Procedure Computation List

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        {
            "ob_id": 32162,
            "uuid": "e560269d82274ed4a096af819206fe37",
            "title": "Computation and post processing of IGP MRR data using IMProToo software",
            "abstract": "During the campaign, both ProcessedData (.pro) and RawSpectra (.raw)\r\nwere saved in daily files from 3 February to 22 March 2018. No averaged data files (.ave)\r\n were saved. Short breaks in data acquisition lead to several data gaps during the campaign, which are apparent as missing data in the data files.\r\nThe ProcessedData files were converted to compressed netCDF format, using a modified version of mrr2c V1.0.2 (c) 2017-2020 by Peter Kuma (https://github.com/peterkuma/mrr2c) Output from this conversion is stored as daily datafiles (naming: bergen-mrr2_yyyymmdd_processed-v1.nc; format: netcdf) in directory: MRR_Alliance_Pro_v1/\r\nIn addition, data files were processed with the tool IMProToo v0.101\r\n (https://github.com/maahn/IMProToo) based on the *.raw data files. Output of this processing is stored as daily datafiles (naming: bergen-mrr2_alliance_yyyymmdd_IMProToo-v0.nc; format: netcdf) in directory: MRR_Alliance_IMProToo_v0/\r\nPost processing was done by Harald Sodemann (UiB), who also acts as data contact.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32165,
            "uuid": "54e1d465d6154a2a82bd76f4d82f89c8",
            "title": "Derivation of the Glaciers_cci Inventory of Ice-Marginal Lakes in Greenland dataset",
            "abstract": "Ice marginal lakes were identified using three independent remote sensing methods: \r\n1) multi-temporal backscatter classification from Sentinel-1 synthetic aperture radar imagery;\r\n2) multi-spectral indices classification from Sentinel-2 optical imagery; \r\nand 3) sink detection from the ArcticDEM (v3).  (The ArcticDEM is an NGA-NSF public-private initiative to automatically produce a high-resolution, high quality, digital surface model (DSM) of the Arctic using optical stereo imagery, high-performance computing, and open source photogrammetry software.)\r\n\r\nAll data were compiled and filtered in a semi-automated approach, using a modified version of the MEaSUREs GIMP ice mask (https://nsidc.org/data/NSIDC-0714/versions/1) to clip the dataset to within 1 km of the ice margin. Each detected lake was then verified manually.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32226,
            "uuid": "28a7188563934ccb9e44eb0fd534069f",
            "title": "Algorithm for the  ESA Soil Moisture Climate Change Initiative, v06.1",
            "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors.   For information on the algorithm see the Algorithm Theoretical Baseline Document.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32241,
            "uuid": "d56fbdd4ae02464a81d994372ce0863d",
            "title": "Temperature and precipitation projections by the CMIP5 global models averaged over SREX regions",
            "abstract": "Chapter 14 of The IPCC 5th Assessment Report, WG1 includes, in the supplementary material of chapter 14 a table of extremes (Table 14.SM1). The underlying data are provided in spreadsheet format.\r\n\r\nThe tabulated data are presented in two spreadsheet formats. The first of these has a row corresponding to each row in AR5 table 14.1: each row contains 10 data values, 5 for temperature and 5 for precipitation. A 2nd file is arranged with only 2 data values on each row (one temperature and one precipitation value), with a different row for each statistic (median, 25th percentile etc). This 2nd approach is easier to read in to a R program for plotting. A simple demonstration script, which produces illustration 2 below, is provided in /software/ar5/extremes/extremesBoxPlot.r.\r\n\r\nThe 26 SREX regions are: Alaska/NW Canada (ALA), Eastern Canada/Greenland/Iceland (CGI), Western North America (WNA), Central North America (CNA), Eastern North America (ENA), Central America/Mexico (CAM), Amazon (AMZ), NE Brazil (NEB), West Coast South America (WSA), South- Eastern South America (SSA), Northern Europe (NEU), Central Europe (CEU), Southern Europe/the Mediterranean (MED), Sahara (SAH), Western Africa (WAF), Eastern Africa (EAF), Southern Africa (SAF), Northern Asia (NAS), Western Asia (WAS), Central Asia (CAS), Tibetan Plateau (TIB), Eastern Asia (EAS), Southern Asia (SAS), Southeast Asia (SEA), Northern Australia (NAS) and Southern Australia/New Zealand (SAU).\r\n\r\nThe non-SREX reference regions are: Antarctica (ANT), Arctic (ARC), Caribbean (CAR), Western Indian Ocean (WIO), Northern Tropical Pacific (NTP), Equatorial Tropical Pacific (ETP) and Southern Tropical Pacific (STP). \r\n\r\nErrata: the table is missing a row of data and there are errors in the RCP2.6 Caribbean annual precipitation figures (WG1AR5_Errata_26092014.pdf, 14SM-18 and 14SM-22).",
            "keywords": "IPCC-DDC, IPCC, AR5, WG1, Chapter 14, Table 14.SM.1, Temperature, Precipitation, SREX",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32244,
            "uuid": "b712474c4a8e415b9fa6877652acb93a",
            "title": "Compilation of the ESA Climate Change Initiative Sea Level Budget Closure",
            "abstract": "The compilation is a result from the Sea-level Budget Closure (SLBC_cci) project conducted in the framework of ESA’s Climate Change Initiative (CCI). \r\nData and methods underlying the time series are as follows:\r\n(a) satellite altimetry analysis by the Sea Level CCI project.\r\n(b) a new analysis of Argo drifter data with incorporation of sea surface temperature data; an alternative time series consists in an ensemble mean over previous global mean steric sea level anomaly time series.\r\n(c) analysis of monthly global gravity field solutions from the Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry mission.\r\n(d) results from a global glacier model.\r\n(e) analysis of satellite radar altimetry over the Greenland Ice Sheet, amended by results from the global glacier model for the Greenland peripheral glaciers; an alternative time series consists of results from GRACE satellite gravimetry.\r\n(f) analysis of satellite radar altimetry over the Antarctic Ice Sheet; an alternative time series consists of results from GRACE satellite gravimetry.\r\n(g) results from the WaterGAP global hydrological model.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32285,
            "uuid": "94870c9edbe1431994001d40f20e6779",
            "title": "ESA Cloud Climate Change Initiative ATSR2-AATSR v3 data",
            "abstract": "The ATSR2-AATSR v3 dataset has been derived using the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework.  For further details, see the documentation at https://climate.esa.int/en/projects/cloud/key-documents/",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32287,
            "uuid": "04f7fe1327cd4b92a3ac92712dc5027b",
            "title": "ESA Cloud Climate Change Initiative AVHRR-AM v3 data",
            "abstract": "The AVHRR-AM dataset has been derived using the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework.  For further details, see the documentation at https://climate.esa.int/en/projects/cloud/key-documents/",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32289,
            "uuid": "7c2ab8316ba14b66b6ae1fe35cf74c3d",
            "title": "ESA Cloud Climate Change Initiative AVHRR-PM v3 data",
            "abstract": "The AVHRR-PM dataset has been derived using the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework.  For further details, see the documentation at https://climate.esa.int/en/projects/cloud/key-documents/",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32326,
            "uuid": "f3d479240b1248ae8ba90b5121f7ff32",
            "title": "Derivation of Ocean Colour v5 data from the ESA Climate Change Initiative Ocean Colour project (Ocean_Colour_cci)",
            "abstract": "The ocean colour CCI has calculated ocean colour Essential Climate Variable data, using input data from various satellite instruments, as part of the ESA Climate Change Initiative program.\r\n\r\nFor more information see the Ocean Colour v5 Product User Guide.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32461,
            "uuid": "692a4500a73c4355868b72da0dd0c1a1",
            "title": "ECMWF OpenIFS model deployed on unknown computer",
            "abstract": "ECMWF OpenIFS model deployed on unknown computer",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32472,
            "uuid": "0bfc6301e7c1413da71093e853b03f83",
            "title": "NCEO OLTraj V2.0",
            "abstract": "The trajectories were generated starting from zonal and meridional model velocity fields from the AVISO project; please see the Global ocean gridded L4 sea surface heights and derived variables reprocessed reference in the documentation section for more details on the dataset.  The output of which was integrated using the LAMTA package (6-hour time step) as previously described in Nencioli et al., 2018 (also available in the documentation section).",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32514,
            "uuid": "b69f14b6767e444d93c02a6e0cecf2ae",
            "title": "ESA Snow Climate Change Initiative: Derivation of SCFG MODIS v1 product.",
            "abstract": "The retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of background and forest reflectance maps derived from statistical analyses of MODIS time series replacing the constant values for snow free ground and snow free forest used in the GlobSnow approach, and (ii) the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019). The forest transmissivity map is used to account for the shading effects of the forest canopy and estimate also in forested areas the fractional snow cover on ground.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32516,
            "uuid": "209e3c585d8b4d5b8f36f6c59687e1e9",
            "title": "ESA Snow Climate Change Initiative: Derivation of SCFV MODIS v1 product.",
            "abstract": "The retrieval method of the snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of a background reflectance map derived from statistical analyses of MODIS time series replacing the constant values for snow free ground used in the GlobSnow approach, and (ii) the adaptation of the retrieval method for mapping in forested areas the SCFV. \r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32519,
            "uuid": "0d8dfce486af4455abb556666416887b",
            "title": "ESA Snow Climate Change Initiative: Derivation of SCFG AVHRR v1 product.",
            "abstract": "The retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm and 1.61 µm (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nThe following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32524,
            "uuid": "d60e0d8070734e39951a356adbb8b497",
            "title": "ESA Snow Climate Change Initiative: Derivation of SCFV AVHRR v1 product.",
            "abstract": "The retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm and 1.61 µm (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. \r\n\r\nThe following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32585,
            "uuid": "91ff8a893f4647ab9224e67666d0a345",
            "title": "Met Office Unified Model United Kingdom Variable resolution (UKV) version 8.5, run u-ab978",
            "abstract": "Run u-ab978 of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the United Kingdom Variable resolution (UKV) version 8.5 model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period July 2015 with a flat orography file for the island. See related dataset for output from a complementary run with the island's orography included for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nTechnical details regarding the configuration of these runs follow those described in Vosper (2015, doi:10.1002/qj.2566) - see online resources linked to this record.",
            "keywords": "UKV, gravity wave",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": [
                "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/10880/?format=api"
            ]
        },
        {
            "ob_id": 32586,
            "uuid": "7e791b00ce5e46129f055851325a741c",
            "title": "Met Office Unified Model United Kingdom Variable resolution (UKV) version 8.5, run u-ab326",
            "abstract": "Run u-ab326 of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the United Kingdom Variable resolution (UKV) version 8.5 model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period July 2015 with the island orography included. See related dataset for output from a complementary run with a flat orography file for the island for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nTechnical details regarding the configuration of these runs follow those described in Vosper (2015, doi:10.1002/qj.2566) - see online resources linked to this record.",
            "keywords": "UKV, gravity wave",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": [
                "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/10881/?format=api"
            ]
        },
        {
            "ob_id": 32587,
            "uuid": "da680a867c1e4ddba41bd06f8a4784b0",
            "title": "Met Office Unified Model United Kingdom Variable resolution (UKV) version 8.5, run u-ag706",
            "abstract": "Run u-ag706 of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the United Kingdom Variable resolution (UKV) version 8.5 model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period January 2015 with a flat orography file for the island. See related dataset for output from a complementary run with the island's orography included for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nTechnical details regarding the configuration of these runs follow those described in Vosper (2015, doi:10.1002/qj.2566) - see online resources linked to this record.",
