution for the United Kingdom 1980-2080 derived from UK Climate Projections 2018", "abstract": "Gridded daily meteorological variables over the United Kingdom at 1 km resolution for the years 1980-2080. This dataset is an ensemble of four different realisations of future climate for each of four different representative concentration pathway scenarios (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), provided both with and without bias correction. This dataset contains time series of daily mean values of air temperature (K), specific humidity (kg kg-1), relative humidity (%), wind speed (m s-1), downward longwave radiation (W m-2), downward shortwave radiation (W m-2), precipitation (kg m-2 s-2) and surface air pressure (Pa). It also contains time series of daily minimum air temperature (K), daily maximum air temperature (K) and daily temperature range (K). The data are provided in gridded netCDF files at 1 km resolution aligned to the Ordnance Survey / British National Grid. There is one file for each variable for each month of the daily data. Also provided are monthly, seasonal and annual means, for which there is one file for each variable for each time resolution. Additionally twenty year mean-monthly (Jan-Dec) climatologies at ten year intervals are provided, for which there is one file for each variable for each twenty year time slice. The projections use a 360-day calendar, where each month consists of 30 days." }, "objectObservation": { "ob_id": 26213, "uuid": "589211abeb844070a95d061c8cc7f604", "short_code": "ob", "title": "UKCP18 Regional Projections on a 12km grid over the UK for 1980-2080", "abstract": "\"Regional climate model projections produced as part of the UK Climate Projection 2018 (UKCP18) project. The data produced by the Met Office Hadley Centre provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12km), helping to inform adaptation to a changing climate. \r\n\r\nThe projections cover Europe and a 100-year period, 01/12/1980-30/11/2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations. \r\n\r\nThis dataset contains 12km data for the United Kingdom, the Isle of Man and the Channel Islands provided on the Ordnance Survey's British National Grid. Further information on this dataset and UKCP18 can be found in the documentation section." } }, { "ob_id": 637, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 33060, "uuid": "8847a05eeda646a29da58b42bdf2a87c", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2020), version 2.0", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme.\r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. \r\n\r\nThe global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. \r\n\r\nThe SCFG time series provides daily products for the period 2000 – 2020. \r\n\r\nThe SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. \r\n\r\nThe retrieval method of the Snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The Snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nThe main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of spatially variable background reflectance and forest reflectance maps instead of global constant values for snow free land and forest, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the update of the global forest canopy transmissivity based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) to assure in forested areas consistency of the SCFG and the SCFV CRDP v2.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b) using the same retrieval approach.\r\n\r\nImprovements of the Snow_cci SCFG version 2.0 compared to the Snow_cci version 1.0 include (i) the utilisation of an updated background reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest canopy transmissivity map, and (iii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 µm.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.\r\n\r\nThe SCFG product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development.\r\n\r\nThere are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFG products are available but have data gaps." }, "objectObservation": { "ob_id": 31205, "uuid": "3b3fd2daf3d34c1bb4a09efeaf3b8ea9", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2019), version 1.0", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme.\r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. \r\n\r\nThe global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. \r\n\r\nThe SCFG time series provides daily products for the period 2000 – 2019. \r\n\r\nThe SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. \r\n\r\nThe retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of background and forest reflectance maps derived from statistical analyses of MODIS time series replacing the constant values for snow free ground and snow free forest used in the GlobSnow approach, and (ii) the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019). The forest transmissivity map is used to account for the shading effects of the forest canopy and estimate also in forested areas the fractional snow cover on ground.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.\r\n\r\nThe SCFG product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development.\r\n\r\nThere are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFG products are available but have data gaps." } }, { "ob_id": 638, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 34664, "uuid": "5fb94c8e37f64c95b671278b0e55cdd4", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2021, v3.2.0.2021f", "abstract": "This is version v3.2.0.2021f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data.\r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20220101_v3.2.1.2021f.nc. The station codes can be found under the docs tab. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note.\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." }, "objectObservation": { "ob_id": 32100, "uuid": "f5a674c74cdd427594b6f3793b536cd0", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2020, v3.1.1.2020f", "abstract": "This is version 3.1.1.2020f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v3.1.0.2019f to include 2020 and so spans 1931-2020.\r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20210101_v3-1-1-2020f.nc. The station codes can be found under the docs tab. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note.\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." } }, { "ob_id": 639, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 34669, "uuid": "115d5e4ebf7148ec941423ec86fa9f26", "short_code": "ob", "title": "HadEX3: Global land-surface climate extremes indices v3.0.4 (1901-2018)", "abstract": "HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive). \r\n\r\nSpatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010.\r\n\r\nAll indices are available as annual quantities, with a subset also available on a monthly basis. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110').\r\n\r\nVersion 3.0.4 was added due to an error in how the Rx1day and Rx5day data were being handled for one of the West African data sources. More details can be found in the HadEX3 blog under 'Details/Docs' tab.\r\n\r\nAdditionally, an extension to HadEX3, comprising additional indices recommended by the WMO Expert Team on Sector-specific Climate Indices (ET-SCI), has been produced. These data are available in a separate dataset connected to this record, marked as supplemental to this dataset." }, "objectObservation": { "ob_id": 32055, "uuid": "087d4c75ace04e59a71d95c1c44918f9", "short_code": "ob", "title": "HadEX3: Global land-surface climate extremes indices v3.0.3 (1901-2018)", "abstract": "HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive). \r\n\r\nSpatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010.\r\n\r\nAll indices are available as annual quantities, with a subset also available on a monthly basis. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110').\r\n\r\nVersion 3.0.3 was added due to an error in how the Rx1day and Rx5day data were being handled for one of the West African data sources. More details can be found in the HadEX3 blog under 'Details/Docs' tab." } }, { "ob_id": 640, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 34674, "uuid": "f0f32cf3e7884950a585b7a4fda9dcb6", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1973-2015, v1.0.4.2015f", "abstract": "This is version 1.0.4.2015f of HadISD (27 April 2015) the Met Office Hadley Centre's global sub-daily data, extending v1.0.3.2014f to span 1/1/1973 - 31/12/2015. \r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed in the quality control process are also provided along with a station listing with IDs, names and location information. The data are provided as one NetCDF file per station. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20151231_v1-0-4-2015f.nc (note, the filenames incorrectly show the start date of 19310101, instead of 19730101). The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Climate of the Past\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." }, "objectObservation": { "ob_id": 19173, "uuid": "7b6993cbf7ec45f9ad01b86bed537e4c", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1973-2015, v1.0.4.2015p", "abstract": "This is version 1.0.4.2015p of HadISD (27 April 2015) the Met Office Hadley Centre's global sub-daily data, extending v1.0.3.2014f to span 1/1/1973 - 31/12/2015. \r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, their quality and completness cannot be guaranteed. Quality control flags and data values which have been removed in the quality control process are also provided along with a station listing with IDs, names and location information. The data are provided as one NetCDF file per station. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20151231_v1-0-4-2015p.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Climate of the Past\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." } }, { "ob_id": 641, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 34674, "uuid": "f0f32cf3e7884950a585b7a4fda9dcb6", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1973-2015, v1.0.4.2015f", "abstract": "This is version 1.0.4.2015f of HadISD (27 April 2015) the Met Office Hadley Centre's global sub-daily data, extending v1.0.3.2014f to span 1/1/1973 - 31/12/2015. \r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed in the quality control process are also provided along with a station listing with IDs, names and location information. The data are provided as one NetCDF file per station. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20151231_v1-0-4-2015f.nc (note, the filenames incorrectly show the start date of 19310101, instead of 19730101). The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Climate of the Past\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." }, "objectObservation": { "ob_id": 13557, "uuid": "229b53d2e44741ecbe70ba6299875a30", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1973-2014, v1.0.3.2014f", "abstract": "This is version 1.0.3.2014f of HadISD (27 April 2015) the Met Office Hadley Centre's global sub-daily data, extending v1.0.2.2013f to span 1/1/1973 - 31/12/2014.\r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19730101-20141231_v1-0-3-2014f.nc T. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." } }, { "ob_id": 642, "relationType": "Continues", "subjectObservation": { "ob_id": 34720, "uuid": "906f6d3957f9439c98dfb662cda8a769", "short_code": "ob", "title": "MSG: Dust imagery in the RGB channels over the full disc at 45.5 degrees East (LEDF41, from 1st June 2022)", "abstract": "The Meteosat Second Generation (MSG) satellites, operated by EUMETSAT (The European Organisation for the Exploitation of Meteorological Satellites), provide almost continuous imagery to meteorologists and researchers in Europe and around the world. These include visible, infra-red, water vapour, High Resolution Visible (HRV) images and derived cloud top height, cloud top temperature, fog, snow detection and volcanic ash products. These images are available for a range of geographical areas. \r\n\r\nThis dataset contains RGB dust images from MSG satellites over the full disc at 45.5 degrees East. Imagery available from 1000 UTC 1st June 2022 onwards at a frequency of 15 minutes (some are hourly) and are at least 24 hours old.\r\n\r\nNOTE - this dataset differs from the previous LEDF41 product produced using imagery from Meteosat-8 located at 41.5E. These new data are from Meteosat-9 which was drifted from previous operations over 3.5 E to 45.5 E between 1st February 2022 to 20th April 2022 to take over as the prime IODC (Indian Ocean Data Coverage) satellite by 1st June 2022. See linked EUMETNET web page regarding this change in operation. The Met Office switched to providing this LEDF41 product from this new satellite at 0915 UTC on 1st June 2022, this dataset. See linked datasets for previous data. These are treated as two distinct datasets due to the shift in locational coverage.\r\n\r\nThe geographic extent for images within this datasets is available via the linked documentation 'MSG satellite imagery product geographic area details'. Each MSG imagery product area can be referenced from the third and fourth character of the image product name giving in the filename. E.g. for EEAO11 the corresponding geographic details can be found under the entry for area code 'AO' (i.e West Africa)." }, "objectObservation": { "ob_id": 33317, "uuid": "b1dacc09b42f4d8ab492c5d5c751efa9", "short_code": "ob", "title": "MSG: Dust imagery in the RGB channels over the full disc at 41.5 degrees East (LEDF41, upto 0900 UTC 1st June 2022)", "abstract": "The Meteosat Second Generation (MSG) satellites, operated by EUMETSAT (The European Organisation for the Exploitation of Meteorological Satellites), provide almost continuous imagery to meteorologists and researchers in Europe and around the world. These include visible, infra-red, water vapour, High Resolution Visible (HRV) images and derived cloud top height, cloud top temperature, fog, snow detection and volcanic ash products. These images are available for a range of geographical areas. \r\n\r\nThis dataset contains RGB dust images from MSG satellites over the full disc at 41.5 degrees East. Imagery available from November 2021 until 0900 UTC 1st June 2022 (see following note) at a frequency of 15 minutes (some are hourly) and are at least 24 hours old.\r\n\r\nNOTE - from 1st February 2022 to 20th April 2022 Meteosat-9 was drifted from 3.5 E to 45.5E at a rate of 0.5 degree a day drift to a new observation location centred over 45.5 degrees East to take over as the prime IODC (Indian Ocean Data Coverage) satellite from 30th May 2022. This role was previously by Meteosat-8, which remains in place for emergency . Data were not made available during this drifting process. The Met Office production of the LEDF41 product switched to using Meteosat-9 from 0915 UTC on 1st June 2022. See linked dataset for the replacement dataset to continue provision of this product over this region of the globe.\r\n\r\nThe geographic extent for images within this datasets is available via the linked documentation 'MSG satellite imagery product geographic area details'. Each MSG imagery product area can be referenced from the third and fourth character of the image product name giving in the filename. E.g. for EEAO11 the corresponding geographic details can be found under the entry for area code 'AO' (i.e West Africa)." } }, { "ob_id": 643, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 31200, "uuid": "4647cc9ad3c044439d6c643208d3c494", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 – 2020), version 2.0", "abstract": "This dataset contains v2.0 of the Daily Snow Water Equivalent (SWE) product from the ESA Climate Change Initiative (CCI) Snow project, at 0.1 degree resolution.\r\n\r\nSnow water equivalent (SWE) indicates the amount of accumulated snow on land surfaces, in other words the amount of water contained within the snowpack. The SWE product time series covers the period from 1979/01 to 2020/05. Northern Hemisphere SWE products are available at daily temporal resolution with alpine areas masked. \r\n\r\nThe product is based on data from the Scanning Multichannel Microwave Radiometer (SMMR) operated on National Aeronautics and Space Administration’s (NASA) Nimbus-7 satellite, the Special Sensor Microwave / Imager (SSM/I) and the Special Sensor Microwave Imager / Sounder (SSMI/S) carried onboard the Defense Meteorological Satellite Program (DMSP) 5D- and F-series satellites. The satellite bands provide spatial resolutions between 15 and 69 km. The retrieval methodology combines satellite passive microwave radiometer (PMR) measurements with ground-based synoptic weather station observations by Bayesian non-linear iterative assimilation. A background snow-depth field from re-gridded surface snow-depth observations and a passive microwave emission model are required components of the retrieval scheme.\r\n\r\nThe dataset is aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modelling, and aspects of hydrology and meteorology.\r\n\r\nThe Finnish Meteorological Institute is responsible for the SWE product development and generation. \r\n\r\nFor the period from 1979 to May 1987, the products are available every second day. From October 1987 till May 2020, the products are available daily. Products are only generated for the Northern Hemisphere winter seasons, usually from beginning of October till the middle of May. A limited number of SWE products are available for days in June and September; products are not available for the months July and August as there is usually no snow information reported on synoptic weather stations, which is required as input for the SWE retrieval. Because of known limitations in alpine terrain, a complex-terrain mask is applied based on the sub-grid variability in elevation determined from a high-resolution digital elevation model. All land ice and large lakes are also masked; retrievals are not produced for coastal regions of Greenland.\r\n\r\nThis version 2 dataset has some notable differences compared to the v1 data. In v2, passive microwave radiometer data are obtained from the recalibrated enhanced resolution CETB ESDR dataset (MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) https://nsidc.org/pmesdr/data-sets/) the grid spacing is reduced from 25 km to 12.5 km, and spatially and temporally varying snow density fields are used to adjust SWE retrievals in post processing. The output grid spacing is reduced from 0.25-degree to 0.10-degree WGS84 latitude / longitude to be compatible with other Snow_cci products. The time series has been extended by two years with data from 2018 to 2020 added.\r\n\r\nThe ESA CCI phased product development framework allowed for a systematic analysis of these changes to the input data and snow density parameterization that occurred between v1 and v2 using a series of step-wise developmental datasets. In comparison with in-situ snow courses, the correlation and RMSE of v2 improved 18% (0.1) and 12% (5mm), respectively, relative to v1. The timing of peak snow mass is shifted two weeks later and a temporal discontinuity in the monthly northern hemisphere snow mass time series associated with the shift from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder (SSMIS) in 2009 is removed in v2." }, "objectObservation": { "ob_id": 29998, "uuid": "fa20aaa2060e40cabf5fedce7a9716d0", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 – 2018), version 1.0", "abstract": "Snow water equivalent (SWE) indicates the amount of accumulated snow on land surfaces; in other words the amount of water contained within the snowpack. The SWE product time series covers the period from 1979 to 2018. Northern Hemisphere SWE products are available at daily temporal resolution with alpine areas masked. \r\n\r\nThe product is based on data from the Scanning Multichannel Microwave Radiometer (SMMR) operated on National Aeronautics and Space Administration’s (NASA) Nimbus-7 satellite, the Special Sensor Microwave / Imager (SSM/I) and the Special Sensor Microwave Imager / Sounder (SSMI/S) carried onboard the Defense Meteorological Satellite Program (DMSP) 5D- and F-series satellites. The satellite bands provide spatial resolutions between 15 and 69 km. The retrieval methodology combines satellite passive microwave radiometer (PMR) measurements with ground-based synoptic weather station observations by Bayesian non-linear iterative assimilation. A background snow-depth field from re-gridded surface snow-depth observations and a passive microwave emission model are required components of the retrieval scheme.\r\n\r\nThe dataset was aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modelling, and aspects of hydrology and meteorology.\r\n\r\nThe Finnish Meteorological Institute is responsible for the SWE product development and generation. For the period from 1979 to May 1987, the products are available every second day. From October 1987 till May 2018, the products are available daily. Products are only generated for the Northern Hemisphere winter seasons, usually from beginning of October till the middle of May. A limited number of SWE products are available for days in June and September; products are not available for the months July and August as there is usually no snow information reported on synoptic weather stations, which is required as input for the SWE retrieval. Because of known limitations in alpine terrain, a complex-terrain mask is applied based on the sub-grid variability in elevation determined from a high-resolution digital elevation model. All land ice and large lakes are also masked; retrievals are not produced for coastal regions of Greenland." } }, { "ob_id": 644, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 33061, "uuid": "763eb87e0682446cafa8c74488dd5fb8", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1982 - 2018), version 2.0", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFV time series provides daily products for the period 1982-2018. \r\n\r\nThe product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. \r\n\r\nThe retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.630 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. \r\n\r\nThe following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water; permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map.\r\n\r\nThe SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern is responsible for the SCFV product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation. \r\n\r\nThe SCFV AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 37 years." }, "objectObservation": { "ob_id": 31209, "uuid": "d9df331e346f4a50b18bcf41a64b98c7", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1982 - 2019), version 1.0", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFV time series provides daily products for the period 1982-2019. \r\n\r\nThe product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. \r\nThe retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm and 1.61 µm (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. \r\n\r\nThe following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map.\r\n\r\nThe SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern is responsible for the SCFV product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation. \r\n\r\nThe SCFV AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 38 years." } }, { "ob_id": 645, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 33062, "uuid": "3f034f4a08854eb59d58e1fa92d207b6", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1982 - 2018), version 2.0", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. \r\n\r\nThe global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFG time series provides daily products for the period 1982-2018. \r\n\r\nThe product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. \r\n\r\nThe retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. \r\n\r\nThe following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground.\r\n\r\nThe SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern is responsible for the SCFG product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nThe SCFG AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 37 years." }, "objectObservation": { "ob_id": 31210, "uuid": "5484dc1392bc43c1ace73ba38a22ac56", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1982 - 2019), version1.0", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. \r\n\r\nThe global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFG time series provides daily products for the period 1982-2019. \r\n\r\nThe product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. \r\n\r\nThe retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm and 1.61 µm (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nThe following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground.\r\n\r\nThe SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern is responsible for the SCFG product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nThe SCFG AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 38 years." } }, { "ob_id": 646, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 33059, "uuid": "ebe625b6f77945a68bda0ab7c78dd76b", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2020), version 2.0", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from the MODIS satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. \r\n\r\nThe SCFV time series provides daily products for the period 2000 – 2020. \r\n\r\nThe SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. \r\n\r\nThe retrieval method of the Snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the Snow_cci SCFV retrieval method is applied. \r\n\r\nThe main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the adaptation of the retrieval method using of a spatially variable ground reflectance instead of global constant values for snow free land, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data to assure in forested areas consistency of the SCFV and the SCFG CRDP v2.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b) using the same retrieval approach.\r\n\r\nImprovements of the Snow_cci SCFV version 2.0 compared to the Snow_cci version 1.0 include (i) the utilisation of an updated ground reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest mask used for the transmissivity estimation, and (iii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 µm.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.\r\n\r\nThe SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nENVEO is responsible for the SCFV product development and generation from MODIS data, SYKE supported the development.\r\n\r\nThere are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFV products are available but have data gaps." }, "objectObservation": { "ob_id": 31204, "uuid": "ef8eb5ff84994f2ca416dbb2df7f72c7", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2019), version 1.0", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from the MODIS satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. \r\n\r\nThe SCFV time series provides daily products for the period 2000 – 2019. \r\n\r\nThe SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. \r\n\r\nThe retrieval method of the snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of a background reflectance map derived from statistical analyses of MODIS time series replacing the constant values for snow free ground used in the GlobSnow approach, and (ii) the adaptation of the retrieval method for mapping in forested areas the SCFV. \r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.\r\n\r\nThe SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nENVEO is responsible for the SCFV product development and generation from MODIS data, SYKE supported the development.\r\n\r\nThere are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFV products are available but have data gaps." } }, { "ob_id": 647, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 34653, "uuid": "ab8d21568c81491fbb9a300c36884af7", "short_code": "ob", "title": "ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.0", "abstract": "This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. \r\n\r\nThis is version 2.0 of the dataset. The five thematic climate variables included in this dataset are:\r\n• Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\r\n• Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .\r\n• Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.\r\n• Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\r\n• Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n\r\nData generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop.\r\n\r\nDetailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website." }, "objectObservation": { "ob_id": 32169, "uuid": "ef1627f523764eae8bbb6b81bf1f7a0a", "short_code": "ob", "title": "ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 1.1", "abstract": "This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. This is version 1.1 of the dataset.\r\n\r\nLakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. \r\n\r\nThe five thematic climate variables included in this dataset are:\r\n•\tLake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.\r\n•\tLake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.\r\n•\tLake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. \r\n•\tLake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. \r\n•\tLake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n\r\nData generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents." } }, { "ob_id": 648, "relationType": "Continues", "subjectObservation": { "ob_id": 32108, "uuid": "f2f0bc7b9d0344babea9e800d9b71535", "short_code": "ob", "title": "ECMWF ERA5t: 10 ensemble member surface level analysis parameter data", "abstract": "This dataset contains ERA5 initial release (ERA5t) surface level analysis parameter data from 10 member ensemble runs. ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. Ensemble means and spreads were calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. See linked datasets for ensemble member and spread data.\r\n\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble mean and ensemble spread data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record." }, "objectObservation": { "ob_id": 32094, "uuid": "bd302093953a48359ab33e4b48324f5f", "short_code": "ob", "title": "ECMWF ERA5: 10 ensemble member surface level analysis parameter data", "abstract": "This dataset contains ERA5 surface level analysis parameter data from 10 ensemble runs. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble members were used to derive means and spread data (see linked datasets). Ensemble means and spreads were calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." } }, { "ob_id": 649, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 37111, "uuid": "c6f1b1ff16f8407386e2d643bc5b916a", "short_code": "ob", "title": "CANDIFLOS : Surface fluxes from ACSE measurement campaign on icebreaker Oden, 2014", "abstract": "Characterising and Interpreting FLuxes Over Sea Ice (CANDIFLOS) is a data analysis project drawing upon data from multiple field campaigns. It aims to improve the parameterization of surface fluxes over sea ice. This data set consists of the processed surface heat fluxes and sea ice fractions from the Arctic Clouds Summer Experiment (ACSE) project (2014) conducted on icebreaker Oden. Matching data from the AO2016 cruise are provided as a separate data set." }, "objectObservation": { "ob_id": 26024, "uuid": "e58fdade3a6c46bbaae7c53e948dd6d0", "short_code": "ob", "title": "Arctic Cloud Summer Expedition (ACSE): composite flux data for Icebreaker Oden", "abstract": "This dataset contains provides the final best estimates of fluxes, mean environmental variables and derived transfer coefficient estimates, along with asociated quality control flags, during the Icebreaker Oden voyage durning the Arctic Cloud Summer Expedition (ACSE) in summer 2014. These were calculated based on instrumentation data from the University of Leeds' Metek sonic anemometer, Licor LI-7500 gas analyzer and XSENS MTi-G-700 motion pack, plus mean surface meteorology data provided from the automatic weather station operated on board by the Department of Meteorology, Stockholm University (MISU).\r\n\r\nOther data from the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the NOAA ACSE and The Bolin Centre for Climate Research SWERUS (SWEdish-Russian-US) holdings - see online resources linked to this record.\r\n\r\n\r\nThe Arctic Cloud Summer Expedition (ACSE) was a collaboration between the University of Leeds, the University of Stockholm, and NOAA-CIRES. ACSE aimed to study the response of Arctic boundary layer cloud to changes in surface conditions in the Arctic Ocean as a working package of the larger Swedish-Russian-US Investigation of Climate, Cryosphere and Carbon interaction (SWERUS-C3) Expedition in Summer 2014. This expedition was a core component to the overall SWERUS-C3 programme and was supported by the Swedish Polar Research Secretariat.\r\n\r\nACSE took place during a 3-month cruise of the Swedish Icebreaker Oden from Tromso, Norway to Barrow, Alaska and back over the summer of 2014. During this cruise ACSE scientists measured surface turbulent exchange, boundary layer structure, and cloud properties. Many of the measurements used remote sensing approaches - radar, lidar, and microwave radiometers - to retrieve vertical profiles of the dynamic and microphysical properties of the lower atmosphere and cloud.\r\n\r\nThe UK participation of ACSE was funded by the Natural Environment Research Council (NERC, grant: NE/K011820/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF). This dataset collection contains data mainy from the UK contribution with some additional data from other institutes also archived to complement the suite of meteorological measurements.\r\n\r\nThe document \"ACSE_turbulent_fluxes_readme.txt\" in the archive contains fuller details of the flux calculations. The final data, prepared for archiving as NetCDF data at the Centre for Environmental Data Analysis (CEDA) by Ian Brooks, University of Leeds, contain:\r\n\r\n1) The final quality controlled best estimates of 20-min averaged dynamic fluxes, associated mean environmental variables (10m wind, etc), transfer coefficients, and quality control flags.\r\n\r\n2) The raw kinematic fluxes, etc that go into generating (1), along with the quality control variables used in generating the QC flags, and the QC flags.\r\n\r\n3) Other environmental variables (in some cases with duplicates from multiple different sensors) averaged onto the same time base as the flux estimates.\r\n\r\nThe authors note that in all cases a lot of work has been done on quality control and applying suitable corrections to raw measurements. In many cases other choices could have been made, and additional QC measures may need to be applied.\r\n\r\nMost of the work on the flux data processing has been done by John Prytherch, with additional input from Ian Brooks and Dominic Salisbury. Additional work on ancillary data was undertaken by other members of the ACSE science team." } }, { "ob_id": 650, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 37207, "uuid": "39b1337028d147d9b572ae352490bed0", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK river basins, v1.1.0.0 (1836-2021)", "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 2021, 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 v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) 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." }, "objectObservation": { "ob_id": 32984, "uuid": "0cb035c7598a4dcb8aecb6b6558c83e9", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK river basins, v1.0.3.0 (1862-2020)", "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 1862 to 2020, 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\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." } }, { "ob_id": 651, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 37209, "uuid": "6f4ac352b19341eb8c5b26644845ac35", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 60km grid over the UK, v1.1.0.0 (1836-2021)", "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 2021, 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 v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) 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." }, "objectObservation": { "ob_id": 32986, "uuid": "bc774f1b83524437a8046d8b9a9e3c6d", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 60km grid over the UK, v1.0.3.0 (1862-2020)", "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 1862 to 2020, 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\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." } }, { "ob_id": 652, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 37210, "uuid": "e6866698e5bd46cfa726cd82a7971f9a", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 25km grid over the UK, v1.1.0.0 (1836-2021)", "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 2021, 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 v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) 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." }, "objectObservation": { "ob_id": 32987, "uuid": "616e6194a8c742d790f3b43bf66a534d", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 25km grid over the UK, v1.0.3.0 (1862-2020)", "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 1862 to 2020, 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\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." } }, { "ob_id": 653, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 37211, "uuid": "652cea3b8b4446f7bff73be0ce99ba0f", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 12km grid over the UK, v1.1.0.0 (1836-2021)", "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 2021, 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 v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) 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." }, "objectObservation": { "ob_id": 32988, "uuid": "54a99222c1e741a4a70ef1caa8f10c7e", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 12km grid over the UK, v1.0.3.0 (1862-2020)", "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 1862 to 2020, 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\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." } }, { "ob_id": 654, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 37212, "uuid": "bbca3267dc7d4219af484976734c9527", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.1.0.0 (1836-2021)", "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 2021, 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 v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) 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." }, "objectObservation": { "ob_id": 32989, "uuid": "786b3ce6be54468496a3e11ce2f2669c", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.0.3.0 (1862-2020)", "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 1862 to 2020, 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\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." } }, { "ob_id": 655, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 37213, "uuid": "aeb4ca481d634ec597831282c3baed32", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 5km grid over the UK, v1.1.0.0 (1836-2021)", "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 2021, 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 v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) 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." }, "objectObservation": { "ob_id": 32990, "uuid": "f2da35c56afb4fa6aebf44094b65dff3", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 5km grid over the UK, v1.0.3.0 (1862-2020)", "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 1862 to 2020, 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\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." } }, { "ob_id": 656, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 37214, "uuid": "7edd216fcf794b1f9a5889d496d50e54", "short_code": "ob", "title": "HadUK-Grid Climate Observations by Administrative Regions over the UK, v1.1.0.0 (1836-2021)", "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 2021 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 v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) 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." }, "objectObservation": { "ob_id": 32991, "uuid": "97bc0b64bc354898a242a42238e1b45c", "short_code": "ob", "title": "HadUK-Grid Climate Observations by Administrative Regions over the UK, v1.0.3.0 (1862-2020)", "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 1862 to 2020, 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\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." } }, { "ob_id": 657, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 37092, "uuid": "03c935c6890c4b2ebf4aae4d84cd9472", "short_code": "ob", "title": "ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.0.1", "abstract": "This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. \r\n\r\nThis is version 2.0.1 of the dataset. The five thematic climate variables included in this dataset are:\r\n• Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\r\n• Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .