Result List

HTTP 200 OK
Allow: GET, POST, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

{
    "count": 11555,
    "next": "https://api.catalogue.ceda.ac.uk/api/v3/results/?format=api&limit=100&offset=9500",
    "previous": "https://api.catalogue.ceda.ac.uk/api/v3/results/?format=api&limit=100&offset=9300",
    "results": [
        {
            "ob_id": 37216,
            "uuid": "54754344635f4035941cb1030ff3c0ae",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.1.0.0/country",
            "numberOfFiles": 163,
            "volume": 13487610,
            "fileFormat": "Data are NetCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37208,
                "uuid": "59a7cd0dcd474f5f906ead4073a9be8b",
                "short_code": "ob",
                "title": "HadUK-Grid Climate Observations by UK countries, 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 countries 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snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations.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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37217,
            "uuid": "f1a0b38437b44371b428c19959580177",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.1.0.0/region",
            "numberOfFiles": 163,
            "volume": 20685180,
            "fileFormat": "Data are NetCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37218,
            "uuid": "f976ead364384450929158694b2c943e",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.1.0.0/60km",
            "numberOfFiles": 6340,
            "volume": 550187706,
            "fileFormat": "Data are NetCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37219,
            "uuid": "8549bbd4b00d43f9ac8f6500a4195a0e",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.1.0.0/25km",
            "numberOfFiles": 6340,
            "volume": 2184083579,
            "fileFormat": "Data are NetCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37220,
            "uuid": "2159bc7f755f4126a8a6a9e82ddb70aa",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.1.0.0/12km",
            "numberOfFiles": 6340,
            "volume": 9323967714,
            "fileFormat": "Data are NetCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37221,
            "uuid": "2bcc374d730c42439605d7f898290d8f",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.1.0.0/1km",
            "numberOfFiles": 6340,
            "volume": 1299119852240,
            "fileFormat": "Data are NetCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37222,
            "uuid": "d1a7d41ba06248a589758228468e1d77",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.1.0.0/5km",
            "numberOfFiles": 6340,
            "volume": 52174651514,
            "fileFormat": "Data are NetCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37223,
            "uuid": "39d948be41dd423dba6f0204887c01d0",
            "short_code": "result",
            "curationCategory": "C",
            "dataPath": "/badc/ukmo-nimrod/data/single-site/jersey/raw-dual-polar/",
            "numberOfFiles": 1,
            "volume": 967,
            "fileFormat": "Data are NIMROD binary formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37157,
                "uuid": "231b2448a02a45dfa810739f8d6abbe3",
                "short_code": "ob",
                "title": "Jersey C-band rain radar dual polar products",
                "abstract": "Dual-polar products from the Met Office's Jersey C-band rain radar, Channel Islands. Data from this site include augmented ldr (linear depolarisation ratio) and zdr (differential reflectivity) scan data (both long and short pulse) available from June 2018 at present. The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37224,
            "uuid": "ecd3b586e7114c778e66048c2f9894d7",
            "short_code": "result",
            "curationCategory": "C",
            "dataPath": "/badc/ukmo-nimrod/data/single-site/ingham/raw-dual-polar/",
            "numberOfFiles": 1,
            "volume": 967,
            "fileFormat": "Data are NIMROD binary formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37158,
                "uuid": "16e105340d384a018c6b677e8e1d55e0",
                "short_code": "ob",
                "title": "Ingham C-band rain radar dual polar products",
                "abstract": "Dual-polar products from the Met Office's Ingham C-band rain radar, Lincolnshire, England. Data from this site include augmented ldr (linear depolarisation ratio) and zdr (differential reflectivity) scan data (both long and short pulse) available from August 2018 at present. The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37226,
            "uuid": "e901aad52c1f4f02a3f30db096f2dc19",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/deposited2022/WW_leaf_clip_species/",
            "numberOfFiles": 0,
            "volume": 0,
            "fileFormat": "Data are BADC-CSV formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": null,
            "onlineresource_set": []
        },
        {
            "ob_id": 37245,
            "uuid": "c0d17c34dd9b461497b6b5d292000625",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/deposited2022/WW_leaf_clip_species/",
            "numberOfFiles": 2,
            "volume": 22189306,
            "fileFormat": "Data are BADC-CSV formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37244,
                "uuid": "8a798466b9f74b3da500004a94ee5fee",
                "short_code": "ob",
                "title": "Intra-annual multi-temporal reflectance of 17 herbaceous species of chalk grassland using leaf-clip mounted on spectrometer",
                "abstract": "A time-series of leaf-level optical hyperspectral reflectance captured using a leaf-clip and handheld spectrometer for 17 herbaceous species typical of chalk grassland habitat in Kent UK, over 13 sampling dates in 2021.  Data were collected using a non-imaging spectrometer manufactured by Spectra-vista Corporation fitted with a leaf-clip; capturing reflectance from 350 - 2500nm.  Funded by NERC Grant NE/L002566/1."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37249,
            "uuid": "1d4a777daf01440399a2509d2f648436",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/esacci/lakes/data/lake_products/L3S/v2.0.1/",
            "numberOfFiles": 10326,
            "volume": 49364168500,
            "fileFormat": "Data are in NetCDF format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37251,
            "uuid": "5444f33708d94172a5feb93410a06cbc",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/neodc/avhrr3_metop_c/data/l1b/",
            "numberOfFiles": 22002,
            "volume": 9239305557868,
            "fileFormat": "Data are in Eumetsat native format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37250,
                "uuid": "d881fc23e0dd489ba1bf8e3f870a172c",
                "short_code": "ob",
                "title": "L1b AVHRR-3 Radiometric imager data onboard MetOp-C",
                "abstract": "Data from the Advanced Very High Resolution Radiometer-3 (AVHRR-3) on board the Eumetsat Polar System (EPS) MetOp-C satellite.\r\n\r\nAVHRR-3 scans the Earth's surface in six spectral bands in the range of 0.58-12.5 microns, to provide day and night imaging of land, water and clouds and measurements of sea surface temperature, ice snow and vegetation cover.  The instruments were provided by the National Oceanic and Atmospheric Administration (NOAA) and is flown on the EPS-METOP series of satellites.\r\n\r\nCEDA currently provides a copy of the L1B data, which were acquired directly from EUMETSAT."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37256,
            "uuid": "70dca229f6d74f3b903328a56320d2c1",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/link/data/ddc",
            "numberOfFiles": 1668,
            "volume": 390657717,
            "fileFormat": "Unknown",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37255,
                "uuid": "4555b38d2c5a48edbbe5f1e72670d7a4",
                "short_code": "ob",
                "title": "IPCC Data Distribution Centre subsets provided by the LINK project.",
                "abstract": "This dataset contains output data from a number of models  associated  with the IPCC Third Assessment Report.  This data was processed at the Climate Research Unit at the University of East Anglia. The data extraction was intended for use by the Climate Impacts Community (and was funded by the UK Department of Environment Food and Rural Affairs, Defra).\r\n\r\nData from various modelling centres and models: CCCMA, CSIRO, ECHAM4, GFDL99, HADCM3, NIES99."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37260,
            "uuid": "e880f56dfa7e4749969f0fdc465b53e9",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/acsis/CPOM-model-sea-ice",
            "numberOfFiles": 0,
            "volume": 0,
            "fileFormat": "Data are NetCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": null,
            "onlineresource_set": []
        },
        {
            "ob_id": 37271,
            "uuid": "5d749abd75f442af879244fa05883236",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/acsis/cpom-model-sea-ice",
            "numberOfFiles": 0,
            "volume": 0,
            "fileFormat": "Data are NetCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": null,
            "onlineresource_set": []
        },
        {
            "ob_id": 37281,
            "uuid": "2040bcd0737e4deb8050b5c51be88a77",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig11/v20220510",
            "numberOfFiles": 6,
            "volume": 61916,
            "fileFormat": "csv\r\ntxt",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37282,
            "uuid": "d985cd3ed37040c7b24ccc2a148aa4e6",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/esacci/land_surface_temperature/data/MULTISENSOR_IRCDR/L3S/0.01/v2.00/monthly/",
            "numberOfFiles": 589,
            "volume": 853935291448,
            "fileFormat": "Data are in NetCDF format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37126,
                "uuid": "785ef9d3965442669bff899540747e28",
                "short_code": "ob",
                "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2020), version 2.00",
                "abstract": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer on Sentinel 3A (SLSTRA); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra) to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from August 1995 to July 2002; AATSR from August 2002 to March 2012; MODIS Terra from April 2012 to July 2016; and SLSTRA from August 2016 to December 2020. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width.\r\n\r\nThe dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml).\r\n\r\nLSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. Full Earth coverage is achieved in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st August 1995 and ends on 31st December 2020. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies. \r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37283,
            "uuid": "1cb1e0cdc69a4a338b96f2758a259ed0",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/esacci/land_surface_temperature/data/TERRA_MODIS/L3C/0.01/v3.00/monthly/",
            "numberOfFiles": 453,
            "volume": 1094815295871,
            "fileFormat": "Data are in NetCDF format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37125,
                "uuid": "32d7bc64c7b740e9ad7a43589ab91592",
                "short_code": "ob",
                "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from  MODIS (Moderate resolution Infra-red Spectroradiometer) on Terra, level 3 collated (L3C) global product (2000-2018), version 3.00",
                "abstract": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Terra (Terra). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Terra equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nThe monthly dataset starts from March 2000 and ends  December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37284,
            "uuid": "2906f7defb784600a918440ecdade33c",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/esacci/land_surface_temperature/data/AQUA_MODIS/L3C/0.01/v3.00/monthly/",
            "numberOfFiles": 397,
            "volume": 955324716523,
            "fileFormat": "Data are in NetCDF format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37124,
                "uuid": "fe98aa1c666d42b9a2a0d19a72bb8a36",
                "short_code": "ob",
                "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from  MODIS (Moderate resolution Infra-red Spectroradiometer) on Aqua, level 3 collated (L3C) global product (2002-2018), version 3.00",
                "abstract": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Aqua (Aqua). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the daytime and night-time Aqua equator crossing times which are 13:30 and 01:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 4th July 2002 and ends on 31st December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37285,
            "uuid": "9c792d0a5cc54cd4bdc8aeb0cee93eaf",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/esacci/land_surface_temperature/data/SENTINEL3B_SLSTR/L3C/0.01/v3.00/monthly/",
            "numberOfFiles": 51,
            "volume": 97348034301,
            "fileFormat": "Data are in NetCDF format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37123,
                "uuid": "b54d5f1c08594879a05929ce09951c56",
                "short_code": "ob",
                "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2020), version 3.00",
                "abstract": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage runs from December 2018 to December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37286,
            "uuid": "7423663a8e4341bd831ce1f54d49faf6",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/esacci/land_surface_temperature/data/SENTINEL3A_SLSTR/L3C/0.01/v3.00/monthly/",
            "numberOfFiles": 113,
            "volume": 225180016553,
            "fileFormat": "Data are in NetCDF format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37122,
                "uuid": "aa8268e2ca0e48d98aee372795722253",
                "short_code": "ob",
                "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2020), version 3.00",
                "abstract": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st May 2016 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37287,
            "uuid": "edc15b78c9ab41bcbfa2ebba18c6dbac",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/esacci/land_surface_temperature/data/ENVISAT_ATSR/L3C/0.01/v3.00/monthly/",
            "numberOfFiles": 233,
            "volume": 424023897857,
            "fileFormat": "Data are in NetCDF format.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37121,