            "keywords": "UKV, gravity wave",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": [
                "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/10882/?format=api"
            ]
        },
        {
            "ob_id": 32588,
            "uuid": "1de8ce38a5e142669f7c8a2b6a188faf",
            "title": "Met Office Unified Model United Kingdom Variable resolution (UKV) version 8.5, run u-ag477",
            "abstract": "Run u-ag477 of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the United Kingdom Variable resolution (UKV) version 8.5 model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period January 2015 with the island orography included. See related dataset for output from a complementary run with a flat orography file for the island for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nTechnical details regarding the configuration of these runs follow those described in Vosper (2015, doi:10.1002/qj.2566) - see online resources linked to this record.",
            "keywords": "UKV, gravity wave",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": [
                "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/10883/?format=api"
            ]
        },
        {
            "ob_id": 32589,
            "uuid": "def5b5d175a643abb8f9dbe624a752f5",
            "title": "Met Office Unified Model United Kingdom Variable resolution (UKV) version 8.5, run u-ae766",
            "abstract": "Run u-ae766 of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the United Kingdom Variable resolution (UKV) version 8.5 model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period June-July 2015 with the island orography included. See related dataset for output from a complementary run with a flat orography file for the island for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nTechnical details regarding the configuration of these runs follow those described in Vosper (2015, doi:10.1002/qj.2566) - see online resources linked to this record.",
            "keywords": "UKV, gravity wave",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": [
                "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/10884/?format=api"
            ]
        },
        {
            "ob_id": 32590,
            "uuid": "948d8cd87c0543818e178437b39ac2f8",
            "title": "Met Office Unified Model United Kingdom Variable resolution (UKV) version 8.5, run u-af015",
            "abstract": "Run u-af015 of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the United Kingdom Variable resolution (UKV) version 8.5 model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period June-July 2015 with a flat orography file for the island. See related dataset for output from a complementary run with the island's orography included for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nTechnical details regarding the configuration of these runs follow those described in Vosper (2015, doi:10.1002/qj.2566) - see online resources linked to this record.",
            "keywords": "UKV, gravity wave",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": [
                "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/10885/?format=api"
            ]
        },
        {
            "ob_id": 32595,
            "uuid": "6802c93ca9d64942bd1cdb88e23b3546",
            "title": "NCEO Aboveground Biomass Map v21 2015",
            "abstract": "Algorithm / method The map shows aboveground woody biomass (AGB) in Kenyan forests. The map was generated by combining field inventory plots (KFS) with L-band SAR (JAXA ALOS-2 PALSAR-2) and multispectral optical data (NASA Landsat 8), by means of a Random Forests algorithm within a k-Fold calibration / validation framework. \r\n\r\nTraining dataset Forest inventory dataset collected consisting of 30 m diameter plots gathered in 4-plot clusters. The AGB pools measured were trees, bamboos and lianas. Pantropical allometries (1) were used to estimate AGB. A few plots with extremely large AGB (potentially due to the small plot size) were excluded (>98 percentile) \r\n\r\nSpatial data inputs ALOS-2 PALSAR-2 dual-polarization (2015)  and  Landsat 8 Surface Reflectance (2015±1)",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
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            "identifier_set": []
        },
        {
            "ob_id": 32672,
            "uuid": "81f04df0424245d4b54f4b8e64de73d2",
            "title": "Computation for Adverse Weather Scenarios for Future Electricity Systems",
            "abstract": "The data provided are derived from the Met Office decadal prediction system hindcast (https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/climate-models/depresys) and UKCP18 (https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/index) according to the climate change impacts identified from UKCP18.\r\n\r\nThe methods developed for characterising and representing these adverse weather scenarios, and the approach used to compile the final dataset are presented in the accompanying documentation.\r\n\r\nUse of this data is subject to the terms of the Open Government Licence (http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/) The following acknowledgment must be given when using the data: © Crown Copyright 2021, Met Office, funded by the National Infrastructure Commission.",
            "keywords": "Adverse weather scenarios, future electricity systems, adverse weather scenarios for future electricity systems, UK, Europe, weather risk, climate risk, energy system, extreme weather scenarios, electricity system resilience, climate change",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32673,
            "uuid": "7fe79daa538147ff84380b779ee1f72f",
            "title": "First principle quantum mechanics",
            "abstract": "A mixture of first principle quantum mechanics and input from the experiment",
            "keywords": "",
            "inputDescription": null,
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            "identifier_set": []
        },
        {
            "ob_id": 32677,
            "uuid": "c763ca9f2a874b8ca75d9576efe6673b",
            "title": "Derivation of Grounding Line Location data from the ESA Antarctic Ice Sheet Climate Change Initiative project",
            "abstract": "For information on the derivation of the Grounding Line location dataset see the documentation in https://climate.esa.int/projects/ice-sheets-antarctic/key-documents/",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32691,
            "uuid": "c201b5be02374fb99c777c3602638453",
            "title": "Standardized Precipitation Evapotranspiration Index (SPEI)",
            "abstract": "The SPEI dataset was developed by first aligning and formatting the precipitation and potential evaporation dataset using Climate Data Operators (CDO). Then a water deficit dataset was produced by subtracting these two datasets. Later, using R programming languages SPEI package, created by Santiago Beguería and Sergio M. Vicente-Serrano, the SPEI values were estimated for forty-eight different timescales. Finally, the dataset was validated using Climate Research Unit’s dataset, soil moisture dataset and Normalized Difference Vegetation Index dataset.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32697,
            "uuid": "29ac5e1fed1243ee8ad453ba74c5d6a4",
            "title": "Derivation of Gravimetric Mass Balance products from the Antarctic Ice Sheet CCI  (v3.0)",
            "abstract": "TU Dresden has generated a Gravimetric Mass Balance (GMB) product for the Antarctic Ice Sheet (AIS) based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through July 2020. The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 187 monthly solutions. The mass change estimation is based on the tailored sensitivity kernel approach developed at TU Dresden.\r\n\r\nThe methodology is described in: Groh, A. & Horwath, M. (2021). Antarctic Ice Mass Change Products from GRACE/GRACE-FO Using Tailored Sensitivity Kernels. Remote Sens., 13(9), 1736. doi:10.3390/rs13091736",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32700,
            "uuid": "2b2b435fec204442a44ff2bf173d78b1",
            "title": "Derivation of the Antarctic Ice Sheet CCI Ice Velocity v1 dataset from Sentinel-1",
            "abstract": "The surface velocity is derived by applying feature tracking techniques using Sentinel-1 synthetic aperture radar (SAR) data acquired in the Interferometric Wide (IW) swath mode. Ice velocity is provided at 200m grid spacing in Polar Stereographic projection (EPSG: 3031). The horizontal velocity components are provided in true meters per day, towards easting and northing direction of the grid. The vertical displacement is derived from a digital elevation model. Provided is a NetCDF file with the velocity components: vx, vy, vz, along with maps showing the magnitude of the horizontal components, the valid pixel count and uncertainty. The product combines all ice velocity maps, based on 6- and 12-day repeats, acquired within a single month in a monthly averaged product.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32710,
            "uuid": "dcb605e6db9b41c3959153f5686d6302",
            "title": "Level 1 processing algorithm applied to Landsat 5 data",
            "abstract": "Landsat Collection 1 Level-1 data products are produced by the Landsat Product Generation System (LPGS). LPGS also generates the 16-bit Quality Assessment Band (QA) and an angle coefficient file that are included in the Level-1 product, as well as a Full Resolution Browse (FRB) and an 8-bit Quality Image. The system then delivers the data products and images to the online cache for distribution. The Landsat 5 TM data is processed to full Precision Terrain correction.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32713,
            "uuid": "c8de973897f042e29caf9edd71d5d705",
            "title": "Level 2 Sulphur Dioxide (SO2) total column processing algorithm applied to Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI) raw data",
            "abstract": "The baseline operation flow of the scheme is based on a DOAS retrieval algorithm and is identical to that implemented in the retrieval algorithm for HCHO (also developed by BIRA-IASB, see S5P HCHO ATBD [RD12]). The main output of the algorithm are SO2 vertical column density, slant column density, air mass factor, Averaging Kernels (AK), and error estimates. Here, we will first briefly discuss the principle of the DOAS VCD retrieval before discussing the separate steps of the process in more detail.\r\n\r\nFirst, the radiance and irradiance data are read from an S5P L1b file, along with geolocation data such as pixel coordinates and observation geometry (sun and viewing angles). At this stage also cloud cover information is obtained from the S5P cloud L2 data, as required for the calculation of the AMF, later in the scheme. Then relevant absorption cross-section data (SO2), as well as characteristics of the instrument (e.g., slit functions) are used as input for the SO2 slant column density determination. As a baseline, the slant column fit is done in a sensitive window from 312 to 326 nm. For pixels with a strong SO2 signal, results from alternative windows, where the SO2 absorption is weaker, can be used instead. An empirical offset correction (dependent on the fitting window used) is then applied to the SCD. The latter correction accounts for systematic biases in the SCDs. Following the SCD determination, the AMF is estimated. For computational efficiency, the algorithm makes no ‘on the fly’ calculation but uses a pre-calculated box air mass factor look-up table (LUT). This lookup-table is generated using the LIDORT radiative transfer code and has several entries: cloud cover data, topographic information, observation geometry, surface albedo, effective wavelength (representative of the fitting window used), total ozone column, and the shape of the vertical SO2 profile. The algorithm also includes an error calculation and retrieval characterization module that computes the so-called DOAS-type averaging kernels (Eskes & Boersma, 2003), which characterize the vertical sensitivity of the measurement and which are required for comparison with other types of data (Veefkind et al., 2012). For more information please look at the ATBD document on the TROPOMI  website.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32737,
            "uuid": "fbfa5bb9c569492bb7ec68678dff7d52",
            "title": "Computation of Permafrost v3 datsets by the ESA Permafrost CCI",
            "abstract": "The  Permafrost CCI project has created Earth Observation (EO) based products for the permafrost Essential Climate Variable (ECV) spanning the last two decades. Since ground temperature  and  thaw  depth  cannot be  directly  observed  from  space-borne  sensors,  a  variety  of satellite  and  reanalysis  data  are  combined  in  a  ground  thermal  model.  The  algorithm  uses  remotely sensed  data  sets  of  Land  Surface  Temperature  (MODIS  LST/  ESA  LST  CCI)  and  landcover  (ESA LandcoverCCI)  to  drive  the  transient  permafrost  model  CryoGrid  2,  which  yields  thaw  depth  and ground  temperature  at  various  depths,  while  ground  temperature  forms  the  basis  for  permafrost fraction.\r\n\r\nInput data: MODIS Land surface temperature is used as the main input for the L4 production for 2003-2019 data. Sensors of auxiliary data are listed in the meta data.\r\nDownscaled and bias corrected ERA reanalyses data based on statistics of the overlap period between ERA reanalysis and MODIS LST are used for data before 2003. Sensors of auxiliary data are listed in the meta data.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32740,