\r\n• Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.\r\n• Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\r\n• Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n\r\nData generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop.\r\n\r\nDetailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website." }, "objectObservation": { "ob_id": 34653, "uuid": "ab8d21568c81491fbb9a300c36884af7", "short_code": "ob", "title": "ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.0", "abstract": "This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. \r\n\r\nThis is version 2.0 of the dataset. The five thematic climate variables included in this dataset are:\r\n• Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\r\n• Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .\r\n• Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.\r\n• Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\r\n• Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n\r\nData generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop.\r\n\r\nDetailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website." } }, { "ob_id": 658, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 37280, "uuid": "f3515388768344bfb2be0521f82388be", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.11 (v20220510)", "abstract": "Data for Figure 2.11 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 2.11 includes mapped and time-series data showing global surface temperature relative to 1850 - 1900 over multiple time scales.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n---------------------------------------------------\r\n Figure has three panels, with data provided for panel (a) (center and right part), and panel (c).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n---------------------------------------------------\r\n Global surface temperature, relative to 1850 - 1900 for:\r\n\r\n Panel a: \r\n \r\n - 1000 to 1900 CE - from PAGES 2k Consortium (modified from the version 2019: 10.1038/s41561-019-0400-0)\r\n - 1850 to 2020 from AR6 assessed mean (same as Figure 2.11c).\r\n\r\n Panel c: \r\n \r\n - Annual and decadal means from instrumental data for 1850–2020, along with the uncertainty range from HadCRUT5.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n---------------------------------------------------\r\n Panel a:\r\n \r\n - Data file: Figure_2_11a-PAGES_2k_Consortium.csv (yearly data, 1000 to 1900); relates to the center part of the figure showing global surface temperature relative to 1850 -1900. (bold solid green line, column 2, median 10-yr smooth adjusted (+0.37°C), thin solid green lines: 5th (column 3) and 95th (column 4) percentiles of the ensemble members).\r\n - Data file: Figure2_11_panel_a.csv (yearly data, 1850 to 2020); relates to the right part of the figure showing global temperature anomaly AR6 assessed mean. (bold solid violet line, column 2)\r\n\r\nPanel c: \r\n \r\n - Data file: Figure_2_11c-land_and_ocean_time_series.csv (yearly data, 1850 to 2020); relates to the upper part of the figure showing global surface temperature relative to 1850 -1900. (Land, column 2, red line; Ocean, column 3, blue line).\r\n\r\n---------------------------------------------------\r\nNotes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nInput data and code to reproduce panel b and panel c (lower part) plots are provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to input data figure 2.11.\r\n - Link to the code for the figure, archived on Zenodo." }, "objectObservation": { "ob_id": 33420, "uuid": "b953c9c8b41d4339b2a0a80fdc3cb840", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.11 (v20211207)", "abstract": "Data for Figure 2.11 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 2.11 includes mapped and time-series data showing global surface temperature relative to 1850 - 1900 over multiple time scales\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n---------------------------------------------------\r\n Figure has three panels, with data provided for panel (a) (center and right part), and panel (c).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n---------------------------------------------------\r\n Global surface temperature, relative to 1850 - 1900 for:\r\n\r\n Panel a: \r\n \r\n - 1000 to 1900 CE - from PAGES 2k Consortium (modified from the version 2019: 10.1038/s41561-019-0400-0)\r\n - 1850 to 2020 from AR6 assessed mean (same as Figure 2.11c).\r\n\r\n Panel c: \r\n \r\n - Annual and decadal means from instrumental data for 1850–2020, along with the uncertainty range from HadCRUT5.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n---------------------------------------------------\r\n Panel a:\r\n \r\n - Data file: Figure_2_11a-PAGES_2k_Consortium.csv (yearly data, 1000 to 1900); relates to the center part of the figure showing global surface temperature relative to 1850 -1900. (bold solid green line, column 2, median 10-yr smooth adjusted (+0.37°C), thin solid green lines: 5th (column 3) and 95th (column 4) percentiles of the ensemble members).\r\n - Data file: Figure2_11_panel_a.csv (yearly data, 1850 to 2020); relates to the right part of the figure showing global temperature anomaly AR6 assessed mean. (bold solid violet line, column 2)\r\n\r\nPanel c: \r\n \r\n - Data file: Figure_2_11c-land_and_ocean_time_series.csv (yearly data, 1850 to 2020); relates to the upper part of the figure showing global surface temperature relative to 1850 -1900. (Land, column 2, red line; Ocean, column 3, blue line).\r\n - Data file: Figure_2_11c-lower_panel.csv, (annual and decadal mean, 1850 to 2020); relates to the lower part of the figure. (black line, column 2, HadCRUT 5.0; cyan line, column 3, NOAA Global Temp; pink line, column 4, Berkeley Earth; orange line, column 5, Kadow et al.; grey shadow, columns 6 and 7, HadCRUT confidence limit)\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 659, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 37276, "uuid": "48cd535e93574c8da8e80b91e06c7d51", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by DTU Space, v2.2", "abstract": "This dataset provides a Gravimetric Mass Balance (GMB) product for the Greenland Ice Sheet (GIS), generated by DTU Space, based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through August 2021.\r\n\r\nThe GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 200 monthly solutions. The mass change estimation is based on inversion method developed at DTU Space.\r\n\r\nTwo different types of products are available. First, the gridded mass trends product is comprised of ice mass change trends for cells of equal area with 50 km resolution covering the whole GIS. Second, the mass change time series product provides time series of integrated mass changes for 8 drainage basins and the entire GIS.\r\n\r\nReference:\r\nBarletta, V. R., Sørensen, L. S., and Forsberg, R. (2013) 'Scatter of mass changes estimates at basin scale for Greenland and Antarctica', The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013.\"," }, "objectObservation": { "ob_id": 26782, "uuid": "ff4bfe39b7fe42fc993341d3cebdabb5", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by DTU Space, v1.5", "abstract": "This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by DTU Space. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to June 2016; and mass trend grids for different 5-year periods between 2003 and 2016. This version (1.5) is derived from GRACE monthly solutions from the CSR RL06 product.\r\n\r\nThe mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.\r\n\r\nMass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. \r\n\r\nBasin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. \r\n\r\nCitation: \r\nBarletta, V. R., Sørensen, L. S., and Forsberg, R.: Scatter of mass changes estimates at basin scale for Greenland and Antarctica, The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013, 2013." } }, { "ob_id": 660, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 37289, "uuid": "062942e96a6e4567b2bc47045be910a7", "short_code": "ob", "title": "HadISDH.land: gridded global monthly land surface humidity data version 4.4.0.2021f", "abstract": "This is the HadISDH.land 4.4.0.2021f 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 31/12/2021. \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 2021. 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.3.1.2.202101p, 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." }, "objectObservation": { "ob_id": 32371, "uuid": "82b0164a4d06467ab450ff67006729c1", "short_code": "ob", "title": "HadISDH land: gridded global monthly land surface humidity data version 4.3.1.2020f", "abstract": "This is the HadISDH land 4.3.1.2020f 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 31/12/2020. \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 4.2.0.2019f version to the end of 2020 and constitutes a minor update to HadISDH due to changing some of the code base from IDL and Python 2.7 to Python 3, detecting and fixing a bug in the process, and retrieving the missing April 2015 station data. These have led to small changes in regional and global average values and coverage. All other processing steps for HadISDH remain identical. Users are advised to read the update document in the Docs section for full details.\r\n\r\nAs in previous years, the annual scrape of NOAAs Integrated Surface Dataset for HadISD.3.1.2.202101p, 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." } }, { "ob_id": 661, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 37290, "uuid": "54d3408edd9e41bca226924754619812", "short_code": "ob", "title": "HadISDH.marine: gridded global monthly ocean surface humidity data version 1.3.0.2021f", "abstract": "This is the HadISDH.marine 1.3.0.2021f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.marine is a near-global gridded monthly mean marine surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships. The observations have been quality controlled and bias-adjusted. 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 31/12/2021.\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 2021. 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\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., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775." }, "objectObservation": { "ob_id": 32372, "uuid": "c928ef392244426b9473af92a16b0daf", "short_code": "ob", "title": "HadISDH marine: gridded global monthly ocean surface humidity data version 1.1.0.2020f", "abstract": "This is the HadISDH marine 1.1.0.2020f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH-marine s a near-global gridded monthly mean marine surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships. The observations have been quality controlled and bias-adjusted. 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 31/12/2020.\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 1.0.0.2019f version to the end of 2020 and constitutes a minor update to HadISDH due to change in method for calculating gridbox monthly means. All other processing steps for HadISDH remain identical. Users are advised to read the update document in the Docs section for full details.\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., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775." } }, { "ob_id": 662, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 37291, "uuid": "563cb665bc6e43f99b355a9bb8134317", "short_code": "ob", "title": "HadISDH.blend: gridded global monthly land and ocean surface humidity data version 1.3.0.2021f", "abstract": "This is the HadISDH.blend 1.3.0.2021f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.blend is a near-global gridded monthly mean surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships and weather stations. The observations have been quality controlled and homogenised / bias adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). These data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2021.\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 2021. It combines the latest version of HadISDH.land and HadISDH.marine. and therefore their respective update notes. Users are advised to read the update documents in the Docs section for full details.\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., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775.\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." }, "objectObservation": { "ob_id": 32373, "uuid": "8e90b16ddd2a484897ab9737c46d6204", "short_code": "ob", "title": "HadISDH blend: gridded global monthly land and ocean surface humidity data version 1.1.1.2020f", "abstract": "This is the HadISDH blend 1.1.1.2020f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH-blend is a near-global gridded monthly mean surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships and weather stations. The observations have been quality controlled and homogenised / bias adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). These data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2020.\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 1.0.0.2019f version to the end of 2020. It combines HadISDH.land.4.3.1.2020f and HadISDH.marine.1.1.0.2020f and therefore their respective update notes. Users are advised to read the update documents in the Docs section for full details.\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., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775.\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." } }, { "ob_id": 663, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 37291, "uuid": "563cb665bc6e43f99b355a9bb8134317", "short_code": "ob", "title": "HadISDH.blend: gridded global monthly land and ocean surface humidity data version 1.3.0.2021f", "abstract": "This is the HadISDH.blend 1.3.0.2021f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.blend is a near-global gridded monthly mean surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships and weather stations. The observations have been quality controlled and homogenised / bias adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). These data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2021.\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 2021. It combines the latest version of HadISDH.land and HadISDH.marine. and therefore their respective update notes. Users are advised to read the update documents in the Docs section for full details.\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., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775.\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." }, "objectObservation": { "ob_id": 37290, "uuid": "54d3408edd9e41bca226924754619812", "short_code": "ob", "title": "HadISDH.marine: gridded global monthly ocean surface humidity data version 1.3.0.2021f", "abstract": "This is the HadISDH.marine 1.3.0.2021f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.marine is a near-global gridded monthly mean marine surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships. The observations have been quality controlled and bias-adjusted. 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 31/12/2021.\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 2021. 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\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., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775." } }, { "ob_id": 664, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 37291, "uuid": "563cb665bc6e43f99b355a9bb8134317", "short_code": "ob", "title": "HadISDH.blend: gridded global monthly land and ocean surface humidity data version 1.3.0.2021f", "abstract": "This is the HadISDH.blend 1.3.0.2021f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.blend is a near-global gridded monthly mean surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships and weather stations. The observations have been quality controlled and homogenised / bias adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). These data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2021.\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 2021. It combines the latest version of HadISDH.land and HadISDH.marine. and therefore their respective update notes. Users are advised to read the update documents in the Docs section for full details.\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., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775.\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." }, "objectObservation": { "ob_id": 37289, "uuid": "062942e96a6e4567b2bc47045be910a7", "short_code": "ob", "title": "HadISDH.land: gridded global monthly land surface humidity data version 4.4.0.2021f", "abstract": "This is the HadISDH.land 4.4.0.2021f 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 31/12/2021. \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 2021. 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.3.1.2.202101p, 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." } }, { "ob_id": 670, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 35186, "uuid": "b7706cbc226f4e2a9999d74ee12eac96", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument D deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." }, "objectObservation": { "ob_id": 37594, "uuid": "af83232f73bf42a88a1df88d1067825b", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument C deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." } }, { "ob_id": 671, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 35186, "uuid": "b7706cbc226f4e2a9999d74ee12eac96", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument D deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." }, "objectObservation": { "ob_id": 37591, "uuid": "32dcaf1485de444c8ba273afde964f46", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument B deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." } }, { "ob_id": 672, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 35186, "uuid": "b7706cbc226f4e2a9999d74ee12eac96", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument D deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." }, "objectObservation": { "ob_id": 37588, "uuid": "10af126c9dcf40e2aff7c3cf835d7e6b", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument A deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." } }, { "ob_id": 673, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 37594, "uuid": "af83232f73bf42a88a1df88d1067825b", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument C deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." }, "objectObservation": { "ob_id": 37591, "uuid": "32dcaf1485de444c8ba273afde964f46", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument B deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." } }, { "ob_id": 674, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 37594, "uuid": "af83232f73bf42a88a1df88d1067825b", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument C deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." }, "objectObservation": { "ob_id": 37588, "uuid": "10af126c9dcf40e2aff7c3cf835d7e6b", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument A deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." } }, { "ob_id": 675, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 37594, "uuid": "af83232f73bf42a88a1df88d1067825b", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument C deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." }, "objectObservation": { "ob_id": 35186, "uuid": "b7706cbc226f4e2a9999d74ee12eac96", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument D deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." } }, { "ob_id": 676, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 37591, "uuid": "32dcaf1485de444c8ba273afde964f46", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument B deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." }, "objectObservation": { "ob_id": 37594, "uuid": "af83232f73bf42a88a1df88d1067825b", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument C deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." } }, { "ob_id": 677, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 37591, "uuid": "32dcaf1485de444c8ba273afde964f46", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument B deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." }, "objectObservation": { "ob_id": 37588, "uuid": "10af126c9dcf40e2aff7c3cf835d7e6b", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument A deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." } }, { "ob_id": 678, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 37591, "uuid": "32dcaf1485de444c8ba273afde964f46", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument B deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." }, "objectObservation": { "ob_id": 35186, "uuid": "b7706cbc226f4e2a9999d74ee12eac96", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument D deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." } }, { "ob_id": 679, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 37588, "uuid": "10af126c9dcf40e2aff7c3cf835d7e6b", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument A deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." }, "objectObservation": { "ob_id": 37594, "uuid": "af83232f73bf42a88a1df88d1067825b", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument C deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." } }, { "ob_id": 680, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 37588, "uuid": "10af126c9dcf40e2aff7c3cf835d7e6b", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument A deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." }, "objectObservation": { "ob_id": 37591, "uuid": "32dcaf1485de444c8ba273afde964f46", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument B deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." } }, { "ob_id": 681, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 37588, "uuid": "10af126c9dcf40e2aff7c3cf835d7e6b", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument A deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." }, "objectObservation": { "ob_id": 35186, "uuid": "b7706cbc226f4e2a9999d74ee12eac96", "short_code": "ob", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument D deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities." } }, { "ob_id": 682, "relationType": "IsIdenticalTo", "subjectObservation": { "ob_id": 33333, "uuid": "38bac9051d064d4da183fff2361f5de8", "short_code": "ob", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 3.27 (v20220621)", "abstract": "Input data for figure 3.27 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.27 shows maps of multi-decadal salinity trends for the near-surface ocean.\r\n\r\n ---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n ---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two subpanels, with input data provided for the upper panel.\r\n\r\n ---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains the global ocean salinity estimates from Durack & Wijffels (2010) based on observations from 01-01-1950 to 12-31-2019:\r\n \r\n - Mean salinity (for the Jan/1950 to Dec/2019 period, units in PSS).\r\n - Salinity change (for the same period, PSS/70-years).\r\n - Salinity change error (same period, PSS/70-years).\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThe observational data from here (top panel) is taken from the file:\r\nDurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc. The fields of interest are salinity_mean (shown as black contours) and salinity_change (shown in colourscale). DurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc is an updated file from Durack & Wijffels (2010).\r\n\r\nThe data file DurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc is an intermediate file used in 3.27, please refer to the code to generate the figure using the corresponding tools (see the link to the code in the 'Related document' section README_AR6_WG1_Chap3_Figure3_27_GlobalSeaSurfaceSalinitytrends.md at main ESMValGroup/ESMValTool-AR6-OriginalCode-FinalFigures).\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to input data figure 2.27.\r\n - Link to the figure on the IPCC AR6 website." }, "objectObservation": { "ob_id": 33307, "uuid": "78ad6999f2d743d2a7db16757c27b549", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 2.27 (v20211112)", "abstract": "Input data for Figure 2.27 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 2.27 presents the ocean salinity trends during historical period (1950-2019) for the near surface (global map, panel a) and zonal mean sub-surface (panel b), with regions of non-significant changes masked by 'x' marks. \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 two panels, with data provided for all panels (DurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc) and processed via the MATLAB script (Figure_2_27.m) linked in the Related Documens section.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains the global ocean salinity estimates from Durack & Wijffels (2010) based on observations from 01-01-1950 to 12-31-2019:\r\n \r\n - Mean salinity (for the Jan/1950 to Dec/2019 period, units in PSS).\r\n - Salinity change (for the same period, PSS/70-years).\r\n - Salinity change error (same period, PSS/70-years).\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n The processing of the salinity estimates from Durack & Wejffels (2010), is done in the MATLAB script (Figure_2_27.m).\r\n\r\n Panel a: \r\n - Ocean surface salinity change (1950-2019) and time mean (for isohalines).\r\n\r\n Panel b:\r\n - Zonal mean ocean subsurface salinity (0-2000m) change (1950-2019) and time mean (for isohalines).\r\n\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data.\r\n ---------------------------------------------------\r\n The salinity change from the dataset has unit PSS/70-years. Units for salinity change and salinity change error have been converted to PSS/decade.\r\n\r\n Salinity change error from the dataset must be multiplied by 1.09 (to account for the resolved error when a bootstrap resampling was undertaken) x 1.64485 (i.e., z-value for 90% confidence interval) in order to get the uncertainty for the stippling.\r\n\r\n The stippling in both panels is done for regions (either surface salinity, panel a, or zonal mean salinity, panel b) where the salinity uncertainties are larger than the salinity trend.\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\r\n - Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 683, "relationType": "IsIdenticalTo", "subjectObservation": { "ob_id": 33307, "uuid": "78ad6999f2d743d2a7db16757c27b549", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 2.27 (v20211112)", "abstract": "Input data for Figure 2.27 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 2.27 presents the ocean salinity trends during historical period (1950-2019) for the near surface (global map, panel a) and zonal mean sub-surface (panel b), with regions of non-significant changes masked by 'x' marks. \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 two panels, with data provided for all panels (DurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc) and processed via the MATLAB script (Figure_2_27.m) linked in the Related Documens section.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains the global ocean salinity estimates from Durack & Wijffels (2010) based on observations from 01-01-1950 to 12-31-2019:\r\n \r\n - Mean salinity (for the Jan/1950 to Dec/2019 period, units in PSS).\r\n - Salinity change (for the same period, PSS/70-years).\r\n - Salinity change error (same period, PSS/70-years).\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n The processing of the salinity estimates from Durack & Wejffels (2010), is done in the MATLAB script (Figure_2_27.m).\r\n\r\n Panel a: \r\n - Ocean surface salinity change (1950-2019) and time mean (for isohalines).\r\n\r\n Panel b:\r\n - Zonal mean ocean subsurface salinity (0-2000m) change (1950-2019) and time mean (for isohalines).\r\n\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data.\r\n ---------------------------------------------------\r\n The salinity change from the dataset has unit PSS/70-years. Units for salinity change and salinity change error have been converted to PSS/decade.\r\n\r\n Salinity change error from the dataset must be multiplied by 1.09 (to account for the resolved error when a bootstrap resampling was undertaken) x 1.64485 (i.e., z-value for 90% confidence interval) in order to get the uncertainty for the stippling.\r\n\r\n The stippling in both panels is done for regions (either surface salinity, panel a, or zonal mean salinity, panel b) where the salinity uncertainties are larger than the salinity trend.\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\r\n - Link to the code for the figure, archived on Zenodo." }, "objectObservation": { "ob_id": 33333, "uuid": "38bac9051d064d4da183fff2361f5de8", "short_code": "ob", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 3.27 (v20220621)", "abstract": "Input data for figure 3.27 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.27 shows maps of multi-decadal salinity trends for the near-surface ocean.\r\n\r\n ---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n ---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two subpanels, with input data provided for the upper panel.\r\n\r\n ---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains the global ocean salinity estimates from Durack & Wijffels (2010) based on observations from 01-01-1950 to 12-31-2019:\r\n \r\n - Mean salinity (for the Jan/1950 to Dec/2019 period, units in PSS).\r\n - Salinity change (for the same period, PSS/70-years).\r\n - Salinity change error (same period, PSS/70-years).\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThe observational data from here (top panel) is taken from the file:\r\nDurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc. The fields of interest are salinity_mean (shown as black contours) and salinity_change (shown in colourscale). DurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc is an updated file from Durack & Wijffels (2010).\r\n\r\nThe data file DurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc is an intermediate file used in 3.27, please refer to the code to generate the figure using the corresponding tools (see the link to the code in the 'Related document' section README_AR6_WG1_Chap3_Figure3_27_GlobalSeaSurfaceSalinitytrends.md at main ESMValGroup/ESMValTool-AR6-OriginalCode-FinalFigures).\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to input data figure 2.27.\r\n - Link to the figure on the IPCC AR6 website." } }, { "ob_id": 685, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 33394, "uuid": "f7c3f3cbf65447b9a43207dcc30219d9", "short_code": "ob", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 3.28 (v20220621)", "abstract": "Input Data for Figure 3.28 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.28 shows long-term trends in halosteric and thermosteric sea level in CMIP6 models and observations. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The data is used in left upper and left lower panels (scatter panels), as well as right upper panels (D&W, EN4, Ishii) \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n 210127_DurackandWijffels_V1.0_70yr_steric_1950-2019_0-2000db_210122-205355_beta.nc is input data for D&W. The variables steric_height_halo_anom_depthInterp and steric_height_thermo_anom_depthInterp are used.\r\n 210201_EN4.2.1.g10_annual_steric_1950-2019_5-5350m.nc is input data for EN4\r\n 210201_Ishii17_v7.3_annual_steric_1955-2019_0-3000m.nc is input data for Ishii\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data.\r\n ---------------------------------------------------\r\n This data is an input observational data for the Figure 3.28. It is used for scatter plots and contour maps.\r\n In addition, shapefiles are required to calculate the regional boundaries: Pacific.shp, Atlantic.shp. These regions should be standarised throught AR6.\r\n\r\n\r\nThe following changes to filenames were made to archive the data (due to filenaming restrictions). To use the data with any associated figure code, the filenames should be reverted.\r\n\r\n 210127_DurackandWijffels_V1_0_70yr_steric_1950-2019_0-2000db_210122-205355_beta.nc -> 210127_DurackandWijffels_V1.0_70yr_steric_1950-2019_0-2000db_210122-205355_beta.nc \r\n 210201_EN4_2_1_g10_annual_steric_1950-2019_5-5350m.nc -> 210201_EN4.2.1.g10_annual_steric_1950-2019_5-5350m.nc \r\n 210201_Ishii17_v7_3_annual_steric_1955-2019_0-3000m.nc -> 210201_Ishii17_v7.3_annual_steric_1955-2019_0-3000m.nc \r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website" }, "objectObservation": { "ob_id": 37520, "uuid": "38512cd8209b4669a0743e9672f70a6e", "short_code": "ob", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.28 (v20220614)", "abstract": "Data for Figure 3.28 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.28 shows long-term trends in halosteric and thermosteric sea level in CMIP6 models and observations.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has panels (a), (b), (c), (d), (e), (f), with data provided for all panels in subdirectories named panel_a, panel_b, panel_c, panel_d, panel_e and panel_f.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The datasets contains: \r\n \r\n - Atlantic and Pacific halosteric sea level trend from CMIP6 models (1950-2014)\r\n - Atlantic and Pacific halosteric sea level trend from Durack&Wijffels observations (1950-2019)\r\n - Atlantic and Pacific halosteric sea level trend from EN4 observations (1950-2019)\r\n - Atlantic and Pacific halosteric sea level trend from Ishii observations (1955-2019)\r\n - Atlantic and Pacific thermosteric sea level trend from CMIP6 models (1950-2014)\r\n - Atlantic and Pacific thermosteric sea level trend from Durack&Wijffels observations (1950-2019)\r\n - Atlantic and Pacific thermosteric sea level trend from EN4 observations (1950-2019)\r\n - Atlantic and Pacific thermosteric sea level trend from Ishii observations (1955-2019)\r\n - Global halosteric sea level trends from Durack&Wijffels observations (1950-2019)\r\n - Global halosteric sea level trends from EN4 observations (1950-2019)\r\n - Global halosteric sea level trends from Ishii observations (1955-2019)\r\n - Global halosteric sea level trends from CMIP6 multi-model mean (1950-2014)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/halosteric_trends_hist-nat.csv has data for green and black markers.\r\n - panel_a/halosteric_trends_historical.csv has data for orange and black markers.\r\n - panel_b/thermosteric_trends_hist-nat.csv has data for green and black markers.\r\n - panel_b/thermosteric_trends_historical.csv has data for orange and black markers.\r\n - panel_c/halosteric_trends_map_DW.nc has data for filled colored contours.\r\n - panel_d/halosteric_trends_map_EN4.nc has data for filled colored contours.\r\n - panel_e/halosteric_trends_map_Ishii.nc has data for filled colored contours.\r\n - panel_f/halosteric_trends_map_cmip6.nc has data for filled colored contours.\r\n For panels a and b details about the data provided in relation to the figure in the header of every file.\r\n\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\n The observational data from here (top right panel) is taken from the file:\r\n\r\nDurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc. The field of interest are salinity_mean (shown as black contours) and salinity_change (shown in colourscale). The file was archived as input data for Figure 2.27. The link to this dataset is provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the input dataset for figure 3.28\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website" } }, { "ob_id": 686, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 33333, "uuid": "38bac9051d064d4da183fff2361f5de8", "short_code": "ob", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 3.27 (v20220621)", "abstract": "Input data for figure 3.27 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.27 shows maps of multi-decadal salinity trends for the near-surface ocean.