                "uuid": "2ac9a3e7bdeb41b58b226a2fa612a4a3",
                "short_code": "ob",
                "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from AATSR (Advanced Along-Track Scanning Radiometer), level 3 collated (L3C) global product (2002-2012), version 3.00",
                "abstract": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Envisat equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. AATSR achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage for the monthly dataset starts from August 2002 and ends March 2012. There is a twelve day gap in the underlying data due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37288,
            "uuid": "b9f48f8971bc4d18b7109c29d42aa2b4",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/esacci/land_surface_temperature/data/ERS-2_ATSR/L3C/0.01/v3.00/monthly/",
            "numberOfFiles": 169,
            "volume": 304719474314,
            "fileFormat": "Data are in NetCDF format.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37120,
                "uuid": "16c633f003ef4d8481420f052356c11c",
                "short_code": "ob",
                "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2),  level 3 collated (L3C) global product (1995-2013), version 3.00",
                "abstract": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time. \r\n\r\nPer pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.\r\n\r\nAlso provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag,  and land cover class.\r\n\r\nThe dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml.\r\n\r\nLSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st August 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37292,
            "uuid": "40b02425001344e88d163a42722648cd",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadISDH/mon/HadISDHTable/r1/v4-4-0-2021f/",
            "numberOfFiles": 8,
            "volume": 93467418,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37293,
            "uuid": "a042271e6b0a458088b37dff4eafe6d6",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadISDH-marine/mon/HadISDHTable/r1/v1-3-0-2021f/",
            "numberOfFiles": 8,
            "volume": 240979716,
            "fileFormat": "Data are NetCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37294,
            "uuid": "9f11d1587a334bd7939294e24c5c17f8",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadISDH-blend/mon/HadISDHTable/r1/v1-3-0-2021f/",
            "numberOfFiles": 8,
            "volume": 169930678,
            "fileFormat": "Data are NetCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37295,
            "uuid": "9e13eed9d9ef4172840a5931e77b59be",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/neodc/comet/publications_data/Ou_et_al_JGR_2022/v1.0/",
            "numberOfFiles": 127,
            "volume": 4380628626,
            "fileFormat": "These data are provided in GeoTIFF (.tif) or NETCDF (.grd) formats.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37273,
                "uuid": "7fbb95c288af44ab8b40e74fef0e7cbc",
                "short_code": "ob",
                "title": "Velocity and strain rate fields of the northeast Tibetan Plateau",
                "abstract": "This data set contains velocity and strain rate fields over the northeast Tibetan Plateau, which are derived from Sentinel-1A and -1B synthetic aperture radar satellite data (SAR) and stored in GeoTIFF (.tif) or NETCDF (.grd) formats.\r\nThe velocities in the line-of-sights (LOS) of the satellites were processed at ~100 m resolution from time series in ~250km x 250km frames. The data set consists of velocities from 10 frames in ascending tracks and 13 frames in descending tracks of the satellites' orbits. The spatial extent of the velocities spans 96E-108E and 32N-43N, covering an area of 660,000 km^2. The temporal coverages of the data span from October 2014 to December 2019 across 65-110 acquisition epochs. The uncertainties of the velocities average to <1 mm/yr. The time series are inverted from fully-connected networks of short-temporal-baseline interferograms which are generated from interfering and unwrapping pairs of SAR imagery. The velocities represent the average velocity through the displacement time series. \r\n\r\nThe LOS velocities were decomposed into east and vertical velocities which are also archived with associated uncertainties. These Cartesian fields cover the overlapping areas between ascending and descending tracks and total 440,000 km^2. By combining the horizontal gradients of the filtered east velocities and interpolated north velocities from Global Navigational Satellite System, we derive second invariant, maximum shear, and dilatation strain rate fields for the same area with 1 km sampling intervals. \r\nThese strain rate fields highlight creeping sections and strain concentration on faults and fault junctions. The velocity fields reveal fault kinematics in terms of slip rates and partitioning. The vertical velocities also show non-tectonic signals such as subsidence related to permafrost melting, groundwater extraction, and reservoir loading, as well uplift from blocked drainages. \r\n\r\nThe data are collected and processed by Qi Ou with the automatic processing tools developed by Milan Lazecky. Velocity and strain rate fields were interpreted by all authors. By default, interferograms were generated from each epoch to six consecutive epochs and between acquisition pairs with six-month and nine-month temporal baselines. Interferograms with the unwrapping error were removed from the network and all networks were continuous and fully connected."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37297,
            "uuid": "e9c2e74f43cc4acfbf748d41358920de",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/deposited2022/aerosol_inhibited_paper_data/",
            "numberOfFiles": 1617,
            "volume": 2436431192253,
            "fileFormat": "Data are NetCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37296,
                "uuid": "406f88ee14f34177934b1dbd0be6aac7",
                "short_code": "ob",
                "title": "Simulation data used in the Suppression of surface ozone by an aerosol-inhibited photochemical ozone regime journal article",
                "abstract": "This dataset contains the data used to plot results found in the Suppression of surface ozone by an aerosol-inhibited photochemical ozone regime journal article published in Nature Geoscience. The simulations were run using the GEOS-Chem V12.8 chemical transport model at 0.5-degree horizontal resolution over the domain 170W-170E, 10S-60N using 2017 meteorological data for 1750, 1970 and 2014 emissions scenarios.   July 2017 GEOS-FP (forward-processing) meteorological fields were used for all simulations.  \r\n\r\nThree experimental runs were performed using 1750 emissions; no sea salt, no dust and no biomass burning emissions. One experiment was run using 1970 emissions; no shipping emissions. Three experimental runs were performed using 2014 emissions with three different HO2 uptake coefficients; 0.1, 0.05 and 0 (no uptake). Surface data is archived for all simulations, additionally, data at pressure levels 200 hPa, and 500 hPa 800 hPa were archived for 2014."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37306,
            "uuid": "72b3c9cb16494d6e87cf94c74c1bf4a3",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/deposited2022/bioarc-realtime-bioaerosol/data/",
            "numberOfFiles": 8,
            "volume": 74595685,
            "fileFormat": "Data are NetCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37305,
                "uuid": "14dfd0ba5212422c9c72b5184cbf5330",
                "short_code": "ob",
                "title": "BIOARC: ground site real-time bioaerosol spectrometer datasets (2019-2021)",
                "abstract": "These datasets contain total, non-fluorescent and bio-fluorescent aerosol particle concentrations and particle size distributions collected with University of Manchester WIBS-4M an MBS-M spectrometers during the Towards a UK Airborne Bioaerosol Climatology (BIOARC) project.  \r\n\r\nData was collected at the following ground sites:\r\nCardington Meteorological Research Unit: MBS-M, 11/04/2019 - 09/06/2019\r\nChilbolton Observatory: WIBS-4D, 14/05/2019 - 14/06/2019\r\nWeybourne Atmospheric Observatory: WIBS-4M, 03/06/2019 - 01/08/2019\r\nChilbolton Observatory: WIBS-4M, 10/09/2020 - 21/06/2021\r\nWeybourne Atmospheric Observatory: MBS-M, 15/09/2020 - 03/11/2019\r\nWeybourne Atmospheric Observatory: MBS-M, 15/04/2021 - 16/07/2021\r\n\r\nNERC reference NE/S002049/1"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37309,
            "uuid": "77b73bbc2997499c94f57e29a2a98269",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_02/inputdata_ch2_fig11/v20220428",
            "numberOfFiles": 10,
            "volume": 152140,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37312,
            "uuid": "1f8f3e2e1cbd416c9181f193035806a3",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/capeverde/data/cv-met-davis/",
            "numberOfFiles": 0,
            "volume": 0,
            "fileFormat": "Data are NASA Ames formatted up to 2019 then netcdf",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": null,
            "onlineresource_set": []
        },
        {
            "ob_id": 37314,
            "uuid": "f81862e3ae1642158c6a17d82a9a6a54",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/capeverde/data/cv-met-7.5m",
            "numberOfFiles": 138,
            "volume": 352016722,
            "fileFormat": "Data are NASA Ames formatted until 2019 then netcdf",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37313,
                "uuid": "b939606648494fa1b35a1fee04a459e6",
                "short_code": "ob",
                "title": "Cape Verde Atmospheric Observatory: 7.5 meter tower meteorological measurements (2006 onwards)",
                "abstract": "Data from observations made at the Cape Verde Atmospheric Observatory (CVAO) which exists to advance understanding of climatically significant interactions between the atmosphere and ocean and to provide a regional focal point and long-term data. \r\n\r\nThe observatory is based on Calhau Island of São Vicente, Cape Verde at 16.848N, 24.871W, in the tropical Eastern North Atlantic Ocean, a region which is data poor but plays a key role in atmosphere-ocean interactions of climate-related and biogeochemical parameters including greenhouse gases. It is an open-ocean site that is representative of a region likely to be sensitive to future climate change, and is minimally influenced by local effects and intermittent continental pollution. \r\n\r\nThe dataset contains meteorological measurements (wind speed, wind direction, atmospheric pressure, air temperature, relative humidity, solar radiation, rainfall) made at 7.5m height."
            },
            "onlineresource_set": [
                52128
            ]
        },
        {
            "ob_id": 37315,
            "uuid": "da1ba6ec606746cf8ecb0e9ad97dd7fb",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/capeverde/data/cv-met-campbell",
            "numberOfFiles": 0,
            "volume": 0,
            "fileFormat": "Data are NASA Ames formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": null,
            "onlineresource_set": [
                52129
            ]
        },
        {
            "ob_id": 37324,
            "uuid": "f7b5dc3ac2174a09ba07ba08c742828e",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/acsis/cpom-model-sea-ice/data/CICE2018_v5.1_ORCA1_1980_2020_NCEP2_CPOM",
            "numberOfFiles": 497,
            "volume": 4905241296,
            "fileFormat": "Data are NetCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37323,
                "uuid": "888a2e5b9177455586127b48031461a6",
                "short_code": "ob",
                "title": "ACSIS: Pan-Arctic sea ice simulations CICEv5.1.2 with prognostic melt pond model and EAP rheology with  modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter with NCEP Reanalysis-2 atmospheric forcing data from 1980 - 2020",
                "abstract": "This dataset includes model output from a stand-alone ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) with an atmospheric forcing data set are applied: NCEP Reanalysis-2 (NCEP2) data (Kanamitsu et al., 2002, updated 2020). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. \r\n\r\nThe specific parameters for this dataset are:\r\nsea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology, but with several modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter (see Schroeder et al., TC 2019)\r\nocean model: mixed-layer\r\nperiod: 1980-2020\r\natmospheric forcing: NCEP2\r\ndomain: pan-Arctic\r\ngrid resolution: 1deg ORCA\r\n\r\nThe simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37326,
            "uuid": "dfbd5b43bd714b3588299e693fdf42de",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/acsis/cpom-model-sea-ice/data/CICE2018_v5.1_ORCA1_1980_2020_NCEP2_def",
            "numberOfFiles": 501,
            "volume": 4513488284,
            "fileFormat": "Data are in NETCDF format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37325,
                "uuid": "e0a895453c7f4ba8b6864580d0a4b56a",
                "short_code": "ob",
                "title": "ACSIS: Pan-Arctic sea ice simulations CICEv5.1.2 with prognostic melt pond model and EAP rheology without modifications with NCEP Reanalysis-2 atmospheric forcing data from 1980 - 2020",
                "abstract": "This dataset includes model output from a stand-alone ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) with an atmospheric forcing data set are applied: NCEP Reanalysis-2 (NCEP2) data (Kanamitsu et al., 2002, updated 2020). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. \r\n\r\nThe specific parameters for this dataset are:\r\nsea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology\r\nocean model: mixed-layer\r\nperiod: 1980-2020\r\natmospheric forcing: NCEP2\r\ndomain: pan-Arctic\r\ngrid resolution: 1deg ORCA\r\n\r\nThe simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37327,
            "uuid": "76b575e9c30b40489aeaa78399be180d",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/acsis/cpom-model-sea-ice/data/CICE2018_v5.1_ORCA1_1980_2020_NCEP2_CPOM",
            "numberOfFiles": 0,
            "volume": 0,
            "fileFormat": "Data are NetCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": null,
            "onlineresource_set": []
        },
        {
            "ob_id": 37329,
            "uuid": "1e2e610a869a43afaa8795a675d11e2a",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/acsis/cpom-model-sea-ice/data/NEMOCICE2018_ORCA025_1969_2015_DFS5.2_CICECPOM",
            "numberOfFiles": 487,
            "volume": 129317544210,