            "uuid": "e14a7e11c35f423a8887647f59d5027f",
            "title": "NCEO OLTraj V2.2",
            "abstract": "The trajectories were generated starting from zonal and meridional model velocity fields from the AVISO project; please see the Global ocean gridded L4 sea surface heights and derived variables reprocessed reference in the documentation section for more details on the dataset.  The output of which was integrated using the LAMTA package (6-hour time step) as previously described in Nencioli et al., 2018 (also available in the documentation section).\r\n\r\nThe computing is similar to that of OLTraj v2.0 but is of higher resolution and supports double value for time variables instead of int64",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32756,
            "uuid": "1d05ff37e7124eaa8dbc7a3aedbba431",
            "title": "Level 2 processing algorithm applied to Sentinel 1 raw data, Instrument Processing Facility (IPF) version 2",
            "abstract": "Level-2 consists of geo-located geophysical products derived from Level-1. Level-2 Ocean (OCN) products for wind, wave and currents applications may contain the following geophysical components derived from the SAR data:\r\n- Ocean Wind field (OWI)\r\n- Ocean Swell spectra (OSW)\r\n- Surface Radial Velocity (RVL)\r\nThe availability of components depends on the acquisition mode. The OSW component cannot be generated from IW and EW mode, since individual looks with sufficient time separation are required. The obtained inter look time separation within one burst is too short due to the progressive scanning (i.e. short dwell time).\r\n\r\nThe metadata referring to OWI are derived from an internally processed GRD product. The metadata referring to RVL (and OSW, for SM and WV mode) are derived from an internally processed SLC product.\r\n\r\nFor more information on the changes for this processing version please see the Sentinel 1 document libary under the docs tab.",
            "keywords": "Synthetic Aperture Radar, Sentinel 1, Level 2, algorithm, SAR",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32760,
            "uuid": "c445ebc429d84b86b9e62e01bac93ba2",
            "title": "WRF model version 3.6.1 deployed on the ARCHER UK National Supercomputing Service.",
            "abstract": "WRF model version 3.6.1 deployed on the ARCHER UK National Supercomputing Service.\r\n\r\nMeteorology data from the European Centre for Medium-range Weather Forecasts (ECMWF) ERA-interim reanalysis data for initial and lateral boundary conditions.\r\n\r\nThe WRF v3.6.1 model set up implemented in this study included four nested domains. The domains had grid resolutions of 36 km × 36 km, 12 km × 12 km, 3 km × 3 km and 1 km × 1 km. The finest domain covered the West Midlands, centering over Birmingham. The multi-layer building energy parametrization (BEP) scheme with three land-use types (low-intensity residential, high-intensity residential and industrial/commercial) was also used.\r\n\r\nFull model set-up details match those in: Heaviside et al. (2015) - see linked documentation.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32778,
            "uuid": "e71c879b049f4ccca07542044df25f09",
            "title": "Level 1 processing algorithm applied to Sentinel 1 raw data, Instrument Processing Facility (IPF) v2",
            "abstract": "This computation involves the Level 1 processing algorithm applied to raw Synthetic Aperture Radar (SAR) data. This consists of Level 1 preprocessing, special handling for TOPSAR mode, Doppler centroid estimation, Level 1 Single Look Complex (SLC) processing algorithms and Level 1 post-processing to generate the output Single Look Complex (SLC) and Ground Range Detected (GRD) products as well as quicklook images. \r\n\r\nLevel-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track.\r\n\r\nThe products include a single look in each dimension using the full available signal bandwidth and complex samples (real and imaginary) preserving the phase information. The products have been geo-referenced using the orbit and attitude data from the satellite and have been corrected for azimuth bi-static delay, elevation antenna pattern and range spreading loss.\r\n\r\nFor more information on the changes for this processing version please see the Sentinel 1 document libary under the docs tab.",
            "keywords": "Synthetic Aperture Radar, Sentinel 1, Level 1, algorithm, SAR",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32808,
            "uuid": "feb90994cd0c439384bf1819eaa575ff",
            "title": "CCM SOCOLv4.0 deployed on CSCS Piz Daint computer",
            "abstract": "CCM SOCOLv4.0 deployed on CSCS Piz Daint computer",
            "keywords": "CCMI-2022, SOCOL",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32825,
            "uuid": "b58a89dcbf5b43bb96145e5f3833bede",
            "title": "Computation Component: Level 1A processing algorithm applied to Sentinel 3 SRAL raw data.",
            "abstract": "This computation involves the Level 1A processing algorithm applied to raw Synthetic Aperture Radar Altimeter (SRAL) data. \r\n\r\nThe main algorithms of the Level-1 SAR_Ku chain are:\r\nDetermine surface type: This algorithm computes the surface type (\"open ocean or semi-enclosed seas\", \"enclosed seas or lakes\", \"continental ice\" or \"land\") determining the position of a \"land-sea mask\" Auxiliary Data File nearest to the geolocated measurement. The latitude and longitude resolution of this land-sea mask is 2 minutes.\r\nCompute tracker ranges corrected for USO frequency drift: This algorithm computes the USO correction from an Auxiliary Data File called \"USO file\" and this correction is applied to the tracker range. The \"USO file\" provides the real USO frequency drift measured on-board wrt the USO frequency nominal value. This algorithm also computes the tracker range rate converted into distance versus time.\r\nCompute tracker ranges corrected for internal path correction: This algorithm computes the internal path correction from an Auxiliary Data File called \"CAL1 LTM file\" and this correction is applied to the tracker range. The \"CAL1 LTM file\" provides the internal path delay measured on-board thanks to the CAL1 calibration mode, which measures the difference of travel between the transmission and the reference lines within the altimeter. This algorithm also computes and applies the instrumental delay correction measured on-ground, due to the distance between the duplexer and the antenna reference point.\r\nCorrect the AGC for instrumental errors: This algorithm computes the Automatic Gain Control (AGC) instrumental correction and applies this correction to the AGC. The AGC instrumental correction is computed taking into account the real gain value applied on-board and stored as a matrix table on an Auxiliary Data File called \"characterisation file\".\r\nCorrect and apply power & phase corrections: This algorithm computes and applies to each burst the phase and power variations within all the echoes of every burst. These phase and power corrections are measured on-board through a sequence of calibration echoes in CAL1 calibration mode.\r\nCorrect the waveforms: On-board, there is a calibration mode called CAL2 that is able to compute the Gain Profile Range Window (GPRW) that provides the information of the attenuation of the samples of the Level. The GPRW accounts for several instrumental effects (e.g. intermediate frequency filters gain response) that have an impact on the Level 0 waveforms power. This algorithm corrects these Level-0 waveforms by the GPRW instrumental effects.\r\nCompute surface locations: In the SAR_Ku processing chain, the output measurements are referenced to surface locations along the satellite track. These surface locations correspond with the intersection of the Doppler beams with an estimation of the surface elevations. These surface locations are used along all L1 SAR_Ku processing.\r\nDetermine Doppler beams direction: This algorithm determines the angular spacing between the instantaneous zero Doppler plane and the lines defined by the burst centre and the reference surface locations \"observed\" within the burst sequence.\r\nDoppler beams generation: This algorithm generates the Doppler beams in the frequency domain. Each burst of pulse-limited time domain echoes are transformed into the frequency domain using an FFT (Fast Fourier Transform) in the along track direction.\r\nCompute and apply Doppler correction: This algorithm computes and applied the Doppler correction to the tracker ranges. This correction is needed to remove the echoes frequency shifts due to sensor-target velocity. The Doppler correction is computed and applied in the frequency domain to each Doppler beam. This correction is a function of the emitted frequency, the pulse emitted duration, the satellite velocity of the beams, the emitted bandwidth and the sign of the slope of the transmitted chirp.\r\nCompute and apply slant range corrections: This algorithm computes the slant corrections (both fine and coarse) that correct the range-migration due to the motion of the sensor along the orbit.\r\nRange compression: This algorithm performs a range compression of the waveform that is the conversion of each Doppler processed burst of pulse-width time domain echoes to the frequency domain.\r\nTracker alignment correction: This algorithm corrects the azimuth processed echo stack for on-board tracker variation. It means that for each surface location, the waveforms are aligned before multi-looking.\r\nDoppler beams stack & multi-looking: This algorithm computes the stacked Doppler beams (I2+Q2 power waveforms) through the non-coherent summation of all the beams corresponding to each surface location.\r\nCompute sigma0 scaling factor: This algorithm computes the sigma0 scaling factor that is used at Level2 to determine the backscatter coefficients from the retracked amplitudes. The sigma0 scaling factor accounts for all power attenuations and gains which have an impact on the signal received on-board.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32839,
            "uuid": "be9fb3c9bda3425286c3fdc9f94bf04c",
            "title": "Derivation of the ESA Lakes Climate Change Initiative dataset",
            "abstract": "The data generated by the Lakes_cci project have been derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview.\r\n\r\nFor information on the derivation of the lake products, please see the documentation at https://climate.esa.int/en/projects/lakes/key-documents-lakes/.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32850,
            "uuid": "fb97f236192748289c4b0f5102097ffa",
            "title": "UKCP18 local time-mean sea level calculations for selected tide gauge locations around the world",
            "abstract": "The UKCP18 local time-mean sea level projections are based on CMIP5 climate model simulations of global ocean thermal expansion and global average surface temperature (GST) change. The extended 2300 projections make use of a simple climate model emulator tuned to CMIP5 models to generate estimates of thermal expansion and GST change beyond 2100. Global contributions from ice sheet surface mass balance and glaciers are related to GST following methods described in IPCC AR5 WG1 Chapter 13 (Church et al, 2013) and as described by Palmer et al (2020). Global sea level contributions for dynamic ice discharge for Greenland and Antarctica are based on IPCC AR5 and Levermann et al (2014), respectively. Estimates of land water storage changes follow those reported in IPCC AR5. These global components are regionalised for worldwide tide gauge locations using: (i) sea level GRD (gravitational, rotational and deformation) \"fingerprints\" for mass changes in land-based ice and water; (ii) local regression relationships across CMIP5 models for the sterodynamic component; (iii) an ensemble of estimate of the local relative sea level change from glacial isostatic adjustment. A 100,000 sample Monte Carlo is used to provide uncertainties for regional sea level change based on the 5th and 95th percentiles of the Monte Carlo sample. All projections are expressed relative to the 1986-2005 average value.   Please refer to Palmer et al (2020) for detailed methods. Further details are available in Palmer et al (2020), https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019EF001413",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32854,
            "uuid": "680031416869407292a724be855271f3",
            "title": "The SRPR (Remotec) Proxy retrieval algorithm",
            "abstract": "The RemoTeC retrieval algorithm  has been jointly developed at SRON and KIT to retrieve column-averaged methane, using a Proxy retrieval technique.   \r\n\r\nDetails of the technical aspects of the retrievals can be found in the ATBD (see documentation links)",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32860,
            "uuid": "0396726bba52483588fd9db95aebed52",
            "title": "The SRFP (Remotec) Full Physics retrieval algorithm",
            "abstract": "The RemoTeC retrieval algorithm  has been jointly developed at SRON and KIT to retrieve column-averaged methane and carbon dioxide, using a 'Full Physics' retrieval technique.   \r\n\r\nDetails of the technical aspects of the retrievals can be found in the ATBD (see documentation links)",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32866,
            "uuid": "bec27be51fdd44a69ce1223ae068e518",
            "title": "Derivation of the CH4_S5P_WFMD product from the  WFM-DOAS Retrieval algorithm",
