copernicus_climate.json

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        "id": "reanalysis-era5-single-levels",
        "title": "ERA5 hourly data on single levels from 1940 to present",
        "description": "ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.\nData is available from 1940 onwards.\nERA5 replaces the ERA-Interim reanalysis.\nReanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\nERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities.\nAn uncertainty estimate is sampled by an underlying 10-member ensemble\nat three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience.\nSuch uncertainty estimates are closely related to the information content of the available observing system which\nhas evolved considerably over time. They also indicate flow-dependent sensitive areas.\nTo facilitate many climate applications, monthly-mean averages have been pre-calculated too,\nthough monthly means are not available for the ensemble mean and spread.\nERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified.\nThe data set presented here is a regridded subset of the full ERA5 data set on native resolution.\nIt is online on spinning disk, which should ensure fast and easy access.\nIt should satisfy the requirements for most common applications.\nAn overview of all ERA5 datasets can be found in this article.\nInformation on access to ERA5 data on native resolution is provided in these guidelines.\nData has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for\nthe uncertainty estimate (0.5 and 1 degree respectively for ocean waves).\nThere are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities).\nThe present entry is \"ERA5 hourly data on single levels from 1940 to present\".",
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            "Spatial coverage: Global",
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        "title": "ERA5 hourly data on pressure levels from 1940 to present",
        "description": "ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.\nData is available from 1940 onwards.\nERA5 replaces the ERA-Interim reanalysis.\nReanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\nERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities.\nAn uncertainty estimate is sampled by an underlying 10-member ensemble\nat three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience.\nSuch uncertainty estimates are closely related to the information content of the available observing system which\nhas evolved considerably over time. They also indicate flow-dependent sensitive areas.\nTo facilitate many climate applications, monthly-mean averages have been pre-calculated too,\nthough monthly means are not available for the ensemble mean and spread.\nERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified.\nThe data set presented here is a regridded subset of the full ERA5 data set on native resolution.\nIt is online on spinning disk, which should ensure fast and easy access.\nIt should satisfy the requirements for most common applications.\nAn overview of all ERA5 datasets can be found in this article.\nInformation on access to ERA5 data on native resolution is provided in these guidelines.\nData has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for\nthe uncertainty estimate (0.5 and 1 degree respectively for ocean waves).\nThere are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities).\nThe present entry is \"ERA5 hourly data on pressure levels from 1940 to present\".",
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        "title": "ERA5-Land hourly data from 1950 to present",
        "description": "ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. \nERA5-Land uses as input to control the simulated land fields ERA5 atmospheric variables, such as air temperature and air humidity. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'.    \nThe ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. \nThe temporal and spatial resolutions of ERA5-Land makes this dataset very useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states.",
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        "title": "ERA5 hourly time-series data on single levels from 1940 to present",
        "description": "ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.\nData is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis.\nReanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\nERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities.\nAn uncertainty estimate is sampled by an underlying 10-member ensemble\nat three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience.\nSuch uncertainty estimates are closely related to the information content of the available observing system which\nhas evolved considerably over time. They also indicate flow-dependent sensitive areas.\nTo facilitate many climate applications, monthly-mean averages have been pre-calculated too,\nthough monthly means are not available for the ensemble mean and spread.\nERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified.\nThe dataset presented here is a regridded subset of the full ERA5 data set on native resolution that is stored in a format designed for retrieving long time-series for a single point. When the requested location does not match the exact location of a grid point then the nearest grid point is used instead. It is this source of ERA5 data that is used by the ERA-Explorer to ensure response times required for the interactive web-application.\nAn overview of all ERA5 datasets can be found in this article.\nInformation on access to ERA5 data on native resolution is provided in these guidelines.",