            "fileFormat": "Files are NETCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37328,
                "uuid": "632ccda332f54b85bdb190e44ad1b493",
                "short_code": "ob",
                "title": "ACSIS: Global ocean-ice sea ice simulations CICEv5.1.2 with prognostic melt pond model and EAP rheology with modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter with DFS5.2 atmospheric forcing data from 1969 - 2015",
                "abstract": "This dataset includes model output from a forced ocean-ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This simulation uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) and the ocean-ice ones the same ocean model NEMO GO6.0 (Storkey et al., 2018) as HadGEM3. The atmospheric forcing data set are applied: DFS5.2 (Dussin et al., 2016). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. \r\n\r\nThe specific parameters for this dataset are:\r\nsea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology, but with several modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter (see Schroeder et al., TC 2019)\r\nocean model: NEMOv3.6\r\nperiod: 1969-2015\r\natmospheric forcing: DFS5.2 (Drakkar)\r\ndomain: global\r\ngrid resolution: 0.25deg ORCA\r\n\r\nThe simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37331,
            "uuid": "8ab4bbc0f8534343b5edda1b60acb5de",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/acsis/cpom-model-sea-ice/data/NEMOCICE2018_ORCA025_1969_2015_DFS5.2_CICEdef",
            "numberOfFiles": 567,
            "volume": 669973964026,
            "fileFormat": "Files are NETCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37330,
                "uuid": "d43b0d44716a4a76bfe757952bab582a",
                "short_code": "ob",
                "title": "ACSIS: Global ocean-ice sea ice simulations CICEv5.1.2 with prognostic melt pond model and EAP rheology without modifications with DFS5.2 atmospheric forcing data from 1969 - 2015",
                "abstract": "This dataset includes model output from a forced ocean-ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This simulation uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) and the ocean-ice ones the same ocean model NEMO GO6.0 (Storkey et al., 2018) as HadGEM3. The atmospheric forcing data set are applied: DFS5.2 (Dussin et al., 2016). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. \r\n\r\nThe specific parameters for this dataset are:\r\nsea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology\r\nocean model: NEMOv3.6\r\nperiod: 1969-2015\r\natmospheric forcing: DFS5.2 (Drakkar)\r\ndomain: global\r\ngrid resolution: 0.25deg ORCA\r\n\r\nThe simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37333,
            "uuid": "133d13e8352e4336b4774db61311acaf",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/acsis/cpom-model-sea-ice/data/NEMOCICE2020_ORCA1_1960_2009_COREII_CICECPOM_u-bn925",
            "numberOfFiles": 908,
            "volume": 1104565083011,
            "fileFormat": "Files are NETCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37332,
                "uuid": "6c34c573dd214991a515c8927933e7c4",
                "short_code": "ob",
                "title": "ACSIS: Global ocean-ice sea ice simulations CICEv5.1.2 with prognostic melt pond model and EAP rheology with modifications  including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter with CORE II atmospheric forcing data from 1960 - 2009",
                "abstract": "This dataset includes model output from a forced ocean-ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This simulation uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) and the ocean-ice ones the same ocean model NEMO GO6.0 (Storkey et al., 2018) as HadGEM3. The atmospheric forcing data set are applied: CORE II surface data (Large & Yeager, 2009). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. \r\n\r\nThe specific parameters for this dataset are:\r\nsea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology, but with several modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter (see Schroeder et al., TC 2019)\r\nocean model: NEMOv3.6\r\nperiod: 1960-2009\r\natmospheric forcing: CORE II\r\ndomain: global\r\ngrid resolution: 1deg ORCA\r\n\r\nThe simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37335,
            "uuid": "60de77575fbd4ff48aaa8a4e4b54088e",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/acsis/cpom-model-sea-ice/data/NEMOCICE2020_ORCA1_1960_2009_COREII_CICEdef_u-bn845",
            "numberOfFiles": 909,
            "volume": 1105885778990,
            "fileFormat": "Files are NETCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37334,
                "uuid": "16760feb788a4c86ae94d11887be265f",
                "short_code": "ob",
                "title": "ACSIS: Global ocean-ice sea ice simulations CICEv5.1.2 with prognostic melt pond model and EAP rheology without modifications with CORE II atmospheric forcing data from 1960 - 2009",
                "abstract": "This dataset includes model output from a forced ocean-ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This simulation uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) and the ocean-ice ones the same ocean model NEMO GO6.0 (Storkey et al., 2018) as HadGEM3. The atmospheric forcing data set are applied: CORE II surface data (Large & Yeager, 2009). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. \r\n\r\nThe specific parameters for this dataset are:\r\nsea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology\r\nocean model: NEMOv3.6\r\nperiod: 1960-2009\r\natmospheric forcing: COREII\r\ndomain: global\r\ngrid resolution: 1deg ORCA\r\n\r\nThe simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37337,
            "uuid": "dffdc9d0432b466d88332745d5c2ef8b",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/acsis/cpom-model-sea-ice/data/NEMOCICE2020_ORCA1_1960_2014_DFS5.2_CICECPOM_u-bo229",
            "numberOfFiles": 982,
            "volume": 1238012822420,
            "fileFormat": "Files are NETCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37336,
                "uuid": "2a925024e89449eab009e1b32e925c38",
                "short_code": "ob",
                "title": "ACSIS: Global ocean-ice sea ice simulations CICEv5.1.2 with prognostic melt pond model and EAP rheology with modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter with DFS5.2 atmospheric forcing data from 1960 - 2015",
                "abstract": "This dataset includes model output from a forced ocean-ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This simulation uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) and the ocean-ice ones the same ocean model NEMO GO6.0 (Storkey et al., 2018) as HadGEM3. The atmospheric forcing data set are applied: DFS5.2 (Dussin et al., 2016). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. \r\n\r\nThe specific parameters for this dataset are:\r\nsea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology, but with several modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter (see Schroeder et al., TC 2019)\r\nocean model: NEMOv3.6\r\nperiod: 1960-2015\r\natmospheric forcing: DFS5.2 (Drakkar)\r\ndomain: global\r\ngrid resolution: 1deg ORCA\r\n\r\nThe simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37339,
            "uuid": "4424dbd8e80b4e38ad48d6791ea839b2",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/acsis/cpom-model-sea-ice/data/NEMOCICE2020_ORCA1_2000_2020_NCEP2_CICECPOM_u-cc335",
            "numberOfFiles": 255,
            "volume": 22907814840,
            "fileFormat": "Files are NETCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37338,
                "uuid": "c50c863cca4f408ebe69847565548cb5",
                "short_code": "ob",
                "title": "ACSIS: Global ocean-ice sea ice simulations CICEv5.1.2 with prognostic melt pond model and EAP rheology with  modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter with NCEP Reanalysis-2 atmospheric forcing data from 1960 - 2015",
                "abstract": "This dataset includes model output from a forced ocean-ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) with an atmospheric forcing data set are applied: NCEP Reanalysis-2 (NCEP2) data (Kanamitsu et al., 2002, updated 2020). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. \r\n\r\nThe specific parameters for this dataset are:\r\nsea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology, but with several modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter (see Schroeder et al., TC 2019)\r\nocean model: NEMOv3.6\r\nperiod: 1960-2015\r\natmospheric forcing: NCEP2\r\ndomain: global\r\ngrid resolution: 1deg ORCA\r\n\r\nThe simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37342,
            "uuid": "6ca7e1a93ad54791a3af75ed23bfad06",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ccmi/data/post-cmip6/ccmi-2022/CSIRO/ACCESS-CM2-Chem/refD1",
            "numberOfFiles": 886,
            "volume": 462515915486,
            "fileFormat": "netCDF",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37341,
                "uuid": "031313dc6bfc4a0baabd58a51629bc21",
                "short_code": "ob",
                "title": "CCMI-2022: REF-D1 data produced by the ACCESS-CM2-Chem model at CSIRO",
                "abstract": "This dataset contains model data for CCMI-2022 experiment refD1 produced by the ACCESS-CM2-Chem chemistry-climate model run by the modelling team at the CSIRO (Commonwealth Scientific and Industrial Research Organisation) ARCCSS (Australian Research Council Centre of Excellence for Climate System Science).\r\n\r\nThe refD1 experiment is a hindcast of the atmospheric state, using a prescribed evolution of sea surface temperature and sea ice from observations along with forcings for the extra-terrestrial solar flux, long-lived greenhouse gases and ozone depleting substances, stratospheric aerosols and an imposed quasi-biennial oscillation that approximate the observed variations over the historical period to the fullest extent possible.\r\n\r\nThe CCMI-2022 Chemistry-climate model initiative is a set of model experiments focused on the stratosphere, with the goals of providing updated projections towards the future evolution of the ozone layer and improving our understanding of chemistry-climate interactions from models.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n-  Review of the global models used within phase 1 of the Chemistry-Climate Model Initiative (CCMI)\r\n-  A new set of Chemistry-Climate Model Initiative (CCMI) Community Simulations to Update the Assessment of Models and Support Upcoming Ozone Assessment Activities, David Plummer and Tatsuya Nagashima and Simone Tilmes and Alex Archibald and Gabriel Chiodo and Suvarna Fadnavis and Hella Garny and Beatrice Josse and Joowan Kim and Jean-Francois Lamarque and Olaf Morgenstern and Lee Murray and Clara Orbe and Amos Tai and Martyn Chipperfield and Bernd Funke and Martin Juckes and Doug Kinnison and Markus Kunze and Beiping Luo and Katja Matthes and Paul A. Newman and Charlotte Pascoe and Thomas Peter (2021), SPARC Newsletter, volume 57, pp 22-30"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37345,
            "uuid": "b8f0a750a3294bc8a327bd3e7e1de685",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/deposited2022/invisible_tracks/data/",
            "numberOfFiles": 74,
            "volume": 656568837112,
            "fileFormat": "Data are in CSV format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37320,
                "uuid": "2d0f8bb3927b4f75ae75276705858f68",
                "short_code": "ob",
                "title": "Invisible Tracks: Collocation of wind-advected ship locations and shipping emissions with data from the MODIS cloud product",
                "abstract": "This dataset contains data from the MODIS (Moderate Resolution Imaging Spectroradiometer) cloud product, collocated to wind-advected ship locations and shipping emissions. Most importantly, it includes effective droplet radii, calculated droplet number concentration, liquid water path, and cloud optical depth for locations where clouds have been polluted by shipping and to either side of a ship trajectory. Cloud data in the trajectory is labelled with the variable name only, data on either side additionally with [property]_1 and [property]_3 for the western and eastern side, respectively. The data is ungridded and comes in the form of csv files. It covers the period of 2014-2019. \r\n\r\nThe dataset is the product of three data sources: AIS data giving ship locations, ERA5 winds used to advect the emissions up to the time of the Aqua and Terra overpasses, as well as the level-2 cloud product MOD06.\r\n\r\nThis data was collected for the study of shipping aerosols' effect on marine liquid clouds, in particular when the emissions do not produce a satellite-visible ship track, which could be hand-logged."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37355,
            "uuid": "11b2b2e5c8414dfc93047b62eb2a0f08",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/deposited2022/below-cloud-scavenging-aerosol/",
            "numberOfFiles": 165,
            "volume": 86597715864,
            "fileFormat": "Data are NetCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37354,
                "uuid": "2e36fe8eb7ee4bd0a0833d3e1edd795a",
                "short_code": "ob",
                "title": "Data to support Below-cloud scavenging of aerosol by rain: A review of numerical modelling approaches and sensitivity simulations with mineral dust",