            "abstract": "The Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS) algorithm is a least-squares retrieval method based on scaling (or shifting) pre-selected atmospheric vertical profiles.   The column-averaged dry air mole fractions of  methane (denoted XCH4) are derived from the vertical column amounts of methane by normalising with the dry air column, which is obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis. The corresponding vertical columns of CH4 are retrieved from the measured sun-normalised radiance using spectral fitting windows in the SWIR spectral region (2311-2315.5 nm and 2320-2338 nm).\r\nFor further details see the documentation section.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32876,
            "uuid": "54e09534daab4b988933a7bbef02c204",
            "title": "Caption for Figure SPM.5 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Panel a) Comparison of observed and simulated annual mean surface temperature change. The left map shows the observed changes in annual mean surface temperature in the period of 1850–2020 per °C of global warming (°C). The local (i.e., grid point) observed annual mean surface temperature changes are linearly regressed against the global surface temperature in the period 1850–2020. Observed temperature data are from Berkeley Earth, the dataset with the largest coverage and highest horizontal resolution. Linear regression is applied to all years for which data at the corresponding grid point is available. The regression method was used to take into account the complete observational time series and thereby reduce the role of internal variability at the grid point level. White indicates areas where time coverage was 100 years or less and thereby too short to calculate a reliable linear regression. The right map is based on model simulations and shows change in annual multi-model mean simulated temperatures at a global warming level of 1°C (20-year mean global surface temperature change relative to 1850–1900). The triangles at each end of the color bar indicate out-of-bound values, that is, values above or below the given limits. \r\n \r\nPanel b) Simulated annual mean temperature change (°C), panel c) precipitation change (%), and panel d) total column soil moisture change (standard deviation of interannual variability) at global warming levels of 1.5°C, 2°C and 4°C (20-yr mean global surface temperature change relative to 1850–1900). Simulated changes correspond to CMIP6 multi-model mean change (median change for soil moisture) at the corresponding global warming level, i.e. the same method as for the right map in panel a). In panel c), high positive percentage changes in dry regions may correspond to small absolute changes. In panel d), the unit is the standard deviation of interannual variability in soil moisture during 1850–1900. Standard deviation is a widely used metric in characterizing drought severity. A projected reduction in mean soil moisture by one standard deviation corresponds to soil moisture conditions typical of droughts that occurred about once every six years during 1850–1900. In panel d), large changes in dry regions with little interannual variability in the baseline conditions can correspond to small absolute change. The triangles at each end of the color bars indicate out-of-bound values, that is, values above or below the given limits. Results from all models reaching the corresponding warming level in any of the five illustrative scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) are averaged. Maps of annual mean temperature and precipitation changes at a global warming level of 3°C are available in Figure 4.31 and Figure 4.32 in Section 4.6.\r\nCorresponding maps of panels b), c) and d) including hatching to indicate the level of model agreement at grid-cell level are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in CC-box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability leading to an increase in robustness.\r\n\r\n{TS.1.3.2, Figure TS.3, Figure TS.5, Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1}",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32889,
            "uuid": "c517236b3f3c41ef80522e20fc64bcd5",
            "title": "Computation for extreme precipitation return level changes derived from UKCP Local Projections on a 5km grid for the FUTURE-DRAINAGE Project",
            "abstract": "A spatial statistical model was used to estimate extreme short-duration precipitation changes, derived from the UKCP Local projections at 5km resolution (Kendon et al 2021).",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 32901,
            "uuid": "e6e6d5b2de9440ac98bcfca57a97264e",
            "title": "Caption for Figure SPM.8 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Selected indicators of global climate change under the five illustrative scenarios used in this report. \r\n\r\nThe projections for each of the five scenarios are shown in colour. Shades represent uncertainty ranges – more detail is provided for each panel below. The black curves represent the historical simulations (panels a, b, c) or the observations (panel d). Historical values are included in all graphs to provide context for the projected future changes. \r\n\r\nPanel a) Global surface temperature changes in °C relative to 1850–1900. These changes were obtained by combining CMIP6 model simulations with observational constraints based on past simulated warming, as well as an updated assessment of equilibrium climate sensitivity (see Box SPM.1). Changes relative to 1850–1900 based on 20-year averaging periods are calculated by adding 0.85°C (the observed global surface temperature increase from 1850–1900 to 1995–2014) to simulated changes relative to 1995–2014. Very likely ranges are shown for SSP1-2.6 and SSP3-7.0.\r\n\r\nPanel b) September Arctic sea ice area in 10^6 km^2 based on CMIP6 model simulations. Very likely ranges are shown for SSP1-2.6 and SSP3-7.0. The Arctic is projected to be practically ice-free near mid-century under mid- and high GHG emissions scenarios.\r\n\r\nPanel c) Global ocean surface pH (a measure of acidity) based on CMIP6 model simulations. Very likely ranges are shown for SSP1-2.6 and SSP3-7.0.\r\n\r\nPanel d) Global mean sea level change in meters relative to 1900. The historical changes are observed (from tide gauges before 1992 and altimeters afterwards), and the future changes are assessed consistently with observational constraints based on emulation of CMIP, ice sheet, and glacier models. Likely ranges are shown for SSP1-2.6 and SSP3-7.0. Only likely ranges are assessed for sea level changes due to difficulties in estimating the distribution of deeply uncertain processes. The dashed curve indicates the potential impact of these deeply uncertain processes. It shows the 83rd percentile of SSP5-8.5 projections that include low-likelihood, high-impact ice sheet processes that cannot be ruled out; because of low confidence in projections of these processes, this curve does not constitute part of a likely range. Changes relative to 1900 are calculated by adding 0.158 m (observed global mean sea level rise from 1900 to 1995–2014) to simulated and observed changes relative to 1995–2014.\r\n\r\nPanel e): Global mean sea level change at 2300 in meters relative to 1900. Only SSP1-2.6 and SSP5-8.5 are projected at 2300, as simulations that extend beyond 2100 for the other scenarios are too few for robust results. The 17th–83rd percentile ranges are shaded. The dashed arrow illustrates the 83rd percentile of SSP5-8.5 projections that include low-likelihood, high-impact ice sheet processes that cannot be ruled out.\r\n\r\nPanels b) and c) are based on single simulations from each model, and so include a component of internal variability. Panels a), d) and e) are based on long-term averages, and hence the contributions from internal variability are small.\r\n\r\n{Figure TS.8, Figure TS.11, Box TS.4 Figure 1, Box TS.4 Figure 1, 4.3, 9.6, Figure 4.2, Figure 4.8, Figure 4.11, Figure 9.27}",
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        },
        {
            "ob_id": 32908,
            "uuid": "5e51855a28ff48babd9b881ad5b899d9",
            "title": "Caption for Figure SPM.6 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Projected changes in the intensity and frequency of hot temperature extremes over land, extreme precipitation over land, and agricultural and ecological droughts in drying regions.\r\n \r\nProjected changes are shown at global warming levels of 1°C, 1.5°C, 2°C, and 4°C and are relative to 1850-1900 {Footnote } representing a climate without human influence. The figure depicts frequencies and increases in intensity of 10- or 50-year extreme events from the base period (1850-1900) under different global warming levels.\r\nHot temperature extremes are defined as the daily maximum temperatures over land that were exceeded on average once in a decade (10-year event) or once in 50 years (50-year event) during the 1850–1900 reference period. Extreme precipitation events are defined as the daily precipitation amount over land that was exceeded on average once in a decade during the 1850–1900 reference period. Agricultural and ecological drought events are defined as the annual average of total column soil moisture below the 10th percentile of the 1850–1900 base period. These extremes are defined on model grid box scale. For hot temperature extremes and extreme precipitation, results are shown for the global land. For agricultural and ecological drought, results are shown for drying regions only, which correspond to the AR6 regions in which there is at least medium confidence in a projected increase in agricultural/ecological drought at the 2°C warming level compared to the 1850–1900 base period in CMIP6. These regions include W. North-America, C. North-America, N. Central-America, S. Central-America, Caribbean, N. South-America, N.E. South-America, South-American-Monsoon, S.W. South-America, S. South-America, West & Central-Europe, Mediterranean, W. Southern-Africa, E. Southern-Africa, Madagascar, E. Australia, S. Australia (Caribbean is not included in the calculation of the figure because of the too small number of full land grid cells). The non-drying regions do not show an overall increase or decrease in drought severity. Projections of changes in agricultural and ecological droughts in the CMIP5 multi-model ensemble differ from those in CMIP6 in some regions, including in part of Africa and Asia. Assessments on projected changes in meteorological and hydrological droughts are provided in Chapter 11. {11.6, 11.9}\r\n \r\nIn the ‘frequency’ section, each year is represented by a dot. The dark dots indicate years in which the extreme threshold is exceeded, while light dots are years when the threshold is not exceeded. Values correspond to the medians (in bold) and their respective 5%-95% range based on the multi-model ensemble from simulations of CMIP6 under different SSP scenarios. For consistency, the number of dark dots is based on the rounded-up median. In the ‘intensity’ section, medians and their 5%-95% range, also based on the multi-model ensemble from simulations of CMIP6, are displayed as dark and light bars, respectively. Changes in the intensity of hot temperature extremes and extreme precipitations are expressed as degree celsius and percentage. As for agricultural and ecological drought, intensity changes are expressed as fractions of standard deviation of annual soil moisture.\r\n \r\n{11.1, 11.3, 11.4, 11.6, Figure 11.12, Figure 11.15, Figure 11.6, Figure 11.7, Figure 11.18}\r\n\r\nFootnote: The period 1850–1900 represents the earliest period of sufficiently globally complete observations to estimate global surface temperature and, consistent with AR5 and SR1.5, is used as an approximation for pre-industrial conditions.",
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        },
        {
            "ob_id": 32911,
            "uuid": "b670de871d43428f83305a9b442d81b0",
            "title": "Caption for Figure SPM.1 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "History of global temperature change and causes of recent warming\r\n\r\nPanel a): Changes in global surface temperature reconstructed from paleoclimate archives (solid grey line, 1–2000) and from direct observations (solid black line, 1850–2020), both relative to 1850–1900 and decadally averaged. The vertical bar on the left shows the estimated temperature (very likely range) during the warmest multi-century period in at least the last 100,000 years, which occurred around 6500 years ago during the current interglacial period (Holocene). The Last Interglacial, around 125,000 years ago, is the next most recent candidate for a period of higher temperature. These past warm periods were caused by slow (multi-millennial) orbital variations. The grey shading with white diagonal lines shows the very likely ranges for the temperature reconstructions. \r\n\r\nPanel b): Changes in global surface temperature over the past 170 years (black line) relative to 1850–1900 and annually averaged, compared to CMIP6 climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown), and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (see Figure SPM.2 for the assessed contributions to warming). \r\n\r\n{2.3.1, 3.3, Cross-Chapter Box 2.3, Cross-Section Box TS.1, Figure 1a, TS.2.2}",
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        },
        {
            "ob_id": 32915,
            "uuid": "1c2ecbedc7754e4cbe92f12f8b4bfa21",