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        "id": "derived-era5-pressure-levels-daily-statistics",
        "title": "ERA5 post-processed daily statistics on pressure levels from 1940 to present",
        "description": "ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.\nData is available from 1940 onwards.\nERA5 replaces the ERA-Interim reanalysis.\nReanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\nThis catalogue entry provides post-processed ERA5 hourly pressure-level data aggregated to daily time steps.  In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:\n\nThe daily aggregation statistic (daily mean, daily max, daily min)\nThe sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours)\nThe option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)\n\nUsers should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code,  please see the documentation.\nFor more details on the hourly data used to calculate the daily statistics, please refer to the ERA5 hourly pressure-level data catalogue entry and the documentation found therein.",
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            "Spatial coverage: Global",
            "Variable domain: Atmosphere (surface)",
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        "title": "ERA5 post-processed daily statistics on single levels from 1940 to present",
        "description": "ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.\nData is available from 1940 onwards.\nERA5 replaces the ERA-Interim reanalysis.\nReanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\nThis catalogue entry provides post-processed ERA5 hourly single-level data aggregated to daily time steps. In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:\n\nThe daily aggregation statistic (daily mean, daily max, daily min, daily sum*)\nThe sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours)\nThe option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)\n\n*The daily sum is only available for the accumulated variables (see ERA5 documentation for more details).\nUsers should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code, please see the documentation.\nFor more details on the hourly data used to calculate the daily statistics, please refer to the ERA5 hourly single-level data catalogue entry and the documentation found therein.",
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        "title": "Thermal comfort indices derived from ERA5 reanalysis",
        "description": "This dataset provides a complete historical reconstruction for a set of indices representing human thermal stress and discomfort in outdoor conditions. This dataset, also known as ERA5-HEAT (Human thErmAl comforT) represents the current state-of-the-art for bioclimatology data record production.\nThe dataset is organised around two main variables:\n\nthe mean radiant temperature (MRT)\nthe universal thermal climate index (UTCI)\n\nThese variables describe how the human body experiences atmospheric conditions, specifically air temperature, humidity, ventilation and radiation.\nThe dataset is computed using the ERA5 reanalysis from the European Centre for Medium-Range Forecasts (ECMWF). ERA5 combines model data with observations from across the world to provide a globally complete and consistent description of the Earth\u2019s climate and its evolution in recent decades. ERA5 is regarded as a good proxy for observed atmospheric conditions.\nThe dataset currently covers 01/01/1940 to near real time and is regularly extended as ERA5 data become available.\nThe dataset is produced by the European Centre for Medium-range Weather Forecasts.",
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        "id": "reanalysis-era5-land-timeseries",
        "title": "ERA5 Land hourly time-series data from 1950 to present",
        "description": "ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to the \nFifth Generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5).\nProduced by replaying only the land component of the ECMWF ERA5 climate reanalysis, it benefits from the same physical data-assimilation framework but runs offline at \nhigher spatial detail (9 km grid) to deliver richer land-surface information. \nReanalysis merges numerical model output with global observations into a globally complete, physically consistent climate record; \nthis \u201cdata assimilation\u201d approach mirrors operational weather forecasting but is optimised for historical completeness rather than forecast timeliness.\nReanalysis datasets extend back several decades by sacrificing forecast deadlines, allowing additional time to gather observations and retrospectively ingest improved data, \nthereby enhancing data quality in earlier periods.\nERA5-Land uses atmospheric fields from ERA5\u2014air temperature, humidity, pressure\u2014as \u201cforcing\u201d inputs to drive its land-surface model, \npreventing rapid drift from reality that unconstrained simulations would suffer.\n Although observations do not enter the land model directly, they shape the atmospheric forcing through assimilation, giving ERA5-Land an indirect observational anchor.