                "abstract": "This dataset contains all the UM-GA8.0 climate model output needed to reproduce Table 2 and Figures 7-10 in the paper Below-cloud scavenging of aerosol by rain: A review of numerical modelling approaches and sensitivity simulations with mineral dust by Anthony C. Jones, Adrian Hill, John Hemmings, Pascal Lemaitre, Arnaud Querel, Claire L. Ryder, and Stephanie Woodward, Submitted to Atmospheric Chemistry and Physics, May 2022, as well as Figures S7-S13 in the Supplementary material. UM-GA8.0 is the Met Office Unified Model General Atmosphere vn8.0 in a climate configuration (N96L85) and using AMIP protocol (see Jones et al., 2022 for more details).\r\n\r\nAll files are CF-1.7 compliant and in NetCDF format with appropriate metadata. Each file contains monthly mean data for the 15 simulated years used in the paper (the 5 year spin up is not provided).\r\n\r\nThe files are separated into folders by experiment name:\r\n\r\nFolder      | Simulation name (Table 1 in Jones et al., 2022)\r\n-------------------------------------------------------------\r\nSlinn       | Slinn\r\nSlinnph     | Slinn+ph\r\nSlinnphrc   | Slinn+ph+rc\r\nWang        | Wang\r\nLaakso      | Laakso\r\nSlinnPhRc1M | Slinn+ph+rc(1M)\r\nSlinnPhRcDm | Slinn+ph+rc(dm)\r\nLaaksoDm    | Laakso(dm)\r\n\r\nThe files use standard CMIP naming conventions with one slight modification: before the 'nc' suffix, the aerosol mode that the variable applies to is generally given (either coarse (cor) or accumulation (acc) mode). The STARTDATE and ENDDATES are the same for all files (12/1993 and 11/2008 respectively). As is the TIMEPERIOD (Amon, i.e., monthly mean data) and the MODEL (MetUM, else known as UM-GA8.0). \r\n\r\nVARIABLE_TIMEPERIOD_MODEL_EXPERIMENT_STARTDATE_ENDDATE.MODE.nc\r\n\r\nThe variables comprise:\r\n\r\nShort name  | Description (units, if any)\r\n-------------------------------------------------------------\r\nconccn      | Particle number concentration (m-3)\r\nconcdust    | Particle mass concentration (kg.m-3)\r\ndiamdust    | Modal median diameter (m)\r\ndrydust     | Dust dry deposition rate (kg.m-2.s-1)\r\nemidust     | Dust surface emission rate (kg.m-2.s-1)\r\nloaddust    | Vertically integrated dust load (kg.m-2)\r\nmmratedust  | Dust mode-merging (cor->acc) rate (kg.m-3.s-1)\r\nod443dust   | Dust optical depth at 443 nm\r\nod550dust   | Dust optical depth at 443 nm\r\norog        | Surface Altitude (m)\r\nwetdust     | Dust wet deposition rate (kg.m-2.s-1)\r\nzfull       | Model altitude at the top of the gridcell (m)\r\n\r\nThe mass concentration (concdust), number concentration (conccn), and mass burden (loaddust) were calculated from monthly-mean mass or number mixing ratios and monthly mean potential temperature, pressure, and specific humidity fields (not supplied). The mode mixing rate (mmratedust) is only available for the downard mode merging simulations (SlinnPhRcDm and LaaksoDm).\r\n\r\nAll figures in the paper were produced using Python (3.6) and Iris scientific analysis software.\r\n\r\nAll data is Crown Copyright, Met Office, and is made available under the terms of the Non-Commercial Government Licence: http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37366,
            "uuid": "d75d65022a104967a54105793a09abd5",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/igp/data/IGP_climate_model/",
            "numberOfFiles": 457,
            "volume": 136695138100,
            "fileFormat": "Data are NetCDF formatted.\r\n\r\nFiles ending _daymean.nc or ydaymean.nc were processed using CDO.  the CDO command that the filename ends with.  IE for MSLP, the file u_au087_MSLP_9110_daymean.nc was created using\r\n\r\ncdo daymean u_au087_MSLP_9110.nc u_au087_MSLP_9110_daymean.nc\r\n\r\nThe folder splitmon used the CDO command splitmon to break each file down into January, February, March and April, identified by the suffix 01, 02, 03 and 04 respectively.  Daymean and ydaymean versions were created.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37365,
                "uuid": "9fb47f1d6d294b0dba553adc2253e6cf",
                "short_code": "ob",
                "title": "Idealised climate model simulations for the Iceland Greenland Sea's Project (IGP)",
                "abstract": "This dataset contains idealised climate model simulations for the Atmospheric Forcing of the Iceland Sea (AFIS) project  which was the UK component, funded by NERC, of the Iceland Greenland Sea's Project (IGP).  The UK Met Office Unified Model (MetUM) version 10.6 with a regional nested domain was used to carry out a suite of simulations of the atmosphere over the NE North Atlantic region. The set up of the MetUM uses the Global Atmosphere 6 and Global Land 6 (GA6/GL6)  configurations including the ENDGame dynamical core.\r\nModel simulations were run on the Met Office supercomputer accessed through Monsoon.  \r\n\r\nThis dataset contains the output for one of 7 model outputs. \r\nThese are:\r\nTemperature - 1.5m Surface Temperature output from themodel simulations.\r\nLatent HF - Latent Heat Flux output from the model simulations.\r\nSensible HF - Sensible Heat Flux output from the model simulations.\r\nRelative Humidity - Relative Humidity output from the model simulations.\r\nSpecific Humidity - Specific Humidity output from the model simulations.\r\nSurface Winds - 10 m U and V wind component output from the model simulations.\r\nMSLP - Mean Sea Level Pressure output from the model simulations.\r\n\r\nA list of papers related to this dataset can be found in the linked online resources on this record."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37371,
            "uuid": "0dfa54be08fd4abca117605a3e3c1904",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/deposited2022/Ottoman_data_halkali_1896_1917",
            "numberOfFiles": 275,
            "volume": 694017645,
            "fileFormat": "Data are BADC-CSV and JPEG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37370,
                "uuid": "c6e27bda1fc849c098a7fff7ff69fd5a",
                "short_code": "ob",
                "title": "Halkali Agricultural School (Istanbul, Turkey): Daily Meteorological Observations 1896-1917",
                "abstract": "Daily weather observations measured by students and staff at Halkali Agricultural School (a school opened in 1892 for agriculture and animal husbandry during the Ottoman period) from 1896 to 1917 in Istanbul, Turkey have been transcribed from the original publications into digital form and translated from Ottoman Turkish (the Perso-Arabic script) to English (Latin alphabet). Over 55 thousand observations of daily maximum, minimum and average temperature, rainfall, soil and under soil (0.25m) temperature, humidity, pressure, and wind speed were recovered."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37383,
            "uuid": "f5394010ba95439e88556cc3b5f172dd",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig02/v20220610",
            "numberOfFiles": 0,
            "volume": 0,
            "fileFormat": "Data are csv formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": null,
            "onlineresource_set": []
        },
        {
            "ob_id": 37386,
            "uuid": "6d9e47db74604e28beec7b37d1232249",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig02/v20220610",
            "numberOfFiles": 41,
            "volume": 628815,
            "fileFormat": "Data are BADC-CSV formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37392,
            "uuid": "6dbcfe44b22a471f9f58c920032ff169",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig07/v20220613",
            "numberOfFiles": 7,
            "volume": 20243,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37391,
                "uuid": "392c8351349b4436923c102c558873d9",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.7 (v20220613)",
                "abstract": "Data for Figure 3.7 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 3.7 shows regression coefficients and corresponding attributable warming estimates for individual CMIP6 models.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\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 four panels, with data provided for all panels in subdirectories named panel_a, panel_b, panel_c and panel_d.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains information on global temperature attributable warming (2010-2019 relative to 1850-1900) from CMIP6 models: \r\n \r\n - Regression coefficients for two way regression (2010-2019 relative to 1850-1900)\r\n - Regression coefficients for three way regression (2010-2019 relative to 1850-1900)\r\n - Attributable warming for two way regression (2010-2019 relative to 1850-1900)\r\n - Attributable warming for three way regression (2010-2019 relative to 1850-1900)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/regression_coeff_two_way_regression.csv has data for brown and green crosses\r\n - panel_b/regression_coeff_three_way_regression.csv has data for grey, green and blue crosses\r\n - panel_c/attributable_warming_two_way_regression.csv has data for brown and green crosses\r\n - panel_d/attributable_warming_three_way_regression.csv has data for grey, green and blue crosses\r\n\r\n Details about the data provided in relation to the figure in the header of every file.\r\n\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37395,
            "uuid": "d561cc52dede4ebd8a0735af1c2b9402",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig08/v20220613",
            "numberOfFiles": 4,
            "volume": 8653,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37394,
                "uuid": "bf3d0b8a8c0d4ae19cfd994b6fef4a5c",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.8 (v20220613)",
                "abstract": "Data for Figure 3.8 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 3.8 shows assessed contributions to observed warming, and supporting 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\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---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains the drivers of the attributable warming (2010-2019 relative to 1850-1900):\r\n \r\n - Observed global warming (2010-2019)\r\n - Global warming and its drivers reported in the literature sources  (2010-2019)\r\n - Global warming and its drivers calculated from CMIP6 models (2010-2019)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - drivers_observed_warming.csv has data for the shadings and markers in the figure.\r\n Additional details of data provided in relation to figure in the file header (BADC-CSV file).\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, 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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37397,
            "uuid": "4bbc777e3ebb44c2b16d2724338f39ed",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c267-jan-26",
            "numberOfFiles": 55,
            "volume": 3536936195,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37398,
                "uuid": "28afd9beff544fc5a5823dd1749a58c2",
                "short_code": "ob",
                "title": "FAAM C267 Instrument Test flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for FAAM Test, Calibration, Training and Non-science Flights and other non-specified flight projects (Instrument) project."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37401,
            "uuid": "5efdac079ddb4da28f9e9db023f57766",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c268-feb-07",
            "numberOfFiles": 63,
            "volume": 3669285409,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37402,
                "uuid": "0122ac2714794007ae49a02070e8a45b",
                "short_code": "ob",
                "title": "FAAM C268 Instrument Test flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for FAAM Test, Calibration, Training and Non-science Flights and other non-specified flight projects (Instrument) project."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37405,
            "uuid": "e487194712a1410d90194436e5c3a479",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c270-feb-25",
            "numberOfFiles": 88,
            "volume": 4393500570,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37406,
                "uuid": "4f62fa21159b4b0cbf6bed9f451d2775",
                "short_code": "ob",
                "title": "FAAM C270 Instrument Test flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for FAAM Test, Calibration, Training and Non-science Flights and other non-specified flight projects (Instrument) project."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37409,
            "uuid": "1088ce9b77b1426882dc102229aee816",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c271-mar-11",
            "numberOfFiles": 79,
            "volume": 6237869625,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37410,
                "uuid": "18189d378d884d4c91fabdfcc343ed8e",
                "short_code": "ob",
                "title": "FAAM C271 ACAO flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for Arctic Cold Air Outbreak (ACAO) project. Flight C271 took place on 16th March 2022 in the Kiruna-Svalbard, Sweden region."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37413,
            "uuid": "6c340cb7722943e7874095da4c21f899",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c272-mar-16",
            "numberOfFiles": 73,
            "volume": 5640619394,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37414,
                "uuid": "43ce3ca3dbf34ee6889d5e2a408e1214",
                "short_code": "ob",
                "title": "FAAM C272 ACAO flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for Arctic Cold Air Outbreak (ACAO) project. Flight C272 took place on 16th March 2022 in the Kiruna, Sweden region."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37417,
            "uuid": "39c00ea74ab2444aa16466a8d9fc7f7c",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c273-mar-19",
            "numberOfFiles": 77,
            "volume": 6188260784,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37418,
                "uuid": "c1b4cb2719514b3c8f1610acc99d57a5",
                "short_code": "ob",
                "title": "FAAM C273 ACAO flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for Arctic Cold Air Outbreak (ACAO) project. Flight C273 took place on 19th March 2022 in the Kiruna-Svalbard, Sweden region."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37421,
            "uuid": "7ed2b9ece66a499b90456bb3250699d4",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c275-mar-23",
            "numberOfFiles": 81,
            "volume": 4709033791,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37422,
                "uuid": "eaff9591f2d64e4390abffa4881bdab5",
                "short_code": "ob",
                "title": "FAAM C275 ACAO flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for Arctic Cold Air Outbreak (ACAO) project. Flight C282 took place on 23rd March 2022 in the Kiruna-Svalbard, Sweden region."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37425,
            "uuid": "563c442227c746ba8129600e89b32a02",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c276-mar-23",
            "numberOfFiles": 95,
            "volume": 5296619632,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37426,
                "uuid": "b8047bf1742542c681506c34e0d6ded1",
                "short_code": "ob",
                "title": "FAAM C276 ACAO flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for Arctic Cold Air Outbreak (ACAO) project. Flight C276 took place on 23rd March 2022 in the Kiruna-Svalbard, Sweden region."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37429,
            "uuid": "405b6d8772584edca3820a02d9a60952",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c277-mar-24",
            "numberOfFiles": 73,
            "volume": 4365041860,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37430,
                "uuid": "a6313cb338234bec9c5edefb42f1ee31",
                "short_code": "ob",
                "title": "FAAM C277 ACAO flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for Arctic Cold Air Outbreak (ACAO) FAAM Project project. Flight C277 took place on 24th March 2022 in the Kiruna-Svalbard, Sweden region."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37433,