\r\n\r\n ---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n ---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two subpanels, with input data provided for the upper panel.\r\n\r\n ---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains the global ocean salinity estimates from Durack & Wijffels (2010) based on observations from 01-01-1950 to 12-31-2019:\r\n \r\n - Mean salinity (for the Jan/1950 to Dec/2019 period, units in PSS).\r\n - Salinity change (for the same period, PSS/70-years).\r\n - Salinity change error (same period, PSS/70-years).\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThe observational data from here (top panel) is taken from the file:\r\nDurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc. The fields of interest are salinity_mean (shown as black contours) and salinity_change (shown in colourscale). DurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc is an updated file from Durack & Wijffels (2010).\r\n\r\nThe data file DurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc is an intermediate file used in 3.27, please refer to the code to generate the figure using the corresponding tools (see the link to the code in the 'Related document' section README_AR6_WG1_Chap3_Figure3_27_GlobalSeaSurfaceSalinitytrends.md at main ESMValGroup/ESMValTool-AR6-OriginalCode-FinalFigures).\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to input data figure 2.27.\r\n - Link to the figure on the IPCC AR6 website." }, "objectObservation": { "ob_id": 37567, "uuid": "ceae289f1a56414ea708f43db83fc2c6", "short_code": "ob", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.27 (v20220616)", "abstract": "Data for Figure 3.27 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.27 shows maps of multi-decadal salinity trends for the near-surface ocean.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n Technically there are two panels, they are named in the datasets as top and bottom, but the data is stored in the parent directory. Data provided for bottom panel.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains salinity data:\r\n \r\n - climatological mean from CMIP6 models (1950-2014)\r\n - simulated trend from CMIP6 models (1950-2014)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - ocean_salinity_cmip6.nc: climatological salinity (1950-2014) from CMIP6 models (black contours) in a bottom panel\r\n - ocean_salinity_trends_cmip6.nc: salinity trends (1950-2014) from CMIP6 models (colored shades) in a bottom panel\r\n\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\n The observational data from here (top panel) is taken from the file:\r\n\r\nDurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc. The field of interest are salinity_mean (shown as black contours) and salinity_change (shown in colourscale). The file was archived as input data for Figure 2.27. The link to this dataset is provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to input data figure 2.27\r\n - Link to the figure on the IPCC AR6 website" } }, { "ob_id": 687, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 37308, "uuid": "033cd690801741c9bc745b8da55faef4", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 2.11 (v20220428)", "abstract": "Input Data for Figure 2.11 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 2.11 shows observed global temperature change over a wide range of timescales.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three subpanels. Input data are provided for panel b and panel c (lower panel).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Panel b:\r\n - gridded file of observed trends (as ASCII text) and significance overlay. Separate notes document.\r\n \r\n Panel c (lower panel):\r\n - Global surface temperature, relative to 1850 - 1900 for annual and decadal means from instrumental data for 1850–2020, along with the uncertainty range from HadCRUT5.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel b:\r\n - IndermediateData_Figure-2_11-HadCRUT_significance_overlay_1981-2020.txt\r\n - IntermediateData_Figure-2_11-HadCRUT_significance_overlay_1900-1980.txt\r\n - IntermediateData_Figure-2_11-HadCRUT_trends_1900-1980.txt\r\n - IntermediateData_Figure-2_11-HadCRUT_trends_1981-2020.txt\r\n \r\n Panel c:\r\n - Figure_2_11c-lower_panel.csv; relates to the lower part of the figure. (black line, column 2, HadCRUT 5.0; cyan line, column 3, NOAA Global Temp; pink line, column 4, Berkeley Earth; orange line, column 5, Kadow et al.; grey shadow, columns 6 and 7, HadCRUT confidence limit)\r\n\r\nHadCRUT5 is a gridded dataset of global historical surface temperature anomalies relative to a 1961-1990 reference period produced by the Met Office Hadley Centre. \r\nNOAA Global Temp is a gridded dataset of global historical surface temperature anomalies relative to a 1971-2000 reference period produced by the National Oceanic and Atmospheric Administration. \r\nBerkeley Earth is a global historical land-ocean temperature index produced by Berkeley Earth.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Figure 2.11b - this is an ASCII grid (described in Figure_2_11-notes_on_HadCRUT_trend_files.txt) with a significance overlay. Should be approximately reproducible with any standard software to produce maps from gridded data.\r\n\r\n\r\nFigure 2.11c (lower panel), link to the code to reproduce this part of the figure is provided in the Related Documents section of this catalogue record.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to the code for the figure, archived on Zenodo." }, "objectObservation": { "ob_id": 37280, "uuid": "f3515388768344bfb2be0521f82388be", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.11 (v20220510)", "abstract": "Data for Figure 2.11 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 2.11 includes mapped and time-series data showing global surface temperature relative to 1850 - 1900 over multiple time scales.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n---------------------------------------------------\r\n Figure has three panels, with data provided for panel (a) (center and right part), and panel (c).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n---------------------------------------------------\r\n Global surface temperature, relative to 1850 - 1900 for:\r\n\r\n Panel a: \r\n \r\n - 1000 to 1900 CE - from PAGES 2k Consortium (modified from the version 2019: 10.1038/s41561-019-0400-0)\r\n - 1850 to 2020 from AR6 assessed mean (same as Figure 2.11c).\r\n\r\n Panel c: \r\n \r\n - Annual and decadal means from instrumental data for 1850–2020, along with the uncertainty range from HadCRUT5.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n---------------------------------------------------\r\n Panel a:\r\n \r\n - Data file: Figure_2_11a-PAGES_2k_Consortium.csv (yearly data, 1000 to 1900); relates to the center part of the figure showing global surface temperature relative to 1850 -1900. (bold solid green line, column 2, median 10-yr smooth adjusted (+0.37°C), thin solid green lines: 5th (column 3) and 95th (column 4) percentiles of the ensemble members).\r\n - Data file: Figure2_11_panel_a.csv (yearly data, 1850 to 2020); relates to the right part of the figure showing global temperature anomaly AR6 assessed mean. (bold solid violet line, column 2)\r\n\r\nPanel c: \r\n \r\n - Data file: Figure_2_11c-land_and_ocean_time_series.csv (yearly data, 1850 to 2020); relates to the upper part of the figure showing global surface temperature relative to 1850 -1900. (Land, column 2, red line; Ocean, column 3, blue line).\r\n\r\n---------------------------------------------------\r\nNotes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nInput data and code to reproduce panel b and panel c (lower part) plots are provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to input data figure 2.11.\r\n - Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 688, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 34657, "uuid": "528c3543bc394134916aa792c4a2e700", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 2.25 (v20220119)", "abstract": "Input data for Figure 2.25 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.25 shows changes in permafrost temperature for 4 Arctic regions over the period 1974-2019 shown as average departures from the International Polar Year (2007-2009) baseline.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Annual mean permafrost temperatures(deg C) for sites in 4 regions at depths indicated based on Romanovsky et al. (2020) in State of the Climate in 2019 BAMS 101(8) p S265-S269 https://doi.org/10.1175/BAMS-D-20-0086.1\r\n Regions based on those in Romanovsky et al. (2017) Ch 4 in Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nTime series for each site that was used to determine the regional anomalies shown in the figure\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1" }, "objectObservation": { "ob_id": 33207, "uuid": "0e80e12edb7e4dc9b9219df77c0fe9d6", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.25 (v20211005)", "abstract": "Data for Figure 2.25 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.25 shows changes in permafrost temperature for 4 Arctic regions over the period 1974-2019 shown as average departures from the International Polar Year (2007-2009) baseline.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains regional average departures (anomalies) of observed permafrost temperature relative to International Polar Year (2007-20909) baseline from 1974-2019 for 4 regions as defined in Romanovsky et al. (2017):\r\n \r\n - Permafrost temperature Barents region (1974-2019)\r\n - Permafrost temperature Baffin Bay Davis Strait region (1979-2019)\r\n - Permafrost temperature anomaly Beaufort-Chukchi Sea region (1978-2019)\r\n - Permafrost temperature Interior Alaska and Central Mackenzie Valley NWT (1983-2019)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 2.25\r\n Data file: Arctic_permafrost_temperature_anomaly.csv; (annual data, average regional anomalies) relates to green line (Barents), purple line (Baffin), blue line (Beaufort-Chukchi) and red line (Interior Alaska and Central Mackenzie Valley)\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to input data figure 2.25." } }, { "ob_id": 689, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 33257, "uuid": "02fd1d886bad40f3bb2eef3271900823", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 2.13 (v20211022)", "abstract": "Input Data for Figure 2.13 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.13 shows the global trends in surface specific humidity and surface relative humidity over 1973-2019\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with input data provided for panels (a) HadISDH_blendq_1_0_0_2019f.nc and (c) HadISDH_blendRH_1_0_0_2019f.nc \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains global maps of specific and relative humidity trends over 1973-2019\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n This dataset is the input data used in the code that generates panel (a) and panel (c) for figure 2.13 \r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The following changes to filenames were made to archive the data (due to filenaming restrictions). To use the data with any associated figure code, the filenames should be reverted.\r\n\r\n HadISDH_blendq_1_0_0_2019f.nc -> HadISDH.blendq.1.0.0.2019f.nc \r\n HadISDH_blendRH_1_0_0_2019f.nc -> HadISDH.blendRH.1.0.0.2019f.nc \r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to the figure on the IPCC AR6 website" }, "objectObservation": { "ob_id": 33342, "uuid": "967313bee45c48998c5027896e3da53c", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.13 (v20211126)", "abstract": "Data for Figure 2.13 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.13 shows the global trends in surface specific humidity and surface relative humidity over 1973 - 2019\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for panel (b) and (d) in one single file (qrh2.csv). Code for plotting panels (a) and (c) is archived on zenodo and a link is provided in the Related Documents section of this catalogue record.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains time-series of observed global average surface specific and relative humidity during 1973-2019\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel b. Global average surface specific humidity annual anomalies (1981–2010 base period):\r\n \r\n - Data file: qrh2.csv - column 2 cyan solid line\r\n - Data file: qrh2.csv - column 3 orange solid line\r\n - Data file: qrh2.csv - column 4 blue solid line\r\n - Data file: qrh2.csv - column 5 black solid line\r\n\r\n\r\nPanel d. Global average surface relative humidity annual anomalies (1981–2010 base period):\r\n - Data file: qrh2.csv - column 7 cyan solid line\r\n - Data file: qrh2.csv - column 8 orange solid line\r\n - Data file: qrh2.csv - column 9 blue solid line\r\n - Data file: qrh2.csv - column 10 black solid line\r\n\r\nRH stands for relative humidity.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2).\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1.\r\n - Link to input data figure 2.13.\r\n - Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 690, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 33260, "uuid": "8ec2d4b94f8e4756ad31858ff8256464", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 2.15 (v20211022)", "abstract": "Input data for Figure 2.15 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.15 provides global precipitation trend maps and time series for a variety of data sources\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has six panels, with input data provided for panel (a) (cru_masked_2019_2), panels (b) and (e) (gpcc_v2020_msk2.nc), panel (d) (cru_masked_2019_2.nc), and panel (f) (gpcp2019.nc)\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains observed global precipitation data from a variety of sources covering the period 1891-2019\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n This dataset is the input data used in the code that generates panels (a), (b), (d), (e) and (f) for figure 2.15. \r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to the figure on the IPCC AR6 website" }, "objectObservation": { "ob_id": 33350, "uuid": "70276cf6b04e4b638b4fe9b37f7651dd", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.15 (v20211126)", "abstract": "Data for Figure 2.15 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.15 provides global precipitation trend maps and time series for a variety of data sources.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has six panels, with data provided for panel c (precipglobalavedata2019_4.csv). Code for plotting all panels is archived on zenodo and a link is provided in the Related Documents section of this catalogue record.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains observed global precipitation data from a variety of sources covering the period 1891-2019.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel c:\r\n \r\n - Data file precipglobalavedata2019_4.csv: column 2 orange solid line (upper panel)\r\n - Data file precipglobalavedata2019_4.csv: column 3 cyan solid line (upper panel)\r\n - Data file precipglobalavedata2019_4.csv: column 4 black solid line (upper panel)\r\n - Data file precipglobalavedata2019_4.csv: column 5 black dotted line (upper panel)\r\n - Data file precipglobalavedata2019_4.csv: column 7 orange solid line (lower panel)\r\n - Data file precipglobalavedata2019_4.csv: column 8 cyan solid line (lower panel)\r\n - Data file precipglobalavedata2019_4.csv: column 9 black solid line (lower panel)\r\n - Data file precipglobalavedata2019_4.csv: column 10 black dotted line (lower panel)\r\n\r\nGCPP stands for the Global Precipitation Climatology Centre.\r\nCRU TS stands for Climatic Research Unit Timeseries.\r\nGPCP stands for the Global Precipitation Climatology Project.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2).\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1.\r\n - Link to input data figure 2.15.\r\n - Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 692, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 37681, "uuid": "c9397680d08442b9a1d21e7c50df4aba", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input Data for Figure 2.12 (v20220630)", "abstract": "Input Data for Figure 2.12 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 2.12 shows changes in temperature through the troposphere and stratosphere, both on near-global scales and in the tropics.\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 Gulev, 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 subpanels, with intermediate data provided for panels b to e.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains trends in temperature at various atmospheric heights for 1980–2019 and 2002–2019\r\n \r\n - for the near-global (70°N–70°S) domain.\r\n - for the tropical (20°N–20°S) region.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panels b-e: each line shows the observed trend for the specified time period/region for a given data set as a function of height.\r\n \r\n - Data files: *ROM_SAF*.nc: Radio occultation RO (ROM SAF). Violet line\r\n - Data files: *UCAR*.nc: Radio occultation RO (UCAR/NOAA). Cyan line\r\n - Data files: *Wegener*.nc: Radio occultation RO (WEGC). Blue line\r\n - Data files: *ERA5*.nc: Modern reanalysis. Cyan dotted lines.\r\n - Data files: *RICH*.nc: Radiosonde. Orange line\r\n - Data files: *RAOBCORE*.nc: Radiosonde. Yellow line\r\n - Data files: *AIRS*.nc: Infrared satellite. Green line\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThere are notes guiding the user to reproduce the figure in the code associated to this dataset. Link to the code that reproduces the figure in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to the code for the figure, archived on Zenodo." }, "objectObservation": { "ob_id": 37707, "uuid": "e9f67cfb456845b3b406328c6ae43e2d", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.12 (v20220701)", "abstract": "Data for Figure 2.12 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 2.12 shows changes in temperature through the troposphere and stratosphere, both on near-global scales and in the tropics.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 5 subpanels, with data provided for panel a.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains observed temperature anomaly trends for 2002-2019 from the ROM SAF dataset, plotted as a trend/height/latitude contour plot.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 2.12:\r\n \r\n - Data file: Figure_2_12a_data_file.nc: tdry_trends filled contours plot\r\n - Data file: Figure_2_12a_data_file.nc: lrt_temprature_altitude grey line\r\n\r\nROM SAF stands for Radio Occultation Meteorology Satellite Application Facilities.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Panel (a) is plotted using standard matplotlib software. \r\nThere are notes guiding the user to reproduce the figure in the code associated to this dataset. Link to the code that reproduces the figure in the Related Documents section of this catalogue record.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to input data figure 2.12.\r\n- Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 693, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 37381, "uuid": "a07deacaffb8453e93d57ee214676304", "short_code": "ob", "title": "ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.0.2", "abstract": "This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. \r\n\r\nThis is version 2.0.2 of the dataset. \r\n\r\nThe five thematic climate variables included in this dataset are:\r\n• Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\r\n• Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .\r\n• Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.\r\n• Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\r\n• Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n\r\nData generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop.\r\n\r\nDetailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website and in Carrea, L., Crétaux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z" }, "objectObservation": { "ob_id": 37092, "uuid": "03c935c6890c4b2ebf4aae4d84cd9472", "short_code": "ob", "title": "ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.0.1", "abstract": "This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. \r\n\r\nThis is version 2.0.1 of the dataset. The five thematic climate variables included in this dataset are:\r\n• Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\r\n• Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .\r\n• Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.\r\n• Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\r\n• Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n\r\nData generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop.\r\n\r\nDetailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website." } }, { "ob_id": 694, "relationType": "Continues", "subjectObservation": { "ob_id": 37832, "uuid": "45283390b97c4a27861d74b3d915b0bd", "short_code": "ob", "title": "Monthly mean climate data from a transient simulation with the Whole Atmosphere Community Climate Model eXtension (WACCM-X) from 2015 to 2070", "abstract": "This dataset comprises monthly mean data from a global, transient simulation with the Whole Atmosphere Community Climate Model eXtension (WACCM-X) from 2015 to 2070. WACCM-X is a global atmosphere model covering altitudes from the surface up to ~500 km, i.e., including the troposphere, stratosphere, mesosphere and thermosphere. WACCM-X version 2.0 (Liu et al., 2018) was used, part of the Community Earth System Model (CESM) release 2.1.0 (http://www.cesm.ucar.edu/models/cesm2) made available by the National Center for Atmospheric Research. The model was run in free-running mode with a horizontal resolution of 1.9 degrees latitude and 2.5 degrees longitude (giving 96 latitude points and 144 longitude points) and 126 vertical levels. Further description of the model and simulation setup is provided by Cnossen (2022) and references therein. A large number of variables is included on standard monthly mean output files on the model grid, while selected variables are also offered interpolated to a constant height grid or vertically integrated in height (details below). Zonal mean and global mean output files are included as well.\r\n\r\nThe data are provided in NetCDF format and file names have the following structure: \r\n\r\nf.e210.FXHIST.f19_f19.h1a.cam.h0.[YYYY]-[MM][DFT].nc\r\n\r\nwhere [YYYY] gives the year with 4 digits, [MM] gives the month (2 digits) and [DFT] specifies the data file type. The following data file types are included:\r\n\r\n1)\tMonthly mean output on the full grid for the full set of variables; [DFT] = \r\n2)\tZonal mean monthly mean output for the full set of variables; [DFT] = _zm\t\r\n3)\tGlobal mean monthly mean output for the full set of variables; [DFT] = _gm\r\n4)\tHeight-interpolated/-integrated output on the full grid for selected variables; [DFT] = _ht\r\n\r\nA cos(latitude) weighting was used when calculating the global means.\r\n\r\nData were interpolated to a set of constant heights (61 levels in total) using the Z3GM variable (for variables output on midpoints, with 'lev' as the vertical coordinate) or the Z3GMI variable (for variables output on interfaces, with ilev as the vertical coordinate) stored on the original output files (type 1 above). Interpolation was done separately for each longitude, latitude and time. \r\n\r\nMass density (DEN [g/cm3]) was calculated from the M_dens, N2_vmr, O2, and O variables on the original data files before interpolation to constant height levels. \r\n\r\nThe Joule heating power QJ [W/m3] was calculated using \r\nQ_J = (sigma_P*B^2)*((u_i - U_n)^2 + (v_i-v_n)^2 + (w_i-w_n)^2) \r\nwith sigma_P = Pedersen conductivity[S], B = geomagnetic field strength [T], ui, vi, and wi = zonal, meridional, and vertical ion velocities [m/s] and un, vn, and wn = neutral wind velocities [m/s]. QJ was integrated vertically in height (using a 2.5 km height grid spacing rather than the 61 levels on output file type 4) to give the JHH variable on the type 4 data files. The QJOULE variable also given is the Joule heating rate [K/s] at each of the 61 height levels.\r\n\r\nAll data are provided as monthly mean files with one time record per file, giving 672 files for each data file type for the period 2015-2070 (56 years).\r\n\r\nReferences:\r\n\r\nCnossen, I. (2022), A realistic projection of climate change in the upper atmosphere into the 21st century, in preparation.\r\n\r\nLiu, H.-L., C.G. Bardeen, B.T. Foster, et al. (2018), Development and validation of the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X 2.0), Journal of Advances in Modeling Earth Systems, 10(2), 381-402, doi:10.1002/2017ms001232." }, "objectObservation": { "ob_id": 31926, "uuid": "dc91f5e39ae34fd883af81dfdbaf659c", "short_code": "ob", "title": "Monthly mean climate data from a transient simulation with the Whole Atmosphere Community Climate Model eXtension (WACCM-X) from 1950 to 2015", "abstract": "This dataset comprises monthly mean data from a global, transient simulation with the Whole Atmosphere Community Climate Model eXtension (WACCM-X) from 1950 to 2015. WACCM-X is a global atmosphere model covering altitudes from the surface up to ~500 km, i.e. including the troposphere, stratosphere, mesosphere and thermosphere. \r\n\r\nWACCM-X version 2.0 (Liu et al., 2018) was used, part of the Community Earth System Model (CESM) release 2.1.0 made available by the US National Center for Atmospheric Research. The model was run in free-running mode with a horizontal resolution of 1.9° latitude 2.5° longitude (giving 96 latitude points and 144 longitude points) and 126 vertical levels. Further description of the model and simulation setup is provided by Cnossen (2020) and references therein. A large number of variables are included on standard monthly mean output files on the model grid, while selected variables are also offered interpolated to a constant height grid or vertically integrated in height (details below). Zonal mean and global mean output files are included as well.\r\n\r\nThe following data file types are included:\r\n1)Monthly mean output on the full grid for the full set of variables; [DFT] = ''\r\n2)Zonal mean monthly mean output for the full set of variables; [DFT] = _zm\r\n3)Global mean monthly mean output for the full set of variables; [DFT] = _gm\r\n4)Height-interpolated/-integrated output on the full grid for selected variables; [DFT] = _ht\r\n\r\nA cos(latitude) weighting was used when calculating the global means.\r\n\r\nData were interpolated to a set of constant heights (61 levels in total) using the Z3GM variable (for variables output on midpoints, with \"lev\" as the vertical coordinate) or the Z3GMI variable (for variables output on interfaces, with \"ilev\" as the vertical coordinate) stored on the original output files (type 1 above). Interpolation was done separately for each longitude, latitude and time. \r\n\r\nMass density (DEN [g/cm3]) was calculated from the M_dens, N2_vmr, O2, and O variables on the original data files before interpolation to constant height levels. \r\n\r\nThe Joule heating power QJ [W/m3] was calculated using Q_J=_P B^2 [(u_i-u_n )^2+(v_i-v_n )^2+(w_i-w_n )^2] with P = Pedersen conductivity [S], B = geomagnetic field strength [T], ui, vi, and wi = zonal, meridional, and vertical ion velocities [m/s] and un, vn, and wn = neutral wind velocities [m/s]. QJ was integrated vertically in height (using a 2.5 km height grid spacing rather than the 61 levels on output file type 4) to give the JHH variable on the type 4 data files. The QJOULE variable also given is the Joule heating rate [K/s] at each of the 61 height levels.\r\n\r\nAll data are provided as monthly mean files with one time record per file, giving 792 files for each data file type for the period 1950-2015 (66 years)." } }, { "ob_id": 695, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 37842, "uuid": "b0beb88af3b748b98afc4b35f77bebf8", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, for global land areas, version 1.2", "abstract": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (CO2), derived from the TANSAT satellite, using the University of Leicester Full-Physics Retrieval Algorithm (UoL-FP, also known as OCFP). This dataset is also referred to as CO2_TAN_OCFP. This version of the dataset provides data globally over land. For further information on the dataset, please see the linked documentation.\r\n\r\nInitially this dataset contains data from the period from March 2017 to May 2018, delivered as part of the GHG_cci Climate Research Data Package 7. Additional time periods may be delivered in the future.\r\n\r\nThis data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO)." }, "objectObservation": { "ob_id": 32607, "uuid": "9252ff9ddeb249a2bd8433e9ae9dfe13", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, for global land areas, version 1.0", "abstract": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (CO2), derived from the TANSAT satellite, using the University of Leicester Full-Physics Retrieval Algorithm (UoL-FP, also known as OCFP). This dataset is also referred to as CO2_TAN_OCFP. This version of the dataset provides data globally over land. For further information on the dataset, please see the linked documentation.\r\n\r\nInitially this dataset contains two months of data (June and August 2017), delivered as part of the GHG_cci Climate Research Data Package 6. Additional time periods will be added in the future.\r\n\r\n\r\nThis data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO)." } }, { "ob_id": 696, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 37846, "uuid": "070522ac6a5d4973a95c544beef714b4", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm, version 10", "abstract": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (XCO2), using the fast atmospheric trace gas retrieval for OCO2 (FOCAL-OCO2). The FOCAL-OCO2 algorithm which has been setup to retrieve XCO2 by analysing hyper spectral solar backscattered radiance measurements from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. FOCAL includes a radiative transfer model which has been developed to approximate light scattering effects by multiple scattering at an optically thin scattering layer. This reduces the computational costs by several orders of magnitude. FOCAL's radiative transfer model is utilised to simulate the radiance in all three OCO-2 spectral bands allowing the simultaneous retrieval of CO2, H2O, and solar induced chlorophyll fluorescence. The product is limited to cloud-free scenes on the Earth's day side. This dataset is also referred to as CO2_OC2_FOCA.\r\n\r\nThis version of the data (v10) was produced as part of the European Space Agency's (ESA) \r\nClimate Change Initiative (CCI) Greenhouse Gases (GHG) project (GHG-CCI+, http://cci.esa.int/ghg)\r\nand got co-funding from the Univ. Bremen and EU H2020 projects CHE (grant agreement no. 776186) and VERIFY (grant agreement no. 776810).\r\n\r\nWhen citing this data, please also cite the following peer-reviewed publications:\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, V.Rozanov, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 1: Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup, Remote Sensing, 9(11), 1159; doi:10.3390/rs9111159, 2017\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 2: Application to XCO2 Retrievals from OCO-2, Remote Sensing, 9(11), 1102; doi:10.3390/rs9111102, 2017" }, "objectObservation": { "ob_id": 32609, "uuid": "b0de069568a141b0b074ca0f7cee004b", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm, version 09", "abstract": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (XCO2), using the fast atmospheric trace gas retrieval for OCO2 (FOCAL-OCO2). The FOCAL-OCO2 algorithm which has been setup to retrieve XCO2 by analysing hyper spectral solar backscattered radiance measurements from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. FOCAL includes a radiative transfer model which has been developed to approximate light scattering effects by multiple scattering at an optically thin scattering layer. This reduces the computational costs by several orders of magnitude. FOCAL's radiative transfer model is utilised to simulate the radiance in all three OCO-2 spectral bands allowing the simultaneous retrieval of CO2, H2O, and solar induced chlorophyll fluorescence. The product is limited to cloud-free scenes on the Earth's day side. This dataset is also referred to as CO2_OC2_FOCA.\r\n\r\nThis version of the data (v09) was produced as part of the European Space Agency's (ESA) \r\nClimate Change Initiative (CCI) Greenhouse Gases (GHG) project (GHG-CCI+, http://cci.esa.int/ghg)\r\nand got co-funding from the Univ. Bremen and EU H2020 projects CHE (grant agreement no. 776186) and VERIFY (grant agreement no. 776810).\r\n\r\nWhen citing this data, please also cite the following peer-reviewed publications:\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, V.Rozanov, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 1: Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup, Remote Sensing, 9(11), 1159; doi:10.3390/rs9111159, 2017\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 2: Application to XCO2 Retrievals from OCO-2, Remote Sensing, 9(11), 1102; doi:10.3390/rs9111102, 2017" } }, { "ob_id": 697, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 38074, "uuid": "e3a7f3336ff8464f9ae6534a8e8676e5", "short_code": "ob", "title": "MIDAS Open: UK hourly solar radiation data, v202207", "abstract": "The UK hourly solar radiation data contain the amount of solar irradiance received during the hour ending at the specified time. All sites report 'global' radiation amounts. This is also known as 'total sky radiation' as it includes both direct solar irradiance and 'diffuse' irradiance as a result of light scattering. Some sites also provide separate diffuse and direct irradiation amounts, depending on the instrumentation at the site. For these the sun's path is tracked with two pyrometers - one where the path to the sun is blocked by a suitable disc to allow the scattered sunlight to be measured to give the diffuse measurement, while the other has a tube pointing at the sun to measure direct solar irradiance whilst blanking out scattered sun light. \r\n\r\nFor details about the different measurements made and the limited number of sites making them please see the MIDAS Solar Irradiance table linked to in the online resources section of this record.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2021.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, MODLERAD, ESAWRADT and DRADR35 messages. The data spans from 1947 to 2021.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." }, "objectObservation": { "ob_id": 32975, "uuid": "625f5ea4ddac4578a2aacf47bcf39657", "short_code": "ob", "title": "MIDAS Open: UK hourly solar radiation data, v202107", "abstract": "The UK hourly solar radiation data contain the amount of solar irradiance received during the hour ending at the specified time. All sites report 'global' radiation amounts. This is also known as 'total sky radiation' as it includes both direct solar irradiance and 'diffuse' irradiance as a result of light scattering. Some sites also provide separate diffuse and direct irradiation amounts, depending on the instrumentation at the site. For these the sun's path is tracked with two pyrometers - one where the path to the sun is blocked by a suitable disc to allow the scattered sunlight to be measured to give the diffuse measurement, while the other has a tube pointing at the sun to measure direct solar irradiance whilst blanking out scattered sun light.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, MODLERAD, ESAWRADT and DRADR35 messages. The data spans from 1947 to 2020.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." } }, { "ob_id": 698, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 38073, "uuid": "4ecbf3fa1b084c5a9080248433275124", "short_code": "ob", "title": "MIDAS Open: UK soil temperature data, v202207", "abstract": "The UK soil temperature data contain daily and hourly values of soil temperatures at depths of 5, 10, 20, 30, 50, and 100 centimetres. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM or DLY3208 messages. The data spans from 1900 to 2021.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2021.\r\n\r\nAt many stations temperatures below the surface are measured at various depths. The depths used today are 5, 10, 20, 30 and 100cm, although measurements are not necessarily made at all these depths at a station and exceptionally measurements may be made at other depths. When imperial units were in general use, typically before 1961, the normal depths of measurement were 4, 8, 12, 24 and 48 inches.\r\n\r\nLiquid-in-glass soil thermometers at a depth of 20 cm or less are unsheathed and have a bend in the stem between the bulb and the lowest graduation. At greater depths the thermometer is suspended in a steel tube and has its bulb encased in wax.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." }, "objectObservation": { "ob_id": 32969, "uuid": "cabc37d867fa4f2a84302350df908693", "short_code": "ob", "title": "MIDAS Open: UK soil temperature data, v202107", "abstract": "The UK soil temperature data contain daily and hourly values of soil temperatures at depths of 5, 10, 20, 30, 50, and 100 centimetres. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM or DLY3208 messages. The data spans from 1900 to 2020.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nAt many stations temperatures below the surface are measured at various depths. The depths used today are 5, 10, 20, 30 and 100cm, although measurements are not necessarily made at all these depths at a station and exceptionally measurements may be made at other depths. When imperial units were in general use, typically before 1961, the normal depths of measurement were 4, 8, 12, 24 and 48 inches.