\n To reconcile ERA5\u2019s coarser grid with ERA5-Land\u2019s finer 9 km grid, a lapse-rate correction adjusts input temperatures, humidity, and pressures for altitude differences.\nLike all numerical simulations, ERA5-Land carries uncertainty that generally grows backward in time as fewer observations were available to constrain the forcing.\n Users can combine ERA5-Land fields with the uncertainty estimates from equivalent ERA5 variables to assess confidence bounds.\nThe temporal resolution (hourly) and spatial detail (9 km) of ERA5-Land make it invaluable for land-surface applications such as flood and drought forecasting, \nagricultural monitoring, and hydrological studies.\nThe dataset presented here is a regridded subset of the full ERA5-Land archive, stored in an Analysis-Ready, \nCloud-Optimised (ARCO) format specifically designed for retrieving long time-series for individual points. \nWhen a user\u2019s requested location does not exactly match a grid point, the nearest grid point is automatically selected. This optimised data source ensures rapid response times.",
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            "Spatial coverage: Global",
            "Variable domain: Land (biosphere)",
            "Variable domain: Land (hydrology)",
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            "Variable domain: Land (physics)"
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        "license": "CC-BY-4.0",
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        "id": "reanalysis-carra-pressure-levels",
        "title": "Arctic regional reanalysis on pressure levels from 1991 to present",
        "description": "The C3S Arctic Regional Reanalysis (CARRA) dataset contains hourly data including 3-hourly analyses and hourly short term forecasts of atmospheric pressure level meteorological variables (temperature, humidity, wind, and other thermodynamic variables) at 2.5 km resolution. Additionally, forecasts up to 30 hours initialised from the analyses at 00 and 12 UTC are available.\nThe dataset includes two domains. The West domain covers Greenland, the Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, the Greenland Sea and Svalbard. The East domain covers Svalbard, Jan Mayen, Franz Josef Land, Novaya Zemlya, the Barents Sea, and the northern parts of the Norwegian Sea and Scandinavia.\nThe dataset has been produced with the use of the HARMONIE-AROME state-of-the-art non-hydrostatic regional numerical weather prediction model. High resolution reanalysis for the Arctic region is particularly important because climate change is more pronounced in the Arctic region than elsewhere on Earth. This fact calls for a better description of this region providing additional details with respect to the global reanalyses (ERA5 for instance). The additional information is provided by the higher horizontal resolution, more local observations (from the Nordic countries and Greenland), a better description of surface characteristics (high resolution satellite and physiographic data), high resolution non-hydrostatic dynamics and improved physical parameterisation of clouds and precipitation in particular.\nThe inputs to CARRA reanalysis are the observations, the ERA5 global reanalysis as lateral boundary conditions and the physiographic datasets describing the surface characteristics of the model. The observation values and information about their quality are used together to constrain the reanalysis where observations are available and provide information for the data assimilation system in areas in where fewer observations are available.\nMore details about the reanalysis dataset and the extensive input data are given in the Documentation section.",
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        "id": "reanalysis-carra-model-levels",
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        "description": "The C3S Arctic Regional Reanalysis (CARRA) dataset contains hourly data including 3-hourly analyses and hourly short term forecasts of atmospheric model level meteorological variables (temperature, humidity, wind, cloud, precipitation and turbulent kinetic energy) at 2.5 km resolution. Additionally, forecasts up to 30 hours initialised from the analyses at 00 and 12 UTC are available.\nThe dataset includes two domains. The West domain covers Greenland, the Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, the Greenland Sea and Svalbard. The East domain covers Svalbard, Jan Mayen, Franz Josef Land, Novaya Zemlya, the Barents Sea, and the northern parts of the Norwegian Sea and Scandinavia.\nThe dataset has been produced with the use of the HARMONIE-AROME state-of-the-art non-hydrostatic regional numerical weather prediction model. High resolution reanalysis for the Arctic region is particularly important because climate change is more pronounced in the Arctic region than elsewhere in on Earth. This fact calls for a better description of this region providing additional details with respect to the global reanalyses (ERA5 for instance). The additional information is provided by the higher horizontal resolution, more local observations (from the Nordic countries and Greenland), a better description of surface characteristics (high resolution satellite and physiographic data), high resolution non-hydrostatic dynamics and improved physical parameterisation of clouds and precipitation in particular.\nThe inputs to CARRA reanalysis are the observations, the ERA5 global reanalysis as lateral boundary conditions and the physiographic datasets describing the surface characteristics of the model. The observation values and information about their quality are used together to constrain the reanalysis where observations are available and provide information for the data assimilation system in areas in where fewer observations are available.\nMore details about the reanalysis dataset and the extensive input data are given in the Documentation section.",