            "uuid": "dd93c9b4532b4012a4ac96f9f9c42561",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c278-mar-24",
            "numberOfFiles": 64,
            "volume": 3465148696,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37434,
                "uuid": "3849fd9c3c1c4b48a8987fe2b2363c9f",
                "short_code": "ob",
                "title": "FAAM C278 ACAO flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for Arctic Cold Air Outbreak (ACAO) FAAM Project project. Flight C278 took place on 24th March 2022 in the Kiruna-Svalbard, Sweden region."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37437,
            "uuid": "e1ab28a7d5b74963a3daaaa0ee934d01",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c279-mar-25",
            "numberOfFiles": 76,
            "volume": 6077369761,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37438,
                "uuid": "04dad6d09bef46f6b42f06419d36372e",
                "short_code": "ob",
                "title": "FAAM C279 ACAO flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for Arctic Cold Air Outbreak (ACAO) FAAM Project project. Flight C279 took place on 25th March 2022 in the Kiruna-Svalbard, Sweden region."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37442,
            "uuid": "9d8fe3cf2776496e89fdef7c8aed4c83",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c280-mar-29",
            "numberOfFiles": 97,
            "volume": 6084197210,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37443,
                "uuid": "a8c47d03a6af49719fe64ab434d9002c",
                "short_code": "ob",
                "title": "FAAM C280 ACAO flight: Airborne atmospheric measurements from instruments on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from instrument suites on board the FAAM BAE-146 aircraft collected for the Arctic Cold Air Outbreak Project (ACAO). Flight C280 took place on 29th March 2022 in the Kiruna-Svalbard, Sweden region."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37445,
            "uuid": "7641d7a8b4c94864968458b2ff39a22d",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c281-mar-30",
            "numberOfFiles": 64,
            "volume": 3378671479,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37446,
                "uuid": "3e08843892a24e1b9fb642ffd3e4659a",
                "short_code": "ob",
                "title": "FAAM C281 ACAO flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for the Arctic Cold Air Outbreak Project (ACAO). Flight C281 took place on 30th March 2022 in the Kiruna-Svalbard, Sweden region."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37449,
            "uuid": "3f687db2066344c9834d90843565e7c3",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c282-mar-30",
            "numberOfFiles": 76,
            "volume": 6083005303,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37450,
                "uuid": "c8e4139b23404092923a753abba15db8",
                "short_code": "ob",
                "title": "FAAM C282 ACAO flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for the Arctic Cold Air Outbreak Project (ACAO).  Flight C282 took place on 30th March 2022 in the Kiruna-Svalbard, Sweden region."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37453,
            "uuid": "fd53f350438f41e9a5f200d767372bd1",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c283-apr-27",
            "numberOfFiles": 54,
            "volume": 4793003193,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37454,
                "uuid": "9d8303ca10fd4c9b8fdb5e718daf6b26",
                "short_code": "ob",
                "title": "FAAM C283 ACSIS flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37457,
            "uuid": "8c4ccecf9e044ca4b5062b385ba85b05",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c284-apr-29",
            "numberOfFiles": 126,
            "volume": 6742367946,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37458,
                "uuid": "3419c3583acc487e9c78e6b8f3f9bde0",
                "short_code": "ob",
                "title": "FAAM C284 ACRUISE flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for ACRUISE : Atmospheric Composition and Radiative forcing changes due to  UN International Ship Emissions regulations (ACRUISE) project."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37461,
            "uuid": "27042003f91d4ab2b2f3363740c1066d",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c285-apr-30",
            "numberOfFiles": 150,
            "volume": 8004528927,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37462,
                "uuid": "8d6c7296be464b26a01747bc61cb97bd",
                "short_code": "ob",
                "title": "FAAM C285 ACRUISE flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for ACRUISE : Atmospheric Composition and Radiative forcing changes due to  UN International Ship Emissions regulations (ACRUISE) project."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37465,
            "uuid": "7da1e43182cb48c4aec4a3ebf1fd23f7",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c286-may-01",
            "numberOfFiles": 148,
            "volume": 7788257171,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37466,
                "uuid": "20ee9eb360da44fca27ae70a996471d9",
                "short_code": "ob",
                "title": "FAAM C286 ACRUISE flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for ACRUISE : Atmospheric Composition and Radiative forcing changes due to  UN International Ship Emissions regulations (ACRUISE) project."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37469,
            "uuid": "e6a960a0daa649659cf34a1d3fb7c32b",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c287-may-02",
            "numberOfFiles": 145,
            "volume": 8067731963,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37470,
                "uuid": "b58b6af48cc642b1a84e936fcafd6352",
                "short_code": "ob",
                "title": "FAAM C287 ACRUISE flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for ACRUISE : Atmospheric Composition and Radiative forcing changes due to  UN International Ship Emissions regulations (ACRUISE) project."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37473,
            "uuid": "1096f5ead71748619ae7804871a91228",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c288-may-03",
            "numberOfFiles": 75,
            "volume": 3675139908,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37474,
                "uuid": "7b16d480d42248ccbf2e144456de38db",
                "short_code": "ob",
                "title": "FAAM C288 ACSIS Transit flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37477,
            "uuid": "518c3ef0397f4579b3a0ad128cc68f83",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c289-may-03",
            "numberOfFiles": 104,
            "volume": 4792018739,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37478,
                "uuid": "65494ea159d847a3bfcf38ddc1e476f6",
                "short_code": "ob",
                "title": "FAAM C289 ACSIS flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37481,
            "uuid": "c68855d249ce4ec1ab93cf0f4b92b4d3",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c290-may-05",
            "numberOfFiles": 127,
            "volume": 6428700903,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37482,
                "uuid": "fb6891a046e448bf87661c7297052a38",
                "short_code": "ob",
                "title": "FAAM C290 ACSIS flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37485,
            "uuid": "69c709441ef34235abac53500009975f",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c291-may-06",
            "numberOfFiles": 135,
            "volume": 7338774206,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37486,
                "uuid": "7ab38af8367b4c84acf8f9472f097b98",
                "short_code": "ob",
                "title": "FAAM C291 ACSIS flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37489,
            "uuid": "2924618309d2405da066fb2767e2c073",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c292-may-07",
            "numberOfFiles": 50,
            "volume": 7657483522,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37490,
                "uuid": "834f5a562aac4a97ba8d84d8cf2d571f",
                "short_code": "ob",
                "title": "FAAM C292 ACSIS flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37493,
            "uuid": "23c8a0801fc94f179157f5fb47b09d25",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c293-may-08",
            "numberOfFiles": 51,
            "volume": 6647991981,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37494,
                "uuid": "ae5cd3a3914541a592ce1f9ccba00959",
                "short_code": "ob",
                "title": "FAAM C293 ACSIS flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37497,
            "uuid": "ff7c98d5a16940dc9da02b62bc2d2700",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c294-may-09",
            "numberOfFiles": 42,
            "volume": 4948237270,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37498,
                "uuid": "e525619875834d63a2a6ce84288cb130",
                "short_code": "ob",
                "title": "FAAM C294 ACSIS flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37502,
            "uuid": "794adcaaebe14c4d85e7ec7a4cc28ce7",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c295-may-24",
            "numberOfFiles": 37,
            "volume": 4493846712,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37503,
                "uuid": "d6620263855c4327bcc51b7480b0dba6",
                "short_code": "ob",
                "title": "FAAM C295 WESCON flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for WESCON FAAM Aircraft Project project."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37506,
            "uuid": "712947ffd5784f989d4de73ca8e081b6",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig21/v20220613",
            "numberOfFiles": 25,
            "volume": 298735,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37505,
                "uuid": "6f800cbda88d424cbcc59181b8b85aaa",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.21 (v20220613)",
                "abstract": "Data for Figure 3.21 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 3.21 shows the seasonal evolution of observed and simulated Arctic and Antarctic sea ice area (SIA) over 1979-2017.\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 Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has several subplots, but they are unidentified, so the data is stored in the parent directory.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains Sea Ice Area anomalies over 1979-2017 relative to the 1979-2000 means from:\r\n \r\n - Observations (OSISAF, NASA Team, and Bootstrap)\r\n - Historical simulations from CMIP5 and CMIP6 multi-model means\r\n - Natural only simulations from CMIP5 and CMIP6 multi-model means\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - *_arctic_* files are used for the plots on the left side of the figure\r\n - *_antarctic_* files are used for the plots on the right side of the figure\r\n - *_OBS_NASATeam* files are used for the first row of the plot\r\n - *_OBS_Bootstrap* are used for the second row of the plot\r\n - *_OBS_OSISAF* are used for the third row of the plot\r\n - *_ALL_CMIP5* are used in the fourth row of the plot\r\n - *_ALL_CMIP6* are used in the fifth row of the plot\r\n - *_NAT_CMIP5* are used in the sixth row of the plot\r\n - *_NAT_CMIP6* are used in the seventh row of the plot\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The significance are for the grey dots, it's nan or 1 values. The data has to be overplotted to colored squares. Grey dots indicate multi-model mean anomalies stronger than inter-model spread (beyond ± 1 standard deviation).\r\n\r\n\r\nThe coordinates of the data are indices, but in global attributes 'comments' of each file there are relations of indices to months, since months are the y coordinate.\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, 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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37509,
            "uuid": "cee56315cf91421a9117235b3752416f",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig22/v20220613",
            "numberOfFiles": 6,
            "volume": 14968,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37508,
                "uuid": "0915a82fa8a84e21bcb5467be84d49fc",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.22 (v20220613)",
                "abstract": "Data for Figure 3.22 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 3.22 shows time series of Northern Hemisphere March-April mean snow cover extent (SCE) from observations, CMIP5 and CMIP6 simulations.\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 There are technically two panels top and bottom (CMIP5 and CMIP6), however, the data is stored in the parent directory.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The data is for the Northern Hemisphere snow cover extent anomalies (SCEA) from models and observations:\r\n \r\n - The SCEA observational data from GLDAS-NOAH (1948-2012), Brown-NOAA (1923-2017), Mudryk et al 2020 (1968-2017)\r\n - The SCEA modelled by CMIP5 historical-rcp45 experiment (1923-2017)\r\n - The SCEA modelled by CMIP5 historicalNat experiment (1923-2012)\r\n - The SCEA modelled by CMIP6 historical-ssp245 experiment (1923-2017)\r\n - The SCEA modelled by CMIP6 hist-nat experiment (1923-2017)\r\n - The SCEA modelled by CMIP5 and CMIP6 piControl experiments\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n snow_cover_extent_cmip5_obs.csv is the data for the green and brown lines and shadings in the upper panel and grey lines (1923-2017)\r\n snow_cover_extent_cmip6_obs.csv is the data for the green and brown lines and shadings in the lower panel and grey lines (1923-2017)\r\n snow_cover_extent_piControl.csv for the blue error bars in the both panels\r\n Additional details of data provided in relation to figure in the file header (BADC-CSV file)\r\n\r\nCMIP5 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\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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37512,
            "uuid": "d674fce875b04d96b3242ae121fd46e9",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig20/v20220613",
            "numberOfFiles": 7,
            "volume": 13025,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37511,
                "uuid": "becdaa43cf884c299435dc319e758f4e",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.20 (v20220613)",