\r\n\r\nLiquid-in-glass soil thermometers at a depth of 20 cm or less are unsheathed and have a bend in the stem between the bulb and the lowest graduation. At greater depths the thermometer is suspended in a steel tube and has its bulb encased in wax.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." } }, { "ob_id": 699, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 38072, "uuid": "64f5d7be890a4ac08cb2b4e78eb5fcc1", "short_code": "ob", "title": "MIDAS Open: UK hourly rainfall data, v202207", "abstract": "The UK hourly rainfall data contain the rainfall amount (and duration from tilting syphon gauges) during the hour (or hours) ending at the specified time. The data also contains precipitation amounts, however precipitation measured over 24 hours are not stored. Over time a range of rain gauges have been used - see the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSHRLY, DLY3208, SREW and SSER. The data spans from 1915 to 2021.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset." }, "objectObservation": { "ob_id": 32970, "uuid": "f7ae919f96b44a1c9695f40a9cf988dd", "short_code": "ob", "title": "MIDAS Open: UK hourly rainfall data, v202107", "abstract": "The UK hourly rainfall data contain the rainfall amount (and duration from tilting syphon gauges) during the hour (or hours) ending at the specified time. The data also contains precipitation amounts, however precipitation measured over 24 hours are not stored. Over time a range of rain gauges have been used - see the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSHRLY, DLY3208, SREW and SSER. The data spans from 1915 to 2020.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset." } }, { "ob_id": 700, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 38071, "uuid": "15deeb29cdcd4524b07560e5aad45ded", "short_code": "ob", "title": "MIDAS Open: UK daily rainfall data, v202207", "abstract": "The UK daily rainfall data contain rainfall accumulation and precipitation amounts over a 24 hour period. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSDLY, DLY3208 and SSER. The data spans from 1853 to 2021. Over time a range of rain gauges have been used - see section 5.6 and the relevant message type information in the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version (202107) of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2021, and additional historical data for Colmonell (Ayrshire, 1924-1960), Camps Reservoir (Lanarkshire, 1934-1960), and Greenock (Renfrewshire, 1910-1960).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset. Currently this represents approximately 13% of available daily rainfall observations within the full MIDAS collection." }, "objectObservation": { "ob_id": 32971, "uuid": "d6bcf4171c2f4754a7455d00deda0f72", "short_code": "ob", "title": "MIDAS Open: UK daily rainfall data, v202107", "abstract": "The UK daily rainfall data contain rainfall accumulation and precipitation amounts over a 24 hour period. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSDLY, DLY3208 and SSER. The data spans from 1853 to 2020. Over time a range of rain gauges have been used - see section 5.6 and the relevant message type information in the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset. Currently this represents approximately 13% of available daily rainfall observations within the full MIDAS collection." } }, { "ob_id": 701, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 38070, "uuid": "6180fb7ed76a442eb1b8f3f152fd08d7", "short_code": "ob", "title": "MIDAS Open: UK hourly weather observation data, v202207", "abstract": "The UK hourly weather observation data contain meteorological values measured on an hourly time scale. The measurements of the concrete state, wind speed and direction, cloud type and amount, visibility, and temperature were recorded by observation stations operated by the Met Office across the UK and transmitted within SYNOP, DLY3208, AWSHRLY and NCM messages. The sunshine duration measurements were transmitted in the HSUN3445 message. The data spans from 1875 to 2021.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2021, and additional historical data for Sheffield (South Yorkshire, 1882-1935).\r\n\r\nFor details on observing practice see the message type information in the MIDAS User Guide linked from this record and relevant sections for parameter types.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Note, METAR message types are not included in the Open version of this dataset. Those data may be accessed via the full MIDAS hourly weather data." }, "objectObservation": { "ob_id": 32974, "uuid": "3bd7221d4844435dad2fa030f26ab5fd", "short_code": "ob", "title": "MIDAS Open: UK hourly weather observation data, v202107", "abstract": "The UK hourly weather observation data contain meteorological values measured on an hourly time scale. The measurements of the concrete state, wind speed and direction, cloud type and amount, visibility, and temperature were recorded by observation stations operated by the Met Office across the UK and transmitted within SYNOP, DLY3208, AWSHRLY and NCM messages. The sunshine duration measurements were transmitted in the HSUN3445 message. The data spans from 1875 to 2020.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. Of particular note, however, is that as well as including data for 2020, historical data recovery has added further data for Eskdalemuir (1914-1944) and Eastbourne (1887-1910).\r\n\r\nFor details on observing practice see the message type information in the MIDAS User Guide linked from this record and relevant sections for parameter types.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Note, METAR message types are not included in the Open version of this dataset. Those data may be accessed via the full MIDAS hourly weather data." } }, { "ob_id": 702, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 38069, "uuid": "4b44cec2f9a846f39d5007983b7eaaab", "short_code": "ob", "title": "MIDAS Open: UK daily weather observation data, v202207", "abstract": "The UK daily weather observation data contain meteorological values measured on a 24 hour time scale. The measurements of sunshine duration, concrete state, snow depth, fresh snow depth, and days of snow, hail, thunder and gail were attained by observation stations operated by the Met Office across the UK operated and transmitted within DLY3208, NCM, AWSDLY and SYNOP messages. The data span from 1887 to 2021. For details of observations see the relevant sections of the MIDAS User Guide linked from this record for the various message types.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2021, and additional historical data for Sheffield (South Yorkshire, 1898-1935).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily weather observations within the full MIDAS collection." }, "objectObservation": { "ob_id": 32973, "uuid": "d399794d81fa41779a925b6d4758a5cd", "short_code": "ob", "title": "MIDAS Open: UK daily weather observation data, v202107", "abstract": "The UK daily weather observation data contain meteorological values measured on a 24 hour time scale. The measurements of sunshine duration, concrete state, snow depth, fresh snow depth, and days of snow, hail, thunder and gail were attained by observation stations operated by the Met Office across the UK operated and transmitted within DLY3208, NCM, AWSDLY and SYNOP messages. The data span from 1887 to 2020. For details of observations see the relevant sections of the MIDAS User Guide linked from this record for the various message types.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. Of particular note, however, is that as well as including data for 2020, historical data recovery has added further data for Eastbourne (1887-1910).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily weather observations within the full MIDAS collection." } }, { "ob_id": 703, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 38068, "uuid": "fa83484e57854d6fbde16ff945ff6dc0", "short_code": "ob", "title": "MIDAS Open: UK mean wind data, v202207", "abstract": "The UK mean wind data contain the mean wind speed and direction, and the direction, speed and time of the maximum gust, all during 1 or more hours, ending at the stated time and date. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, DLY3208, HWNDAUTO and HWND6910. The data spans from 1949 to 2021.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2021.\r\n\r\nFor further details on observing practice, including measurement accuracies for the message types, see relevant sections of the MIDAS User Guide linked from this record (e.g. section 3.3 details the wind network in the UK, section 5.5 covers wind measurements in general and section 4 details message type information).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." }, "objectObservation": { "ob_id": 32972, "uuid": "4d48efaaeb7f47a7963df75d6d1dbdc5", "short_code": "ob", "title": "MIDAS Open: UK mean wind data, v202107", "abstract": "The UK mean wind data contain the mean wind speed and direction, and the direction, speed and time of the maximum gust, all during 1 or more hours, ending at the stated time and date. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, DLY3208, HWNDAUTO and HWND6910. The data spans from 1949 to 2020.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nFor further details on observing practice, including measurement accuracies for the message types, see relevant sections of the MIDAS User Guide linked from this record (e.g. section 3.3 details the wind network in the UK, section 5.5 covers wind measurements in general and section 4 details message type information).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." } }, { "ob_id": 704, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 38067, "uuid": "8bcf6925cddc4681b96f94d424537b9e", "short_code": "ob", "title": "MIDAS Open: UK daily temperature data, v202207", "abstract": "The UK daily temperature data contain maximum and minimum temperatures (air, grass and concrete slab) measured over a period of up to 24 hours. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM, DLY3208 or AWSDLY messages. The data span from 1853 to 2021. For details on measurement techniques, including calibration information and changes in measurements, see section 5.2 of the MIDAS User Guide linked to from this record. Soil temperature data may be found in the UK soil temperature datasets linked from this record.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2021.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily temperature observations within the full MIDAS collection." }, "objectObservation": { "ob_id": 32968, "uuid": "92e823b277cc4f439803a87f5246db5f", "short_code": "ob", "title": "MIDAS Open: UK daily temperature data, v202107", "abstract": "The UK daily temperature data contain maximum and minimum temperatures (air, grass and concrete slab) measured over a period of up to 24 hours. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM, DLY3208 or AWSDLY messages. The data span from 1853 to 2020. For details on measurement techniques, including calibration information and changes in measurements, see section 5.2 of the MIDAS User Guide linked to from this record. Soil temperature data may be found in the UK soil temperature datasets linked from this record.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. Of particular note, however, is that as well as including data for 2020, historical data recovery has added further data for Eskdalemuir (1915-1948) and Eastbourne (1887-1910).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily temperature observations within the full MIDAS collection." } }, { "ob_id": 705, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38128, "uuid": "1f359da21c4041b4ab0977d05c7d38f0", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.15 (v20220916)", "abstract": "Data for Figure TS.15 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure TS.15 shows contribution to ERF and global surface temperature change from component emissions between 1750 to 2019 based on CMIP6 models, and net aerosol effective radiative forcing (ERF) from different lines of evidence.\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---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three panels with data provided for all panels in the underlying chapter figures (6.12 and 7.5).\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n Figure 6.12:\r\n - Contribution to effective radiative forcing (ERF) (a) and global mean surface air temperature (GSAT) change (b) from component emissions between 1750 to 2019 based on CMIP6 models\r\n\r\n\r\nFigure 7.5:\r\n - Net aerosol effective radiative forcing (ERF), in W m-2, from:\r\n - AR5 assessment\r\n - AR6 assessment comprising the following:\r\n (Energy balance constraints [–2 to 0 W m–2 with no best estimate])\r\n (Observational evidence from satellite retrievals of –1.4 [–2.2 to –0.6] W m–2)\r\n (Combined model-based evidence of –1.25 [–2.1 to –0.4] W m–2)\r\n\r\n\r\nDetails about the dataset in the catalogue records of the underlying chapter figures (6.12 and 7.5)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a and panel b:\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_ERF.csv.\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_ERF_uncertainty.csv.\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_dT.csv.\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_dT_uncertainty.csv.\r\n \r\n Panel c:\r\n - Data file: table7.6.csv: input data for figure 7.5\r\n\r\n CMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n ERFari stands for Effective Radiative Forcing of aerosol-radiation interaction.\r\n ERFaci stands for Effective Radiative Forcing of aerosol-cloud interaction.\r\n IRFari stands for Instantaneous Radiative Forcing of aerosol-radiation interaction.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Panel a and panel b are identical to panel a and panel b of figure 6.12. Panel c is identical to figure 7.5.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to underlying chapter figures from which the figure was generated (Figure 6.12, Figure 7.5)\r\n - Link to code used to produce figure 7.5 on the Chapter 7 GitHub repository." }, "objectObservation": { "ob_id": 39759, "uuid": "7b3d379fc1f040978df4806c6775a0df", "short_code": "ob", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 7.5 (v20230221)", "abstract": "Input Data for Figure 7.5 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 7.5 shows net aerosol effective radiative forcing (ERF) from different lines of evidence. \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 input data provided for this panel.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Net aerosol effective radiative forcing (ERF), in W m-2, from:\r\n - AR5 assessment\r\n - AR6 assessment comprising the following:\r\n (Energy balance constraints [–2 to 0 W m–2 with no best estimate])\r\n (Observational evidence from satellite retrievals of –1.4 [–2.2 to –0.6] W m–2)\r\n (Combined model-based evidence of –1.25 [–2.1 to –0.4] W m–2)\r\n\r\nThe headline AR6 assessment of –1.3 [–2.0 to –0.6] W m–2 is highlighted in purple for 1750–2014 and compared to the AR5 assessment of –0.9 [–1.9 to –0.1] W m–2 for 1750–2011. The evidence comprising the AR6 assessment is shown below this (shown in brackets in the list of data provided). \r\n\r\nEstimates from individual CMIP5 (Zelinka et al., 2014) and CMIP6 (Smith et al., 2020b and Table 7.6) models are depicted by blue and red crosses respectively. \r\nFor each line of evidence the assessed best-estimate contributions from ERFari and ERFaci are shown with darker and paler shading respectively. \r\nThe observational assessment for ERFari is taken from the IRFari. \r\nUncertainty ranges are represented by black bars for the total aerosol ERF and depict very likely ranges. \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 7.5\r\n \r\n - Data file: table7.6.csv\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\nERFari stands for Effective Radiative Forcing of aerosol-radiation interaction.\r\nERFaci stands for Effective Radiative Forcing of aerosol-cloud interaction.\r\nIRFari stands for Instantaneous Radiative Forcing of aerosol-radiation interaction.\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 of the Chapter 7 GitHub repository. \r\nThe notebook to produce this figure uses Table 7.6 from the report chapter and data from Zelinka et al., 2014 written into the code.\r\nTo reproduce the figure from the input data provided here ('table7.6.csv'), you will need to edit the path in box 5 of 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 (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 for plotting the figure from the Chapter 7 GitHub repository which also contains input data files" } }, { "ob_id": 706, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38128, "uuid": "1f359da21c4041b4ab0977d05c7d38f0", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.15 (v20220916)", "abstract": "Data for Figure TS.15 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure TS.15 shows contribution to ERF and global surface temperature change from component emissions between 1750 to 2019 based on CMIP6 models, and net aerosol effective radiative forcing (ERF) from different lines of evidence.\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---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three panels with data provided for all panels in the underlying chapter figures (6.12 and 7.5).\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n Figure 6.12:\r\n - Contribution to effective radiative forcing (ERF) (a) and global mean surface air temperature (GSAT) change (b) from component emissions between 1750 to 2019 based on CMIP6 models\r\n\r\n\r\nFigure 7.5:\r\n - Net aerosol effective radiative forcing (ERF), in W m-2, from:\r\n - AR5 assessment\r\n - AR6 assessment comprising the following:\r\n (Energy balance constraints [–2 to 0 W m–2 with no best estimate])\r\n (Observational evidence from satellite retrievals of –1.4 [–2.2 to –0.6] W m–2)\r\n (Combined model-based evidence of –1.25 [–2.1 to –0.4] W m–2)\r\n\r\n\r\nDetails about the dataset in the catalogue records of the underlying chapter figures (6.12 and 7.5)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a and panel b:\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_ERF.csv.\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_ERF_uncertainty.csv.\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_dT.csv.\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_dT_uncertainty.csv.\r\n \r\n Panel c:\r\n - Data file: table7.6.csv: input data for figure 7.5\r\n\r\n CMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n ERFari stands for Effective Radiative Forcing of aerosol-radiation interaction.\r\n ERFaci stands for Effective Radiative Forcing of aerosol-cloud interaction.\r\n IRFari stands for Instantaneous Radiative Forcing of aerosol-radiation interaction.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Panel a and panel b are identical to panel a and panel b of figure 6.12. Panel c is identical to figure 7.5.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to underlying chapter figures from which the figure was generated (Figure 6.12, Figure 7.5)\r\n - Link to code used to produce figure 7.5 on the Chapter 7 GitHub repository." }, "objectObservation": { "ob_id": 37887, "uuid": "8855e410adf547b4afd039a5b88487f4", "short_code": "ob", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.12 (v20220815)", "abstract": "Data for Figure 6.12 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 6.12 shows contribution to effective radiative forcing (ERF) and global mean surface air temperature (GSAT) change from component emissions between 1750 to 2019 based on CMIP6 models. \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\nSzopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 817–922, doi:10.1017/9781009157896.008.\r\n---------------------------------------------------\r\n 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- Contribution to effective radiative forcing (ERF) (a) and global mean surface air temperature (GSAT) change (b) from component emissions between 1750 to 2019 based on CMIP6 models\r\n\r\nERFs for the direct effect of well-mixed greenhouse gases (WMGHGs) are from the analytical formulae in section 7.3.2, H2O (strat) is from Table 7.8. ERFs for other components are multi-model means from Thornhill et al. (2021b) and are based on ESM simulations in which emissions of one species at a time are increased from 1850 to 2014 levels. The derived emissions-based ERFs are rescaled to match the concentration-based ERFs in Figure 7.6.\r\n\r\nError bars are 5–95% and for the ERF account for uncertainty in radiative efficiencies and multi-model error in the means. ERFs due to aerosol–radiation (ERFari) and cloud effects are calculated from separate radiation calls for clear-sky and aerosol-free conditions (Ghan, 2013; Thornhill et al., 2021b). \r\n\r\n‘Cloud’ includes cloud adjustments (semi-direct effect) and ERF from indirect aerosol-cloud to –0.22 W m–2 for ERFari and –0.84 W m–2 interactions (ERFaci). The aerosol components (SO2, organic carbon and black carbon) are scaled to sum to –0.22 W m–2 for ERFari and –0.84 W m–2 for ‘cloud’ (Section 7.3.3). \r\n\r\nFor GSAT estimates, time series (1750–2019) for the ERFs have been estimated by scaling with concentrations for WMGHGs and with historical emissions for SLCFs. The time variation of ERFaci for aerosols is from Chapter 7. The global mean temperature response is calculated from the ERF time series using an impulse response function (Cross-Chapter Box 7.1) with a climate feedback parameter of –1.31 W m–2 °C–1. \r\n\r\nContributions to ERF and GSAT change from contrails and light-absorbing particles on snow and ice are not represented, but their estimates can be seen on Figure 7.6 and 7.7, respectively. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 6.SM.3)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 6.12:\r\n \r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_ERF.csv\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_ERF_uncertainty.csv\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_dT.csv\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_dT_uncertainty.csv\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nERFari stands for Effective Radiative Forcing of aerosol-radiation interactions.\r\nERFaci stands for Effective Radiative Forcing of aerosol-cloud interactions. \r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nPanels were plotted using Python and the code has been embedded in Jupyter notebooks for reproducibility - code is available in the GitHub repository linked 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 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n- Link to the GitHub repository containing the Jupyter notebooks used to run the code associated with this figure.\r\n- Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 707, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38227, "uuid": "f99ec964a6f345beadb000e295ac2e5b", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.9 v20220922", "abstract": "Data for Figure TS.9 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure TS.9 shows changes in well-mixed greenhouse gas (WMGHG) concentrations and effective radiative forcing (EFR).\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\nPlease also include citations of the related publications for Figure 2.4b provided at the end of this abstract.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for panels b and c. Links to the code which contains the data for other panels are provided in the Related Documents section of this catalogue record.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n - Panel TS.9a => Figure 2.3 c\r\n - Panel TS.9b => Figure 2.4 b\r\n - Panel TS.9c => Figure 2.5 a,b,c\r\n - Panel TS.9d => Figure 2.10\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - Panel TS.9b => Figure 2.4 b\r\n - Panel TS.9c => Figure 2.5 a,b,c\r\n\r\n\r\n---------------------------------------------------\r\nTemporal Range of Paleoclimate Data\r\n---------------------------------------------------\r\nThis dataset covers a paleoclimate timespan from 450 Ma to 2020. \r\nMa refers to millions of years before present.\r\n\r\n\r\n---------------------------------------------------\r\nNotes on reproducing the figure from the provided data\r\n---------------------------------------------------\r\nData for figures 2.3 c and 2.10 are contained within the code to generate the figures which is linked in the Related Documents section of this catalogue record and data for Figure 2.5 and 2.4 panel b are provided. The corresponding catalogue records for Figure 2.5 and 2.4 are linked in the Related Records section below.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to the report component of the underlying chapter figures from which this figure was generated (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n- Links to catalogue records of relevant figures the data is taken from in the Related Records section of this catalogue record\r\n- Link to code which contains data for figure 2.3 and 2.10\r\n\r\n\r\n---------------------------------------------------\r\nRelated publications for figure 2.4 panel b datasets\r\n---------------------------------------------------\r\nPlease include the following citations of related publications from which the figure 2.4 panel b datasets originate. Relations to individual datasets are listed at the top of each dataset. Links are provided in the Related Documents section of the figure 2.4 catalogue record which is linked to this record.\r\n\r\nAhn, J., Brook, E. J., Mitchell, L., Rosen, J. McConnell, J. R., Taylor, K., Etheridge, D., and Rubino, M. (2012b). Atmospheric CO2 over the last 1000 years: A high-resolution record from the West Antarctic Ice Sheet (WAIS) Divide ice core, Global Biogeochemical Cycles, 26, GB2027 , doi:10.1029/2011GB004247.\r\n\r\nBauska, T. K., Joos, F., Mix, A. C., Roth, R., Ahn, J., & Brook, E. J. (2015). Links between atmospheric carbon dioxide, the land carbon reservoir and climate over the past millennium. Nature Geoscience. https://doi.org/10.1038/ngeo2422\r\n\r\nRubino, M., Etheridge, D. M., Thornton, D. P., Howden, R., Allison, C. E., Francey, R. J., Langenfelds, R. L., Steele, L. P., Trudinger, C. M., Spencer, D. A., Curran, M. A. J., van Ommen, T. D., & Smith, A. M. (2019). Revised records of atmospheric trace gases CO2, CH4, N2O, and d13C-CO2 over the last 2000 years from Law Dome, Antarctica. Earth System Science Data, 11(2), 473–492. https://doi.org/10.5194/essd-11-473-2019\r\n\r\nSIEGENTHALER, U. R. S., MONNIN, E., KAWAMURA, K., SPAHNI, R., SCHWANDER, J., STAUFFER, B., STOCKER, T. F., BARNOLA, J.-M., & FISCHER, H. (2005). Supporting evidence from the EPICA Dronning Maud Land ice core for atmospheric CO2 changes during the past millennium. Tellus B, 57(1), 51–57. https://doi.org/10.1111/j.1600-0889.2005.00131.x\r\n\r\nMitchell, L., Brook, E., Lee, J. E., Buizert, C., & Sowers, T. (2013). Constraints on the late Holocene anthropogenic contribution to the atmospheric methane budget. Science. https://doi.org/10.1126/science.1238920\r\n\r\nFlückiger, J., Dällenbach, A., Blunier, T., Stauffer, B., Stocker, T. F., Raynaud, D., & Barnola, J. M. (1999). Variations in atmospheric N2O concentration during abrupt climatic changes. Science. https://doi.org/10.1126/science.285.5425.227\r\n\r\nMachida, T., Nakazawa, T., Fujii, Y., Aoki, S., & Watanabe, O. (1995). Increase in the atmospheric nitrous oxide concentration during the last 250 years. Geophysical Research Letters, 22(21), 2921–2924. https://doi.org/10.1029/95GL02822\r\n\r\nRyu, Y., Ahn, J., Yang, J.-W., Jang, Y., Brook, E., Timmermann, A., Hong, S., Han, Y., Hur, S., & Kim, S. (2020). Atmospheric nitrous oxide during the past two millennia, Global Biogeochemical Cycles, 34, e2020GB006568. https://doi.org/10.1029/2020GB006568\r\n\r\nSowers, T. (2001). N2O record spanning the penultimate deglaciation from the Vostok ice core. Journal of Geophysical Research: Atmospheres, 106(D23), 31903–31914. https://doi.org/10.1029/2000JD900707" }, "objectObservation": { "ob_id": 34599, "uuid": "c6f19b27f8e74e98968c17c8e1f74c60", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.5 (v20221116)", "abstract": "Data for Figure 2.5 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 2.5 shows globally-averaged dry-air mole fractions of CO2, CH4, and N2O derived from surface observations.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\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 three panels (a, b, c), and each panel contains an insert. Data provided for all panels in one single file (Data_Figure_2_5.csv).\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains time series for:\r\n \r\n - Observed atmospheric global carbon dioxide (CO2) (1958-2019).\r\n - Observed atmospheric global methane (CH4) (1979-2019).\r\n - Observed atmospheric global nitrous oxide (N2O) (1979-2019).\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n \r\n - Data file Data_Figure_2_5.csv, column 2, grey line.\r\n - Data file Data_Figure_2_5.csv, column 17, cyan line.\r\n - Data file Data_Figure_2_5.csv, column 29, yellow line.\r\n\r\n\r\nInset panel a:\r\n \r\n - Data file Data_Figure_2_5.csv, column 3, grey line.\r\n - Data file Data_Figure_2_5.csv, column 18, cyan line.\r\n - Data file Data_Figure_2_5.csv, column 30, yellow line.\r\n\r\n\r\nPanel b:\r\n \r\n - Data file Data_Figure_2_5.csv, column 5, grey line.\r\n - Data file Data_Figure_2_5.csv, column 20, cyan line.\r\n - Data file Data_Figure_2_5.csv, column 11, red line.\r\n - Data file Data_Figure_2_5.csv, column 26, green circles.\r\n\r\n\r\nInset panel b:\r\n \r\n - Data file Data_Figure_2_5.csv, column 6, grey line.\r\n - Data file Data_Figure_2_5.csv, column 21, cyan line.\r\n - Data file Data_Figure_2_5.csv, column 12, red line.\r\n - Data file Data_Figure_2_5.csv, column 27, green line.\r\n\r\n\r\nPanel c:\r\n \r\n - Data file Data_Figure_2_5.csv, column 8, grey line.\r\n - Data file Data_Figure_2_5.csv, column 23, cyan line.\r\n - Data file Data_Figure_2_5.csv, column 14, red line.\r\n\r\n\r\nInset panel c:\r\n \r\n - Data file Data_Figure_2_5.csv, column 9, grey line.\r\n - Data file Data_Figure_2_5.csv, column 24, cyan line.\r\n - Data file Data_Figure_2_5.csv, column 15, red line.\r\n\r\n\r\nAcronyms: \r\nNOAA - National Oceanic and Atmospheric Administration\r\nAGAGE - Advanced Global Atmospheric Gases Experiment\r\nCSIRO - Commonwealth Scientific and Industrial Research Organisation\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1" } }, { "ob_id": 708, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38235, "uuid": "29a0282f3b494c54a5e6c59f61e9202b", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.19 v20220923", "abstract": "Data for Figure TS.19 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure TS.19 shows carbon sink response in a scenario with net carbon dioxide (CO2) removal from the atmosphere. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains data for 50-year periods during 2000-2300 for:\r\n \r\n - Atmospheric CO2 concentration\r\n - Net CO2 emissions (accumulated over 50 yer periods)\r\n - Net land flux (accumulated over 50 yer periods)\r\n - Net ocean flux (accumulated over 50 yer periods)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file: Data_Figure_5_33.csv:\r\n \r\n - row 1: x-axis values.\r\n - row 2: light blue bars.\r\n - row 3: orange bars.\r\n - row 4: green bars.\r\n - row 5: blue bars\r\n - row 6: relates with the values written in black over the corresponding arrows (row 2 values plus values written in black)\r\n - row 7: Standard deviation over orange bars.\r\n - row 8: Standard deviation over green bars.\r\n - row 9: Standard deviation over blue bars.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n This figure was created in Excel and the error bars (standard deviation) were added in Adobe Illustrator.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to the report component of the underlying chapter figures from which this figure was generated (Chapter 5)\r\n - Link to the Supplementary Material for Chapter 5, which contains details on the input data used in Table 5.SM.6" }, "objectObservation": { "ob_id": 37608, "uuid": "85409987ce6a4976b0845b512baa2843", "short_code": "ob", "title": "Chapter 5 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 5.33 (v20220623)", "abstract": "Data for Figure 5.33 from Chapter 5 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 5.33 shows carbon sink response in a scenario with net carbon dioxide (CO2) removal from the atmosphere. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nCanadell, J.G., P.M.S. Monteiro, M.H. Costa, L. Cotrim da Cunha, P.M. Cox, A.V. Eliseev, S. Henson, M. Ishii, S. Jaccard, C. Koven, A. Lohila, P.K. Patra, S. Piao, J. Rogelj, S. Syampungani, S. Zaehle, and K. Zickfeld, 2021: Global Carbon and other Biogeochemical Cycles and Feedbacks. 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. 673–816, doi:10.1017/9781009157896.007.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains data for 50-year periods during 2000-2300 for:\r\n \r\n - Atmospheric CO2 concentration\r\n - Net CO2 emissions (accumulated over 50 year periods)\r\n - Net land flux (accumulated over 50 year periods)\r\n - Net ocean flux (accumulated over 50 year periods)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file: Data_Figure_5_33.csv:\r\n \r\n - row 1: x-axis values.\r\n - row 2: light blue bars.\r\n - row 3: orange bars.\r\n - row 4: green bars.\r\n - row 5: blue bars\r\n - row 6: relates with the values written in black over the corresponding arrows (row 2 values plus values written in black)\r\n - row 7: Standard deviation over orange bars.\r\n - row 8: Standard deviation over green bars.\r\n - row 9: Standard deviation over blue bars.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n This figure was created in Excel and the error bars (standard deviation) were added in Adobe \r\n Illustrator.\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 5)\r\n - Link to the Supplementary Material for Chapter 5, which contains details on the input data used in Table 5.SM.6" } }, { "ob_id": 709, "relationType": "IsIdenticalTo", "subjectObservation": { "ob_id": 38235, "uuid": "29a0282f3b494c54a5e6c59f61e9202b", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.19 v20220923", "abstract": "Data for Figure TS.19 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure TS.19 shows carbon sink response in a scenario with net carbon dioxide (CO2) removal from the atmosphere. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains data for 50-year periods during 2000-2300 for:\r\n \r\n - Atmospheric CO2 concentration\r\n - Net CO2 emissions (accumulated over 50 yer periods)\r\n - Net land flux (accumulated over 50 yer periods)\r\n - Net ocean flux (accumulated over 50 yer periods)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file: Data_Figure_5_33.csv:\r\n \r\n - row 1: x-axis values.\r\n - row 2: light blue bars.\r\n - row 3: orange bars.\r\n - row 4: green bars.\r\n - row 5: blue bars\r\n - row 6: relates with the values written in black over the corresponding arrows (row 2 values plus values written in black)\r\n - row 7: Standard deviation over orange bars.\r\n - row 8: Standard deviation over green bars.\r\n - row 9: Standard deviation over blue bars.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n This figure was created in Excel and the error bars (standard deviation) were added in Adobe Illustrator.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to the report component of the underlying chapter figures from which this figure was generated (Chapter 5)\r\n - Link to the Supplementary Material for Chapter 5, which contains details on the input data used in Table 5.SM.6" }, "objectObservation": { "ob_id": 37608, "uuid": "85409987ce6a4976b0845b512baa2843", "short_code": "ob", "title": "Chapter 5 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 5.33 (v20220623)", "abstract": "Data for Figure 5.33 from Chapter 5 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 5.33 shows carbon sink response in a scenario with net carbon dioxide (CO2) removal from the atmosphere. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nCanadell, J.G., P.M.S. Monteiro, M.H. Costa, L. Cotrim da Cunha, P.M. Cox, A.V. Eliseev, S. Henson, M. Ishii, S. Jaccard, C. Koven, A. Lohila, P.K. Patra, S. Piao, J. Rogelj, S. Syampungani, S. Zaehle, and K. Zickfeld, 2021: Global Carbon and other Biogeochemical Cycles and Feedbacks. 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. 673–816, doi:10.1017/9781009157896.007.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains data for 50-year periods during 2000-2300 for:\r\n \r\n - Atmospheric CO2 concentration\r\n - Net CO2 emissions (accumulated over 50 year periods)\r\n - Net land flux (accumulated over 50 year periods)\r\n - Net ocean flux (accumulated over 50 year periods)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file: Data_Figure_5_33.csv:\r\n \r\n - row 1: x-axis values.\r\n - row 2: light blue bars.\r\n - row 3: orange bars.\r\n - row 4: green bars.\r\n - row 5: blue bars\r\n - row 6: relates with the values written in black over the corresponding arrows (row 2 values plus values written in black)\r\n - row 7: Standard deviation over orange bars.\r\n - row 8: Standard deviation over green bars.\r\n - row 9: Standard deviation over blue bars.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n This figure was created in Excel and the error bars (standard deviation) were added in Adobe \r\n Illustrator.\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 5)\r\n - Link to the Supplementary Material for Chapter 5, which contains details on the input data used in Table 5.SM.6" } }, { "ob_id": 710, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38230, "uuid": "d75fd35a7444433c9b5b78ef110495ab", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.22 v20220923", "abstract": "Data for Figure TS.22 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure TS.22 shows a synthesis of the geographical distribution of climatic impact-drivers changes and the number of AR6 WGI reference regions where they are projected to change.\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 two panels with data provided for all panels.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - geographical location of regions belonging to one of five groups characterized by a specific combination of changing climatic impact-drivers (CIDs).