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        "license": "CC-BY-4.0",
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        ],
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        "id": "reanalysis-carra-height-levels",
        "title": "Arctic regional reanalysis on height levels from 1991 to present",
        "description": "The C3S Arctic Regional Reanalysis (CARRA) dataset contains hourly data including 3-hourly analyses and hourly short term forecasts of atmospheric height level meteorological variables (temperature, humidity, wind, and other thermodynamic variables) at 2.5 km resolution. Additionally, forecasts up to 30 hours initialised from the analyses at 00 and 12 UTC are available.\nThe dataset includes two domains. The West domain covers Greenland, the Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, the Greenland Sea and Svalbard. The East domain covers Svalbard, Jan Mayen, Franz Josef Land, Novaya Zemlya, the Barents Sea, and the northern parts of the Norwegian Sea and Scandinavia.\nThe dataset has been produced with the use of the HARMONIE-AROME state-of-the-art non-hydrostatic regional numerical weather prediction model. High resolution reanalysis for the Arctic region is particularly important because climate change is more pronounced in the Arctic region than elsewhere on Earth. This fact calls for a better description of this region providing additional details with respect to the global reanalyses (ERA5 for instance). The additional information is provided by the higher horizontal resolution, more local observations (from the Nordic countries and Greenland), a better description of surface characteristics (high resolution satellite and physiographic data), high resolution non-hydrostatic dynamics and improved physical parameterisation of clouds and precipitation in particular.\nThe inputs to CARRA reanalysis are the observations, the ERA5 global reanalysis as lateral boundary conditions and the physiographic datasets describing the surface characteristics of the model. The observation values and information about their quality are used together to constrain the reanalysis where observations are available and provide information for the data assimilation system in areas in where fewer observations are available.\nMore details about the reanalysis dataset and the extensive input data are given in the Documentation section.",
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        ],
        "license": "CC-BY-4.0",
        "providers": [
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    {
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        "id": "reanalysis-carra-single-levels",
        "title": "Arctic regional reanalysis on single levels from 1991 to present",
        "description": "The C3S Arctic Regional Reanalysis (CARRA) dataset contains 3-hourly analyses and hourly short term forecasts of atmospheric and surface meteorological variables (surface and near-surface temperature, surface and top of atmosphere fluxes, precipitation, cloud, humidity, wind,  pressure, snow and sea variables) at 2.5 km resolution. Additionally, forecasts up to 30 hours initialised from the analyses at 00 and 12 UTC are available.\nThe dataset includes two domains. The West domain covers Greenland, the Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, the Greenland Sea and Svalbard. The East domain covers Svalbard, Jan Mayen, Franz Josef Land, Novaya Zemlya, the Barents Sea, and the northern parts of the Norwegian Sea and Scandinavia.\nThe dataset has been produced with the use of the HARMONIE-AROME state-of-the-art non-hydrostatic regional numerical weather prediction model. High resolution reanalysis for the Arctic region is particularly important because the climate change is more pronounced in the Arctic region than elsewhere in the Earth. This fact calls for a better description of this region providing additional details with respect to the global reanalyses (ERA5 for instance). The additional information is provided by the higher horizontal resolution, more local observations (from the Nordic countries and Greenland), better description of surface characteristics (high resolution satellite and physiographic data), high resolution non-hydrostatic dynamics and improved physical parameterisation of clouds and precipitation in particular.\nThe inputs to CARRA reanalysis are the observations, the ERA5 global reanalysis as lateral boundary conditions and the physiographic datasets describing the surface characteristics of the model. The observation values and information about their quality are used together to constrain the reanalysis where observations are available and provide information for the data assimilation system in areas in where less observations are available.\nMore details about the reanalysis dataset and the extensive input data are given in the Documentation section.",