                "abstract": "Data for Figure 3.20 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 3.20 shows means and trends in Arctic sea ice area (SIA) in September and Antarctic SIA in February for 1979-2017 from CMIP5 and CMIP6 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 Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n Technically figure has four panels, but they are not named so the data is stored in the parent directory. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Data is for September Arctic and February Antarctic Sea Ice Areas (SIAs) and their trends from models and observations:\r\n \r\n - SIAs from Bootstrap, NASA-Team and OSISAF (1979-2017)\r\n - SIAs from CMIP5 historical-rcp45 experiment (1979-2017)\r\n - SIAs from CMIP6 historical-ssp245 experiment (1979-2017)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - sia_point_nh_cmip5.csv has Arctic sea ice area means and decadal trends for September calculated from CMIP5 and observations from 1979-2017\r\n - sia_point_nh_cmip6.csv has Arctic sea ice area means and decadal trends for September calculated from CMIP6 and observations from 1979-2017\r\n - sia_point_sh_cmip5.csv has Antarctic sea ice area means and decadal trends for February calculated from CMIP5 and observations from 1979-2017\r\n - sia_point_sh_cmip6.csv has Antarctic sea ice area means and decadal trends for February calculated from CMIP6 and observations from 1979-2017\r\n\r\n Additional details of data provided in relation to figure in the files header (BADC-CSV files)\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\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The black line which is shown in each panel is written in the comments.\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, 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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37515,
            "uuid": "ddd223788e9b4e4d9904cf1f78845f4d",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig24/v20220614",
            "numberOfFiles": 6,
            "volume": 104720,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37514,
                "uuid": "a71383af93af4f58ae27d66ba15b3543",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.24 (v20220614)",
                "abstract": "Data for Figure 3.24 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 3.24 shows biases in zonal mean and equatorial sea surface temperature (SST) in CMIP5 and CMIP6 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\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 panels (a), (b), (c), with data provided for all panels in subdirectories named panel_a, panel_b and panel_c.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains sea surface temperature (SST) data (1979-1999): \r\n \r\n - Modelled zonal mean SST biases from CMIP5\r\n - Modelled zonal mean SST biases from CMIP6\r\n - Modelled zonal mean SST biases from HighResMIP\r\n - Modelled equatorial SST biases from CMIP5\r\n - Modelled equatorial SST biases from CMIP6\r\n - Modelled equatorial SST biases from HighResMIP\r\n - Modelled mean equatorial SST from CMIP5\r\n - Modelled mean equatorial SST from CMIP6\r\n - Modelled mean equatorial SST from HighResMIP\r\n - Observed mean equatorial SST from HadISST v1\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/zonal_sst_bias.csv has zonal mean sea surface temperature bias over the period 1979-1999, there are data for blue (CMIP5), red (CMIP6) and green (HighResMIP) shadings representing 5th and 95th percentile over ensemble\r\n - panel_b/equatorial_sst_bias.csv  has equatorial mean sea surface temperature bias over the period 1979-1999, there are data for blue (CMIP5), red (CMIP6) and green (HighResMIP) shadings representing 5th and 95th percentile over ensemble\r\n - panel_c/equatorial_sst_means.csv  has equatorial mean sea surface temperature over the period 1979-1999, there are data for black (HadISSTv1),  blue (CMIP5), red (CMIP6) and green (HighResMIP) shadings representing 5th and 95th percentile over ensemble\r\n Details about the data provided in relation to the figure in the header of every file.\r\n\r\nCMIP5 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\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n For equatorial SSTs and equatorial SST biases, the data has longitude coordinate which goes 20 to 380 degrees. It was done with python package iris not to break the lines through Atlantic.\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 code for the figure, archived on Zenodo."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37518,
            "uuid": "40ea7ba170d446b1a4faa6fba444712d",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig25/v20220614",
            "numberOfFiles": 21,
            "volume": 791937,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37517,
                "uuid": "dce3253d984c4342899b01548f52ba5f",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.25 (v20220614)",
                "abstract": "Data for Figure 3.25 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 3.25 shows CMIP6 potential temperature and salinity biases for the global ocean, Atlantic, Pacific and Indian Oceans.\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 There are panels (a), (b), (c), (d), (e), (f), (g), (h). The data is in respective subdirectories.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains modelled and observational ocean data (1981-2010) for different ocean basins (global, Atlantic, Pacific, Indian): \r\n \r\n - Potential temperature from WOA18 observations\r\n - Salinity from WOA18 observations\r\n - Potential temperature bias (CMIP6 - WOA18)\r\n - Salinity bias (CMIP6 - WOA18)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a\r\n - panel_a/potential_temperature_bias_global_panel_a.nc: data for colored filled contours showing temperature bias from 1981 to 2010\r\n - panel_a/WOA_potential_temperature_global_panel_a.nc: data for black contours showing WOA18 temperature from 1981 to 2010\r\n \r\n Panel b\r\n - panel_b/salinity_bias_global_panel_b.nc:  data for colored filled contours showing salinity bias from 1981 to 2010\r\n - panel_b/WOA_salinity_global_panel_b.nc: data for black contours showing WOA18 salinity from 1981 to 2010\r\n \r\n Panel c\r\n - panel_c/potential_temperature_bias_atlantic_panel_c.nc: data for colored filled contours showing temperature bias from 1981 to 2010\r\n - panel_c/WOA_potential_temperature_atlantic_panel_c.nc: data for black contours showing WOA18 temperature from 1981 to 2010\r\n \r\n Panel d\r\n - panel_d/salinity_bias_atlantic_panel_d.nc: data for colored filled contours showing salinity bias from 1981 to 2010\r\n - panel_d/WOA_salinity_atlantic_panel_d.nc: data for black contours showing WOA18 salinity from 1981 to 2010\r\n \r\n Panel e\r\n - panel_e/potential_temperature_bias_pacific_panel_e.nc: data for colored filled contours showing temperature bias from 1981 to 2010\r\n - panel_e/WOA_potential_temperature_pacific_panel_e.nc:  data for black contours showing WOA18 temperature from 1981 to 2010\r\n \r\n Panel f\r\n - panel_f/salinity_bias_pacific_panel_f.nc: data for colored filled contours showing salinity bias from 1981 to 2010\r\n - panel_f/WOA_salinity_pacific_panel_f.nc:  data for black contours showing WOA18 salinity from 1981 to 2010\r\n \r\n Panel g\r\n - panel_g/potential_temperature_bias_indian_panel_g.nc: data for colored filled contours showing temperature bias from 1981 to 2010\r\n - panel_g/WOA_potential_temperature_indian_panel_g.nc:  data for black contours showing WOA18 temperature from 1981 to 2010\r\n \r\n Panel h\r\n - panel_h/salinity_bias_indian_panel_h.nc: data for colored filled contours showing salinity bias from 1981 to 2010\r\n - panel_h/WOA_salinity_indian_panel_h.nc:  data for black contours showing WOA18 salinity from 1981 to 2010\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37521,
            "uuid": "c7c4a3a9379340758827aa31aaa49785",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig28/v20220614",
            "numberOfFiles": 11,
            "volume": 1063413,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37524,
            "uuid": "15480d0c97fc45db8536d4655ee85e4b",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig30/v20220614",
            "numberOfFiles": 14,
            "volume": 201303,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37523,
                "uuid": "a3902bb4d1b543b39cc85380df8d1586",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.30 (v20220614)",
                "abstract": "Data for Figure 3.30 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 3.30 shows observed and CMIP6 simulated AMOC mean state, variability and long-term trends.\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 The figure has 6 subpanels 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 List of data provided\r\n ---------------------------------------------------\r\n This dataset contains: \r\n \r\n - AMOC streamfunction profiles from CMIP5 (1860-2004) and CMIP6 (1860-2014) historical simulations\r\n - AMOC mean maximum overturning depth from CMIP5 (1860-2004) and CMIP6 (1860-2014) historical simulations\r\n - AMOC mean maximum overturning depth from RAPID observational dataset (2004-2018)\r\n - AMOC mean maximum overturning streamfunction from CMIP5 (1860-2004) and CMIP6 (1860-2014) historical simulations\r\n - AMOC mean maximum overturning streamfunction from RAPID observational dataset (2004-2018)\r\n - AMOC 8-year trends from CMIP5 and CMIP6 simulations and RAPID observations (2004-2012)\r\n - Interannual AMOC changes from CMIP5 and CMIP6 simulations and RAPID observations (2008-2010)\r\n - Longterm AMOC trends (1850-2014) from CMIP6 simulations\r\n - Longterm AMOC trends (1940-1985) from CMIP6 simulations\r\n - Longterm AMOC trends (1985-2014) from CMIP6 simulations\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/amoc_mean_state_boxes.csv has the data for the grey observations lines and blue and red boxes with whiskers\r\n - panel_a/amoc_profiles_shadings.csv has data for the blue and red profile shadings.\r\n - panel_a/amoc_profile_cmip5.csv has data for the blue profile\r\n - panel_a/amoc_profile_cmip6.csv has data for the red profile\r\n - panel_b/amoc_trends_2004_2012.csv has data for boxes and whiskers and outlier dots\r\n - panel_b/amoc_trends_cmip5_cmip6_additional_outliers.csv has data for additional outlier dots for CMIP5 and CMIP6\r\n - panel_c/interannual_variability_AMOC.csv has data for boxes and whiskers and outlier dots\r\n - panel_c/interannual_variability_AMOC_cmip5_cmip6_additional_outliers.csv has data for additional outlier dots for CMIP5 and CMIP6\r\n - panel_d/amoc_longtern_trend_1850_2014.csv has data for grey, green, blue and orange boxes and whiskers\r\n - panel_e/amoc_longtern_trend_1940_1985.csv has data for grey, green, blue and orange boxes and whiskers\r\n - panel_f/amoc_longtern_trend_1985_2014.csv has data for grey, green, blue and orange boxes and whiskers\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nAMOC is the Atlantic Meridional Overturning Circulation.\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"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37527,
            "uuid": "2072081687d542eba3f221b606a26023",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig35/v20220614",
            "numberOfFiles": 4,
            "volume": 124650,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37526,
                "uuid": "ef5ca18bcaf441d9993f181a058016ba",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.35 (v20220614)",
                "abstract": "Data for Figure 3.35 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 3.35 shows Southern Annular Mode indices in the last millennium.  \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 two panels, and all the data are provided in sam_millennium.nc.  \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Annual SAM reconstructions.\r\n - Annual-mean SAM index by CMIP5 and CMIP6 Last Millennium simulations extended by historical simulations.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - sam_abram_runmean, sam_datwyler_runmean: thin blue and brown lines\r\n - sam_abram_lowpass, sam_datwyler_lowpass: thick blue and brown lines\r\n\r\nPanel b:\r\n - sam_cmip_runmean: thin lines\r\n . MIROC-ES2L: ensemble = 10 (violet)\r\n . MRI-ESM2-0: ensemble = 11 (green)\r\n . CMIP5: ensemble = 1, 2, 3, 4, 5, 6, 7, 8, 9 (grey)\r\n - sam_cmip_lowpass: thick lines\r\n . MIROC-ES2L: ensemble = 10 (violet)\r\n . MRI-ESM2-0: ensemble = 11 (green)\r\n . CMIP5: ensemble = 1, 2, 3, 4, 5, 6, 7, 8, 9 (grey)\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"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37530,
            "uuid": "ae2564f37be14ff5a78ac77de0072425",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig39/v20220614",
            "numberOfFiles": 8,
            "volume": 43817223,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37529,
                "uuid": "02006a22c33b42039d96be53d332930a",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.39 (v20220614)",