\r\n - number of AR6 WG1 regions where Climatic Impact Drivers are projected to change if a global warming level of 2°C is reached compared to a climatological reference period included within 1960-2014\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - 'Figure-F-Panels_IDL.xlsx' - Datafile containing data for both figures in excel sheets\r\n\r\nIndividual panel data in csv format:\r\n\r\n - Panel a: 'Figure-F-Panel_a_IDL.csv' - Description of the clustering used to generate panel a\r\n \r\n - Panel b: 'consolidated_data_figure_SPM.9.csv' - Same data used for Figure SPM.9 (count of regions with increasing or decreasing changes in climatic impact-drivers). First row relates to darker purple bars, second row to lighter purple bars, third row refers to lighter brown bars and fourth row to darker brown bars.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Link to the related record SPM.9 identical to panel b is provided in the Related Records section under Datasets.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to the report component of the underlying figures from which this figure was generated (Figure SPM.9)\r\n - Link to the SPM.9 catalogue record at CEDA" }, "objectObservation": { "ob_id": 34591, "uuid": "e1ff6e07cd624c59a7e7983ce60add44", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.9 (v20220105)", "abstract": "Data for Figure SPM.9 from the Summary for Policymakers (SPM) 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 SPM.9 provides a synthesis of the number of AR6 WGI reference regions where climatic impact-drivers are projected to change.\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\r\nIPCC, 2021: Summary for Policymakers. 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. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n\r\n---------------------------------------------------\r\nTemporal range\r\n---------------------------------------------------\r\n\r\nNumber of land & coastal regions and open-ocean regions where each Climatic Impact-Drivers (CID) is projected to increase or decrease with high confidence or medium confidence. Changes refer to a 20–30 year period centred around 2050 and/or consistent with 2°C global warming compared to a similar period within 1960-2014, except for hydrological drought and agricultural and ecological drought which is compared to 1850-1900. \r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, with data provided for all panels in a single file named consolidated_data_figure_SPM9.csv\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nThis dataset contains the number of AR6 WGI regions where climatic impact-drivers are projected to change if a global warming level of 2°C is reached compared to a climatological reference period included within 1960-2014.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nData file: consolidated_data_figure_SPM.9.csv (count of regions with increasing or decreasing changes in climatic impact-drivers); relates to panel (a) and panel (b) and it's shown by the bars in the figure. The first row of data relates to the darker purple bars, the second row to the lighter purple bars, the third row to the lighter brown bars and the fourth row to the darker brown bars. Row 5 represents the maximum number of regions for which each climatic impact-driver is relevant. It is shown on the figure as the lighter-shaded ‘envelope’.\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\r\n - Link to origin of figure (IPCC WG1 Summary for Policy Makers)\r\n - Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers)\r\n - Link to the Interactive Atlas webpage\r\n - Link to the figure on the IPCC AR6 website" } }, { "ob_id": 711, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 38216, "uuid": "99120ddac5004caa85358f5250e2eece", "short_code": "ob", "title": "CRU CY4.06: Climatic Research Unit year-by-year variation of selected climate variables by country version 4.06 (Jan. 1901 - Dec. 2021)", "abstract": "The Climatic Research Unit (CRU) Country (CY) data version 4.06 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency: including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure, potential evapotranspiration and wet day frequency. This version uses the updated set of country definitions, please see the appropriate Release Notes.\r\n\r\nThis dataset was produced in 2022 by CRU at the University of East Anglia and extends the CRU CY4.06 data to include 2021. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY4.06 is derived directly from the CRU time series (TS) 4.06 dataset. CRU CY version 4.06 spans the period 1901-2021 for 292 countries.\r\n\r\nTo understand the CRU CY4.06 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.06. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.06 release notes listed in the online documentation on this record.\r\n\r\nCRU CY data are available for download to all CEDA users." }, "objectObservation": { "ob_id": 32803, "uuid": "7a5529a8758041eb83b9c32f8461e50d", "short_code": "ob", "title": "CRU CY4.05: Climatic Research Unit year-by-year variation of selected climate variables by country version 4.05 (Jan. 1901 - Dec. 2020)", "abstract": "The Climatic Research Unit (CRU) Country (CY) data version 4.05 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency: including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure, potential evapotranspiration and wet day frequency. This version uses the updated set of country definitions, please see the appropriate Release Notes.\r\n\r\nThis dataset was produced in 2021 by CRU at the University of East Anglia and extends the CRU CY4.04 data to include 2020. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY4.05 is derived directly from the CRU time series (TS) 4.05 dataset. CRU CY version 4.05 spans the period 1901-2020 for 292 countries.\r\n\r\nTo understand the CRU CY4.05 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.05. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.05 release notes listed in the online documentation on this record.\r\n\r\nCRU CY data are available for download to all CEDA users." } }, { "ob_id": 712, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 38103, "uuid": "e0b4e1e56c1c4460b796073a31366980", "short_code": "ob", "title": "CRU TS4.06: Climatic Research Unit (CRU) Time-Series (TS) version 4.06 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2021)", "abstract": "The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.06 data are month-by-month variations in climate over the period 1901-2021, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia and funded by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre.\r\n\r\nThe CRU TS4.06 variables are cloud cover, diurnal temperature range, frost day frequency, wet day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2021.\r\n\r\nThe CRU TS4.06 data were produced using angular-distance weighting (ADW) interpolation. All versions prior to 4.00 used triangulation routines in IDL. Please see the release notes for full details of this version update. \r\n\r\nThe CRU TS4.06 data are monthly gridded fields based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. The NetCDF versions contain an additional integer variable, ’stn’, which provides, for each datum in the main variable, a count (between 0 and 8) of the number of stations used in that interpolation. The missing value code for 'stn' is -999.\r\n\r\nAll CRU TS output files are actual values - NOT anomalies." }, "objectObservation": { "ob_id": 32804, "uuid": "c26a65020a5e4b80b20018f148556681", "short_code": "ob", "title": "CRU TS4.05: Climatic Research Unit (CRU) Time-Series (TS) version 4.05 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2020)", "abstract": "The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.05 data are month-by-month variations in climate over the period 1901-2020, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia and funded by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre.\r\n\r\nThe CRU TS4.05 variables are cloud cover, diurnal temperature range, frost day frequency, wet day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2020.\r\n\r\nThe CRU TS4.05 data were produced using angular-distance weighting (ADW) interpolation. All versions prior to 4.00 used triangulation routines in IDL. Please see the release notes for full details of this version update. \r\n\r\nThe CRU TS4.05 data are monthly gridded fields based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. The NetCDF versions contain an additional integer variable, ’stn’, which provides, for each datum in the main variable, a count (between 0 and 8) of the number of stations used in that interpolation. The missing value code for 'stn' is -999.\r\n\r\nAll CRU TS output files are actual values - NOT anomalies." } }, { "ob_id": 713, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 38218, "uuid": "38715b12b22043118a208acd61771917", "short_code": "ob", "title": "CRU JRA v2.3: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901 - Dec.2021.", "abstract": "The CRU JRA V2.3 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 deg latitude x 0.5 deg longitude grid, the grid is near global but excludes Antarctica (this is 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 2021.\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 4.06 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\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" }, "objectObservation": { "ob_id": 32817, "uuid": "4bdf41fc10af4caaa489b14745c665a6", "short_code": "ob", "title": "CRU JRA v2.2: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901 - Dec.2020.", "abstract": "The CRU JRA V2.2 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 deg latitude x 0.5 deg longitude grid, the grid is near global but excludes Antarctica (this is 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 2020.\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 4.05 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\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" } }, { "ob_id": 714, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 33299, "uuid": "e6af67fca81c40b7bb3eddaadde06909", "short_code": "ob", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci) : Global remote sensing daily merged multi-mission along-track significant wave height from altimetry, L3 product, version 3", "abstract": "The ESA Sea State Climate Change Initiative (CCI) project has produced global daily merged multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 3 (L3) data) with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 3 Remote Sensing Significant Wave Height product, which provides along-track data at approximately 6 km spatial resolution. It has been generated from upstream Sea State CCI L2P products, edited and merged into daily products, retaining only valid and good quality measurements from all altimeters over one day, with simplified content (only a few key parameters). This is close to what is delivered in Near-Real Time by the CMEMS (Copernicus - Marine Environment Monitoring Service) project. It covers the date range from 2002-2021.\r\n\r\nThe altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions (Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A), therefore spanning over a shorter time range than version 1.1. Unlike version 1.1, this version 3 involved a complete and consistent retracking of all the included altimeters. Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band)." }, "objectObservation": { "ob_id": 30006, "uuid": "3ef6a5a66e9947d39b356251909dc12b", "short_code": "ob", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci) : Global remote sensing daily merged multi-mission along-track significant wave height, L3 product, version 1.1", "abstract": "The ESA Sea State Climate Change Initiative (CCI) project has produced global daily merged multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 3 (L3) data) with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 1.1 edited Remote Sensing Significant Wave Height product, along-track at approximately 6 km spatial resolution, which have been generated from upstream Sea State CCI L2P products, edited and merged into daily products, retaining only valid and good quality measurements from all altimeters over one day, with simplified content (only a few key parameters). This is close to what is delivered in Near-Real Time by the CMEMS (Copernicus - Marine Environment Monitoring Service) project. \r\n\r\nThis first version of Sea State CCI products is inherited from the GlobWave project building on experience and existing outputs. It extends and improves the GlobWave products which were a post-processing over existing L2 altimeter agency products with additional filtering, corrections and variables. A major improvement consists in a new denoised sea surface height variable using Empirical Mode Decomposition.\r\n\r\nThe altimeter data used in the Sea State CCI dataset v1.1 come from multiple satellite missions spanning from 1991 to 2018 (ERS-1, ERS-2, Topex, Envisat, GFO, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL). Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band)." } }, { "ob_id": 715, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 33300, "uuid": "8cb46a5efaa74032bf1833438f499cc3", "short_code": "ob", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height from altimetry, L2P product, version 3", "abstract": "The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 2P (L2P) data) with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 3 Remote Sensing Significant Wave Height product, which provides along-track data at approximately 6 km spatial resolution, separated per satellite and pass, including all measurements with flags, corrections and extra parameters from other sources. These are expert products with rich content and no data loss. \r\n\r\nThe altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions spanning from 2002 to 2022021 (Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A), therefore spanning over a shorter time range than version 1.1. Unlike version 1.1, this version 3 involved a complete and consistent retracking of all the included altimeters. Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band)." }, "objectObservation": { "ob_id": 30003, "uuid": "f91cd3ee7b6243d5b7d41b9beaf397e1", "short_code": "ob", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height, L2P product, version 1.1", "abstract": "The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 2P (L2P) data) with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 1.1 Remote Sensing Significant Wave Height product, along-track at approximately 6 km spatial resolution, separated per satellite and pass, including all measurements with flags, corrections and extra parameters from other sources. These are expert products with rich content and no data loss. \r\n\r\nThis first version of the Sea State CCI products is inherited from the GlobWave project building on experience and existing outputs. It extends and improves the GlobWave products which were a post-processing over existing L2 altimeter agency products with additional filtering, corrections and variables. A major improvement consists in a new denoised sea surface height variable using Empirical Mode Decomposition.\r\n\r\nThe altimeter data used in the Sea State CCI dataset v1.1 come from multiple satellite missions spanning from 1991 to 2018 (ERS-1, ERS-2, Topex, Envisat, GFO, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL). Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in the Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band)." } }, { "ob_id": 716, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 33298, "uuid": "9c350d4ff7ee438f9f1fc7252cbb2282", "short_code": "ob", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci) : Global remote sensing merged multi-mission monthly gridded significant wave height from altimetry, L4 product, version 3", "abstract": "The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 4 (L4) data) with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 3 Remote Sensing Significant Wave Height product, gridded over a global regular cylindrical projection (1°x1° resolution), averaging valid and good measurements from all available altimeters on a monthly basis (using the L2P products also available). These L4 products are meant for statistics and visualization.\r\n\r\nThe altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions spanning from 2002 to 2021 ( Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A), therefore spanning over a shorter time range than version 1.1. Unlike version 1.1, this version 3 involved a complete and consistent retracking of all the included altimeters. Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band)." }, "objectObservation": { "ob_id": 30013, "uuid": "47140d618dcc40309e1edbca7e773478", "short_code": "ob", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci) : Global remote sensing merged multi-mission monthly gridded significant wave height, L4 product, version 1.1", "abstract": "The ESA Sea State Climate Change Initiative (CCI) project has produced global merged multi-sensor time-series of monthly gridded satellite altimeter significant wave height (referred to as Level 4 (L4) data) with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 1.1 Remote Sensing Sea Surface Height product, gridded over a global regular cylindrical projection (1°x1° resolution), averaging valid and good measurements from all available altimeters on a monthly basis (using the L2P products also available). These L4 products are meant for statistics and visualization.\r\n\r\nThis first version of the Sea State CCI products is inherited from the GlobWave project, building on experience and existing outputs. It extends and improves the GlobWave products ,which were a post-processing over existing L2 altimeter agency products with additional filtering, corrections and variables. A major improvement consists in a new denoised sea surface height variable using Empirical Mode Decomposition, which was used as input to these monthly statistical fields.\r\n\r\nThe altimeter data used in the Sea State CCI dataset v1.1 come from multiple satellite missions spanning from 1991 to 2018 (ERS-1, ERS-2, Topex, Envisat, GFO, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL). Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band)." } }, { "ob_id": 717, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38866, "uuid": "3d16a09c21c9440288608276b615c11f", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.1 v20221110", "abstract": "Data for Figure TS.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\nFigure TS.1 shows changes in atmospheric CO2 and global surface temperature (relative to 1850-1900) from the deep past to the next 300 years.\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 multiple subplots. Metadata provided for all the plots in the figure, and data is provided for the maps of surface temperature (projections and 2020) and for the atmospheric CO2 concentration corresponding to the paleo 60 - 1 million years time series, and paleo and direct measurements from 800 thousand years to 1980.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Atmospherics CO2 concentration (ppm) corresponding to the paleo 60–1 million years\r\n - Atmospherics CO2 concentration (ppm), paleo and direct measurements from 800 thousand years to 1980\r\n - Global surface temperature for 2020 (estimate of the total observed warming since 1850–1900).\r\n - Global surface temperature at 2100 and 2300 from CMIP6 models (relative to 1850-1900) for SSP1-2.6 and SSP5-8.5 scenarios.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - Data file: CO2_60_Myr.csv (top row, atmospheric CO2 concentration corresponding to the paleo 60–10 million years time series)\r\n - Data file: fig2_4a_main_figure_data.csv (top row, paleo and direct measurements from 800 thousand years to 1980)\r\n - Data file: TS_BK_2020.nc (Global surface temperature map for 2020, estimate of the total observed warming since 1850–1900).\r\n - Data file: ensmean_tas_ssp126_2100-historical_1850_regrid.nc (Global surface temperature map at 2100 relative to 1850-1900 for SSP1-2.6 scenario)\r\n - Data file: ensmean_tas_ssp126_2300-historical_1850_regrid.nc (Global surface temperature map at 2300 relative to 1850-1900 for SSP1-2.6 scenario)\r\n - Data file: ensmean_tas_ssp585_2100-historical_1850_regrid.nc (Global surface temperature map at 2100 relative to 1850-1900 for SSP5-8.5 scenario)\r\n - Data file: ensmean_tas_ssp585_2300-historical_1850_regrid.nc (Global surface temperature map at 2300 relative to 1850-1900 for SSP5-8.5 scenario)\r\n\r\nCSV files were converted for archival from Excel workbooks.\r\n\r\nSSP stands for Shared Socioeconomic Pathway.\r\nppm stands for parts per million.\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\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\nSSP5-8.5 is based on Shares Socioeconomic Pathway SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\n\r\n---------------------------------------------------\r\nTemporal Range of Paleoclimate Data\r\n---------------------------------------------------\r\nThis dataset covers a paleoclimate timespan from 60 Myr to 2300.\r\nMyr refers to millions of years before present.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nNotes on reproducing this figure are linked in a computation record found in the Process section of this catalogue record.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Links to the report components of the underlying chapter figures from which part of this figure was generated (Chapter 2 and Chapter 7)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in the figure\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in the figure\r\n - Link to the data for 2300 emissions scenarios described in section 4.7, archived on Zenodo.\r\n - Link to the data for 2300 projections from Figure 4.40a (section 4.7.1), archived on Zenodo." }, "objectObservation": { "ob_id": 37280, "uuid": "f3515388768344bfb2be0521f82388be", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.11 (v20220510)", "abstract": "Data for Figure 2.11 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 2.11 includes mapped and time-series data showing global surface temperature relative to 1850 - 1900 over multiple time scales.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n---------------------------------------------------\r\n Figure has three panels, with data provided for panel (a) (center and right part), and panel (c).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n---------------------------------------------------\r\n Global surface temperature, relative to 1850 - 1900 for:\r\n\r\n Panel a: \r\n \r\n - 1000 to 1900 CE - from PAGES 2k Consortium (modified from the version 2019: 10.1038/s41561-019-0400-0)\r\n - 1850 to 2020 from AR6 assessed mean (same as Figure 2.11c).\r\n\r\n Panel c: \r\n \r\n - Annual and decadal means from instrumental data for 1850–2020, along with the uncertainty range from HadCRUT5.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n---------------------------------------------------\r\n Panel a:\r\n \r\n - Data file: Figure_2_11a-PAGES_2k_Consortium.csv (yearly data, 1000 to 1900); relates to the center part of the figure showing global surface temperature relative to 1850 -1900. (bold solid green line, column 2, median 10-yr smooth adjusted (+0.37°C), thin solid green lines: 5th (column 3) and 95th (column 4) percentiles of the ensemble members).\r\n - Data file: Figure2_11_panel_a.csv (yearly data, 1850 to 2020); relates to the right part of the figure showing global temperature anomaly AR6 assessed mean. (bold solid violet line, column 2)\r\n\r\nPanel c: \r\n \r\n - Data file: Figure_2_11c-land_and_ocean_time_series.csv (yearly data, 1850 to 2020); relates to the upper part of the figure showing global surface temperature relative to 1850 -1900. (Land, column 2, red line; Ocean, column 3, blue line).\r\n\r\n---------------------------------------------------\r\nNotes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nInput data and code to reproduce panel b and panel c (lower part) plots are provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to input data figure 2.11.\r\n - Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 718, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38227, "uuid": "f99ec964a6f345beadb000e295ac2e5b", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.9 v20220922", "abstract": "Data for Figure TS.9 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure TS.9 shows changes in well-mixed greenhouse gas (WMGHG) concentrations and effective radiative forcing (EFR).\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\nPlease also include citations of the related publications for Figure 2.4b provided at the end of this abstract.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for panels b and c. Links to the code which contains the data for other panels are provided in the Related Documents section of this catalogue record.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n - Panel TS.9a => Figure 2.3 c\r\n - Panel TS.9b => Figure 2.4 b\r\n - Panel TS.9c => Figure 2.5 a,b,c\r\n - Panel TS.9d => Figure 2.10\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - Panel TS.9b => Figure 2.4 b\r\n - Panel TS.9c => Figure 2.5 a,b,c\r\n\r\n\r\n---------------------------------------------------\r\nTemporal Range of Paleoclimate Data\r\n---------------------------------------------------\r\nThis dataset covers a paleoclimate timespan from 450 Ma to 2020. \r\nMa refers to millions of years before present.\r\n\r\n\r\n---------------------------------------------------\r\nNotes on reproducing the figure from the provided data\r\n---------------------------------------------------\r\nData for figures 2.3 c and 2.10 are contained within the code to generate the figures which is linked in the Related Documents section of this catalogue record and data for Figure 2.5 and 2.4 panel b are provided. The corresponding catalogue records for Figure 2.5 and 2.4 are linked in the Related Records section below.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to the report component of the underlying chapter figures from which this figure was generated (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n- Links to catalogue records of relevant figures the data is taken from in the Related Records section of this catalogue record\r\n- Link to code which contains data for figure 2.3 and 2.10\r\n\r\n\r\n---------------------------------------------------\r\nRelated publications for figure 2.4 panel b datasets\r\n---------------------------------------------------\r\nPlease include the following citations of related publications from which the figure 2.4 panel b datasets originate. Relations to individual datasets are listed at the top of each dataset. Links are provided in the Related Documents section of the figure 2.4 catalogue record which is linked to this record.\r\n\r\nAhn, J., Brook, E. J., Mitchell, L., Rosen, J. McConnell, J. R., Taylor, K., Etheridge, D., and Rubino, M. (2012b). Atmospheric CO2 over the last 1000 years: A high-resolution record from the West Antarctic Ice Sheet (WAIS) Divide ice core, Global Biogeochemical Cycles, 26, GB2027 , doi:10.1029/2011GB004247.\r\n\r\nBauska, T. K., Joos, F., Mix, A. C., Roth, R., Ahn, J., & Brook, E. J. (2015). Links between atmospheric carbon dioxide, the land carbon reservoir and climate over the past millennium. Nature Geoscience. https://doi.org/10.1038/ngeo2422\r\n\r\nRubino, M., Etheridge, D. M., Thornton, D. P., Howden, R., Allison, C. E., Francey, R. J., Langenfelds, R. L., Steele, L. P., Trudinger, C. M., Spencer, D. A., Curran, M. A. J., van Ommen, T. D., & Smith, A. M. (2019). Revised records of atmospheric trace gases CO2, CH4, N2O, and d13C-CO2 over the last 2000 years from Law Dome, Antarctica. Earth System Science Data, 11(2), 473–492. https://doi.org/10.5194/essd-11-473-2019\r\n\r\nSIEGENTHALER, U. R. S., MONNIN, E., KAWAMURA, K., SPAHNI, R., SCHWANDER, J., STAUFFER, B., STOCKER, T. F., BARNOLA, J.-M., & FISCHER, H. (2005). Supporting evidence from the EPICA Dronning Maud Land ice core for atmospheric CO2 changes during the past millennium. Tellus B, 57(1), 51–57. https://doi.org/10.1111/j.1600-0889.2005.00131.x\r\n\r\nMitchell, L., Brook, E., Lee, J. E., Buizert, C., & Sowers, T. (2013). Constraints on the late Holocene anthropogenic contribution to the atmospheric methane budget. Science. https://doi.org/10.1126/science.1238920\r\n\r\nFlückiger, J., Dällenbach, A., Blunier, T., Stauffer, B., Stocker, T. F., Raynaud, D., & Barnola, J. M. (1999). Variations in atmospheric N2O concentration during abrupt climatic changes. Science. https://doi.org/10.1126/science.285.5425.227\r\n\r\nMachida, T., Nakazawa, T., Fujii, Y., Aoki, S., & Watanabe, O. (1995). Increase in the atmospheric nitrous oxide concentration during the last 250 years. Geophysical Research Letters, 22(21), 2921–2924. https://doi.org/10.1029/95GL02822\r\n\r\nRyu, Y., Ahn, J., Yang, J.-W., Jang, Y., Brook, E., Timmermann, A., Hong, S., Han, Y., Hur, S., & Kim, S. (2020). Atmospheric nitrous oxide during the past two millennia, Global Biogeochemical Cycles, 34, e2020GB006568. https://doi.org/10.1029/2020GB006568\r\n\r\nSowers, T. (2001). N2O record spanning the penultimate deglaciation from the Vostok ice core. Journal of Geophysical Research: Atmospheres, 106(D23), 31903–31914. https://doi.org/10.1029/2000JD900707" }, "objectObservation": { "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" } }, { "ob_id": 719, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38866, "uuid": "3d16a09c21c9440288608276b615c11f", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.1 v20221110", "abstract": "Data for Figure TS.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\nFigure TS.1 shows changes in atmospheric CO2 and global surface temperature (relative to 1850-1900) from the deep past to the next 300 years.\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 multiple subplots. Metadata provided for all the plots in the figure, and data is provided for the maps of surface temperature (projections and 2020) and for the atmospheric CO2 concentration corresponding to the paleo 60 - 1 million years time series, and paleo and direct measurements from 800 thousand years to 1980.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Atmospherics CO2 concentration (ppm) corresponding to the paleo 60–1 million years\r\n - Atmospherics CO2 concentration (ppm), paleo and direct measurements from 800 thousand years to 1980\r\n - Global surface temperature for 2020 (estimate of the total observed warming since 1850–1900).\r\n - Global surface temperature at 2100 and 2300 from CMIP6 models (relative to 1850-1900) for SSP1-2.6 and SSP5-8.5 scenarios.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - Data file: CO2_60_Myr.csv (top row, atmospheric CO2 concentration corresponding to the paleo 60–10 million years time series)\r\n - Data file: fig2_4a_main_figure_data.csv (top row, paleo and direct measurements from 800 thousand years to 1980)\r\n - Data file: TS_BK_2020.nc (Global surface temperature map for 2020, estimate of the total observed warming since 1850–1900).\r\n - Data file: ensmean_tas_ssp126_2100-historical_1850_regrid.nc (Global surface temperature map at 2100 relative to 1850-1900 for SSP1-2.6 scenario)\r\n - Data file: ensmean_tas_ssp126_2300-historical_1850_regrid.nc (Global surface temperature map at 2300 relative to 1850-1900 for SSP1-2.6 scenario)\r\n - Data file: ensmean_tas_ssp585_2100-historical_1850_regrid.nc (Global surface temperature map at 2100 relative to 1850-1900 for SSP5-8.5 scenario)\r\n - Data file: ensmean_tas_ssp585_2300-historical_1850_regrid.nc (Global surface temperature map at 2300 relative to 1850-1900 for SSP5-8.5 scenario)\r\n\r\nCSV files were converted for archival from Excel workbooks.\r\n\r\nSSP stands for Shared Socioeconomic Pathway.\r\nppm stands for parts per million.\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\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\nSSP5-8.5 is based on Shares Socioeconomic Pathway SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\n\r\n---------------------------------------------------\r\nTemporal Range of Paleoclimate Data\r\n---------------------------------------------------\r\nThis dataset covers a paleoclimate timespan from 60 Myr to 2300.\r\nMyr refers to millions of years before present.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nNotes on reproducing this figure are linked in a computation record found in the Process section of this catalogue record.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Links to the report components of the underlying chapter figures from which part of this figure was generated (Chapter 2 and Chapter 7)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in the figure\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in the figure\r\n - Link to the data for 2300 emissions scenarios described in section 4.7, archived on Zenodo.\r\n - Link to the data for 2300 projections from Figure 4.40a (section 4.7.1), archived on Zenodo." }, "objectObservation": { "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" } }, { "ob_id": 720, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 38883, "uuid": "b9bbc5ea2d3f4e44ae06d19a010cea9c", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Box TS.2, Figure 2 (v20220817)", "abstract": "Data for Box TS.2, Figure 2 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.2 figure 2, shows global surface temperature as estimated from proxy records and climate models.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\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 two panels, with data provided for all panels in the underlying chapter figures. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Proxy-based and model-simulated estimates of global surface temperature agree across multiple reference periods.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - Data file: BoxTS_2_Fig_2a.csv: The 'reconstructed temperature' is the same as the global surface temperature in BoxTS.2 Fig. 1. The 'simulated temperature' is the average values for model outputs shown in Fig 3.44a\r\n\r\nPanel b:\r\n - The data for BoxTS.2 Fig 2b uses the same those in Fig 3.2c, except the TS shows 10-year smooth whereas Fig 3.2c shows 20-year smooth, and the TS shows the averages (with 5-95% ranges) of the 32 simulations that are shown separately in Fig. 3.2c.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n List of underlying chapter figures from which the figure was generated: Figure 2.34, Figure 3.2c, Figure 3.44a \r\n \r\n Paleoclimate reference periods are listed and described in Cross-Chapter Box 2.1\r\n \r\n Observed temperature (1850-2020) from Table 2.SM.1\r\n\r\n---------------------------------------------------\r\nTemporal Range of Paleoclimate Data\r\n---------------------------------------------------\r\nThis dataset covers a paleoclimate timespan from the Cenozoic, including multiple paleoclimate reference periods, to the past millennium. \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 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to report component containing underlying chapter figures from which the figure was generated (Figure 3.2c, Figure 3.44a)\r\n\r\n Catalogue records of related figures from Chapter 3 are linked in the Related Records section of this catalogue record." }, "objectObservation": { "ob_id": 37385, "uuid": "4394898334094551bfb29fb37d2f054c", "short_code": "ob", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.2 (v20220610)", "abstract": "Data for Figure 3.2 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.2 shows changes in surface temperature for different paleoclimates.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three subpanels, the data provided for all panels in subdirectories named panel_a, panel_b, panel_c\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n For panel (a):\r\n - PMIP3 global temperature anomalies over continents and oceans reconstruction sites\r\n - PMIP4 CMIP6 global temperature anomalies over continents and oceans reconstruction sites\r\n - PMIP4 non-CMIP6 global temperature anomalies over continents and oceans reconstruction sites\r\n - Tierney 2020 reconstructions of marine temperature\r\n - Cleator 2020 reconstructions of continental temperature\r\n \r\n For panel (b):\r\n - CMIP5 temperature data for paleoclimate periods\r\n - CMIP6 temperature data for paleoclimate periods\r\n - non-CMIP temperature data for paleoclimate periods\r\n - Instrumental observational and observations from reconstructions\r\n \r\n For panel (c):\r\n - Volcanic forcing from TS17, CU12, GRA08\r\n - CMIP6 GMST anomaly with respect to 1850-1900 modelled with TS17 volcanic forcing\r\n - CMIP5 GMST anomaly with respect to 1850-1900 modelled with CU12 volcanic forcing\r\n - CMIP5 GMST anomaly with respect to 1850-1900 modelled with GRA08 volcanic forcing\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/temperature_anomalies_scatter_points.csv relates to the scatter points and their standard deviation for panel (a)\r\n - For panel (b) the datasets are stored as following panel_b/temperature_{color}_{marker}_{period}_{model_group}_{additional_info}.csv and relates to the scatter points for panel (b).\r\n - For panel (c) the data is stored in panel_c/gmst_changes_paleo_volcanic_forcings.csv and relates to red, green, blue and black lines on the panel as well as grey shadings.\r\n Additional information about data provided in relation to figure in files headers.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nPMIP4 is the Paleoclimate Modelling Intercomparison Project phase 4\r\nPMIP3 is the Paleoclimate Modelling Intercomparison Project phase 3\r\n\r\n ---------------------------------------------------\r\n Temporal Range of Paleoclimate Data\r\n ---------------------------------------------------\r\n This dataset covers a paleoclimate timespan from 56 Ma (56 million years ago) to 2010.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data.