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            "Temporal coverage: Past",
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        "license": "CC-BY-4.0",
        "providers": [
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    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-soil-moisture",
        "title": "Soil moisture gridded data from 1978 to present",
        "description": "The C3S satellite soil moisture (SM) service provides a range of global soil moisture variables derived from numerous satellite sensors, including scatterometers and radiometers. The products are designed primarily for applications related to climate variability and change, land\u2013atmosphere interactions, global biogeochemical cycles, ecology, hydrological and land surface modelling, drought monitoring, and meteorology. \nAll C3S SM data are based on algorithms and scientific methods developed within the ESA Climate Change Initiative for Soil Moisture (ESA CCI SM) programme. They represent the current state of the art in the production of satellite-based soil moisture climate data records and comply with the Systematic Observation Requirements for Satellite-Based Products for Climate defined by the Global Climate Observing System (GCOS). The data are provided on a regular latitude\u2013longitude grid, with potential gaps in space and time due to missing satellite overpasses. \nIn satellite-based climate datasets, it is common to distinguish between Climate Data Records (CDR) and Interim Climate Data Records (ICDR). In this dataset, both the CDR and ICDR components are generated using the same software and algorithms. The CDR provides sufficiently long, consistent, and continuous time series suitable for detecting climate variability and change. The ICDR offers short-latency access to near-real-time data, with consistency maintained relative to the CDR baseline. \nAvailable Products: \n- Surface Soil Moisture (SSM) combines observations different sources. C3S SM provides separate ACTIVE (multi-scatterometer) and PASSIVE (multi-radiometer) - products, as well as a COMBINED product that merges all available satellite input sources to achieve the highest possible data quality. The COMBINED dataset is recommended for most applications.\n- Root-Zone Soil Moisture (RZSM) uses the COMBINED SSM product as input together with a water infiltration model to estimate moisture in multiple depth layers within the plant root zone (0\u201310, 10\u201340, 40\u2013100, and 0\u2013100 cm). RZSM is commonly preferred over SSM for drought and large-scale agricultural applications.\n- Freeze/Thaw (F/T) provides a binary classification of the surface soil moisture state (frozen or unfrozen) based on K-band radiometer brightness temperatures.\nThis dataset is produced on behalf of the Copernicus Climate Change Service (C3S) by TU Wien, Planet, and EODC GmbH.",
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            "Spatial coverage: Global",
            "Variable domain: Land (hydrology)",
            "Product type: Satellite observations",
            "Provider: Copernicus C3S"
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    {
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        "id": "satellite-cloud-properties",
        "title": "Cloud properties global gridded monthly and daily data from 1979 to present derived from satellite observations",
        "description": "Cloud properties directly influence the state of the Earth\u00b4s upper-air atmosphere. Clouds have an effect on weather and climate through their contribution to the Earth\u2019s water cycle and impact on the Earth\u2019s energy budget. They impact atmospheric motions on a range of spatial and temporal scales and contribute to changes in the atmospheric composition. Water vapour originating from surface evaporation and transpiration condensates to form clouds, eventually leading to precipitation, which constitutes a fundamental mechanism of water redistribution around the globe.  Therefore, knowledge of cloud properties allows for a deeper understanding of the  feedback mechanisms within the hydrological cycle, directly in terms of precipitation, but also indirectly on the Earth\u00b4s energy budget through the changes in radiative fluxes. According to the 6th Intergovernmental Panel on Climate Change (IPCC) assessment report, clouds (together with aerosols) are among the largest sources of uncertainty of the estimates of the Earth\u00b4s energy budget, as well as of the potential feedback mechanisms and responses to climate change.\nThe Cloud Properties ECV contains four main variables, which can be separated  into averaged cloudiness, cloud height products, and cloud physical properties for both the ice and liquid water phases. These variables are provided in  two \u201cproduct families\u201d, according to the data provenance. The \"CCI product family\" originates from the European Space Agency (ESA) Cloud_cci project, which produced a Thematic Climate Data Record (TCDR) based on ATSR2 and AATSR (Origin: \u201cESA\u201d), and extensions using SLSTR produced specifically for the Copernicus Climate Change Service (Origin: \u201cC3S\u201d). The \u201cCLARA (CM SAF cLoud, Albedo and surface Radiation) product family\u201d originates in the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF), who produced a TCDR based on AVHRR observations. Both product families provide two complementary  data records, differing in algorithms, input data, temporal and horizontal resolution. These TCDR timeseries are intended to have sufficient length, consistency, and continuity to detect climate variability and change. They are frequently updated with Interim Climate Data Records (ICDR) or simply extensions, generated using the same software and algorithms to cover more recent periods. Further details on algorithms, data description, and extensive validation results are given in the Documentation section.",