                "abstract": "Data for Figure 3.39 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 3.39 shows the observed and simulated Pacific Decadal Variability (PDV).\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 Figure subpanels\r\n ---------------------------------------------------\r\n The figure has six panels. Files are not separated according to the panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n pdv.obs.nc contains\r\n - Observed SST anomalies associated with the PDV pattern\r\n - Observed PDV index time series (unfiltered)\r\n - Observed PDV index time series (low-pass filtered)\r\n - Taylor statistics of the observed PDV patterns\r\n - Statistical significance of the observed SST anomalies associated with the PDV pattern\r\n \r\n pdv.hist.cmip6.nc contains\r\n - Simulated SST anomalies associated with the PDV pattern\r\n - Simulated PDV index time series (unfiltered)\r\n - Simulated PDV index time series (low-pass filtered)\r\n - Taylor statistics of the simulated PDV patterns\r\n based on CMIP6 historical simulations.\r\n \r\n pdv.hist.cmip5.nc contains\r\n - Simulated SST anomalies associated with the PDV pattern\r\n - Simulated PDV index time series (unfiltered)\r\n - Simulated PDV index time series (low-pass filtered)\r\n - Taylor statistics of the simulated PDV patterns\r\n based on CMIP5 historical simulations.\r\n \r\n pdv.piControl.cmip6.nc contains\r\n - Simulated SST anomalies associated with the PDV pattern\r\n - Simulated PDV index time series (unfiltered)\r\n - Simulated PDV index time series (low-pass filtered)\r\n - Taylor statistics of the simulated PDV patterns\r\n based on CMIP6 piControl simulations.\r\n \r\n pdv.piControl.cmip5.nc contains\r\n - Simulated SST anomalies associated with the PDV pattern\r\n - Simulated PDV index time series (unfiltered)\r\n - Simulated PDV index time series (low-pass filtered)\r\n - Taylor statistics of the simulated PDV patterns\r\n based on CMIP5 piControl simulations.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - ipo_pattern_obs_ref in pdv.obs.nc: shading\r\n - ipo_pattern_obs_signif (dataset = 1) in pdv.obs.nc: cross markers\r\n \r\n Panel b:\r\n - Multimodel ensemble mean of ipo_model_pattern in pdv.hist.cmip6.nc: shading, with their sign agreement for hatching\r\n \r\n Panel c:\r\n - tay_stats (stat = 0, 1) in pdv.obs.nc: black dots\r\n - tay_stats (stat = 0, 1) in pdv.hist.cmip6.nc: red crosses, and their multimodel ensemble mean for the red dot\r\n - tay_stats (stat = 0, 1) in pdv.hist.cmip5.nc: blue crosses, and their multimodel ensemble mean for the blue dot\r\n \r\n Panel d:\r\n - Lag-1 autocorrelation of tpi in pdv.obs.nc: black horizontal lines in left\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of tpi in pdv.piControl.cmip5.nc: blue open box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of tpi in pdv.piControl.cmip6.nc: red open box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of tpi in pdv.hist.cmip5.nc: blue filled box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of tpi in pdv.hist.cmip6.nc: red filled box-whisker in the left\r\n - Lag-10 autocorrelation of tpi_lp in pdv.obs.nc: black horizontal lines in right\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of tpi_lp in pdv.piControl.cmip5.nc: blue open box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of tpi_lp in pdv.piControl.cmip6.nc: red open box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of tpi_lp in pdv.hist.cmip5.nc: blue filled box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of tpi_lp in pdv.hist.cmip6.nc: red filled box-whisker in the right\r\n \r\n Panel e:\r\n - Standard deviation of tpi in pdv.obs.nc: black horizontal lines in left\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - Multimodel ensemble mean and percentiles of standard deviation of tpi in pdv.piControl.cmip5.nc: blue open box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of standard deviation of tpi in pdv.piControl.cmip6.nc: red open box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of standard deviation of tpi in pdv.hist.cmip5.nc: blue filled box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of standard deviation of tpi in pdv.hist.cmip6.nc: red filled box-whisker in the left\r\n - Standard deviation of tpi_lp in pdv.obs.nc: black horizontal lines in right\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - Multimodel ensemble mean and percentiles of standard deviation of tpi_lp in pdv.piControl.cmip5.nc: blue open box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of standard deviation of tpi_lp in pdv.piControl.cmip6.nc: red open box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of standard deviation of tpi_lp in pdv.hist.cmip5.nc: blue filled box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of standard deviation of tpi_lp in pdv.hist.cmip6.nc: red filled box-whisker in the right\r\n \r\n Panel f:\r\n - tpi_lp in pdv.obs.nc: black curves\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - tpi_lp in pdv.hist.cmip6.nc: 5th-95th percentiles in red shading, multimodel ensemble mean and its 5-95% confidence interval for red curves\r\n - tpi_lp in pdv.hist.cmip5.nc: 5th-95th percentiles in blue shading, multimodel ensemble mean for blue curve\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\nSST stands for Sea Surface Temperature. \r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Multimodel ensemble means and percentiles of historical simulations of CMIP5 and CMIP6 are calculated after weighting individual members with the inverse of the ensemble size of the same model. ensemble_assign in each file provides the model number to which each ensemble member belongs. This weighting does not apply to the sign agreement calculation.\r\n\r\n\r\npiControl simulations from CMIP5 and CMIP6 consist of a single member from each model, so the weighting is not applied.\r\n\r\n\r\nMultimodel ensemble means of the pattern correlation in Taylor statistics in (c) and the autocorrelation of the index in (d) are calculated via Fisher z-transformation and back transformation.\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, 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"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37533,
            "uuid": "9095f52e8a534185a05be9568b2a9057",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig40/v20220614",
            "numberOfFiles": 8,
            "volume": 43817985,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37532,
                "uuid": "12f0d7db5ed747d2940210e52211ed6a",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.40 (v20220614)",
                "abstract": "Data for Figure 3.40 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 3.40 shows the observed and simulated Atlantic Multidecadal Variability (AMV).\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 six panels. Files are not separated according to the panels.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n amv.obs.nc contains\r\n - Observed SST anomalies associated with the AMV pattern\r\n - Observed AMV index time series (unfiltered)\r\n - Observed AMV index time series (low-pass filtered)\r\n - Taylor statistics of the observed AMV patterns\r\n \r\n amv.hist.cmip6.nc contains\r\n - Statistical significance of the observed SST anomalies associated with the AMV pattern\r\n - Simulated SST anomalies associated with the AMV pattern\r\n - Simulated AMV index time series (unfiltered)\r\n - Simulated AMV index time series (low-pass filtered)\r\n - Taylor statistics of the simulated AMV patterns\r\n \r\n based on CMIP6 historical simulations.\r\n \r\n amv.hist.cmip5.nc contains\r\n - Simulated SST anomalies associated with the AMV pattern\r\n - Simulated AMV index time series (unfiltered)\r\n - Simulated AMV index time series (low-pass filtered)\r\n - Taylor statistics of the simulated AMV patterns\r\n based on CMIP5 historical simulations.\r\n \r\n amv.piControl.cmip6.nc contains\r\n - Simulated SST anomalies associated with the AMV pattern\r\n - Simulated AMV index time series (unfiltered)\r\n - Simulated AMV index time series (low-pass filtered)\r\n - Taylor statistics of the simulated AMV patterns\r\n based on CMIP6 piControl simulations.\r\n \r\n amv.piControl.cmip5.nc contains\r\n - Simulated SST anomalies associated with the AMV pattern\r\n - Simulated AMV index time series (unfiltered)\r\n - Simulated AMV index time series (low-pass filtered)\r\n - Taylor statistics of the simulated AMV patterns\r\n based on CMIP5 piControl simulations.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - amv_pattern_obs_ref in amv.obs.nc: shading\r\n - amv_pattern_obs_signif (dataset = 1) in amv.obs.nc: cross markers\r\n \r\n Panel b:\r\n - Multimodel ensemble mean of amv_pattern in amv.hist.cmip6.nc: shading, with their sign agreement for hatching\r\n \r\n Panel c:\r\n - tay_stats (stat = 0, 1) in amv.obs.nc: black dots\r\n - tay_stats (stat = 0, 1) in amv.hist.cmip6.nc: red crosses, and their multimodel ensemble mean for the red dot\r\n - tay_stats (stat = 0, 1) in amv.hist.cmip5.nc: blue crosses, and their multimodel ensemble mean for the blue dot\r\n \r\n Panel d:\r\n - Lag-1 autocorrelation of amv_timeseries_raw in amv.obs.nc: black horizontal lines in left\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of amv_timeseries_raw in amv.piControl.cmip5.nc: blue open box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of amv_timeseries_raw in amv.piControl.cmip6.nc: red open box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of amv_timeseries_raw in amv.hist.cmip5.nc: blue filled box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of amv_timeseries_raw in amv.hist.cmip6.nc: red filled box-whisker in the left\r\n - Lag-10 autocorrelation of amv_timeseries in amv.obs.nc: black horizontal lines in right\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of amv_timeseries in amv.piControl.cmip5.nc: blue open box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of amv_timeseries in amv.piControl.cmip6.nc: red open box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of amv_timeseries in amv.hist.cmip5.nc: blue filled box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of amv_timeseries in amv.hist.cmip6.nc: red filled box-whisker in the right\r\n \r\n Panel e:\r\n - Standard deviation of amv_timeseries_raw in amv.obs.nc: black horizontal lines in left\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries_raw in amv.piControl.cmip5.nc: blue open box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries_raw in amv.piControl.cmip6.nc: red open box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries_raw in amv.hist.cmip5.nc: blue filled box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries_raw in amv.hist.cmip6.nc: red filled box-whisker in the left\r\n - Standard deviation of amv_timeseries in amv.obs.nc: black horizontal lines in right\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries in amv.piControl.cmip5.nc: blue open box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries in amv.piControl.cmip6.nc: red open box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries in amv.hist.cmip5.nc: blue filled box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries in amv.hist.cmip6.nc: red filled box-whisker in the right\r\n \r\n Panel f:\r\n - amv_timeseries in amv.obs.nc: black curves\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - amv_timeseries in amv.hist.cmip6.nc: 5th-95th percentiles in red shading, multimodel ensemble mean and its 5-95% confidence interval for red curves\r\n - amv_timeseries in amv.hist.cmip5.nc: 5th-95th percentiles in blue shading, multimodel ensemble mean for blue curve\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\nSST stands for Sea Surface Temperature.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Multimodel ensemble means and percentiles of historical simulations of CMIP5 and CMIP6 are calculated after weighting individual members with the inverse of the ensemble size of the same model. ensemble_assign in each file provides the model number to which each ensemble member belongs. This weighting does not apply to the sign agreement calculation.\r\n\r\n\r\npiControl simulations from CMIP5 and CMIP6 consist of a single member from each model, so the weighting is not applied.\r\n\r\n\r\nMultimodel ensemble means of the pattern correlation in Taylor statistics in (c) and the autocorrelation of the index in (d) are calculated via Fisher z-transformation and back transformation. \r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains 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"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37536,
            "uuid": "8fea8cd0692b42b7a52c3d41ce08bb6d",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig44/v20220614",
            "numberOfFiles": 0,
            "volume": 0,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": null,
            "onlineresource_set": []
        },
        {
            "ob_id": 37540,
            "uuid": "2898f482327649e88aa29cb6f40eb9ef",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig44/v20220614",
            "numberOfFiles": 0,
            "volume": 0,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": null,
            "onlineresource_set": []
        },
        {
            "ob_id": 37542,
            "uuid": "efcb9dd5b34b470ca37878325ddeb6fd",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig44/v20220614",
            "numberOfFiles": 0,
            "volume": 0,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": null,
            "onlineresource_set": []
        },
        {
            "ob_id": 37544,
            "uuid": "d424d20d7b0e46d1a84bb82a684d7c03",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_ccb1_fig1/v20220615",
            "numberOfFiles": 8,
            "volume": 154594,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37543,
                "uuid": "e299379f837142bfb2aa6df64cc66fe7",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Cross-Chapter Box 3.1,  Figure 1 (v20220615)",
                "abstract": "Data for Cross-Chapter Box 3.1,  Figure 1 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\nCross-Chapter Box 3.1, Figure 1 shows 15-year trends of surface global warming for 1998-2012 and 2012-2026.\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 The figure has four panels, with data provided for panels a and b in a subdirectory named panel_ab, and for panels c and d in subdirectories named panel_c and panel_d respectively.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains: \r\n \r\n - Observed and modelled global annual mean surface temperature and surface air temperature trends for 1998-2012\r\n - Modelled global annual mean surface air temperature trends for 2012-2026\r\n - Observed annual mean surface temperature trends for 1998-2012\r\n - Composite of modelled annual mean surface air temperature trends for 1998-2012\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - gmst_trend_1998-2012 in panel_ab/GMST_trend.csv; HadCRUT5 for histogram, ensemble mean of HadCRUT5 and other observations for open triangles at the top, and multimodel ensemble means of CMIP5 and CMIP6 for open diamonds at the top\r\n - gsat_trend_1998-2012 in panel_ab/GSAT_trend.csv; CMIP5 and CMIP6 ensembles for histograms, ERA5 for the top filled triangle, and multimodel ensemble means of CMIP5 and CMIP6 for filled diamonds at the top\r\n \r\n Panel b:\r\n - gmst_trend_2012-2026 in panel_ab/GMST_trend.csv; multimodel ensemble means of CMIP5 and CMIP6 for open diamonds at the top\r\n - gsat_trend_2012-2026 in panel_ab/GSAT_trend.csv; CMIP5 and CMIP6 ensembles for histograms, and multimodel ensemble means of CMIP5 and CMIP6 for filled diamonds at the top\r\n \r\n Panel c:\r\n - tas in panel_c/TrendPattern_HadCRUT5_mean.nc; shading, with the sig attribute for cross markers\r\n \r\n Panel d:\r\n - tas in panel_d/TrendPattern_composite.nc: shading\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\nHadCRUT5 is a gridded dataset of global historical near-surface air temperature anomalies since the year 1850.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Multimodel ensemble means and histograms are calculated after weighting each ensemble member with the inverse of the ensemble size of the same model.\r\n\r\nThe values for panels c and d are stored with the K/year unit but scaled to the K/decade, therefore they need to be multiplied by a factor of 10 in order to be consistent with the plotted 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 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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37547,
            "uuid": "c19d05d18a7940e3828ce9ff9b60f2fd",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_ccb2_fig1/v20220615",