\r\n ---------------------------------------------------\r\n For panel (a) the error bar should be plotted as anomalies from columns 2/4 +/- standard deviation. \r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website." } }, { "ob_id": 721, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38883, "uuid": "b9bbc5ea2d3f4e44ae06d19a010cea9c", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Box TS.2, Figure 2 (v20220817)", "abstract": "Data for Box TS.2, Figure 2 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.2 figure 2, shows global surface temperature as estimated from proxy records and climate models.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\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 two panels, with data provided for all panels in the underlying chapter figures. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Proxy-based and model-simulated estimates of global surface temperature agree across multiple reference periods.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - Data file: BoxTS_2_Fig_2a.csv: The 'reconstructed temperature' is the same as the global surface temperature in BoxTS.2 Fig. 1. The 'simulated temperature' is the average values for model outputs shown in Fig 3.44a\r\n\r\nPanel b:\r\n - The data for BoxTS.2 Fig 2b uses the same those in Fig 3.2c, except the TS shows 10-year smooth whereas Fig 3.2c shows 20-year smooth, and the TS shows the averages (with 5-95% ranges) of the 32 simulations that are shown separately in Fig. 3.2c.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n List of underlying chapter figures from which the figure was generated: Figure 2.34, Figure 3.2c, Figure 3.44a \r\n \r\n Paleoclimate reference periods are listed and described in Cross-Chapter Box 2.1\r\n \r\n Observed temperature (1850-2020) from Table 2.SM.1\r\n\r\n---------------------------------------------------\r\nTemporal Range of Paleoclimate Data\r\n---------------------------------------------------\r\nThis dataset covers a paleoclimate timespan from the Cenozoic, including multiple paleoclimate reference periods, to the past millennium. \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 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to report component containing underlying chapter figures from which the figure was generated (Figure 3.2c, Figure 3.44a)\r\n\r\n Catalogue records of related figures from Chapter 3 are linked in the Related Records section of this catalogue record." }, "objectObservation": { "ob_id": 37564, "uuid": "d35ac1955c264deea9699d08dbc568f2", "short_code": "ob", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.44 (v20220615)", "abstract": "Data for Figure 3.44 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.44 shows multivariate synopsis of paleoclimate model results compared to observational references. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n Figure has three rows (a), (b) and (c). The data is on the DMS in the panel_a, panel_b, panel_c subdirectories. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n - GSAT anomalies in MidHolocene from CMIP6, PMIP3, non-CMIP6 PMIP4 models as well as Bertlein et al. (2011) reconstructions\r\n - GSAT anomalies in LIG, LGM and EECO from CMIP6, PMIP3, non-CMIP6 PMIP4 models as well as Tierney et al. (2020) reconstructions\r\n - Regional Mean Temperature of the Warmest month, Mean Annual Precipitation and Mean Temperature of the Coldest month from CMIP6, PMIP3, non-CMIP6 PMIP4 models as well as Bertlein et al. (2011) reconstructions\r\n - Regional Mean Temperature of the Warmest month, Mean Annual Precipitation and Mean Temperature of the Coldest month from CMIP6, PMIP3, non-CMIP6 PMIP4 models as well as Tierney et al. (2020) reconstructions\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n panel_a/gmst_anomalies_paleo_climate.csv has data for all the markers in all subpanels in panel a\r\n panel_a/gmst_anomalies_paleo_climate_reconstructions.csv: relates to the pale orange (navajowhite) shading in panel (a), column 2 contains the bottom values, column 3 are the top values.\r\n panel_b/temperature_and_precipitation_paleo_midHolocene.csv has data for all the markers in all subpanels in panel b\r\n panel_c/temperature_and_precipitation_paleo_lastGlacialMaximum.csv has data for all markers in all subpanels in panel c\r\n\r\nGSAT stands for Global Surface Air Temperature.\r\nCMIP6 is the sixth stage of the Coupled Model Intercomparison Project. \r\nPMIP3 is the Paleoclimate Modelling Intercomparison Project phase 3.\r\nPMIP4 is the Paleoclimate Modelling Intercomparison Project phase 4.\r\nLIG stands for Last Interglacial.\r\nLGM stands for the Last Glacial Maximum.\r\nEECO stands for Early Eocene Climatic Optimum.\r\n\r\n ---------------------------------------------------\r\n Temporal Range of Paleoclimate Data\r\n ---------------------------------------------------\r\n This dataset also covers a paleoclimate timespan from 55000000-5000 years BP (55000000-5000 years before present). \r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The last column in each file is the color and/or shape of the marker.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the figure on the IPCC AR6 website" } }, { "ob_id": 722, "relationType": "IsDerivedFrom", "subjectObservation": { "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" }, "objectObservation": { "ob_id": 37892, "uuid": "568fb4b2e6464a50a30c7140bb88a497", "short_code": "ob", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Box 7.2, Figure 1. (v20220817)", "abstract": "Input Data for Box 7.2, Figure 1 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nBox 7.2, Figure 1 shows estimates of the net cumulative energy change (ZJ = 1021 Joules) for the period 1971–2018 associated with: (a) observations of changes in the Global Energy Inventory (b) Integrated Radiative Forcing; (c) 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\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 6 subpanels, with input data provided for panels a-f.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This 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\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Box 7.2, Figure 1:\r\n \r\n - Data file: AR6_ERF_1750-2019.csv\r\n - Data file: AR6_energy_GMSL_timeseries_FGD_1971to2018_IMBIEupdate.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\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. Also listed on the 'master' GitHub page linked in the documentation of this catalogue record are external GitHub repositories and locations within the contributed directory where code for figures have been supplied by other authors. These are provided \"as-is\" and are not guaranteed to be reproducible within this environment. For external GitHub locations, check out the relevant repository READMEs.\r\n\r\nThe 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 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 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" } }, { "ob_id": 723, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 39195, "uuid": "94a315924ed54b08a79e331579fd8c2e", "short_code": "ob", "title": "Surface velocity map of the Afar Rift Zone from 2014-19, geotiff version", "abstract": "This dataset contains a map of ground movements covering the Afar Rift Zone in Ethiopia, Eritrea, and Djibouti for the time period between October 2014 and August 2019. The Afar region is located where three tectonic plates are pulling apart, creating rift segments which are 50-100 km long. Surface deformation on these segments is not constant in time, with episodes of rifting occurring periodically and magma intrusions causing sudden ground movements. We use frequent Sentinel-1 satellite Interferometric Synthetic Aperture Radar (InSAR) observations to measure surface displacements through time across the whole region. We relate these to ground based Global Navigation Satellite Systems (GNSS) observations and combine data from different satellite tracks to produce maps of the average surface velocity in three directions (perpendicular to the rift zone, parallel to the rift zone, and vertical). The continued observation of these time-varying ground movements is important for understanding how continents break up, with data here providing evidence of how tightly focussed extension is around the rift segments and of the subsurface magma movement at several volcanic centres.\r\nThese data have been provided in geotiff format instead of the original netcdf format." }, "objectObservation": { "ob_id": 33114, "uuid": "ac43cee2bf5e4942970492209ba95e49", "short_code": "ob", "title": "Surface velocity map of the Afar Rift Zone from 2014-19", "abstract": "This dataset contains a map of ground movements covering the Afar Rift Zone in Ethiopia, Eritrea, and Djibouti for the time period between October 2014 and August 2019. The Afar region is located where three tectonic plates are pulling apart, creating rift segments which are 50-100 km long. Surface deformation on these segments is not constant in time, with episodes of rifting occurring periodically and magma intrusions causing sudden ground movements. We use frequent Sentinel-1 satellite Interferometric Synthetic Aperture Radar (InSAR) observations to measure surface displacements through time across the whole region. We relate these to ground based Global Navigation Satellite Systems (GNSS) observations and combine data from different satellite tracks to produce maps of the average surface velocity in three directions (perpendicular to the rift zone, parallel to the rift zone, and vertical). The continued observation of these time-varying ground movements is important for understanding how continents break up, with data here providing evidence of how tightly focussed extension is around the rift segments and of the subsurface magma movement at several volcanic centres." } }, { "ob_id": 724, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39198, "uuid": "0b2759059ad6474098e40dad73e0a8ec", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.1 (v20221116)", "abstract": "Data for Figure SPM.1 from the Summary for Policymakers (SPM) 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 SPM.1 shows global temperature history and causes of recent 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\r\nIPCC, 2021: Summary for Policymakers. 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. In Press.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThe figure has two panels, with data provided for all panels in subdirectories named panel_a and panel_b.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nPanel a\r\n\r\nThe dataset contains:\r\n\r\n - Estimated temperature during the warmest multi-century period in at least the last 100,000 years, which occurred around 6500 years ago (4500 BCE), multi-centennial average, from AR6 WGI Chapter 2\r\n - Global surface temperature change time series relative to 1850-1900 for 1-2020 from:\r\n• 1-2000 CE reconstruction from paleoclimate archives, decadal smoothed, from PAGES2k Consortium (2019, DOI: 10.1038/s41561-019-0400-0)\r\n• 1850-2020 CE, observations, decadal smoothed, from AR6 WGI Chapter 2 assessed mean\r\n\r\nPanel b:\r\n\r\nThe dataset contains global surface temperature change time series relative to 1850-1900 for 1850-2020 from simulations from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and observations:\r\n\r\n- CMIP6 historical+ssp245 simulations (simulations with human and natural forcing, 1850-2019)\r\n- CMIP6 hist-nat simulations (simulations with natural forcing, 1850-2019)\r\n- Global Surface Temperature Anomalies (GSTA) relative to 1850-1900 from observations assessed in IPCC AR6 WG1 Chapter 2 (1850-2020)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n---------------------------------------------------\r\nPanel a:\r\n\r\n- panel_a/SPM1_1-2000_recon.csv, 1-2000 time series, decadal smoothed, for years centred on 5-1996 CE [column 1 grey line, columns 2 and 3 grey shading]\r\n- panel_a/SPM1_1850-2020_obs.csv, 1850-2020 time series, decadal smoothed, for years centered on 1855-2016 CE [black line]\r\n- panel_a/SPM1_6500_recon.csv, bar for the warmest multi-century period in more than 100,000 years (around 6500 years ago: 4500 BCE) [grey bar]\r\n\r\nPanel b:\r\n\r\n- panel_b/gmst_changes_model_and_obs.csv. Global surface temperature change time series relative to 1850-1900 for 1850-2020 from:\r\n• CMIP6 historical+ssp245 simulations (1850-2019) [mean, brown line]\r\n• CMIP6 historical+ssp245 simulations (1850-2019) [5% range, brown shading, bottom]\r\n• CMIP6 historical+ssp245 simulations (1850-2019) [95% range, brown shading, top]\r\n• CMIP6 hist-nat simulations (1850-2019) [mean, green line]\r\n• CMIP6 hist-nat simulations (1850-2019) [5% range, green shading, bottom]\r\n• CMIP6 hist-nat simulations (1850-2019) [95% range, green shading, top]\r\n• Global Surface Temperature Anomalies (GSTA) relative to 1850-1900 from observations assessed in IPCC AR6 WG1 Chapter 2 (1850-2020) [black line]\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\nThe 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 webpage, which includes the report component containing the figure (Summary for Policymakers), the Technical Summary (Cross-Section Box TS.1, Figure 1a) and the Supplementary Material for Chapters 2 and 3, which contains details on the input data used in Tables 2.SM.1 (Figure 2.11a) and 3.SM.1 (Figure 3.2c; FAQ 3.1, Figure 1).\r\n- Link to related publication for input data\r\n- Link to the webpage of the WGI report" }, "objectObservation": { "ob_id": 32909, "uuid": "76cad0b4f6f141ada1c44a4ce9e7d4bd", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.1 (v20210809)", "abstract": "Data for Figure SPM.1 from the Summary for Policymakers (SPM) 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 SPM.1 shows global temperature history and causes of recent 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\r\nIPCC, 2021: Summary for Policymakers. 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. In Press.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThe figure has two panels, with data provided for all panels in subdirectories named panel_a and panel_b.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nPanel a\r\n\r\nThe dataset contains:\r\n\r\n - Estimated temperature during the warmest multi-century period in at least the last 100,000 years, which occurred around 6500 years ago (4500 BCE), multi-centennial average, from AR6 WGI Chapter 2\r\n - Global surface temperature change time series relative to 1850-1900 for 1-2020 from:\r\n• 1-2000 CE reconstruction from paleoclimate archives, decadal smoothed, from PAGES2k Consortium (2019, DOI: 10.1038/s41561-019-0400-0)\r\n• 1850-2020 CE, observations, decadal smoothed, from AR6 WGI Chapter 2 assessed mean\r\n\r\nPanel b:\r\n\r\nThe dataset contains global surface temperature change time series relative to 1850-1900 for 1850-2020 from simulations from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and observations:\r\n\r\n- CMIP6 historical+ssp245 simulations (simulations with human and natural forcing, 1850-2019)\r\n- CMIP6 hist-nat simulations (simulations with natural forcing, 1850-2019)\r\n- Global Surface Temperature Anomalies (GSTA) relative to 1850-1900 from observations assessed in IPCC AR6 WG1 Chapter 2 (1850-2020)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n---------------------------------------------------\r\nPanel a:\r\n\r\n- panel_a/SPM1_1-2000_recon.txt, 1-2000 time series, decadal smoothed, for years centered on 5-1996 CE [column 1 grey line, columns 2 and 3 grey shading]\r\n- panel_a/SPM1_1850-2020_obs.txt, 1850-2020 time series, decadal smoothed, for years centered on 1855-2016 CE [black line]\r\n- panel_a/SPM1_6500_recon.txt, bar for the warmest multi-century period in more than 100,000 years (around 6500 years ago: 4500 BCE) [grey bar]\r\n\r\nPanel b:\r\n\r\n- panel_b/gmst_changes_model_and_obs.csv. Global surface temperature change time series relative to 1850-1900 for 1850-2020 from:\r\n• CMIP6 historical+ssp245 simulations (1850-2019) [mean, brown line]\r\n• CMIP6 historical+ssp245 simulations (1850-2019) [5% range, brown shading, bottom]\r\n• CMIP6 historical+ssp245 simulations (1850-2019) [95% range, brown shading, top]\r\n• CMIP6 hist-nat simulations (1850-2019) [mean, green line]\r\n• CMIP6 hist-nat simulations (1850-2019) [5% range, green shading, bottom]\r\n• CMIP6 hist-nat simulations (1850-2019) [95% range, green shading, top]\r\n• Global Surface Temperature Anomalies (GSTA) relative to 1850-1900 from observations assessed in IPCC AR6 WG1 Chapter 2 (1850-2020) [black line]\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\nThe following weblinks are provided in the Related Documents section of this catalogue record:\r\n\r\n- Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers), the Technical Summary (Cross-Section Box TS.1, Figure 1a) and the Supplementary Material for Chapters 2 and 3, which contains details on the input data used in Tables 2.SM.1 (Figure 2.11a) and 3.SM.1 (Figure 3.2c; FAQ 3.1, Figure 1).\r\n- Link to related publication for input data" } }, { "ob_id": 725, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39205, "uuid": "1b91153925dd474387bb696d59adbd15", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.5 (v20221116)", "abstract": "Data for Figure SPM.5 from the Summary for Policymakers (SPM) 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 SPM.5 shows changes in annual mean surface temperatures, precipitation, and total column soil moisture.\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\r\nIPCC, 2021: Summary for Policymakers. 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. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels with 11 maps. All data is provided, except for panel a1.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n\r\n- Annual mean temperature change (°C) (relative to 1850-1900)\r\n- Annual mean precipitation change (%) (relative to 1850-1900)\r\n- Annual mean soil moisture change (standard deviation of interannual variability) (relative to 1850-1900)\r\n\r\nThe data is given for global warming levels (GWLs), namely +1.0°C (temperature only), +1.5°C, 2.0°C, and +4.0°C.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nPanel a:\r\n- Data file: Panel_a2_Simulated_temperature_change_at_1C.nc, simulated annual mean temperature change (°C) at 1°C global warming relative to 1850-1900 (right).\r\n\r\nPanel b:\r\n- Data file: Panel_b1_Simulated_temperature_change_at_1_5C.nc, simulated annual mean temperature change (°C) at 1.5°C global warming relative to 1850-1900 (left).\r\n- Data file: Panel_b2_Simulated_temperature_change_at_2C.nc, simulated annual mean temperature change (°C) at 2.0°C global warming relative to 1850-1900 (center).\r\n- Data file: Panel_b3_Simulated_temperature_change_at_4C.nc, simulated annual mean temperature change (°C) at 4.0°C global warming relative to 1850-1900 (right).\r\n\r\nPanel c:\r\n- Data file: Panel_c1_Simulated_precipitation_change_at_1_5C.nc, simulated annual mean precipitation change (%) at 1.5°C global warming relative to 1850-1900 (left).\r\n- Data file: Panel_c2_Simulated_precipitation_change_at_2C.nc, simulated annual mean precipitation change (%) at 2.0°C global warming relative to 1850-1900 (center).\r\n- Data file: Panel_c3_Simulated_precipitation_change_at_4C.nc, simulated annual mean precipitation change (%) at 4.0°C global warming relative to 1850-1900 (right).\r\n\r\nPanel d:\r\n- Data file: Figure_SPM5_d1_cmip6_SM_tot_change_at_1_5C.nc, simulated annual mean total column soil moisture change (standard deviation) at 1.5°C global warming relative to 1850-1900 (left).\r\n- Data file: Figure_SPM5_d2_cmip6_SM_tot_change_at_2C.nc, simulated annual mean total column soil moisture change (standard deviation) at 2.0°C global warming relative to 1850-1900 (center).\r\n- Data file: Figure_SPM5_d3_cmip6_SM_tot_change_at_4C.nc, simulated annual mean total column soil moisture change (standard deviation) at 4.0°C global warming relative to 1850-1900 (right).\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblink is provided in the Related Documents section of this catalogue record:\r\n- Link to origin of figure (IPCC WG1 Summary for Policy Makers)\r\n- Link to the report webpage, which includes the component containing the figure (Summary for Policymakers), the Technical Summary (Figures TS.3 and TS.5) and the Supplementary Material for Chapters 1, 4 and 11, which contains details on the input data used in Tables 1.SM.1 (Figure 1.14), 4.SM.1 (Figures 4.31 and 4.32) and 11.SM.9 (Figure 11.19).\r\n- Link to the figure on the IPCC AR6 website" }, "objectObservation": { "ob_id": 32874, "uuid": "2787230b963942009e452255a3880609", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.5 (v20210809)", "abstract": "Data for Figure SPM.5 from the Summary for Policymakers (SPM) 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 SPM.5 shows changes in annual mean surface temperatures, precipitation, and total column soil moisture.\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\r\nIPCC, 2021: Summary for Policymakers. 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. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels with 11 maps. All data is provided, except for panel a1.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n\r\n- Annual mean temperature change (°C) (relative to 1850-1900)\r\n- Annual mean precipitation change (%) (relative to 1850-1900)\r\n- Annual mean soil moisture change (standard deviation of interannual variability) (relative to 1850-1900)\r\n\r\n \r\nThe data is given for global warming levels (GWLs), namely +1.0°C (temperature only), +1.5°C, 2.0°C, and +4.0°C.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nPanel a:\r\n- Data file: Panel_a2_Simulated_temperature_change_at_1C.nc, simulated annual mean temperature change (°C) at 1°C global warming relative to 1850-1900 (right).\r\n\r\nPanel b:\r\n- Data file: Panel_b1_Simulated_temperature_change_at_1_5C.nc, simulated annual mean temperature change (°C) at 1.5°C global warming relative to 1850-1900 (left).\r\n- Data file: Panel_b2_Simulated_temperature_change_at_2C.nc, simulated annual mean temperature change (°C) at 2.0°C global warming relative to 1850-1900 (center).\r\n- Data file: Panel_b3_Simulated_temperature_change_at_4C.nc, simulated annual mean temperature change (°C) at 4.0°C global warming relative to 1850-1900 (right).\r\n\r\nPanel c:\r\n- Data file: Panel_c1_Simulated_precipitation_change_at_1_5C.nc, simulated annual mean precipitation change (%) at 1.5°C global warming relative to 1850-1900 (left).\r\n- Data file: Panel_c2_Simulated_precipitation_change_at_2C.nc, simulated annual mean precipitation change (%) at 2.0°C global warming relative to 1850-1900 (center).\r\n- Data file: Panel_c3_Simulated_precipitation_change_at_4C.nc, simulated annual mean precipitation change (%) at 4.0°C global warming relative to 1850-1900 (right).\r\n\r\nPanel d:\r\n- Data file: Figure_SPM5_d1_cmip6_SM_tot_change_at_1_5C.nc, simulated annual mean total column soil moisture change (standard deviation) at 1.5°C global warming relative to 1850-1900 (left).\r\n- Data file: Figure_SPM5_d2_cmip6_SM_tot_change_at_2C.nc, simulated annual mean total column soil moisture change (standard deviation) at 2.0°C global warming relative to 1850-1900 (center).\r\n- Data file: Figure_SPM5_d3_cmip6_SM_tot_change_at_4C.nc, simulated annual mean total column soil moisture change (standard deviation) at 4.0°C global warming relative to 1850-1900 (right).\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblink is provided in the Related Documents section of this catalogue record:\r\n\r\n- Link to the report webpage, which includes the component containing the figure (Summary for Policymakers), the Technical Summary (Figures TS.3 and TS.5) and the Supplementary Material for Chapters 1, 4 and 11, which contains details on the input data used in Tables 1.SM.1 (Figure 1.14), 4.SM.1 (Figures 4.31 and 4.32) and 11.SM.9 (Figure 11.19)." } }, { "ob_id": 726, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 38312, "uuid": "0f51318b226546c3a13e7d8a1451bbd3", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.5, November 2017 - December 2020", "abstract": "This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 µm spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD. This version of the product is version 1.5, and covers the period from November 2017 - December 2020. \r\n\r\nThe WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.\r\n\r\nThese data were produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.\r\n\r\nWhen citing this dataset, please also cite the following peer-reviewed publication: \r\nSchneising, O., Buchwitz, M., Reuter, M., Bovensmann, H., Burrows, J. P., Borsdorff, T., Deutscher, N. M., Feist, D. G., Griffith, D. W. T., Hase, F., Hermans, C., Iraci, L. T., Kivi, R., Landgraf, J., Morino, I., Notholt, J., Petri, C., Pollard, D. F., Roche, S., Shiomi, K., Strong, K., Sussmann, R., Velazco, V. A., Warneke, T., and Wunch, D.: A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor, Atmos. Meas. Tech., 12, 6771–6802, https://doi.org/10.5194/amt-12-6771-2019, 2019." }, "objectObservation": { "ob_id": 32744, "uuid": "1c9c816d0b8a4fbf878e7e0bfef5d79f", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.2, November 2017 - July 2020", "abstract": "This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 µm spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD. This version of the product is version 1.2, and covers the period from November 2017 - July 2020. \r\n\r\nThe WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.\r\n\r\nThese data were produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.\r\n\r\nWhen citing this dataset, please also cite the following peer-reviewed publication: \r\nSchneising, O., Buchwitz, M., Reuter, M., Bovensmann, H., Burrows, J. P., Borsdorff, T., Deutscher, N. M., Feist, D. G., Griffith, D. W. T., Hase, F., Hermans, C., Iraci, L. T., Kivi, R., Landgraf, J., Morino, I., Notholt, J., Petri, C., Pollard, D. F., Roche, S., Shiomi, K., Strong, K., Sussmann, R., Velazco, V. A., Warneke, T., and Wunch, D.: A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor, Atmos. Meas. Tech., 12, 6771–6802, https://doi.org/10.5194/amt-12-6771-2019, 2019." } }, { "ob_id": 731, "relationType": "IsSupplementTo", "subjectObservation": { "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" }, "objectObservation": { "ob_id": 38855, "uuid": "b57ed5886f0a4041b76f2281ba503bed", "short_code": "ob", "title": "Atlas of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure Atlas.29 (v20221104)", "abstract": "Data for Figure Atlas.29 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.29 shows regional changes over land (except for ARO) in annual mean surface air temperature and precipitation relative to the 1995–2014 baseline for the reference regions in Arctic and Antarctica (warming since the 1850–1900 pre-industrial baseline is also provided as an offset).\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 six panels, with data provided for all panels in the master GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nThis dataset contains global monthly precipitation and near surface temperature aggregated by reference region for model output datasets: \r\n- CMIP5, CMIP6 (1850-2100)\r\n- CORDEX (1970-2100)\r\nThese are presented separately for land, sea, and land-sea gridboxes (a single run per model). Regional averages are weighted by the cosine of latitude in all cases. \r\nAn observation-based product (1979-2016) is also provided in the same format for reference: W5E5 (Lange, 2019).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nAll datasets of monthly precipitation and near surface temperature aggregated by region for CMIP5, CMIP6 and CORDEX models are provided in the labelled directories and regions over the Arctic and Antarctica are used for the production of this figure. \r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\nSSP1-2.6 is based on SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP2-4.5 is based on 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\nSSP5-8.5 is based on SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP4.5 is the Representative Concentration Pathway for 4.5 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\nGWL stands for global warming levels.\r\nJJA and DJF stand for June, July, August and December, January, February respectively.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. To reproduce each panel in this figure using the 'regional-scatter-plots_R.ipynb' notebook, in regions: select each of the regions over the Arctic and Antarctica in the top left panel of the figure, area: 'land', cordex.domain: 'ARC' or 'ANT' depending on panel and scatter.seasons: list of months by number e.g. JJA: list(c(12, 1, 2), 6:8). \r\n\r\nThe notebooks describe step by step the basic process followed to generate some key figures of the AR6 WGI Atlas and some products underpinning the Interactive Atlas, such as reference regions, global warming levels, aggregated datasets. They include comments and hints to extend the analysis, thus promoting reusability of the results. These notebooks are provided as guidance for practitioners, more user friendly than the code provided as scripts in the reproducibility folder.\r\n\r\nSome of the notebooks require access to large data volumes out of this repository. To speed up the execution of the notebook, in addition to the full code to access the data, a data loading shortcut is provided, by storing intermediate results in the auxiliary-material folder in this repository. To test other parameter settings, the full data access instructions should be followed, which can take long waiting times.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Atlas)\r\n - Link to the Supplementary Material for Atlas, which contains details on the input data used in Table Atlas.SM.15.\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the necessary notebooks for reproducing the figure from GitHub." } }, { "ob_id": 732, "relationType": "IsSupplementTo", "subjectObservation": { "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" }, "objectObservation": { "ob_id": 38854, "uuid": "89e1b69ad74146cfa8b0a941108811c2", "short_code": "ob", "title": "Atlas of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure Atlas.28 (v20221104)", "abstract": "Data for Figure Atlas.28 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.28 shows changes in annual mean surface air temperature, precipitation and sea level rise relative to the 1995–2014 baseline for the reference regions in the Small Islands region for different lines of evidence (CMIP5, CORDEX and CMIP6).\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 Figure subpanels\r\n ---------------------------------------------------\r\n The figure has twelve panels, with data provided for all panels in the master GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nThis dataset contains global monthly precipitation and near surface temperature aggregated by reference region for model output datasets: \r\n- CMIP5, CMIP6 (1850-2100)\r\n- CORDEX (1970-2100)\r\nThese are presented separately for land, sea, and land-sea gridboxes (a single run per model). Regional averages are weighted by the cosine of latitude in all cases. \r\nAn observation-based product (1979-2016) is also provided in the same format for reference: W5E5 (Lange, 2019).\r\nSea level rise data from the CMIP6 ensemble is also used for the right-hand plot of each panel.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nAll datasets of monthly precipitation and near surface temperature aggregated by region for CMIP5, CMIP6 and CORDEX models are provided in the labelled directories and regions in the Small Islands are used for the production of this figure. \r\n\r\nSea level projections data can be found here: https://www.wdc-climate.de/ui/entry?acronym=IPCC-DDC_AR6_Sup_SLPr\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\nSSP1-2.6 is based on SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP2-4.5 is based on 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\nSSP5-8.5 is based on SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP4.5 is the Representative Concentration Pathway for 4.5 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\nGWL stands for global warming levels.\r\nJJA and DJF stand for June, July, August and December, January, February respectively.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. To reproduce each panel in this figure, use the 'regional-scatter-plots_R.ipynb' notebook. Information on reproducibility can be found in the 'reproducibility/projections' folder of the Atlas GitHub repository.\r\n\r\nThe notebooks describe step by step the basic process followed to generate some key figures of the AR6 WGI Atlas and some products underpinning the Interactive Atlas, such as reference regions, global warming levels, aggregated datasets. They include comments and hints to extend the analysis, thus promoting reusability of the results. These notebooks are provided as guidance for practitioners, more user friendly than the code provided as scripts in the reproducibility folder.\r\n\r\nSome of the notebooks require access to large data volumes out of this repository. To speed up the execution of the notebook, in addition to the full code to access the data, a data loading shortcut is provided, by storing intermediate results in the auxiliary-material folder in this repository. To test other parameter settings, the full data access instructions should be followed, which can take long waiting times.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Atlas)\r\n - Link to the Supplementary Material for Atlas, which contains details on the input data used in Table Atlas.SM.15.\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the necessary notebooks for reproducing the figure from GitHub.\r\n - Link to IPCC AR6 WGI Sea Level Projections" } }, { "ob_id": 733, "relationType": "IsSupplementTo", "subjectObservation": { "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" }, "objectObservation": { "ob_id": 38853, "uuid": "70c57074146147989150a1a37c338fcf", "short_code": "ob", "title": "Atlas of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure Atlas.26 (v20221104)", "abstract": "Data for Figure Atlas.26 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.26 shows changes in annual mean surface air temperature and precipitation from reference regions in North America for different lines of evidence (CMIP5, CORDEX and CMIP6).\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citations:\r\nFor the report component from which the figure originates:\r\nGutiérrez, J.M., R.G. Jones, G.T. Narisma, L.M. Alves, M. Amjad, I.V. Gorodetskaya, M. Grose, N.A.B. Klutse, S. Krakovska, J. Li, D. Martínez-Castro, L.O. Mearns, S.H. Mernild, T. Ngo-Duc, B. van den Hurk, and J.-H. Yoon, 2021: Atlas. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1927–2058, doi:10.1017/9781009157896.021\r\n\r\nIturbide, M. et al., 2021: Repository supporting the implementation of FAIR principles in the IPCC-WG1 Interactive Atlas. Zenodo. Retrieved from: http://doi.org/10.5281/zenodo.5171760\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has nineteen panels, with data provided for all panels in the master GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nThis dataset contains global monthly precipitation and near surface temperature aggregated by reference region for model output datasets: \r\n- CMIP5, CMIP6 (1850-2100)\r\n- CORDEX (1970-2100)\r\nThese are presented separately for land, sea, and land-sea gridboxes (a single run per model). Regional averages are weighted by the cosine of latitude in all cases. \r\nAn observation-based product (1979-2016) is also provided in the same format for reference: W5E5 (Lange, 2019).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nAll datasets of monthly precipitation and near surface temperature aggregated by region for CMIP5, CMIP6 and CORDEX models are provided in the labelled directories and regions over North America are used for the production of this figure. \r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\nSSP1-2.6 is based on SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP2-4.5 is based on 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\nSSP5-8.5 is based on SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP4.5 is the Representative Concentration Pathway for 4.5 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\nGWL stands for global warming levels.\r\nJJA and DJF stand for June, July, August and December, January, February respectively.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. To reproduce each panel in this figure using the 'regional-scatter-plots_R.ipynb' notebook, in regions: select each of the regions over North America in the top panel of the figure, area: 'land', cordex.domain: 'NAM' and scatter.seasons: list of months by number e.g. JJA: list(c(12, 1, 2), 6:8). \r\n\r\nThe notebooks describe step by step the basic process followed to generate some key figures of the AR6 WGI Atlas and some products underpinning the Interactive Atlas, such as reference regions, global warming levels, aggregated datasets. They include comments and hints to extend the analysis, thus promoting reusability of the results. These notebooks are provided as guidance for practitioners, more user friendly than the code provided as scripts in the reproducibility folder.\r\n\r\nSome of the notebooks require access to large data volumes out of this repository. To speed up the execution of the notebook, in addition to the full code to access the data, a data loading shortcut is provided, by storing intermediate results in the auxiliary-material folder in this repository. To test other parameter settings, the full data access instructions should be followed, which can take long waiting times.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Atlas)\r\n - Link to the Supplementary Material for Atlas, which contains details on the input data used in Table Atlas.SM.15.\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the necessary notebooks for reproducing the figure from GitHub." } }, { "ob_id": 734, "relationType": "IsSupplementTo", "subjectObservation": { "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" }, "objectObservation": { "ob_id": 38846, "uuid": "4f314945d3944aeaa12f819fe801dea0", "short_code": "ob", "title": "Atlas of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure Atlas.24 (v20221104)", "abstract": "Data for Figure Atlas.24 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.24 shows changes in annual mean surface air temperature and precipitation from reference regions in Europe for different lines of evidence (CMIP5, CORDEX and CMIP6).