            "title": "Caption for Figure SPM.9 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Synthesis of the number of AR6 WGI reference regions where climatic impact-drivers are projected to change\r\n\r\nA total of 35 climatic impact-drivers (CIDs) grouped into seven types are shown: heat and cold, wet and dry, wind, snow and ice, coastal, open ocean and other. For each CID, the bar in the graph below displays the number of AR6 WGI reference regions where it is projected to change. The colours represent the direction of change and the level of confidence in the change: purple indicates an increase while brown indicates a decrease; darker and lighter shades refer to high and medium confidence, respectively. Lighter background colours represent the maximum number of regions for which each CID is broadly relevant.\r\n\r\nPanel a) shows the 30 CIDs relevant to the land and coastal regions while panel b) shows the 5 CIDs relevant to the open ocean regions. Marine heatwaves and ocean acidity are assessed for coastal ocean regions in panel a) and for open ocean regions in panel b). Changes refer to a 20–30 year period centred around 2050 and/or consistent with 2°C global warming compared to a similar period within 1960-2014, except for hydrological drought and agricultural and ecological drought which is compared to 1850-1900. Definitions of the regions are provided in Atlas.1 and the Interactive Atlas (see interactive-atlas.ipcc.ch). \r\n\r\n{Table TS.5, Figure TS.22, Figure TS.25, 11.9, 12.2, 12.4, Atlas.1} (Table SPM.1)",
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        },
        {
            "ob_id": 32918,
            "uuid": "b6e6f3d33acf46979c6291b8c6255311",
            "title": "Caption for Figure SPM.10 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Near-linear relationship between cumulative CO2 emissions and the increase in global surface temperature. \r\n\r\nTop panel: Historical data (thin black line) shows observed global surface temperature increase in °C since 1850–1900 as a function of historical cumulative carbon dioxide (CO2) emissions in GtCO2 from 1850 to 2019. The grey range with its central line shows a corresponding estimate of the historical human-caused surface warming (see Figure SPM.2). Coloured areas show the assessed very likely range of global surface temperature projections, and thick coloured central lines show the median estimate as a function of cumulative CO2 emissions from 2020 until year 2050 for the set of illustrative scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, see Figure SPM.4). Projections use the cumulative CO2 emissions of each respective scenario, and the projected global warming includes the contribution from all anthropogenic forcers. The relationship is illustrated over the domain of cumulative CO2 emissions for which there is high confidence that the transient climate response to cumulative CO2 emissions (TCRE) remains constant, and for the time period from 1850 to 2050 over which global CO2 emissions remain net positive under all illustrative scenarios as there is limited evidence supporting the quantitative application of TCRE to estimate temperature evolution under net negative CO2 emissions.\r\n\r\nBottom panel: Historical and projected cumulative CO2 emissions in GtCO2 for the respective scenarios.\r\n\r\n{Figure TS.18, Figure 5.31, Section 5.5}",
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        },
        {
            "ob_id": 32923,
            "uuid": "ddad23c4850f407e81e7b847eb8e5ef0",
            "title": "Caption for Figure SPM.4 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Future anthropogenic emissions of key drivers of climate change and warming contributions by groups of drivers for the five illustrative scenarios used in this report.\r\n\r\nThe five scenarios are SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.\r\n\r\nPanel a) Annual anthropogenic (human-caused) emissions over the 2015–2100 period. Shown are emissions trajectories for carbon dioxide (CO2) from all sectors (GtCO2/yr) (left graph) and for a subset of three key non-CO2 drivers considered in the scenarios: methane (CH4, MtCH4/yr, top-right graph), nitrous oxide (N2O, MtN2O/yr, middle-right graph) and sulfur dioxide (SO2, MtSO2/yr, bottom-right graph, contributing to anthropogenic aerosols in panel b).\r\n\r\nPanel b) Warming contributions by groups of anthropogenic drivers and by scenario are shown as change in global surface temperature (°C) in 2081–2100 relative to 1850–1900, with indication of the observed warming to date. Bars and whiskers represent median values and the very likely range, respectively. Within each scenario bar plot, the bars represent total global warming (°C; total bar) (see Table SPM.1) and warming contributions (°C) from changes in CO2 (CO2 bar), from non-CO2 greenhouse gases (non-CO2 GHGs bar; comprising well-mixed greenhouse gases and ozone) and net cooling from other anthropogenic drivers (aerosols and land-use bar; anthropogenic aerosols, changes in reflectance due to land-use and irrigation changes, and contrails from aviation; see Figure SPM.2, panel c, for the warming contributions to date for individual drivers). The best estimate for observed warming in 2010–2019 relative to 1850–1900 (see Figure SPM.2, panel a) is indicated in the darker column in the total bar. Warming contributions in panel b are calculated as explained in Table SPM.1 for the total bar. For the other bars the contribution by groups of drivers are calculated with a physical climate emulator of global surface temperature which relies on climate sensitivity and radiative forcing assessments.\r\n\r\n{Cross-Chapter Box 1.4, 4.6, Figure 4.35, 6.7, Figure 6.18, 6.22 and 6.24, Cross-Chapter Box 7.1, 7.3, Figure 7.7, Box TS.7, Figures TS.4 and TS.15}",
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        },
        {
            "ob_id": 32926,
            "uuid": "fb0ba8552b244598a7594c102121172c",
            "title": "Caption for Figure SPM.7 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Cumulative anthropogenic CO2 emissions taken up by land and ocean sinks by 2100 under the five illustrative scenarios. The cumulative anthropogenic (human-caused) carbon dioxide (CO2) emissions taken up by the land and ocean sinks under the five illustrative scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) are simulated from 1850 to 2100 by CMIP6 climate models in the concentration-driven simulations. Land and ocean carbon sinks respond to past, current and future emissions, therefore cumulative sinks from 1850 to 2100 are presented here. During the historical period (1850-2019) the observed land and ocean sink took up 1430 GtCO2 (59% of the emissions). The bar chart illustrates the projected amount of cumulative anthropogenic CO2 emissions (GtCO2) between 1850 and 2100 remaining in the atmosphere (grey part) and taken up by the land and ocean (coloured part) in the year 2100. The doughnut chart illustrates the proportion of the cumulative anthropogenic CO2 emissions taken up by the land and ocean sinks and remaining in the atmosphere in the year 2100. Values in % indicate the proportion of the cumulative anthropogenic CO2 emissions taken up by the combined land and ocean sinks in the year 2100. The overall anthropogenic carbon emissions are calculated by adding the net global land use emissions from CMIP6 scenario database to the other sectoral emissions calculated from climate model runs with prescribed CO2 concentrations*FOOTNOTE. Land and ocean CO2 uptake since 1850 is calculated from the net biome productivity on land, corrected for CO2 losses due to land-use change by adding the land-use change emissions, and net ocean CO2 flux. {Box TS.5, Box TS.5, Figure 1, 5.2.1, Table 5.1, 5.4.5, Figure 5.25}*Footnote: The other sectoral emissions are calculated as the residual of the net land and ocean CO2 uptake and the prescribed atmospheric CO2 concentration changes in the CMIP6 simulations. These calculated emissions are net emissions and do not separate gross anthropogenic emissions from removals, which are included implicitly.",
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        },
        {
            "ob_id": 32937,
            "uuid": "a3773efdf0c149c5af0ba322e52e8320",
            "title": "Caption for Figure SPM.2 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Assessed contributions to observed warming in 2010–2019 relative to 1850–1900. \r\nPanel a): Observed global warming (increase in global surface temperature) and its very likely range {3.3.1, Cross-Chapter Box 2.3}.\r\nPanel b): Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to total human influence, changes in well-mixed greenhouse gas concentrations, other human drivers due to aerosols, ozone and land-use change (land-use reflectance), solar and volcanic drivers, and internal climate variability. Whiskers show likely ranges {3.3.1}. \r\nPanel c): Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence, including emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct (through radiation) and indirect (through interactions with clouds) effects are considered. {6.4.2, 7.3}",
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        },
        {
            "ob_id": 32939,
            "uuid": "ec4421f8744d414d95e23ba2b788bed0",
            "title": "Derivation of the ESA Ozone CCI ACE-FTS monthly zonal mean ozone profile data",
            "abstract": "The monthly zonal mean ozone profile data are based on  ACE-FTS Level 2 profiles (Bernath et al., 2005; Bernath, 2017), retrieved with the University of Toronto processor UoT v3.5/3.6 and included into the new version of the HARMonized datasets of OZone profiles (HARMOZ_ALT, Sofieva et al., 2013). A more detailed description of the MZM data processing and dataset parameters can be found in the README file with the data and in (Sofieva et al., 2017).",
            "keywords": "",
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        },
        {
            "ob_id": 32946,
            "uuid": "358982e67544438e832a6f1d8c867a6e",
            "title": "Derivation of the ESA Ozone CCI GOMOS monthly zonal mean ozone profile data",
            "abstract": "The monthly zonal mean ozone profile data are based on  GOMOS Level 2 profiles, retrieved with the \r\nscientific ALGOM2s v1 processor (Sofieva et al., 2017a)  and included into the new version of the HARMonized datasets of OZone profiles (HARMOZ_ALT, Sofieva et al., 2013). \r\n\r\nA more detailed description of the GOMOS Monthly zonal mean data can be found in the README and in (Sofieva et al., 2017b).",
            "keywords": "",
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        },
        {
            "ob_id": 32949,
            "uuid": "21dc87fc7da349239858a7e804dccb4b",
            "title": "Derivation of the ESA  Ozone CCI MIPAS monthly zonal mean ozone profile data",
            "abstract": "The monthly zonal mean ozone profile data are based on  MIPAS Level 2 profiles retrieved with the Karlsruhe Institute of Technology processor KIT/IAA V7R_O3_240 (von Clarmann et al., 2003; 2009) and included into the new version of the HARMonized datasets of Ozone profiles (HARMOZ_ALT, Sofieva et al., 2013). \r\n\r\nA more detailed description of the MIPAS Monthly zonal mean data can be found in the README and in (Sofieva et al., 2017).",
            "keywords": "",
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        },
        {
            "ob_id": 32954,
            "uuid": "eb01c2e26aee45969ce71926e5070cb1",
            "title": "Derivation of the ESA Ozone CCI SCIAMACHY monthly zonal mean ozone profile data",
            "abstract": "The monthly zonal mean ozone profile data are based on  SCIAMACHY Level 2 profiles, retrieved with the \r\nUniversity of Bremen processor UBr v3.5 (Jia et al., 2015) and included into the new version of the HARMonized datasets of Ozone profiles (HARMOZ_ALT, Sofieva et al., 2013).\r\n\r\nA more detailed description of the GOMOS Monthly zonal mean data can be found in the README and in (Sofieva et al., 2017b).",
            "keywords": "",
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        },
        {
            "ob_id": 32956,
            "uuid": "4a614e5b61824f508ec7d362e1f187be",
            "title": "Derivation of the ESA Ozone CCI OSIRIS monthly zonal mean ozone profile data",
            "abstract": "The monthly zonal mean ozone profile data are based on OSIRIS Level 2 profiles, retrieved with the University of Saskatchewan processor USask v.5.10 (Bourassa et al., 2017; Degenstein et al., 2009) and included into the new version of the HARMonized datasets of Ozone profiles (HARMOZ_ALT, Sofieva et al., 2013). \r\n\r\nA more detailed description of the GOMOS Monthly zonal mean data can be found in the README and in (Sofieva et al., 2017).",
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        },
        {
            "ob_id": 33008,
            "uuid": "ccabd72bca1c4444a7a538fc8b0a989d",
            "title": "Computation of the MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP)",
            "abstract": "For information on the computation of the MErged Gridded Dataset of Ozone Profiles(MEGRIDOP), see the  associated readme file and the attached documentation.",
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        },
        {
            "ob_id": 33009,
            "uuid": "0f7e38b29ac843a7a8485c9b2fcb36bd",
            "title": "Computation of the Merged SAGE II, Ozone_cci and OMPS-LP dataset of ozone profiles",
            "abstract": "The long-term SAGE-CCI-OMPS+ dataset has been created by computation and merging of deseasonalized anomalies from the individual instruments.\r\n\r\nThe detailed description of the dataset can be found in Sofieva et al. (2017) and Sofieva et al. (2023).",
            "keywords": "",
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        },
        {
            "ob_id": 33013,
            "uuid": "75a592a7130347cb8212599149a3a077",
            "title": "Computation of the ESA Ozone CCI Level 3 Total Ozone Merged Data Product",
            "abstract": "For information on the computation of this dataset see the associated Algorithm Theoretical Basis Document (ATBD).",
            "keywords": "",
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        },
        {
            "ob_id": 33021,
            "uuid": "f7d8ff75cbd04a85b5f7e36676248e23",