            "numberOfFiles": 7,
            "volume": 17543,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37546,
                "uuid": "73c576b685c049258dd578f5487885f2",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Cross-Chapter Box 3.2,  Figure 1 (v20220615)",
                "abstract": "Data for Cross-Chapter Box 3.2,  Figure 1 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\nCross-Chapter Box 3.2, Figure 1 shows a comparison of observed and simulated changes in global mean temperature and precipitation extremes. \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 Technically the figure has four panels, but since they are not marked all the data is in the parent directory.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Global annual maximum daily maximum daily maximum temperature (TXx) anomalies from 1953 to 2017 relative to 1961-1990 from HadEX3 observations and CMIP5 and CMIP6 models (human and natural forcings simulations)\r\n - Global annual maximum daily maximum daily maximum temperature (TXx) anomalies from 1953 to 2017 relative to 1961-1990 from HadEX3 observations and CMIP5 and CMIP6 models (natural forcings simulations)\r\n - Global annual maximum 1-day precipitation (rx1day) anomalies from 1953 to 2017  relative to 1961-1990 from HadEX3 observations and CMIP5 and CMIP6 models (natural forcing only simulations)\r\n - Global annual maximum 1-day precipitation (rx1day) anomalies from 1953 to 2017 relative to 1961-1990 from HadEX3 observations and CMIP5 and CMIP6 models (human and natural forcings simulations)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - txx_anomalies_timeseries_historical.csv has data for the blue (CMIP5), red (CMIP6) and black (HadEX3) lines as well as blue and red shadings showing TXx anomalies (top left panel)\r\n - txx_anomalies_timeseries_natural has data for the blue (CMIP5), red (CMIP6) and black (HadEX3) lines as well as blue and red shadings showing TXx anomalies  (bottom left panel)\r\n - rx1day_anomalies_timeseries_historical has data for the blue (CMIP5), red (CMIP6) and black (HadEX3) lines as well as blue and red shadings showing Rx1day anomalies   (top right panel)\r\n - rx1day_anomalies_timeseries_natural has data for the blue (CMIP5), red (CMIP6) and black (HadEX3) lines as well as blue and red shadings showing Rx1day anomalies  (bottom right panel)\r\n\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparion Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nHadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid covering 1901-2018.\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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37550,
            "uuid": "24310bafd9354dfc90ca6847797ba14a",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_faq1_fig1/v20220615",
            "numberOfFiles": 4,
            "volume": 34308,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37549,
                "uuid": "c03ba3e2a7314f848e41a3a724bd8d25",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for FAQ 3.1, Figure 1 (v20220615)",
                "abstract": "Data for FAQ 3.1, Figure 1 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\nFAQ 3.1 Figure 1 shows that observed warming (1850-2018) is only reproduced in simulations including human influence.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\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 List of data provided\r\n ---------------------------------------------------\r\n The dataset contains global surface temperature changes timeseries relative to 1850-1900 for 1850-2019 from:\r\n \r\n - CMIP6 historical+ssp245 simulations (simulations with human and natural forcing)\r\n - CMIP6 hist-GHG simulations (simulations with anthropogenic green house gases forcing)\r\n - CMIP6 hist-aer simulations (simulation with anthropogenic aerosol forcing)\r\n - CMIP6 hist-nat simulations (simulation with natural forcing only)\r\n - Observations from Chapter 2\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n gmst_anomalies_timeseries.csv. Global surface temperature changes timeseries relative to 1850-1900 for 1850-2019 from:\r\n \r\n - CMIP6 historical+ssp245 simulations (1850-2019) [mean, grey line]\r\n - CMIP6 historical+ssp245 simulations (1850-2019) [5% range, grey shading, bottom]\r\n - CMIP6 historical+ssp245 simulations (1850-2019) [95% range, grey shading, top]\r\n - CMIP6 hist-GHG simulations (1850-2019) [mean, red line]\r\n - CMIP6 hist-GHG simulations (1850-2019) [5% range, red shading, bottom]\r\n - CMIP6 hist-GHG simulations (1850-2019) [95% range, red shading, top]\r\n - CMIP6 hist-aer simulations (1850-2019) [mean, blue line]\r\n - CMIP6 hist-aer simulations (1850-2019) [5% range, blue shading, bottom]\r\n - CMIP6 hist-aer simulations (1850-2019) [95% range, blue 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 - Observations from Chapter 2 (1850-2019) [black line]\r\n\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37553,
            "uuid": "a713247b992b4bf3a0b7eac97d2d4650",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_faq2_fig1/v20220615",
            "numberOfFiles": 6,
            "volume": 113656,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37552,
                "uuid": "f148031d13954c85b873900cd3f47170",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for FAQ 3.2, Figure 1 (v20220615)",
                "abstract": "Data for FAQ 3.2, Figure 1 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\nFAQ 3.2, Figure 1 shows annual, decadal and multi-decadal variations in average global surface temperature. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\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 technically has three panels, but they are not labelled. So the datasets are stored just in the main figure folder. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Dataset contains modelled GSAT anomalies from MPI-ESM grand ensemble (1950-2019):\r\n \r\n - On annual scale\r\n - On decadal scale\r\n - On multi-decadal scale\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - annual_gsat_anomalies_mpi_esm_grand_ens.csv has data for the left panel, GSAT anomalies from 1950 to 2019 from MPI-ESM grand ensemble (black, light green, light marsh green, light dark green lines)\r\n - decadal_gsat_anomalies_mpi_esm_grand_ens.csv  has data for the middle panel, GSAT anomalies from 1950 to 2019 from MPI-ESM grand ensemble  (black, light green, light marsh green, light dark green lines)\r\n - multi_decadal_gsat_anomalies_mpi_esm_grand_ens.csv has data for the right panel, GSAT anomalies from 1950 to 2019 from MPI-ESM grand ensemble  (black, light green, light marsh green, light dark green lines)\r\n\r\n\r\nGSAT stands for Global Surface Air Temperature.\r\nMPI-ESM is a comprehensive Earth-System Model, consisting of component models for the ocean, the atmosphere and the land surface.\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\r\n - Link to the GitHub repo with code for the figure"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37556,
            "uuid": "c04d43c0209248b6880e8b062453eee2",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_faq3_fig1/v20220615",
            "numberOfFiles": 4,
            "volume": 106970,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37555,
                "uuid": "afe80eb32a1c4164a3b84396c6d7a5d6",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for FAQ 3.3, Figure 1 (v20220615)",
                "abstract": "Data for FAQ 3.3, Figure 1 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\nFAQ 3.3 Figure 1 shows pattern correlations between models and observations for three different variables: surface air temperature, precipitation and sea level pressure. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\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 List of data provided\r\n ---------------------------------------------------\r\n This dataset contains all correlation pattern values displayed in the figure.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n fig_FAQ_3_3.nc:\r\n \r\n - variable: 'cor' with two dimensions:\r\n . 'vars': variables on the x-axis (same order as in the figure)\r\n . 'models': name of each models (the attribute 'project' contains mapping to 'CMIP3', 'CMIP5' or 'CMIP6')\r\n\r\nCMIP3 is the third phase of the Coupled Model Intercomparison Project.\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nVar 'cor' contains the values. Coordinate 'var' is the x-axis. Coordinate 'models' is the y-axis. The attribute 'project' of the coordinate 'models' contains as string chain the mapping to CMIP3 (cyan), CMIP5 (blue) and CMIP6 (red). The multi-model mean is not part of the dataset.\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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37559,
            "uuid": "1e9be83c1cb749ab930d629c354cb333",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig38/v20220614",
            "numberOfFiles": 11,
            "volume": 4252781,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37558,
                "uuid": "758419765d0f4926aa70002ec6c856b0",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.38 (v20220614)",
                "abstract": "Data for Figure 3.38 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 3.38 shows model evaluation of ENSO teleconnection for 2m-temperature and precipitation in boreal winter (December-January-February).\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 Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n Data provided for all panels in one single directory\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains observed global patterns for:\r\n \r\n - temperature from the Berkeley Earth dataset over land \r\n - temperature from ERSSTv5 over ocean\r\n - precipitation from GPCC over land (shading, mm day–1)\r\n - precipitation from GPCP worldwide (contours, period: 1979-2014)\r\n \r\n and distributions of regression coefficients in IPCC regions for:\r\n - temperature\r\n - precipitation\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n maps:\r\n \r\n - reg_tas_NINO34_BEST_ERSSTv5_1901_2018_DJF.nc (var = 'rc', upper map over land)\r\n - reg_sst_NINO34_ERSSTv5_ERSSTv5_1901_2018_DJF.nc (var = 'rc', upper map over ocean)\r\n - reg_precip_NINO34_GPCP_ERSST5_1979_2018_DJF.nc (var = 'rc', lower map, contours)\r\n - reg_pr_NINO34_GPCC_ERSSTv5_1901_2016_DJF.nc (var = 'rc', lower map, shading)\r\n \r\n histograms:\r\n - tas_enso_regression_pdf_v4_no_cosweight_DJF.nc\r\n . upper grey histograms: var = 'region_pdfx_hist' and 'region_pdfy_hist'\r\n . MME (black line): var = 'region_ave_hist'\r\n . Observations (blue lines): var = 'region_obs'\r\n - tas_amip_hist_enso_regression_pdf_v4_no_cosweight_DJF.nc (orange dashed line): var = 'region_ave_amip_hist'\r\n \r\n => Fields correspond to regions numbers with labels in the plot, namely for temperature: 'EAU/RFE/RAR/NWN/NCA/ENA/NSA/MED/NWS/ESAF' (see variable region_info with attributes making the association between the region index and the acronym/name). \r\n - pr_enso_regression_pdf_v4_no_cosweight_DJF.nc\r\n . lower grey histograms: var = 'region_pdfx_hist' and 'region_pdfy_hist'\r\n . MME (black line): var = 'region_ave_hist'\r\n . Observations (blue lines): var = 'region_obs'\r\n - pr_amip_hist_enso_regression_pdf_v4_no_cosweight_DJF.nc (orange dahsed line): var = 'region_ave_amip_hist'\r\n \r\n => Fields correspond to regions numbers with labels in the plot, namely for precipitation: 'EAS/SEA/EAU/WNA/NCA/SES/NSA/ESAF/SEAF/MED' (see variable info_region with attributes making the association between the region index and the acronym/name).\r\n\r\n\r\nENSO is the El Niño Southern Oscillation. \r\nGPCC is the Global Precipitation Climatology Centre. \r\nGPCP is the Global Precipitation Climatology Project.\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Data provided in reg_pr_NINO34_GPCC_ERSSTv5_1901_2016_DJF.nc are in mm/month. Values should be divided by 30 for plotting in mm/day.\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, which contains details on the input data used in Table 3.SM.1\r\n - Link to the figure on the IPCC AR6 website"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37562,
            "uuid": "24886522034341c7bd01d6788e12a507",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig26/v20220616",
            "numberOfFiles": 7,
            "volume": 61165,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37561,
                "uuid": "85168e39bfff444ba02bf55e7682f73d",
                "short_code": "ob",
                "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.26 (v20220616)",
                "abstract": "Data for Figure 3.26 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 3.26 shows global ocean heat content in CMIP6 simulations and observations.\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 the figure has 4 panels, but they are not named, so the datasets are stored in the parent directory. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains simulated and observed ocean heat content timeseries:\r\n \r\n - from CMIP6 models at full depth (1850-2014)\r\n - from observations at full depth (1971-2018)\r\n - from CMIP6 models at 0-700 m (1850-2014)\r\n - from observations at 700-200 m (1971-2018)\r\n - from CMIP6 models at 700-200 m (1850-2014)\r\n - from observations at 700-200 m (1971-2018)\r\n - from CMIP6 models at deeper than 2000 m (1850-2014)\r\n - from observations at deeper than 2000 m (1992-2018)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - ocean_heat_content_anomalies_full_depth.csv has the data for the read lines and shadings (CMIP6) from 1850 to 2014 and black lines and shadings (observations) from 1971 to 2018\r\n - ocean_heat_content_anomalies_0_700_m.csv has the data for the read lines and shadings (CMIP6) from 1850 to 2014 and black lines and shadings (observations) from 1971 to 2018\r\n - ocean_heat_content_anomalies_700_2000_m.csv has the data for the read lines and shadings (CMIP6) from 1850 to 2014 and black lines and shadings (observations) from 1971 to 2018\r\n - ocean_heat_content_anomalies_over_2000_m.csv has the data for the read lines and shadings (CMIP6) from 1850 to 2014 and black lines and shadings (observations) from 1992 to 2018\r\n\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 for this figure is taken from the file 'AR6_FGD_assessment_timeseries_OHC.csv' from Cross-Chapter Box1 figure 1, Chapter 9. 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 dataset for figure CCB1 Chapter 9\r\n - Link to the figure on the IPCC AR6 website"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 37565,
            "uuid": "29b5256472a142bfaad9948e7e5c5b54",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig44/v20220614",
            "numberOfFiles": 0,
            "volume": 0,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": null,
            "onlineresource_set": []
        }
    ]
}