\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citations:\r\nFor the report component from which the figure originates:\r\nGutiérrez, J.M., R.G. Jones, G.T. Narisma, L.M. Alves, M. Amjad, I.V. Gorodetskaya, M. Grose, N.A.B. Klutse, S. Krakovska, J. Li, D. Martínez-Castro, L.O. Mearns, S.H. Mernild, T. Ngo-Duc, B. van den Hurk, and J.-H. Yoon, 2021: Atlas. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1927–2058, doi:10.1017/9781009157896.021\r\n\r\nIturbide, M. et al., 2021: Repository supporting the implementation of FAIR principles in the IPCC-WG1 Interactive Atlas. Zenodo. Retrieved from: http://doi.org/10.5281/zenodo.5171760\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has thirteen panels, with data provided for all panels in the master GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nThis dataset contains global monthly precipitation and near surface temperature aggregated by reference region for model output datasets: \r\n- CMIP5, CMIP6 (1850-2100)\r\n- CORDEX (1970-2100)\r\nThese are presented separately for land, sea, and land-sea gridboxes (a single run per model). Regional averages are weighted by the cosine of latitude in all cases. \r\nAn observation-based product (1979-2016) is also provided in the same format for reference: W5E5 (Lange, 2019).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nAll datasets of monthly precipitation and near surface temperature aggregated by region for CMIP5, CMIP6 and CORDEX models are provided in the labelled directories and regions over Europe are used for the production of this figure. \r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\nSSP1-2.6 is based on SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP2-4.5 is based on 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\nSSP5-8.5 is based on SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP4.5 is the Representative Concentration Pathway for 4.5 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\nGWL stands for global warming levels.\r\nJJA and DJF stand for June, July, August and December, January, February respectively.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. To reproduce each panel in this figure using the 'regional-scatter-plots_R.ipynb' notebook, in regions: select each of the regions over Europe in the top panel of the figure, area: 'land', cordex.domain: 'EUR' and scatter.seasons: list of months by number e.g. JJA: list(c(12, 1, 2), 6:8). \r\n\r\nThe notebooks describe step by step the basic process followed to generate some key figures of the AR6 WGI Atlas and some products underpinning the Interactive Atlas, such as reference regions, global warming levels, aggregated datasets. They include comments and hints to extend the analysis, thus promoting reusability of the results. These notebooks are provided as guidance for practitioners, more user friendly than the code provided as scripts in the reproducibility folder.\r\n\r\nSome of the notebooks require access to large data volumes out of this repository. To speed up the execution of the notebook, in addition to the full code to access the data, a data loading shortcut is provided, by storing intermediate results in the auxiliary-material folder in this repository. To test other parameter settings, the full data access instructions should be followed, which can take long waiting times.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Atlas)\r\n - Link to the Supplementary Material for Atlas, which contains details on the input data used in Table Atlas.SM.15.\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the necessary notebooks for reproducing the figure from GitHub." } }, { "ob_id": 735, "relationType": "IsSupplementTo", "subjectObservation": { "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" }, "objectObservation": { "ob_id": 38845, "uuid": "131bb6c7826b467d9f1ea6bd3fa74175", "short_code": "ob", "title": "Atlas of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure Atlas.22 (v20221104)", "abstract": "Data for Figure Atlas.22 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.22 shows changes in annual mean surface air temperature and precipitation from reference regions in Central America, the Caribbean and South America for different lines of evidence (CMIP5, CORDEX and CMIP6).\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citations:\r\nFor the report component from which the figure originates:\r\nGutiérrez, J.M., R.G. Jones, G.T. Narisma, L.M. Alves, M. Amjad, I.V. Gorodetskaya, M. Grose, N.A.B. Klutse, S. Krakovska, J. Li, D. Martínez-Castro, L.O. Mearns, S.H. Mernild, T. Ngo-Duc, B. van den Hurk, and J.-H. Yoon, 2021: Atlas. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1927–2058, doi:10.1017/9781009157896.021\r\n\r\nIturbide, M. et al., 2021: Repository supporting the implementation of FAIR principles in the IPCC-WG1 Interactive Atlas. Zenodo. Retrieved from: http://doi.org/10.5281/zenodo.5171760\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has thirty-one panels, with data provided for all panels in the master GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nThis dataset contains global monthly precipitation and near surface temperature aggregated by reference region for model output datasets: \r\n- CMIP5, CMIP6 (1850-2100)\r\n- CORDEX (1970-2100)\r\nThese are presented separately for land, sea, and land-sea gridboxes (a single run per model). Regional averages are weighted by the cosine of latitude in all cases. \r\nAn observation-based product (1979-2016) is also provided in the same format for reference: W5E5 (Lange, 2019).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nAll datasets of monthly precipitation and near surface temperature aggregated by region for CMIP5, CMIP6 and CORDEX models are provided in the labelled directories and regions over Central America, the Caribbean and South America are used for the production of this figure. \r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\nSSP1-2.6 is based on SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP2-4.5 is based on 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\nSSP5-8.5 is based on SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP4.5 is the Representative Concentration Pathway for 4.5 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\nGWL stands for global warming levels.\r\nJJA and DJF stand for June, July, August and December, January, February respectively.\r\nCAM is the CORDEX region for Central America.\r\nSAM is the CORDEX region for South America.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. To reproduce each panel in this figure using the 'regional-scatter-plots_R.ipynb' notebook, in regions: select each of the regions in the top panel of the figure, area: 'land', cordex.domain: 'CAM' or 'SAM' depending on panel and scatter.seasons: list of months by number e.g. JJA: list(c(12, 1, 2), 6:8). \r\n\r\nThe notebooks describe step by step the basic process followed to generate some key figures of the AR6 WGI Atlas and some products underpinning the Interactive Atlas, such as reference regions, global warming levels, aggregated datasets. They include comments and hints to extend the analysis, thus promoting reusability of the results. These notebooks are provided as guidance for practitioners, more user friendly than the code provided as scripts in the reproducibility folder.\r\n\r\nSome of the notebooks require access to large data volumes out of this repository. To speed up the execution of the notebook, in addition to the full code to access the data, a data loading shortcut is provided, by storing intermediate results in the auxiliary-material folder in this repository. To test other parameter settings, the full data access instructions should be followed, which can take long waiting times.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Atlas)\r\n - Link to the Supplementary Material for Atlas, which contains details on the input data used in Table Atlas.SM.15.\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the necessary notebooks for reproducing the figure from GitHub." } }, { "ob_id": 736, "relationType": "IsSupplementTo", "subjectObservation": { "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" }, "objectObservation": { "ob_id": 38844, "uuid": "3913c555e3054ffa99eb494a1c0bb39d", "short_code": "ob", "title": "Atlas of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure Atlas.21 (v20221104)", "abstract": "Data for Figure Atlas.21 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.21 shows changes in annual mean surface air temperature and precipitation from reference regions in Australasia for different lines of evidence (CMIP5, CORDEX and CMIP6).\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citations:\r\nFor the report component from which the figure originates:\r\nGutiérrez, J.M., R.G. Jones, G.T. Narisma, L.M. Alves, M. Amjad, I.V. Gorodetskaya, M. Grose, N.A.B. Klutse, S. Krakovska, J. Li, D. Martínez-Castro, L.O. Mearns, S.H. Mernild, T. Ngo-Duc, B. van den Hurk, and J.-H. Yoon, 2021: Atlas. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1927–2058, doi:10.1017/9781009157896.021\r\n\r\nIturbide, M. et al., 2021: Repository supporting the implementation of FAIR principles in the IPCC-WG1 Interactive Atlas. Zenodo. Retrieved from: http://doi.org/10.5281/zenodo.5171760\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has sixteen panels, with data provided for all panels in the master GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nThis dataset contains global monthly precipitation and near surface temperature aggregated by reference region for model output datasets: \r\n- CMIP5, CMIP6 (1850-2100)\r\n- CORDEX (1970-2100)\r\nThese are presented separately for land, sea, and land-sea gridboxes (a single run per model). Regional averages are weighted by the cosine of latitude in all cases. \r\nAn observation-based product (1979-2016) is also provided in the same format for reference: W5E5 (Lange, 2019).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nAll datasets of monthly precipitation and near surface temperature aggregated by region for CMIP5, CMIP6 and CORDEX models are provided in the labelled directories and regions over Australasia are used for the production of this figure. \r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\nSSP1-2.6 is based on SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP2-4.5 is based on 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\nSSP5-8.5 is based on SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP4.5 is the Representative Concentration Pathway for 4.5 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\nGWL stands for global warming levels.\r\nJJA and DJF stand for June, July, August and December, January, February respectively.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. To reproduce each panel in this figure using the 'regional-scatter-plots_R.ipynb' notebook, in regions: select each of the regions over Australasia in the top right panel of the figure, area: 'land', cordex.domain: 'AUS' and scatter.seasons: list of months by number e.g. JJA: list(c(12, 1, 2), 6:8). \r\n\r\nThe notebooks describe step by step the basic process followed to generate some key figures of the AR6 WGI Atlas and some products underpinning the Interactive Atlas, such as reference regions, global warming levels, aggregated datasets. They include comments and hints to extend the analysis, thus promoting reusability of the results. These notebooks are provided as guidance for practitioners, more user friendly than the code provided as scripts in the reproducibility folder.\r\n\r\nSome of the notebooks require access to large data volumes out of this repository. To speed up the execution of the notebook, in addition to the full code to access the data, a data loading shortcut is provided, by storing intermediate results in the auxiliary-material folder in this repository. To test other parameter settings, the full data access instructions should be followed, which can take long waiting times.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Atlas)\r\n - Link to the Supplementary Material for Atlas, which contains details on the input data used in Table Atlas.SM.15.\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the necessary notebooks for reproducing the figure from GitHub." } }, { "ob_id": 737, "relationType": "IsSupplementTo", "subjectObservation": { "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" }, "objectObservation": { "ob_id": 38843, "uuid": "bb671075bd194b3bbaa496d90f5310e1", "short_code": "ob", "title": "Atlas of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure Atlas.17 (v20221104)", "abstract": "Data for Figure Atlas.17 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.17 shows changes in annual mean surface air temperature and precipitation from reference regions in Asia for different lines of evidence (CMIP5, CORDEX and CMIP6).\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 thirty-one panels, with data provided for all panels in the master GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nThis dataset contains global monthly precipitation and near surface temperature aggregated by reference region for model output datasets: \r\n- CMIP5, CMIP6 (1850-2100)\r\n- CORDEX (1970-2100)\r\nThese are presented separately for land, sea, and land-sea gridboxes (a single run per model). Regional averages are weighted by the cosine of latitude in all cases. \r\nAn observation-based product (1979-2016) is also provided in the same format for reference: W5E5 (Lange, 2019).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nAll datasets of monthly precipitation and near surface temperature aggregated by region for CMIP5, CMIP6 and CORDEX models are provided in the labelled directories and regions over Asia are used for the production of this figure. \r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\nSSP1-2.6 is based on SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP2-4.5 is based on 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\nSSP5-8.5 is based on SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP4.5 is the Representative Concentration Pathway for 4.5 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\nGWL stands for global warming levels.\r\nJJA and DJF stand for June, July, August and December, January, February respectively.\r\nWAS is the CORDEX region for South Asia.\r\nEAS is the CORDEX region for East Asia.\r\nSEA is the CORDEX region for South East Asia.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. To reproduce each panel in this figure using the 'regional-scatter-plots_R.ipynb' notebook, in regions: select each of the regions over Asia in the top panel of the figure, area: 'land', cordex.domain: 'WAS', 'EAS' or 'SEA' depending on panel and scatter.seasons: list of months by number e.g. JJA: list(c(12, 1, 2), 6:8). \r\n\r\nThe notebooks describe step by step the basic process followed to generate some key figures of the AR6 WGI Atlas and some products underpinning the Interactive Atlas, such as reference regions, global warming levels, aggregated datasets. They include comments and hints to extend the analysis, thus promoting reusability of the results. These notebooks are provided as guidance for practitioners, more user friendly than the code provided as scripts in the reproducibility folder.\r\n\r\nSome of the notebooks require access to large data volumes out of this repository. To speed up the execution of the notebook, in addition to the full code to access the data, a data loading shortcut is provided, by storing intermediate results in the auxiliary-material folder in this repository. To test other parameter settings, the full data access instructions should be followed, which can take long waiting times.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Atlas)\r\n - Link to the Supplementary Material for Atlas, which contains details on the input data used in Table Atlas.SM.15.\r\n- Link to the code for the figure, archived on Zenodo.\r\n- Link to the necessary notebooks for reproducing the figure from GitHub." } }, { "ob_id": 738, "relationType": "IsSupplementTo", "subjectObservation": { "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" }, "objectObservation": { "ob_id": 38842, "uuid": "b140e520e22e45daa8525d18c1c8cced", "short_code": "ob", "title": "Atlas of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure Atlas.16 (v20221104)", "abstract": "Data for Figure Atlas.16 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.16 shows changes in annual mean surface air temperature and precipitation from reference regions in Africa for different lines of evidence (CMIP5, CORDEX and CMIP6).\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 twenty-eight panels, with data provided for all panels in the master GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nThis dataset contains global monthly precipitation and near surface temperature aggregated by reference region for model output datasets: \r\n- CMIP5, CMIP6 (1850-2100)\r\n- CORDEX (1970-2100)\r\nThese are presented separately for land, sea, and land-sea gridboxes (a single run per model). Regional averages are weighted by the cosine of latitude in all cases. \r\nAn observation-based product (1979-2016) is also provided in the same format for reference: W5E5 (Lange, 2019).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nAll datasets of monthly precipitation and near surface temperature aggregated by region for CMIP5, CMIP6 and CORDEX models are provided in the labelled directories and regions over Africa are used for the production of this figure. \r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\nSSP1-2.6 is based on SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP2-4.5 is based on 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\nSSP5-8.5 is based on SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP4.5 is the Representative Concentration Pathway for 4.5 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\nGWL stands for global warming levels.\r\nJJAS and DJFM stand for June, July, August, September and December, January, February, March respectively.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. To reproduce each panel in this figure using the 'regional-scatter-plots_R.ipynb' notebook, in regions: select each of the 9 regions over Africa in the top right panel of the figure, area: 'land', cordex.domain: 'AFR' and scatter.seasons: list of months by number e.g. JJAS: list(c(12, 1, 2),6:9). \r\n\r\nThe notebooks describe step by step the basic process followed to generate some key figures of the AR6 WGI Atlas and some products underpinning the Interactive Atlas, such as reference regions, global warming levels, aggregated datasets. They include comments and hints to extend the analysis, thus promoting reusability of the results. These notebooks are provided as guidance for practitioners, more user friendly than the code provided as scripts in the reproducibility folder.\r\n\r\nSome of the notebooks require access to large data volumes out of this repository. To speed up the execution of the notebook, in addition to the full code to access the data, a data loading shortcut is provided, by storing intermediate results in the auxiliary-material folder in this repository. To test other parameter settings, the full data access instructions should be followed, which can take long waiting times.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Atlas)\r\n - Link to the Supplementary Material for Atlas, which contains details on the input data used in Table Atlas.SM.15.\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the necessary notebooks for reproducing the figure from GitHub." } }, { "ob_id": 739, "relationType": "IsSupplementTo", "subjectObservation": { "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" }, "objectObservation": { "ob_id": 38285, "uuid": "5f8d2c32121a4885b20be2ae96aed72d", "short_code": "ob", "title": "Atlas of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure Atlas.13 (v20221004)", "abstract": "Data for Figure Atlas.13 from Atlas of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure Atlas.13 shows changes in annual mean surface air temperature and precipitation from different lines of evidence (CMIP5 and CMIP6). \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citations:\r\nFor the report component from which the figure originates:\r\nGutiérrez, J.M., R.G. Jones, G.T. Narisma, L.M. Alves, M. Amjad, I.V. Gorodetskaya, M. Grose, N.A.B. Klutse, S. Krakovska, J. Li, D. Martínez-Castro, L.O. Mearns, S.H. Mernild, T. Ngo-Duc, B. van den Hurk, and J.-H. Yoon, 2021: Atlas. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1927–2058, doi:10.1017/9781009157896.021\r\n\r\nIturbide, M. et al., 2021: Repository supporting the implementation of FAIR principles in the IPCC-WG1 Interactive Atlas. Zenodo. Retrieved from: http://doi.org/10.5281/zenodo.5171760\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has eight panels, with data provided for all panels in the master GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nThis dataset contains global monthly precipitation and near surface temperature aggregated by reference region for model output datasets: \r\n- CMIP5, CMIP6 (1850-2100)\r\n- CORDEX (1970-2100)\r\nThese are presented separately for land, sea, and land-sea gridboxes (a single run per model). Regional averages are weighted by the cosine of latitude in all cases. \r\nAn observation-based product (1979-2016) is also provided in the same format for reference: W5E5 (Lange, 2019).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nAll datasets of monthly precipitation and near surface temperature aggregated by region for CMIP5, CMIP6 and CORDEX models are provided in the labelled directories and are used for the production of this figure. \r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\nSSP1-2.6 is based on SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP5-8.5 is based on SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\nGWL stands for global warming levels.\r\nJJA and DJF stand for June, July, August and December, January, February respectively.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. To reproduce each panel in this figure using the 'regional-scatter-plots_R.ipynb' notebook, select the region: 'world', area: 'land' or 'landsea' and scatter.seasons: list of months by number e.g. boreal summer (JJA): list(c(12, 1, 2),6:8). \r\n\r\nThe notebooks describe step by step the basic process followed to generate some key figures of the AR6 WGI Atlas and some products underpinning the Interactive Atlas, such as reference regions, global warming levels, aggregated datasets. They include comments and hints to extend the analysis, thus promoting reusability of the results. These notebooks are provided as guidance for practitioners, more user friendly than the code provided as scripts in the reproducibility folder.\r\n\r\nSome of the notebooks require access to large data volumes out of this repository. To speed up the execution of the notebook, in addition to the full code to access the data, a data loading shortcut is provided, by storing intermediate results in the auxiliary-material folder in this repository. To test other parameter settings, the full data access instructions should be followed, which can take long waiting times.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Atlas)\r\n - Link to the Supplementary Material for Atlas, which contains details on the input data used in Table Atlas.SM.15.\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the necessary notebooks for reproducing the figure from Github." } }, { "ob_id": 740, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38866, "uuid": "3d16a09c21c9440288608276b615c11f", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.1 v20221110", "abstract": "Data for Figure TS.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\nFigure TS.1 shows changes in atmospheric CO2 and global surface temperature (relative to 1850-1900) from the deep past to the next 300 years.\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 multiple subplots. Metadata provided for all the plots in the figure, and data is provided for the maps of surface temperature (projections and 2020) and for the atmospheric CO2 concentration corresponding to the paleo 60 - 1 million years time series, and paleo and direct measurements from 800 thousand years to 1980.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Atmospherics CO2 concentration (ppm) corresponding to the paleo 60–1 million years\r\n - Atmospherics CO2 concentration (ppm), paleo and direct measurements from 800 thousand years to 1980\r\n - Global surface temperature for 2020 (estimate of the total observed warming since 1850–1900).\r\n - Global surface temperature at 2100 and 2300 from CMIP6 models (relative to 1850-1900) for SSP1-2.6 and SSP5-8.5 scenarios.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - Data file: CO2_60_Myr.csv (top row, atmospheric CO2 concentration corresponding to the paleo 60–10 million years time series)\r\n - Data file: fig2_4a_main_figure_data.csv (top row, paleo and direct measurements from 800 thousand years to 1980)\r\n - Data file: TS_BK_2020.nc (Global surface temperature map for 2020, estimate of the total observed warming since 1850–1900).\r\n - Data file: ensmean_tas_ssp126_2100-historical_1850_regrid.nc (Global surface temperature map at 2100 relative to 1850-1900 for SSP1-2.6 scenario)\r\n - Data file: ensmean_tas_ssp126_2300-historical_1850_regrid.nc (Global surface temperature map at 2300 relative to 1850-1900 for SSP1-2.6 scenario)\r\n - Data file: ensmean_tas_ssp585_2100-historical_1850_regrid.nc (Global surface temperature map at 2100 relative to 1850-1900 for SSP5-8.5 scenario)\r\n - Data file: ensmean_tas_ssp585_2300-historical_1850_regrid.nc (Global surface temperature map at 2300 relative to 1850-1900 for SSP5-8.5 scenario)\r\n\r\nCSV files were converted for archival from Excel workbooks.\r\n\r\nSSP stands for Shared Socioeconomic Pathway.\r\nppm stands for parts per million.\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\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\nSSP5-8.5 is based on Shares Socioeconomic Pathway SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\n\r\n---------------------------------------------------\r\nTemporal Range of Paleoclimate Data\r\n---------------------------------------------------\r\nThis dataset covers a paleoclimate timespan from 60 Myr to 2300.\r\nMyr refers to millions of years before present.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nNotes on reproducing this figure are linked in a computation record found in the Process section of this catalogue record.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Links to the report components of the underlying chapter figures from which part of this figure was generated (Chapter 2 and Chapter 7)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in the figure\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in the figure\r\n - Link to the data for 2300 emissions scenarios described in section 4.7, archived on Zenodo.\r\n - Link to the data for 2300 projections from Figure 4.40a (section 4.7.1), archived on Zenodo." }, "objectObservation": { "ob_id": 38911, "uuid": "0f05c2fb8f814d60ac2d657a70e9a7f5", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for CCB 2.1, Figure 1 v20221114", "abstract": "Data for CCB 2.1, Figure 1 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\nCCB 2.1 Figure 1 shows global mean surface temperature over the past 60 million years plotted on three time scales and including projections.\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 Gulev, 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\nPlease also include citations of the related publications provided at the end of this abstract.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Global mean surface temperature adjusted to 1850-1900 reference period for:\r\n \r\n - 60 to 0.02 Ma ( from Westerhold et al. 2020: 10.1126/science.aba6853)\r\n - 60 to 0.02 Ma (from Hansen et al. 2013: 10.1098/rsta.2012.0294)\r\n - 1 to 0.02 Ma (from Snyder 2016: 10.1038/nature19798)\r\n - 20 to 12 ka (from Shakun et al. 2012: 10.1038/nature10915)\r\n - 12 ka to 1900 CE (from Kaufman et al. 2020: 10.1038/s41597-020-0530-7)\r\n - 1850-2020 CE (from Chapter 2, Figure 2.11c)\r\n - Projections - 2100 from Table 4.5, 2300 from Table 4.9\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file: FinalData_CCB2_1_Fig1_DMS.csv contains time series data shown in CCB2.1 Figure 1 for each of the five previously published paleotemperature time series, adjusted to 1850-1900 reference period, plus 1850-2020 from Figure 2.11c.\r\n \r\n - Column A: time reference for Westerhold et al., 2020\r\n - Column C: orange line\r\n - Column D: time reference for Hansen et al., 2013\r\n - Column F: grey line\r\n - Column G: time reference for Snyder, 2016.\r\n - Column I: green line\r\n - Column J: time reference for Shakun et al., 2012\r\n - Column L: black line\r\n - Column M: time reference for Kaufman et al., 2020\r\n - Column O: violet line\r\n - Column P: time reference for AR6 assessed mean\r\n - Column R: red line\r\n\r\nMa stands for million years before present\r\nka represents thousands of years before present.\r\n\r\n\r\n---------------------------------------------------\r\nTemporal Range of Paleoclimate Data\r\n---------------------------------------------------\r\nThis dataset covers a paleoclimate timespan from 60 million years ago to 2020.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Published data were adjusted to 1850-1900 reference period using values specified in 'ReadMe'. \r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Links to related publications listed below\r\n\r\n---------------------------------------------------\r\nRelated publications for figure datasets\r\n---------------------------------------------------\r\nPlease include the following citations of related publications. Relations to individual publications are outlined in the Readme file provided with this data. Links are provided in the Related Documents section of this catalogue record.\r\n\r\nWesterhold, T., et.al. An astronomically dated record of Earth’s climate and its predictability over the last 66 million years. SCIENCE, 11 Sep 2020, Vol 369, Issue 6509, pp. 1383-1387, DOI: 10.1126/science.aba6853\r\n\r\nHansen James, Sato Makiko, Russell Gary and Kharecha Pushker 2013 Climate sensitivity, sea level and atmospheric carbon dioxide Phil. Trans. R. Soc. A.3712012029420120294\r\nhttp://doi.org/10.1098/rsta.2012.0294\r\n\r\nZachos, J., Dickens, G. & Zeebe, R. An early Cenozoic perspective on greenhouse warming and carbon-cycle dynamics. Nature 451, 279–283 (2008). https://doi.org/10.1038/nature06588\r\n\r\nShakun, J., Clark, P., He, F. et al. Global warming preceded by increasing carbon dioxide concentrations during the last deglaciation. Nature 484, 49–54 (2012). https://doi.org/10.1038/nature10915\r\n\r\nKaufman, D., McKay, N., Routson, C. et al. Holocene global mean surface temperature, a multi-method reconstruction approach. Sci Data 7, 201 (2020). https://doi.org/10.1038/s41597-020-0530-7\r\n\r\nPAGES 2k Consortium. Consistent multidecadal variability in global temperature reconstructions and simulations over the Common Era. Nat. Geosci. 12, 643–649 (2019). https://doi.org/10.1038/s41561-019-0400-0\r\n\r\n Gillett, N.P.; Malinina, E.; Kaufman, D.; Neukom, R. (2021): Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.1 (v20210809). NERC EDS Centre for Environmental Data Analysis, date of citation. doi:10.5285/76cad0b4f6f141ada1c44a4ce9e7d4bd. http://dx.doi.org/10.5285/0b2759059ad6474098e40dad73e0a8ec\r\n\r\nTrewin, Blair. (2022). Global temperature time series from IPCC AR6 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6321535\r\n\r\nZebedee Nicholls, Malte Meinshausen, & Jared Lewis. (2022). AR6 WG1 Plots and Processing (v1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6386979" } }, { "ob_id": 741, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38905, "uuid": "1030d40a071d4929bf04e08bfbd22c10", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.25 v20221111", "abstract": "Data for Figure TS.25 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure TS.25 shows the distribution of projected changes in selected climatic impact-driver (CID) indices for selected regions for Coupled Model Intercomparison Project Phases 5 and 6 (CMIP6, CMIP5) and Coordinated Regional Downscaling Experiment (CORDEX) model ensembles.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n For all the panels, the data provided consists of ensemble statistics (q5, median and q95) of the spatial averages over the IPCC AR6 regions of the list of indicators below for CMIP5, CMIP6 and CORDEX, for the recent past (1995-2014), the mid-term (2041-2060) and long-term (2081-2100) future horizons, as well as the +1.5, +2, and +4°C of global warming levels.\r\n The list of indicators shown on the figure is:\r\n - number of days per year with SWE > 100mm (North-America)\r\n - number of days with the NOAA Heat Index exceeding 41°C (Central-America and Asia)\r\n - the 100-yr return period stream flow (South-America, Europe, Africa)\r\n - the number of days per year with Maximum temperature exceeding 35°C (Asia)\r\n - the Shoreline position change (Asia, Australasia)\r\n\r\nSWE stands for snow water equivalent\r\nNOAA stands for National Oceanic and Atmospheric Administration.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nUpper panels of Panel (a):\r\nThe change of the number of days with SWE > 100mm are related with figure 12.10(d) with the corresponding file names:\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)\r\n ** 'NAM-22_CORDEX_NORTH-AMERICA_SWE_mask14_AR6_regional_averages.json' : same as previous file for the CORDEX-core NAM-22 multimodel ensemble\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)\r\n\r\nMiddle panels of Panel (a): \r\nThe change of the NOAA HI exceeding 41°C are related to figure 12.SM.2 with the corresponding file names:\r\n ** 'CMIP5_HI41_AR6_regional_averages.json' : data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_HI41_AR6_regional_averages.json' : data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_HI41_AR6_regional_averages.json' : data for the CORDEX multi-model ensemble\r\n see the description of the data associated with figure 12.SM.2 for more details on the structure of the files\r\n\r\nLower panels of Panel (a) and left panels of Panel (b):\r\n100-yr return period stream flow is shown for South America (figure 12.8(c)), Europe (figure 12.9(c)) and Africa (figure 12.5(c)) with corresponding file names: \r\n ** 'Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.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, 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 - ${CORDEX_domain}: the CORDEX domain\r\n** 'Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt' : files containing the Q100 regional averages of global warming levels with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${GWL}: the Global Warming Level: 1.5, 2 or 4\r\n - ${CORDEX_domain}: the CORDEX domain\r\n\r\nRight panels of Panel (b):\r\nThe Maximum temperature exceeding 35°C (upper right) are related with figure 12.SM.1 with the corresponding file names:\r\n ** 'CMIP5_tx35isimip_AR6_regional_averages.json' : data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_tx35isimip_AR6_regional_averages.json' : data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_tx35isimip_AR6_regional_averages.json' : data for the CORDEX multi-model ensemble\r\n see the description of the data associated with figure 12.SM.1 for more details on the structure of the files\r\n\r\nThe Shoreline position change for EAS and RFE (upper middle right) (related to figure 12.6(d)), and in Australasia (lower right) (related to figure 12.7(d)) have corresponding data file names:\r\n ** 'globalErosionProjections_by_AR6_region_${scenario}_${horizon).json' : regional averages of shoreline position changes for Africa, for the RCP4.5 and RCP8.5 scenarios (${scenario} = RCP45 and ${scenario} = RCP85 respectively) and the 2050 (mid-term, in blue) and 2100 (long-term, in red) future horizons (${horizon}=2050 and ${horizon}=2100 respectively) against the recent past period (2010); the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n\r\nThe four panels on the NOAA Heat Index exceeding 41°C (lower middle right) are related with figure 12.SM.2 with the corresponding file names:\r\n ** 'CMIP5_HI41_AR6_regional_averages.json': data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_HI41_AR6_regional_averages.json': data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_HI41_AR6_regional_averages.json': data for the CORDEX multi-model ensemble\r\n\r\nGWL stands for global warming levels.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n You can find the scripts and the data to reproduce the figures on Github (link in Related Documents section), following the description below. Links to the catalogue records for relevant Chapter 12 figures are in the Related Records section of this catalogue record. \r\n\r\nPanel a:\r\n- the upper panels on the change of the number of days with SWE > 100mm are related with figure 12.10, panel d\r\n- the middle three panels on the change of the NOAA HI exceeding 41°C are related with figure 12.SM.2 \r\n- the lower panels on the 100-yr return period stream flow are related with figure 12.8, panel c\r\n\r\nPanel b:\r\n- upper left panels on the 100-yr return period stream flow in Europe are related with figure 12.9, panel c\r\n- upper right panels on the Maximum temperature exceeding 35°C are related with figure 12.SM.1 \r\n- middle right panels on Shoreline position change for EAS and RFE are associated with figure 12.6, panel d\r\n- the four panels right below on the NOAA Heat Index exceeding 41°C are related with figure 12.SM.2\r\n- the lower left panels on the 100-yr return period stream flow in Africa are related with figure 12.5, panel c\r\n- the lower right panels on the Shoreline position change in Australasia are related with figure 12.7, panel d\r\n\r\nThe final assembling of the panels to get the final figure was done with post-processing.