            "title": "NIWA-UKCA2 deployed at NIWA",
            "abstract": "NIWA-UKCA2 deployed at NIWA",
            "keywords": "CCMI-2022, NIWA-UKCA2",
            "inputDescription": null,
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        },
        {
            "ob_id": 33037,
            "uuid": "f19d3640852647a2989b9ca6d91143c9",
            "title": "weather@home model running on climateprediction.net",
            "abstract": "The weather@home model uses the Hadley Centre atmosphere only model version 3 with improved physics (HadAM3P) as a global driving model at N96 resolution, (1.25 x 1.875 degrees resolution, 19 levels) with 15 minutes timestep for dynamics. One-way nested within this is the Hadley Centre regional model (HadRM3P) run at high resolution. This is the regional model used in PRECIS experiments by the Met Office and in all weather@home experiments.\n\nIn this experiment it has 0.44 x 0.44 degrees resolution with a rotated pole to achieve approx. 50 km x 50 km resolution on 19 levels over South Asia. More info on https://www.climateprediction.net/education/climate-science/models-used/ and validation analysis of the weather@home system can be found in Massey et al 2015  https://doi.org/10.1002/qj.2455, and Guillod et al 2017 https://doi.org/10.5194/gmd-10-1849-2017.",
            "keywords": "",
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        },
        {
            "ob_id": 33039,
            "uuid": "90d3d4ea198c4f0c9cfeb1ee740cc566",
            "title": "1/12deg Nucleus for European Modelling of the Ocean (NEMO) model of the Southern Ocean",
            "abstract": "This computation involved: NEMO-based 1/12 degree grid spacing model of the Southern Ocean as part of the ORCHESTRA LTS-M project. It uses the NEMO \"extended\" grid, although ice cavities are closed. The model was run on Archer, the national HPC platform.",
            "keywords": "",
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        },
        {
            "ob_id": 33040,
            "uuid": "29d77174c7b945fb86ca098e7af410a3",
            "title": "NCEO OLTraj V2.1",
            "abstract": "The trajectories were generated starting from zonal and meridional model velocity fields from the AVISO project; please see the Global ocean gridded L4 sea surface heights and derived variables reprocessed reference in the documentation section for more details on the dataset.  The output of which was integrated using the LAMTA package (6-hour time step) as previously described in Nencioli et al., 2018 (also available in the documentation section).\r\n\r\nThe computing is similar to that of OLTraj v2.0 supports double value for time variables instead of int64",
            "keywords": "",
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        },
        {
            "ob_id": 33042,
            "uuid": "be654a396581427da1a8f977502f098d",
            "title": "Kruger DEM and Orthomosaics; semi-global matching",
            "abstract": "In recent years, semi-global matching (SGM) approaches have proven to be among the most popular and successful algorithms in the fields of stereo vision and photogrammetry (Klette et al. 2011, Michael et al. 2013). To extract the height information from the aerial imagery, we used this matching approach, which utilizes intensity differences, mutual information (as the cost function) and an approximation of the global energy function that is being optimized path-wise (16 paths in this study) from all directions over the image. The cost function is significantly influenced by the use of penalty values, which were chosen based on performance tests and represent varying magnitudes of disparity changes. These variables have a strong impact on the matching performance and the robustness that is related to this processing step. The term 'semi-global arises from the combination of both global and local methods in a way that the complexity of the process is lowered and the quality of the matching is drastically improved. While the computation time for these global methods is often considerably higher, the overall performance increases compared to local matching algorithms. Further, pixel-wise calculated matching cost, contrary to the calculation along image paths, poses negative effects of insufficient correspondences related to low texture and ambiguity (Hirschmüller 2007).",
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        },
        {
            "ob_id": 33050,
            "uuid": "377dd7df67b34831a66befd25a81d60e",
            "title": "ICOsahedral Nonhydrostatic (ICON) Atmospheric Global Circulation Model (GCM) deployed on ARCHER computer",
            "abstract": "ICOsahedral Nonhydrostatic (ICON) Atmospheric Global Circulation Model (GCM) deployed on ARCHER computer",
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        },
        {
            "ob_id": 33057,
            "uuid": "1c59ff1ebc6849d9b31c0dbeffe51a40",
            "title": "Derivation of the ESA GHG CCI column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm",
            "abstract": "Column-averaged carbon dioxide (XCO2) has been retrieved using the FOCAL-OCO2 algorithm, by analysing hyper spectral solar backscattered radiance measurements from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. FOCAL includes a radiative transfer model which has been developed to approximate light scattering effects by multiple scattering at an optically thin scattering layer. This reduces the computational costs by several orders of magnitude. FOCAL's radiative transfer model is utilised to simulate the radiance in all three OCO-2 spectral bands allowing the simultaneous retrieval of CO2, H2O, and solar induced chlorophyll fluorescence. The product is limited to cloud-free scenes on the Earth's day side.",
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        {
            "ob_id": 33070,
            "uuid": "44353c09f9654eb1940b383ae70394d3",
            "title": "UKESM1 deployed on MONSooN computer",
            "abstract": "UKESM1 deployed on MONSooN computer",
            "keywords": "",
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        },
        {
            "ob_id": 33134,
            "uuid": "d54a48cbbd834e78a6c574772a12cc38",
            "title": "Caption for Figure 3.13 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Annual-mean precipitation rate (mm day–1) for the period 1995–2014. (a) Multi-model (ensemble) mean constructed with one realization of the CMIP6 historical experiments from each model. (b) Multi-model mean bias, defined as the difference between the CMIP6 multi-model mean and precipitation analyses from the Global Precipitation Climatology Project (GPCP) version 2.3 (Adler et al., 2003). (c) Multi-model mean of the root mean square error calculated over all months separately and averaged with respect to the precipitation analyses from GPCP version 2.3. (d) Multi-model-mean bias, calculated as the difference between the CMIP6 multi-model mean and the precipitation analyses from GPCP version 2.3. Also shown is the multi-model mean bias as the difference between the multi-model mean of (e) high resolution and (f) low-resolution simulations of four HighResMIP models and the precipitation analyses from GPCP version 2.3. Uncertainty is represented using the advanced approach. No overlay indicates regions with robust signal, where ≥66% of models show change greater than variability threshold and ≥80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where ≥66% of models show change greater than variability threshold and <80% of all models agree on the sign of change. For more information on the advanced approach, please refer to the Cross-Chapter Box Atlas.1. Dots in panel (e) mark areas where the bias in high resolution versions of the HighResMIP models is lower in at least three out of four models than in the corresponding low-resolution versions. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).",
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        },
        {
            "ob_id": 33154,
            "uuid": "f31047a90ad9439593a34b95b0bff03b",
            "title": "Caption for Figure 3.18 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Instantaneous Northern-Hemisphere blocking frequency (% of days) in the extended northern winter season (December–January–February–March – DJFM) for the years 1979–2000. Results are shown for ERA5 reanalysis (black), CMIP5 (blue) and CMIP6 (red) models. Coloured lines show multi-model means and shaded ranges show corresponding 5–95% ranges constructed with one realization from each model. Figure is adapted from Davini and D’Andrea (2020), their Figure 12 and following the D’Andrea et al. (1998) definition of blocking. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).",
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        {
            "ob_id": 33157,
            "uuid": "43505750b2364ce0b581d96a975b2e91",
            "title": "Caption for Figure 3.3 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Annual mean surface (2 m) air temperature (°C) for the period 1995–2014. (a) Multi-model (ensemble) mean constructed with one realization of the CMIP6 historical experiment from each model. (b) Multi-model mean bias, defined as the difference between the CMIP6 multi-model mean and the climatology of the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses of the global climate (ERA5). (c) Multi-model mean of the root mean square error calculated over all months separately and averaged with respect to the climatology from ERA5. (d) Multi-model-mean bias as the difference between the CMIP6 multi-model mean and the climatology from ERA5. Also shown is the multi-model mean bias as the difference between the multi-model mean of (e) high-resolution and (f) low-resolution simulations of four HighResMIP models and the climatology from ERA5. Uncertainty is represented using the advanced approach: No overlay indicates regions with robust signal, where ≥66% of models show change greater than variability threshold and ≥80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where ≥66% of models show change greater than variability threshold and <80% of all models agree on sign of change. For more information on the advanced approach, please refer to Cross-Chapter Box Atlas.1. Dots in panel (e) mark areas where the bias in high resolution versions of the HighResMIP models is lower in at least three out of four models than in the corresponding low-resolution versions. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).",
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        {
            "ob_id": 33160,
            "uuid": "ef8b84ebbd8241038031c269a97e5514",
            "title": "Caption for Figure 3.10 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Observed and simulated tropical mean temperature trends through the atmosphere. Vertical profiles of temperature trends in the tropics (20°S–20°N) for three periods: (a) 1979–2014, (b) 1979–1997 (ozone depletion era) and (c) 1998–2014 (ozone stabilisation era). The black lines show trends in the RICH 1.7 (long dashed) and Radiosonde Observation Correction using Reanalysis (RAOBCORE) 1.7 (dashed) radiosonde datasets (Haimberger et al., 2012), and in the ERA5/5.1 reanalysis (solid). Grey envelopes are centred on the RICH 1.7 trends, but show the uncertainty based on 32 RICH-obs members of version 1.5.1 of the dataset, which used version 1.7.3 of the RICH software but with the parameters of version 1.5.1. ERA5 was used as reference for calculating the adjustments between 2010 and 2019, and ERA-Interim was used for the years before that. Red lines show trends in CMIP6 historical simulations from one realization of 60 models. Blue lines show trends in 46 CMIP6 models that used prescribed, rather than simulated, sea surface temperatures (SSTs). Figure is adapted from Mitchell et al. (2020), their Figure 1. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).",
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        },
        {
            "ob_id": 33163,
            "uuid": "dec11389f39b47f2b64068ad22909741",
            "title": "Caption for Figure 3.4 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Observed and simulated time series of the anomalies in annual and global mean near-surface air temperature (GSAT). All anomalies are differences from the 1850–1900 time-mean of each individual time series. The reference period 1850–1900 is indicated by grey shading. (a) Single simulations from CMIP6 models (thin lines) and the multi-model mean (thick red line). Observational data (thick black lines) are from the Met Office Hadley Centre HadCRUT5, and are blended surface temperature (2 m air temperature over land and sea surface temperature over the ocean). All models have been subsampled using the HadCRUT5 observational data mask. Vertical lines indicate large historical volcanic eruptions. CMIP6 models which are marked with an asterisk are either tuned to reproduce observed warming directly, or indirectly by tuning equilibrium climate sensitivity. Inset: GSAT for each model over the reference period, not masked to any observations. (b) Multi-model means of CMIP5 (blue line) and CMIP6 (red line) ensembles and associated 5th to 95th percentile ranges (shaded regions). Observational data are HadCRUT5, Berkeley Earth, National Oceanic and Atmospheric Administration NOAAGlobalTemp-Interim and Kadow et al. (2020). Masking was done as in (a). CMIP6 historical simulations are extended with SSP2-4.5 simulations for the period 2015–2020 and CMIP5 simulations are extended with RCP4.5 simulations for the period 2006–2020. All available ensemble members were used (see Section 3.2). The multi-model means and percentiles were calculated solely from simulations available for the whole time span (1850–2020). Figure is updated from Bock et al. (2020), their Figures 1 and 2. / CC BY 4.0 https://creativecommons.org/licenses/by/4.0/. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).",
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        {
            "ob_id": 33166,
            "uuid": "99adb852b6a442f9a91c484b09789de2",
            "title": "Caption for Figure 3.5 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "The standard deviation of annually averaged zonal-mean near-surface air temperature. This is shown for four detrended observed temperature datasets (HadCRUT5, Berkeley Earth, NOAAGlobalTemp-Interim and Kadow et al. (2020), for the years 1995-2014) and 59 CMIP6 pre-industrial control simulations (one ensemble member per model, 65 years) (after Jones et al., 2013). For line colours see the legend of Figure 3.4. Additionally, the multi-model mean (red) and standard deviation (grey shading) are shown. Observational and model datasets were detrended by removing the least-squares quadratic trend. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).",
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        {
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            "uuid": "71a0d12647a04cc9a05197cffdd5f24a",
            "title": "Surface velocity map of the Afar Rift Zone from 2014-19",
            "abstract": "We used frequent Sentinel-1 satellite Interferometric Synthetic Aperture Radar (InSAR) observations to measure surface displacements through time across the whole region. We related these to ground based Global Navigation Satellite Systems (GNSS) observations and combine data from different satellite tracks to produce maps of the average surface velocity in three directions (perpendicular to the rift zone, parallel to the rift zone, and vertical).",
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        {
            "ob_id": 33176,
            "uuid": "b445009a1b3d49ceb64985b7e577ee01",
            "title": "Caption for Figure 3.9 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Global, land, ocean and continental annual mean near-surface air temperatures anomalies in CMIP6 models and observations. Time series are shown for CMIP6 historical anthropogenic and natural (brown), natural-only (green), greenhouse gas only (grey) and aerosol only (blue) simulations (multi-model means shown as thick lines, and shaded ranges between the 5th and 95th percentiles) and for HadCRUT5 (black). All models have been subsampled using the HadCRUT5 observational data mask. Temperature anomalies are shown relative to 1950–2010 for Antarctica and relative to 1850–1900 for other continents. CMIP6 historical simulations are expand by the SSP2-4.5 scenario simulations. All available ensemble members were used (see Section 3.2). Regions are defined by Iturbide et al. (2020). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).",
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        {
            "ob_id": 33179,
            "uuid": "6f270d895ab04ebab7c15d98d26fa3a8",
            "title": "Caption for Figure 3.12 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Column water vapour path trends (%/decade) for the period 1998–2019 averaged over the near-global oceans (50°S–50°N). The figure shows satellite data (RSS) and ERA5.1 reanalysis, as well as CMIP5 (sky blue) and CMIP6 (brown) historical simulations. All available ensemble members were used (see Section 3.2). Fits to the model trend probability distributions were performed with kernel density estimation. Figure is updated from Santer et al. (2007). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).",
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        {
            "ob_id": 33182,
            "uuid": "6d55a717cdc044c1b39d34fbd7eb2519",
            "title": "Caption for Figure 3.42 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Relative space–time root-mean-square deviation (RMSD) calculated from the climatological seasonal cycle of the CMIP simulations (1980–1999) compared to observational datasets. (a) CMIP3, CMIP5, and CMIP6 for 16 atmospheric variables (b) CMIP5 and CMIP6 for 10 land variables and four ocean/sea-ice variables. A relative performance measure is displayed, with blue shading indicating better and red shading indicating worse performance than the median of all model results. A diagonal split of a grid square shows the relative error with respect to the reference data set (lower right triangle) and an additional data set (upper left triangle). Reference/additional datasets are from top to bottom in (a): ERA5/NCEP, GPCP-SG/GHCN, CERES-EBAF/-, CERES-EBAF/-, CERES-EBAF/-, CERES-EBAF/-, JRA-55/ERA5, ESACCI-SST/HadISST, ERA5/NCEP, ERA5/NCEP, ERA5/NCEP, ERA5/NCEP, ERA5/NCEP, ERA5/NCEP, AIRS/ERA5, ERA5/NCEP and in (b): CERES-EBAF/-, CERES-EBAF/-, CERES-EBAF/-, CERES-EBAF/-, LandFlux-EVAL/-, Landschuetzer2016/ JMA-TRANSCOM; MTE/FLUXCOM, LAI3g/-, JMA-TRANSCOM, ESACCI-SOILMOISTURE/-, HadISST/ATSR, HadISST/-, HadISST/-, ERA-Interim/-. White boxes are used when data are not available for a given model and variable. Figure is updated and expanded from Bock et al. (2020), their Figure 5 CC BY 4.0 https://creativecommons.org/licenses/by/4.0/. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).",
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        },
        {
            "ob_id": 33191,
            "uuid": "676dcc478d454cd385b7e85d03150881",
            "title": "Caption for Figure 3.23 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Multi-model-mean bias of (a) sea surface temperature and (b) near-surface salinity, defined as the difference between the CMIP6 multi-model mean and the climatology from the World Ocean Atlas 2018. The CMIP6 multi-model mean is constructed with one realization of 46 CMIP6 historical experiments for the period 1995–2014 and the climatology from the World Ocean Atlas 2018 is an average over all available years (1955-2017). Uncertainty is represented using the advanced approach: No overlay indicates regions with robust signal, where ≥66% of models show change greater than variability threshold and ≥80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where ≥66% of models show change greater than the variability threshold and <80% of all models agree on sign of change. For more information on the advanced approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).",
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        {
            "ob_id": 33194,
            "uuid": "2499b863c7604b4eb318916a4c0cd492",
            "title": "Caption for Figure 3.14 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Wet (a) and dry (b) region tropical mean (30°S–30°N) annual precipitation anomalies. Observed data are shown with black lines (GPCP), ERA5 reanalysis in grey, single model simulations results are shown with light blue/red lines (CMIP6), and multi-model-mean results are shown with dark blue/red lines (CMIP6). Wet and dry region annual anomalies are calculated as the running mean over 12 months relative to a 1988–2020 based period. The regions are defined as the wettest third and driest third of the surface area, calculated for the observations and for each model separately for each season (following Polson and Hegerl, 2017). Scaling factors (c, d) are calculated for the combination of the wet and dry region mean, where the observations, reanalysis and all the model simulations are first standardized using the mean standard deviation of the pre-industrial control simulations. Two total least squares regression methods are used: noise in variables (following Polson and Hegerl, 2017) which estimates a best estimate and a 5–95% confidence interval using the pre-industrial controls (circle and thick green line) and the pre-industrial controls with double the variance (thin green line); and a bootstrap method (DelSole et al., 2019) (5–95% confidence interval shown with a purple line and best estimate with a purple circle). Panel (c) shows results for GPCP and panel (d) for ERA5. Figure is adapted from Schurer et al. (2020). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).",
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        },
        {
            "ob_id": 33197,
            "uuid": "d2bc88dbe8c84a48bcc1daeb0349270a",
            "title": "Caption for Figure 3.15 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Observed and simulated time series of anomalies in zonal average annual mean precipitation. (a), (c–f) Evolution of global and zonal average annual mean precipitation (mm day–1) over areas of land where there are observations, expressed relative to the base-line period of 1961–1990, simulated by CMIP6 models (one ensemble member per model) forced with both anthropogenic and natural forcings (brown) and natural forcings only (green). Multi-model means are shown in thick solid lines and shading shows the 5–95% confidence interval of the individual model simulations. The data is smoothed using a low pass filter. Observations from three different datasets are included: gridded values derived from Global Historical Climatology Network (GHCN version 2) station data, updated from Zhang et al. (2007), data from the Global Precipitation Climatology Product (GPCP L3 version 2.3,  Huffman and Bolvin, 2013) and from the Climate Research Unit (CRU TS4.02, Harris et al. (2014)). Also plotted are boxplots showing interquartile and 5–95% ranges of simulated trends over the period for simulations forced with both anthropogenic and natural forcings (brown) and natural forcings only (blue). Observed trends for each observational product are shown as horizontal lines. Panel (b) shows annual mean precipitation rate (mm day–1) of GHCN version 2 for the years 1950–2014 over land areas used to compute the plots. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).",
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        },
        {
            "ob_id": 33200,
            "uuid": "d8f6b26e718945f598541716bfee0328",
            "title": "Caption for Figure 3.19 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Long-term mean (thin black contour) and linear trend (colour) of zonal mean DJF zonal winds over 1985–2014 in the SH. Displayed are (a) ERA5 and (b) CMIP6 multi-model mean (58 CMIP6 models). The solid contours show positive (westerly) and zero long-term mean zonal wind, and the dashed contours show negative (easterly) long-term mean zonal wind. Only one ensemble member per model is included. Figure is modified from Eyring et al. (2013), their Figure 12. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).",
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        },
        {
            "ob_id": 33203,
            "uuid": "41f3a263da774cf9a88ee7661df6ab72",
            "title": "Caption for Figure 3.43 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Centred pattern correlations between models and observations for the annual mean climatology over the period 1980–1999. Results are shown for individual CMIP3 (cyan), CMIP5 (blue) and CMIP6 (red) models (one ensemble member from each model is used) as short lines, along with the corresponding ensemble averages (long lines). Correlations are shown between the models and the primary reference observational data set (from left to right: ERA5, GPCP-SG, CERES-EBAF, CERES-EBAF, CERES-EBAF, CERES-EBAF, JRA-55, ESACCI-SST, ERA5, ERA5, ERA5, ERA5, ERA5, ERA5, AIRS, ERA5). In addition, the correlation between the primary reference and additional observational datasets (from left to right: NCEP, GHCN, -, -, -, -, ERA5, HadISST, NCEP, NCEP, NCEP, NCEP, NCEP, NCEP, ERA5, NCEP) are shown (solid grey circles) if available. To ensure a fair comparison across a range of model resolutions, the pattern correlations are computed after regridding all datasets to a resolution of 4º in longitude and 5º in latitude. Figure is updated and expanded from Bock et al. (2020), their Figure 7 CC BY4.0  https://creativecommons.org/licenses/by/4.0/.. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).",
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        },
        {
            "ob_id": 33206,
            "uuid": "958a4b51b90242d69e3df16b91f10a7d",
            "title": "Caption for Figure 3.41 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Summary figure showing simulated and observed changes in key large-scale indicators of climate change across the climate system, for continental, ocean basin and larger scales. Black lines show observations, brown lines and shading show the multi-model mean and 5th–95th percentile ranges for CMIP6 historical simulations including anthropogenic and natural forcing, and blue lines and shading show corresponding ensemble means and 5th–95th percentile ranges for CMIP6 natural-only simulations. Temperature time series are as in Figure 3.9, but with smoothing using a low pass filter. Precipitation time series are as in Figure 3.15 and ocean heat content as in Figure 3.26. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).",
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        },
        {
            "ob_id": 33209,
            "uuid": "0a5eb3c4c7564750875e4e1310b509e5",
            "title": "Caption for Figure 2.25 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Changes in permafrost temperature. Average departures of permafrost temperature (measured in the upper 20–30 m) from a baseline established during International Polar Year (2007–2009) for Arctic regions. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).",
            "keywords": "",
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        },
        {
            "ob_id": 33212,
            "uuid": "f396a4d88a2f4871a1c5702a7a328669",