\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 Github for chapter 12 containing data and code\r\n - Link to code for Chapter 12 archived on Zenodo" }, "objectObservation": { "ob_id": 37849, "uuid": "91c218d3a80f4c43ac665d0bdf0ed5e7", "short_code": "ob", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 12.5 (v20220804)", "abstract": "Input Data for Figure 12.5 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 12.5 shows projected changes in selected climatic impact-driver indices for Africa.\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 Africa 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 - Shoreline position change over Africa (pointwise) along sandy coasts by the year 2100 relative to 2010 (meters; negative values indicate shoreline retreat) from the CMIP5 based data set presented by Vousdoukas et al. (2020)\r\n - regional averages in Africa 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 in Africa of CMIP5 based projections (mean change estimates and bars the 5th-95th percentile range of associated uncertainty) of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 and RCP4.5 from Vousdoukas et al. (2020)\r\n\r\nSAH, ARP, WAF, CAF, NEAF, SEAF, WSAF, ESAF, MDG, NEU, WCE and MED 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 12.5:\r\n\r\n Panel a:\r\n - Q100_map_panel_a_AFR_less_MED_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 1995-2015 (recent past) for CORDEX RCP8.5; the data is from the AFR CORDEX domain, without the MED AR6 region\r\n - Q100_map_panel_a_MED_for_AFR_from_EUR_divdra.nc: same as previous file but for the MED AR6 region, taken from the EUR CORDEX domain\r\n\r\n Panel b:\r\n - CoastalRecession_AFRICA_RCP85_2100.json: pointwise values (color points on the map) for Africa of shoreline position mean changes between 2100 (long-term) and 2010 (recent past) from the CMIP5 based data set presented by Vousdoukas et al. (2020)\r\n\r\n Panel 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 2080-2099 (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 - ${CORDEX_domain}: the CORDEX domain\r\n\r\n Panel d:\r\n - globalErosionProjections_by_AR6_region_${scenario}_${horizon).json: regional averages of shoreline position changes for Africa, for the RCP4.5 and RCP8.5 scenarios (${scenario} = RCP45 and ${scenario} = RCP85 respectively) and the 2050 (mid-term, in blue) and 2100 (long-term, in red) future horizons (${horizon}=2050 and ${horizon}=2100 respectively) against the recent past period (2010); the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n\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 Coordinated Regional Downscaling Experiment from the WCRP. \r\nWCRP is the World Climate Research Programme. SSP stands for Shared Socioeconomic Pathway. \r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6. \r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\nRCP stands for Representative Concentration Pathway. \r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n For panel a, the plotting script ch12_fig12.5_plotting_code_Q100_AFR.py (see data tables and code on Github) draws the rivers and uses a subroutine (dranetwrite) to identify the rivers to plot them individually with lines, using the data from the Q100_map_panel_a_AFR_less_MED_divdra.nc and Q100_map_panel_a_MED_for_AFR_from_EUR_divdra.nc netcdf files; plotting the Q100 netcdf file will produce dots (and not rivers).\r\n\r\n For 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" } }, { "ob_id": 742, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38905, "uuid": "1030d40a071d4929bf04e08bfbd22c10", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.25 v20221111", "abstract": "Data for Figure TS.25 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure TS.25 shows the distribution of projected changes in selected climatic impact-driver (CID) indices for selected regions for Coupled Model Intercomparison Project Phases 5 and 6 (CMIP6, CMIP5) and Coordinated Regional Downscaling Experiment (CORDEX) model ensembles.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n For all the panels, the data provided consists of ensemble statistics (q5, median and q95) of the spatial averages over the IPCC AR6 regions of the list of indicators below for CMIP5, CMIP6 and CORDEX, for the recent past (1995-2014), the mid-term (2041-2060) and long-term (2081-2100) future horizons, as well as the +1.5, +2, and +4°C of global warming levels.\r\n The list of indicators shown on the figure is:\r\n - number of days per year with SWE > 100mm (North-America)\r\n - number of days with the NOAA Heat Index exceeding 41°C (Central-America and Asia)\r\n - the 100-yr return period stream flow (South-America, Europe, Africa)\r\n - the number of days per year with Maximum temperature exceeding 35°C (Asia)\r\n - the Shoreline position change (Asia, Australasia)\r\n\r\nSWE stands for snow water equivalent\r\nNOAA stands for National Oceanic and Atmospheric Administration.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nUpper panels of Panel (a):\r\nThe change of the number of days with SWE > 100mm are related with figure 12.10(d) with the corresponding file names:\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)\r\n ** 'NAM-22_CORDEX_NORTH-AMERICA_SWE_mask14_AR6_regional_averages.json' : same as previous file for the CORDEX-core NAM-22 multimodel ensemble\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)\r\n\r\nMiddle panels of Panel (a): \r\nThe change of the NOAA HI exceeding 41°C are related to figure 12.SM.2 with the corresponding file names:\r\n ** 'CMIP5_HI41_AR6_regional_averages.json' : data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_HI41_AR6_regional_averages.json' : data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_HI41_AR6_regional_averages.json' : data for the CORDEX multi-model ensemble\r\n see the description of the data associated with figure 12.SM.2 for more details on the structure of the files\r\n\r\nLower panels of Panel (a) and left panels of Panel (b):\r\n100-yr return period stream flow is shown for South America (figure 12.8(c)), Europe (figure 12.9(c)) and Africa (figure 12.5(c)) with corresponding file names: \r\n ** 'Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.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, 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 - ${CORDEX_domain}: the CORDEX domain\r\n** 'Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt' : files containing the Q100 regional averages of global warming levels with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${GWL}: the Global Warming Level: 1.5, 2 or 4\r\n - ${CORDEX_domain}: the CORDEX domain\r\n\r\nRight panels of Panel (b):\r\nThe Maximum temperature exceeding 35°C (upper right) are related with figure 12.SM.1 with the corresponding file names:\r\n ** 'CMIP5_tx35isimip_AR6_regional_averages.json' : data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_tx35isimip_AR6_regional_averages.json' : data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_tx35isimip_AR6_regional_averages.json' : data for the CORDEX multi-model ensemble\r\n see the description of the data associated with figure 12.SM.1 for more details on the structure of the files\r\n\r\nThe Shoreline position change for EAS and RFE (upper middle right) (related to figure 12.6(d)), and in Australasia (lower right) (related to figure 12.7(d)) have corresponding data file names:\r\n ** 'globalErosionProjections_by_AR6_region_${scenario}_${horizon).json' : regional averages of shoreline position changes for Africa, for the RCP4.5 and RCP8.5 scenarios (${scenario} = RCP45 and ${scenario} = RCP85 respectively) and the 2050 (mid-term, in blue) and 2100 (long-term, in red) future horizons (${horizon}=2050 and ${horizon}=2100 respectively) against the recent past period (2010); the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n\r\nThe four panels on the NOAA Heat Index exceeding 41°C (lower middle right) are related with figure 12.SM.2 with the corresponding file names:\r\n ** 'CMIP5_HI41_AR6_regional_averages.json': data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_HI41_AR6_regional_averages.json': data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_HI41_AR6_regional_averages.json': data for the CORDEX multi-model ensemble\r\n\r\nGWL stands for global warming levels.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n You can find the scripts and the data to reproduce the figures on Github (link in Related Documents section), following the description below. Links to the catalogue records for relevant Chapter 12 figures are in the Related Records section of this catalogue record. \r\n\r\nPanel a:\r\n- the upper panels on the change of the number of days with SWE > 100mm are related with figure 12.10, panel d\r\n- the middle three panels on the change of the NOAA HI exceeding 41°C are related with figure 12.SM.2 \r\n- the lower panels on the 100-yr return period stream flow are related with figure 12.8, panel c\r\n\r\nPanel b:\r\n- upper left panels on the 100-yr return period stream flow in Europe are related with figure 12.9, panel c\r\n- upper right panels on the Maximum temperature exceeding 35°C are related with figure 12.SM.1 \r\n- middle right panels on Shoreline position change for EAS and RFE are associated with figure 12.6, panel d\r\n- the four panels right below on the NOAA Heat Index exceeding 41°C are related with figure 12.SM.2\r\n- the lower left panels on the 100-yr return period stream flow in Africa are related with figure 12.5, panel c\r\n- the lower right panels on the Shoreline position change in Australasia are related with figure 12.7, panel d\r\n\r\nThe final assembling of the panels to get the final figure was done with post-processing.\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 Github for chapter 12 containing data and code\r\n - Link to code for Chapter 12 archived on Zenodo" }, "objectObservation": { "ob_id": 37861, "uuid": "d46d733725d64f45afc1e70054f2f51d", "short_code": "ob", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 12.6 (v20220804)", "abstract": "Input Data for Figure 12.6 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 12.6 shows projected changes in selected climatic impact-driver indices for Asia.\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 Asia 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 - Shoreline position change over Asia (pointwise) along sandy coasts by the year 2100 relative to 2010 (meters; negative values indicate shoreline retreat) from the CMIP5 based data set presented by Vousdoukas et al. (2020)\r\n - regional averages in Asia 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 in Asia of CMIP5 based projections (mean change estimates and bars the 5th-95th percentile range of associated uncertainty) of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 and RCP4.5 from Vousdoukas et al. (2020)\r\n\r\nTIB, ECA, EAS, SEA, ARP and SAS 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 12.6:\r\n \r\nPanel a:\r\n - Q100_map_panel_a_EAS_for_ASIA_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 1995-2015 (recent past) for CORDEX RCP8.5; the file contains the data for the regions from the EAS CORDEX domain\r\n - Q100_map_panel_a_SEA_for_ASIA_divdra.nc: same as previous file for the regions from the SEA CORDEX domain\r\n - Q100_map_panel_a_WAS_for_ASIA_divdra.nc: same as previous file for the regions from the WAS CORDEX domain\r\n \r\nPanel b:\r\n - CoastalRecession_ASIA_RCP85_2100.json: pointwise values (color points on the map) for Asia of shoreline position mean changes between 2100 (long-term) and 2010 (recent past) from the CMIP5 based data set presented by Vousdoukas et al. (2020)\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 2080-2099 (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 - ${CORDEX_domain}: the CORDEX domain\r\n \r\nPanel d:\r\n - globalErosionProjections_by_AR6_region_${scenario}_${horizon).json: regional averages of shoreline position changes for Africa, for the RCP4.5 and RCP8.5 scenarios (${scenario} = RCP45 and ${scenario} = RCP85 respectively) and the 2050 (mid-term, in blue) and 2100 (long-term, in red) future horizons (${horizon}=2050 and ${horizon}=2100 respectively) against the recent past period (2010); the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n\r\n\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP. \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\nSSP stands for Shared Socioeconomic Pathway. \r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6. \r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\nRCP stands for Representative Concentration Pathway. \r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\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 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" } }, { "ob_id": 743, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38905, "uuid": "1030d40a071d4929bf04e08bfbd22c10", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.25 v20221111", "abstract": "Data for Figure TS.25 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure TS.25 shows the distribution of projected changes in selected climatic impact-driver (CID) indices for selected regions for Coupled Model Intercomparison Project Phases 5 and 6 (CMIP6, CMIP5) and Coordinated Regional Downscaling Experiment (CORDEX) model ensembles.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n For all the panels, the data provided consists of ensemble statistics (q5, median and q95) of the spatial averages over the IPCC AR6 regions of the list of indicators below for CMIP5, CMIP6 and CORDEX, for the recent past (1995-2014), the mid-term (2041-2060) and long-term (2081-2100) future horizons, as well as the +1.5, +2, and +4°C of global warming levels.\r\n The list of indicators shown on the figure is:\r\n - number of days per year with SWE > 100mm (North-America)\r\n - number of days with the NOAA Heat Index exceeding 41°C (Central-America and Asia)\r\n - the 100-yr return period stream flow (South-America, Europe, Africa)\r\n - the number of days per year with Maximum temperature exceeding 35°C (Asia)\r\n - the Shoreline position change (Asia, Australasia)\r\n\r\nSWE stands for snow water equivalent\r\nNOAA stands for National Oceanic and Atmospheric Administration.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nUpper panels of Panel (a):\r\nThe change of the number of days with SWE > 100mm are related with figure 12.10(d) with the corresponding file names:\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)\r\n ** 'NAM-22_CORDEX_NORTH-AMERICA_SWE_mask14_AR6_regional_averages.json' : same as previous file for the CORDEX-core NAM-22 multimodel ensemble\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)\r\n\r\nMiddle panels of Panel (a): \r\nThe change of the NOAA HI exceeding 41°C are related to figure 12.SM.2 with the corresponding file names:\r\n ** 'CMIP5_HI41_AR6_regional_averages.json' : data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_HI41_AR6_regional_averages.json' : data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_HI41_AR6_regional_averages.json' : data for the CORDEX multi-model ensemble\r\n see the description of the data associated with figure 12.SM.2 for more details on the structure of the files\r\n\r\nLower panels of Panel (a) and left panels of Panel (b):\r\n100-yr return period stream flow is shown for South America (figure 12.8(c)), Europe (figure 12.9(c)) and Africa (figure 12.5(c)) with corresponding file names: \r\n ** 'Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.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, 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 - ${CORDEX_domain}: the CORDEX domain\r\n** 'Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt' : files containing the Q100 regional averages of global warming levels with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${GWL}: the Global Warming Level: 1.5, 2 or 4\r\n - ${CORDEX_domain}: the CORDEX domain\r\n\r\nRight panels of Panel (b):\r\nThe Maximum temperature exceeding 35°C (upper right) are related with figure 12.SM.1 with the corresponding file names:\r\n ** 'CMIP5_tx35isimip_AR6_regional_averages.json' : data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_tx35isimip_AR6_regional_averages.json' : data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_tx35isimip_AR6_regional_averages.json' : data for the CORDEX multi-model ensemble\r\n see the description of the data associated with figure 12.SM.1 for more details on the structure of the files\r\n\r\nThe Shoreline position change for EAS and RFE (upper middle right) (related to figure 12.6(d)), and in Australasia (lower right) (related to figure 12.7(d)) have corresponding data file names:\r\n ** 'globalErosionProjections_by_AR6_region_${scenario}_${horizon).json' : regional averages of shoreline position changes for Africa, for the RCP4.5 and RCP8.5 scenarios (${scenario} = RCP45 and ${scenario} = RCP85 respectively) and the 2050 (mid-term, in blue) and 2100 (long-term, in red) future horizons (${horizon}=2050 and ${horizon}=2100 respectively) against the recent past period (2010); the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n\r\nThe four panels on the NOAA Heat Index exceeding 41°C (lower middle right) are related with figure 12.SM.2 with the corresponding file names:\r\n ** 'CMIP5_HI41_AR6_regional_averages.json': data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_HI41_AR6_regional_averages.json': data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_HI41_AR6_regional_averages.json': data for the CORDEX multi-model ensemble\r\n\r\nGWL stands for global warming levels.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n You can find the scripts and the data to reproduce the figures on Github (link in Related Documents section), following the description below. Links to the catalogue records for relevant Chapter 12 figures are in the Related Records section of this catalogue record. \r\n\r\nPanel a:\r\n- the upper panels on the change of the number of days with SWE > 100mm are related with figure 12.10, panel d\r\n- the middle three panels on the change of the NOAA HI exceeding 41°C are related with figure 12.SM.2 \r\n- the lower panels on the 100-yr return period stream flow are related with figure 12.8, panel c\r\n\r\nPanel b:\r\n- upper left panels on the 100-yr return period stream flow in Europe are related with figure 12.9, panel c\r\n- upper right panels on the Maximum temperature exceeding 35°C are related with figure 12.SM.1 \r\n- middle right panels on Shoreline position change for EAS and RFE are associated with figure 12.6, panel d\r\n- the four panels right below on the NOAA Heat Index exceeding 41°C are related with figure 12.SM.2\r\n- the lower left panels on the 100-yr return period stream flow in Africa are related with figure 12.5, panel c\r\n- the lower right panels on the Shoreline position change in Australasia are related with figure 12.7, panel d\r\n\r\nThe final assembling of the panels to get the final figure was done with post-processing.\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 Github for chapter 12 containing data and code\r\n - Link to code for Chapter 12 archived on Zenodo" }, "objectObservation": { "ob_id": 37852, "uuid": "537b22f0230448fdb9a4ec806ed54d84", "short_code": "ob", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 12.7 (v20220804)", "abstract": "Input Data for Figure 12.7 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 12.7 shows projected changes in selected climatic impact-driver indices for Australasia.\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---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n - spatial field over Australasia 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 - Shoreline position change over Australasia (pointwise) along sandy coasts by the year 2100 relative to 2010 (meters; negative values indicate shoreline retreat) from the CMIP5 based data set presented by Vousdoukas et al. (2020)\r\n - regional averages in Australasia 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 in Australasia of CMIP5 based projections (mean change estimates and bars the 5th-95th percentile range of associated uncertainty) of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 and RCP4.5 from Vousdoukas et al. (2020)\r\n\r\nNAU, CAU, EAU, SAU and NZ 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 12.7:\r\n \r\nPanel a:\r\n - Q100_map_panel_a_AUS_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 1995-2015 (recent past) for CORDEX RCP8.5; the file contains the data for the regions from the AUS CORDEX domain\r\n\r\nPanel b:\r\n - CoastalRecession_Australasia_RCP85_2100.json: pointwise values (color points on the map) for Australasia of shoreline position mean changes between 2100 (long-term) and 2010 (recent past) from the CMIP5 based data set presented by Vousdoukas et al. (2020)\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 2080-2099 (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 - ${CORDEX_domain}: the CORDEX domain\r\n\r\nPanel d:\r\n - globalErosionProjections_by_AR6_region_${scenario}_${horizon).json: regional averages of shoreline position changes for Africa, for the RCP4.5 and RCP8.5 scenarios (${scenario} = RCP45 and ${scenario} = RCP85 respectively) and the 2050 (mid-term, in blue) and 2100 (long-term, in red) future horizons (${horizon}=2050 and ${horizon}=2100 respectively) against the recent past period (2010); the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n\r\n\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP. \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\nSSP stands for Shared Socioeconomic Pathway. \r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6. \r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\nRCP stands for Representative Concentration Pathway. \r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\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\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" } }, { "ob_id": 744, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38905, "uuid": "1030d40a071d4929bf04e08bfbd22c10", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.25 v20221111", "abstract": "Data for Figure TS.25 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure TS.25 shows the distribution of projected changes in selected climatic impact-driver (CID) indices for selected regions for Coupled Model Intercomparison Project Phases 5 and 6 (CMIP6, CMIP5) and Coordinated Regional Downscaling Experiment (CORDEX) model ensembles.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n For all the panels, the data provided consists of ensemble statistics (q5, median and q95) of the spatial averages over the IPCC AR6 regions of the list of indicators below for CMIP5, CMIP6 and CORDEX, for the recent past (1995-2014), the mid-term (2041-2060) and long-term (2081-2100) future horizons, as well as the +1.5, +2, and +4°C of global warming levels.\r\n The list of indicators shown on the figure is:\r\n - number of days per year with SWE > 100mm (North-America)\r\n - number of days with the NOAA Heat Index exceeding 41°C (Central-America and Asia)\r\n - the 100-yr return period stream flow (South-America, Europe, Africa)\r\n - the number of days per year with Maximum temperature exceeding 35°C (Asia)\r\n - the Shoreline position change (Asia, Australasia)\r\n\r\nSWE stands for snow water equivalent\r\nNOAA stands for National Oceanic and Atmospheric Administration.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nUpper panels of Panel (a):\r\nThe change of the number of days with SWE > 100mm are related with figure 12.10(d) with the corresponding file names:\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)\r\n ** 'NAM-22_CORDEX_NORTH-AMERICA_SWE_mask14_AR6_regional_averages.json' : same as previous file for the CORDEX-core NAM-22 multimodel ensemble\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)\r\n\r\nMiddle panels of Panel (a): \r\nThe change of the NOAA HI exceeding 41°C are related to figure 12.SM.2 with the corresponding file names:\r\n ** 'CMIP5_HI41_AR6_regional_averages.json' : data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_HI41_AR6_regional_averages.json' : data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_HI41_AR6_regional_averages.json' : data for the CORDEX multi-model ensemble\r\n see the description of the data associated with figure 12.SM.2 for more details on the structure of the files\r\n\r\nLower panels of Panel (a) and left panels of Panel (b):\r\n100-yr return period stream flow is shown for South America (figure 12.8(c)), Europe (figure 12.9(c)) and Africa (figure 12.5(c)) with corresponding file names: \r\n ** 'Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.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, 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 - ${CORDEX_domain}: the CORDEX domain\r\n** 'Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt' : files containing the Q100 regional averages of global warming levels with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${GWL}: the Global Warming Level: 1.5, 2 or 4\r\n - ${CORDEX_domain}: the CORDEX domain\r\n\r\nRight panels of Panel (b):\r\nThe Maximum temperature exceeding 35°C (upper right) are related with figure 12.SM.1 with the corresponding file names:\r\n ** 'CMIP5_tx35isimip_AR6_regional_averages.json' : data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_tx35isimip_AR6_regional_averages.json' : data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_tx35isimip_AR6_regional_averages.json' : data for the CORDEX multi-model ensemble\r\n see the description of the data associated with figure 12.SM.1 for more details on the structure of the files\r\n\r\nThe Shoreline position change for EAS and RFE (upper middle right) (related to figure 12.6(d)), and in Australasia (lower right) (related to figure 12.7(d)) have corresponding data file names:\r\n ** 'globalErosionProjections_by_AR6_region_${scenario}_${horizon).json' : regional averages of shoreline position changes for Africa, for the RCP4.5 and RCP8.5 scenarios (${scenario} = RCP45 and ${scenario} = RCP85 respectively) and the 2050 (mid-term, in blue) and 2100 (long-term, in red) future horizons (${horizon}=2050 and ${horizon}=2100 respectively) against the recent past period (2010); the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n\r\nThe four panels on the NOAA Heat Index exceeding 41°C (lower middle right) are related with figure 12.SM.2 with the corresponding file names:\r\n ** 'CMIP5_HI41_AR6_regional_averages.json': data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_HI41_AR6_regional_averages.json': data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_HI41_AR6_regional_averages.json': data for the CORDEX multi-model ensemble\r\n\r\nGWL stands for global warming levels.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n You can find the scripts and the data to reproduce the figures on Github (link in Related Documents section), following the description below. Links to the catalogue records for relevant Chapter 12 figures are in the Related Records section of this catalogue record. \r\n\r\nPanel a:\r\n- the upper panels on the change of the number of days with SWE > 100mm are related with figure 12.10, panel d\r\n- the middle three panels on the change of the NOAA HI exceeding 41°C are related with figure 12.SM.2 \r\n- the lower panels on the 100-yr return period stream flow are related with figure 12.8, panel c\r\n\r\nPanel b:\r\n- upper left panels on the 100-yr return period stream flow in Europe are related with figure 12.9, panel c\r\n- upper right panels on the Maximum temperature exceeding 35°C are related with figure 12.SM.1 \r\n- middle right panels on Shoreline position change for EAS and RFE are associated with figure 12.6, panel d\r\n- the four panels right below on the NOAA Heat Index exceeding 41°C are related with figure 12.SM.2\r\n- the lower left panels on the 100-yr return period stream flow in Africa are related with figure 12.5, panel c\r\n- the lower right panels on the Shoreline position change in Australasia are related with figure 12.7, panel d\r\n\r\nThe final assembling of the panels to get the final figure was done with post-processing.\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 Github for chapter 12 containing data and code\r\n - Link to code for Chapter 12 archived on Zenodo" }, "objectObservation": { "ob_id": 37903, "uuid": "0b5c980aa58447508eccdda79554b2b7", "short_code": "ob", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 12.8 (v20220804)", "abstract": "Input Data for Figure 12.8 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 12.8 shows projected changes in selected climatic impact-driver indices for Central and South America.\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---------------------------------------------------\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 South-America and Central-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\r\n- Shoreline position change over South-America (pointwise) along sandy coasts by the year 2100 relative to 2010 (meters; negative values indicate shoreline retreat) from the CMIP5 based data set presented by Vousdoukas et al. (2020)\r\n\r\n- regional averages in South-America and Central-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\r\n - CMIP6 historical, ssp126 and ssp585\r\n\r\n - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5\r\n\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n\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\r\n- regional averages in South-America and Central-America of CMIP5 based projections (mean change estimates and bars the 5th-95th percentile range of associated uncertainty) of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 and RCP4.5 from Vousdoukas et al. (2020)\r\n\r\nNWS, NSA, SAM, NES, SWS, SES, SSA, CAR and SCA 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 12.8:\r\n\r\nPanel a:\r\n\r\n- Q100_map_panel_a_SAM_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 1995-2015 (recent past) for CORDEX RCP8.5; the file contains the data for the regions from the SAM CORDEX domain\r\n\r\n- Q100_map_panel_a_CAM_for_SAM_divdra.nc: same as previous file for the regions from the CAM CORDEX domain\r\n\r\nPanel b:\r\n\r\n- CoastalRecession_SOUTH-AMERICA_RCP85_2100.json: pointwise values (color points on the map) for South-America and Central-America of shoreline position mean changes between 2100 (long-term) and 2010 (recent past) from the CMIP5 based data set presented by Vousdoukas et al. (2020)\r\n\r\nPanel c:\r\n\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 - ${CORDEX_domain}: the CORDEX domain\r\n\r\n- txt files containing the Q100 regional averages of global warming levels: Q100_${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 - ${CORDEX_domain}: the CORDEX domain\r\n\r\nPanel d:\r\n\r\n- globalErosionProjections_by_AR6_region_${scenario}_${horizon).json: regional averages of shoreline position changes for Africa, for the RCP4.5 and RCP8.5 scenarios (${scenario} = RCP45 and ${scenario} = RCP85 respectively) and the 2050 (mid-term, in blue) and 2100 (long-term, in red) future horizons (${horizon}=2050 and ${horizon}=2100 respectively) against the recent past period (2010); the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\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 Coordinated Regional Downscaling Experiment from the WCRP. \r\nWCRP is the World Climate Research Programme. SSP stands for Shared Socioeconomic Pathway. \r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6. \r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\nRCP stands for Representative Concentration Pathway. \r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nFor 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\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 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 12GitHub repository" } }, { "ob_id": 745, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38905, "uuid": "1030d40a071d4929bf04e08bfbd22c10", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.25 v20221111", "abstract": "Data for Figure TS.25 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure TS.25 shows the distribution of projected changes in selected climatic impact-driver (CID) indices for selected regions for Coupled Model Intercomparison Project Phases 5 and 6 (CMIP6, CMIP5) and Coordinated Regional Downscaling Experiment (CORDEX) model ensembles.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n For all the panels, the data provided consists of ensemble statistics (q5, median and q95) of the spatial averages over the IPCC AR6 regions of the list of indicators below for CMIP5, CMIP6 and CORDEX, for the recent past (1995-2014), the mid-term (2041-2060) and long-term (2081-2100) future horizons, as well as the +1.5, +2, and +4°C of global warming levels.\r\n The list of indicators shown on the figure is:\r\n - number of days per year with SWE > 100mm (North-America)\r\n - number of days with the NOAA Heat Index exceeding 41°C (Central-America and Asia)\r\n - the 100-yr return period stream flow (South-America, Europe, Africa)\r\n - the number of days per year with Maximum temperature exceeding 35°C (Asia)\r\n - the Shoreline position change (Asia, Australasia)\r\n\r\nSWE stands for snow water equivalent\r\nNOAA stands for National Oceanic and Atmospheric Administration.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nUpper panels of Panel (a):\r\nThe change of the number of days with SWE > 100mm are related with figure 12.10(d) with the corresponding file names:\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)\r\n ** 'NAM-22_CORDEX_NORTH-AMERICA_SWE_mask14_AR6_regional_averages.json' : same as previous file for the CORDEX-core NAM-22 multimodel ensemble\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)\r\n\r\nMiddle panels of Panel (a): \r\nThe change of the NOAA HI exceeding 41°C are related to figure 12.SM.2 with the corresponding file names:\r\n ** 'CMIP5_HI41_AR6_regional_averages.json' : data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_HI41_AR6_regional_averages.json' : data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_HI41_AR6_regional_averages.json' : data for the CORDEX multi-model ensemble\r\n see the description of the data associated with figure 12.SM.2 for more details on the structure of the files\r\n\r\nLower panels of Panel (a) and left panels of Panel (b):\r\n100-yr return period stream flow is shown for South America (figure 12.8(c)), Europe (figure 12.9(c)) and Africa (figure 12.5(c)) with corresponding file names: \r\n ** 'Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.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, 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 - ${CORDEX_domain}: the CORDEX domain\r\n** 'Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt' : files containing the Q100 regional averages of global warming levels with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${GWL}: the Global Warming Level: 1.5, 2 or 4\r\n - ${CORDEX_domain}: the CORDEX domain\r\n\r\nRight panels of Panel (b):\r\nThe Maximum temperature exceeding 35°C (upper right) are related with figure 12.SM.1 with the corresponding file names:\r\n ** 'CMIP5_tx35isimip_AR6_regional_averages.json' : data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_tx35isimip_AR6_regional_averages.json' : data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_tx35isimip_AR6_regional_averages.json' : data for the CORDEX multi-model ensemble\r\n see the description of the data associated with figure 12.SM.1 for more details on the structure of the files\r\n\r\nThe Shoreline position change for EAS and RFE (upper middle right) (related to figure 12.6(d)), and in Australasia (lower right) (related to figure 12.7(d)) have corresponding data file names:\r\n ** 'globalErosionProjections_by_AR6_region_${scenario}_${horizon).json' : regional averages of shoreline position changes for Africa, for the RCP4.5 and RCP8.5 scenarios (${scenario} = RCP45 and ${scenario} = RCP85 respectively) and the 2050 (mid-term, in blue) and 2100 (long-term, in red) future horizons (${horizon}=2050 and ${horizon}=2100 respectively) against the recent past period (2010); the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n\r\nThe four panels on the NOAA Heat Index exceeding 41°C (lower middle right) are related with figure 12.SM.2 with the corresponding file names:\r\n ** 'CMIP5_HI41_AR6_regional_averages.json': data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_HI41_AR6_regional_averages.json': data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_HI41_AR6_regional_averages.json': data for the CORDEX multi-model ensemble\r\n\r\nGWL stands for global warming levels.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n You can find the scripts and the data to reproduce the figures on Github (link in Related Documents section), following the description below. Links to the catalogue records for relevant Chapter 12 figures are in the Related Records section of this catalogue record. \r\n\r\nPanel a:\r\n- the upper panels on the change of the number of days with SWE > 100mm are related with figure 12.10, panel d\r\n- the middle three panels on the change of the NOAA HI exceeding 41°C are related with figure 12.SM.2 \r\n- the lower panels on the 100-yr return period stream flow are related with figure 12.8, panel c\r\n\r\nPanel b:\r\n- upper left panels on the 100-yr return period stream flow in Europe are related with figure 12.9, panel c\r\n- upper right panels on the Maximum temperature exceeding 35°C are related with figure 12.SM.1 \r\n- middle right panels on Shoreline position change for EAS and RFE are associated with figure 12.6, panel d\r\n- the four panels right below on the NOAA Heat Index exceeding 41°C are related with figure 12.SM.2\r\n- the lower left panels on the 100-yr return period stream flow in Africa are related with figure 12.5, panel c\r\n- the lower right panels on the Shoreline position change in Australasia are related with figure 12.7, panel d\r\n\r\nThe final assembling of the panels to get the final figure was done with post-processing.\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 Github for chapter 12 containing data and code\r\n - Link to code for Chapter 12 archived on Zenodo" }, "objectObservation": { "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." } } ] }