            "title": "Caption for Figure 2.27 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Changes in ocean salinity. Estimates of salinity trends using a total least absolute differences fitting method for (a) global near-surface salinity (SSS) changes and (b) global zonal mean subsurface salinity changes. Black contours show the associated climatological mean salinity (either near-surface (a) or subsurface (b)) for the analysis period (1950–2019). Both panels represent changes of Practical Salinity Scale 1978 [PSS-78], per decade. In both panels green denotes freshening regions and orange/brown denotes regions with enhanced salinities (‘x’ marks denote non-significant changes). Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).",
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        },
        {
            "ob_id": 33216,
            "uuid": "8cddcb183c324fac86e14d62eed44cd5",
            "title": "Caption for Figure 2.22 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "April snow cover extent (SCE) for the Northern Hemisphere (1922–2018). Shading shows very likely range. The trend over the entire 1922–2018 period (black line) is –0.29 (± 0.07) million km2 per decade. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)",
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        },
        {
            "ob_id": 33221,
            "uuid": "5178b8c9b5a949a5875cc625ac17987b",
            "title": "GO6 configuration of the NEMO (Nucleus for European Modelling of the Ocean) ocean and sea-ice model",
            "abstract": "This computation used the GO6 configuration of the NEMO model, which consists of version 3.6 of NEMO and version 5.2.1 of the CICE sea-ice model, on the global eORCA025 1/4° grid\r\nThe sea ice configuration is GSI8..",
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        },
        {
            "ob_id": 33227,
            "uuid": "5a0dba8901934f60b6993a3d6eda4f1e",
            "title": "Computation for  Particulate Organic Carbon estimates from quality-controlled BGC-Argo data",
            "abstract": "The POC estimates were produces using an empirical algorithm (average of the POC:BBP slopes of described Cetinic et al., 2012.  Details of algorithm and input BGC Argo data can be found in the documentation section.",
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        },
        {
            "ob_id": 33238,
            "uuid": "4aebe150972142f1adc2932a40abb295",
            "title": "Caption for Figure SPM.3 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "The IPCC AR6 WGI inhabited regions are displayed as hexagons with identical size in their approximate geographical location (see legend for regional acronyms). All assessments are made for each region as a whole and for the 1950s to the present. Assessments made on different time scales or more local spatial scales might differ from what is shown in the figure. The colours in each panel represent the four outcomes of the assessment on observed changes. White and light grey striped hexagons are used where there is low agreement in the type of change for the region as a whole and grey hexagons are used when there is  limited data and/or literature that prevents an assessment of the region as a whole. Other colours indicate at least medium confidence in the observed change. The confidence level for the human influence on these observed changes is based on assessing trend detection and attribution and event attribution literature, and it is indicated by the number of dots: three dots for high confidence, two dots for medium confidence and one dot for low confidence (filled: limited agreement; empty: limited evidence). \r\n\r\nPanel a) For hot extremes, the evidence is mostly drawn from changes in metrics based on daily maximum temperatures; regional studies using other indices (heatwave duration, frequency and intensity) are used in addition. Red hexagons indicate regions where there is at least medium confidence in an observed increase in hot extremes. \r\n\r\nPanel b) For heavy precipitation, the evidence is mostly drawn from changes in indices based on one-day or five-day precipitation amounts using global and regional studies. Green hexagons indicate regions where there is at least medium confidence in an observed increase in heavy precipitation. \r\n\r\nPanel c) Agricultural and ecological droughts are assessed based on observed and simulated changes in total column soil moisture, complemented by evidence on changes in surface soil moisture, water balance (precipitation minus evapotranspiration) and indices driven by precipitation and atmospheric evaporative demand. Yellow hexagons indicate regions where there is at least medium confidence in an observed increase in this type of drought and green hexagons indicate regions where there is at least medium confidence in an observed decrease in agricultural and ecological drought. \r\n\r\nFor all regions, table TS.5 shows a broader range of observed changes besides the ones shown in this figure.  Note that SSA is the only region that does not display observed changes in the metrics shown in this figure, but is affected by observed increases in mean temperature, decreases in frost, and increases in marine heatwaves. {11.9, Table TS.5, Box TS.10, Figure 1, Atlas 1.3.3, Figure Atlas.2}",
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        },
        {
            "ob_id": 33259,
            "uuid": "847e841295024995b6d3be4ce9268c58",
            "title": "Caption for Figure 2.13 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Changes in surface humidity. Changes in surface humidity. (a) Trends in surface specific humidity over 1973–2019. Trends are calculated using OLS regression with significance assessed following AR(1) adjustment after Santer et al. (2008) ; ‘x’ marks denote non-significant trends). (b) Global average surface specific humidity annual anomalies (1981–2010 base period). (c) as (a) but for the relative humidity. (d) as (b) but for the global average surface relative humidity annual anomalies. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).",
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        },
        {
            "ob_id": 33262,
            "uuid": "3396acfa8103486eb7a86024cfe9c1f3",
            "title": "Caption for Figure 2.15 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Changes in observed precipitation. (a, b) Spatial variability of observed precipitation trends over land for 1901–2019 for two global in-situ products. Trends are calculated using OLS regression with significance assessed following AR(1) adjustment after Santer et al. (2008) (‘x’ marks denote non-significant trends). (c) Annual time series and decadal means from 1891 to date relative to a 1981–2010 climatology (note that different products commence at distinct times). (d, e) as (a, b), but for the periods starting in 1980. (f) is for the same period for the globally complete merged GPCP v2.3 product. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).",
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        },
        {
            "ob_id": 33265,
            "uuid": "648ba0d02e03494192d9b2b0cd10707f",
            "title": "Met Office United Kingdom Earth System Model 1 (UKESM1)",
            "abstract": "Met Office United Kingdom Earth System Model 1 (UKESM1) on the ARCHER HPC platform, submitted via pumatest.",
            "keywords": "",
            "inputDescription": null,
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        },
        {
            "ob_id": 33267,
            "uuid": "c8478b0eb248451c8a938a66a72005d0",
            "title": "The ESA Biomass Climate Change Initiative above ground biomass retrieval algorithm, v3.0",
            "abstract": "For information on the derivation of the Biomass CCI data, please see the ATBD (Algorithm Theoretical Baseline Document).",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
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            "identifier_set": []
        },
        {
            "ob_id": 33271,
            "uuid": "9079ba8ed02144779cf343dc0b3f6d16",
            "title": "Improved Global ocean reanalysis using the smoother algorithm v1.0 June 2016",
            "abstract": "Met Office GloSea5 reanalysis data were processed on the  Met Office Monsoon supercomputer using the smoother algorithm.  Please see the documentation section for further information",
            "keywords": "",
            "inputDescription": null,
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            "identifier_set": []
        },
        {
            "ob_id": 33274,
            "uuid": "c64ed4b79b1247949dfda9671fe4dda8",
            "title": "Empirical Export Production Algorithms",
            "abstract": "Calculations use Export Production algorithms, Sea Surface Temperature (SST), Chlorophyll (Chl), kd490 and primary production.  \r\n\r\nSST is based on reprojected fields from GHRSST/OSTIA (doi:10.5067/GHOST-4FK01, Chl and kd490 is from OC-CCI (https://catalogue.ceda.ac.uk/uuid/5400de38636d43de9808bfc0b500e863, https://catalogue.ceda.ac.uk/uuid/5400de38636d43de9808bfc0b50\r\n0e863) and primary production from BICEP (https://catalogue.ceda.ac.uk/uuid/69b2c9c6c4714517ba10dab3515e4ee6) The algorithms used are described in the papers listed in the docs section.",
            "keywords": "",
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        },
        {
            "ob_id": 33276,
            "uuid": "47f37aea9c174706a348b692669e8b02",
            "title": "Marine phytoplankton carbon",
            "abstract": "A spectrally-resolved photoacclimation model was unified with a primary production model that simulated photosynthesis as a function of irradiance using a two-parameter photosynthesis versus irradiance (P-I) function to estimate the carbon content of marine phytoplankton based on ocean-colour remote sensing products (Sathyendranath et al. 2020 and references therein for details). The photoacclimation model contains a maximum chlorophyll-to-carbon ratio for three different phytoplankton size classes (pico-, nano- and microphytoplankton) that was inferred from field data. Chlorophyll-a products were obtained from the European Space Agency (ESA) Ocean Colour Climate Change Initiative (OC-CCI v5). Photosynthetic Active Radiation (PAR) products were obtained from the National Aeronautics and Space Administration (NASA) and were corrected for inter-sensor bias in products. Mixed Layer Depth (MLD) was obtained from the French Research Institute for Exploration of the Sea (Ifremer). In situ datasets of chlorophyll-a profile parameters and P-I parameters were incorporated as described in Kulk et al. (2020)\r\n\r\n\r\nSathyendranath, S.; Platt, T.; Kovač, Ž.; Dingle, J.; Jackson, T.; Brewin, R.J.W.; Franks, P.; Marañón, E.; Kulk, G.; Bouman, H.A. Reconciling models of primary production and photoacclimation. Applies Optics, 2020, 59, C100. doi.org/10.1364/AO.386252\r\n\r\nKulk, G.; Platt, T.; Dingle, J.; Jackson, T.; Jönsson, B.F.; Bouman, H.A., Babin, M.; Doblin, M.; Estrada, M.; Figueiras, F.G.; Furuya, K.; González, N.; Gudfinnsson, H.G.; Gudmundsson, K.; Huang, B.; Isada, T.; Kovac, Z.; Lutz, V.A.; Marañón,\r\nE.; Raman, M.; Richardson, K.; Rozema, P.D.; Van de Poll, W.H.; Segura, V.; Tilstone, G.H.; Uitz, J.; van Dongen-Vogels, V.; Yoshikawa, T.; Sathyendranath S. Primary production, an index of climate change in the ocean: Satellite-based estimates over two decades. Remote Sens. 2020, 12, 826. doi:10.3390/rs12050826",
            "keywords": "",
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        },
        {
            "ob_id": 33285,
            "uuid": "f9ae51ce1ad24618b22afc7f7bf6fbc6",
            "title": "Derivation of the ESA Water Vapour Climate Change Initiative Total Column Water Vapour over land (TCWV-land) product, v3.1",
            "abstract": "For information on the derivation of the ESA Water Vapour CCI Total Column Water Vapour over land (TCWV-land) data, please see the ATBD (Algorithm Theoretical Baseline Document).",
            "keywords": "",
            "inputDescription": null,
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        },
        {
            "ob_id": 33293,
            "uuid": "786a126f87794677b1804a0742e651bc",
            "title": "Caption for Figure 3.41 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Summary figure showing simulated and observed changes in key large-scale indicators of climate change across the climate system, for continental, ocean basin and larger scales. Black lines show observations, brown lines and shading show the multi-model mean and 5th–95th percentile ranges for CMIP6 historical simulations including anthropogenic and natural forcing, and blue lines and shading show corresponding ensemble means and 5th–95th percentile ranges for CMIP6 natural-only simulations. Temperature time series are as in Figure 3.9, but with smoothing using a low pass filter. Precipitation time series are as in Figure 3.15 and ocean heat content as in Figure 3.26. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).",
            "keywords": "",
            "inputDescription": null,
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        },
        {
            "ob_id": 33306,
            "uuid": "3834577c0ecf490896c2b849b727e9c6",
            "title": "Caption for Figure 2.36 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
            "abstract": "Reconstructed and historical variance ratio of El Niño–Southern Oscillation (ENSO). (a) 30-year running variance of the reconstructed annual mean Niño 3.4 or related indicators from various published reconstructions. (b) Variance of June–November Southern Oscillation Index (SOI) and April–March mean Niño 3.4 (1981–2010 base period) along with the mean reconstruction from (a). Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).",
            "keywords": "",
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        }
    ]
}