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            "Provider: ESA CCI",
            "Provider: EUMETSAT SAF"
        ],
        "license": "other",
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            "temporal": {
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                        "1979-01-01T00:00:00Z",
                        "2025-06-30T00:00:00Z"
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            }
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        "summaries": "{}",
        "assets": {
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    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-surface-radiation-budget",
        "title": "Surface radiation budget from 1979 to present derived from satellite observations",
        "description": "The Surface Radiation Budget (SRB) represents the balance between the heating of the Earth\u2019s surface through absorption of incoming solar radiation and cooling through the emission of infra-red radiation, which is a fundamental component of the surface energy budget. Small changes in the surface radiation budget can lead to large climatological responses, which makes a permanent and precise monitoring indispensable. Accurate understanding of the surface radiation budget at regional and global scales can improve the understanding of the Earth\u2019s climate system and supports research on current and anticipated climate change, as well as the evaluation of radiative processes in climate models. Better knowledge of the global and regional surface radiation is also required for a successful utilisation of renewable energy sources, such as solar power plants.\nThe dataset contains seven variables, which can be separated into solar (shortwave) and thermal (longwave) surface radiation components. Both components include incoming (downwelling), outgoing (upwelling), and the net radiation fluxes. These variables are provided from two \u201cproduct families\u201d, according to the data provenance. The \"CCI product family\" originated from the European Space Agency (ESA) Cloud_cci project, which produced a Thematic Climate Data Record (TCDR) based on ATSR2 and AATSR (\u201cOrigin\u201d:\u201cESA\u201d) and extensions using SLSTR produced specifically for Copernicus Climate Change Service (\u201cOrigin\u201d: \u201cC3S\u201d). The \u201cCLARA (CM SAF cLoud, Albedo and surface Radiation) product family\u201d originates in the EUMETSAT\u00b4s Satellite Application Facility on Climate Monitoring (CM SAF), who produced a TCDR based on AVHRR observations. The two product families follow somewhat different nomenclatures. Therefore, a table relating the names used in the download form with the names used within the files and in other datasets distributed by the Climate Data Store is added at the end of this overview. \nThe \u201cCLARA product family\u201d and \u201cCCI product family\u201d provide complementary data records, differing in temporal and horizontal resolution. These TCDR timeseries are intended to have sufficient length, consistency, and continuity to detect climate variability and change. They are frequently updated with Interim Climate Data Records (ICDRs) or simply extensions, generated using the same software and algorithms to cover more recent periods. Further details on algorithms, data description, and extensive validation results are given in the Documentation section.",
        "links": [
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        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Product type: Satellite observations",
            "Variable domain: Atmosphere (surface)",
            "Provider: Copernicus C3S",
            "Provider: ESA CCI",
            "Provider: EUMETSAT SAF"
        ],
        "license": "other",
        "providers": [],
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        "summaries": "{}",
        "assets": {
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "sis-agrometeorological-indicators",
        "title": "Agrometeorological indicators from 1979 to present derived from reanalysis",
        "description": "This dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. Acquisition and pre-processing of the original ERA5 data is a complex and specialized job. By providing the AgERA5 dataset, users are freed from this work and can directly start with meaningful input for their analyses and modelling. To this end, the variables provided in this dataset match the input needs of most agriculture and agro-ecological models.\nData were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1\u00b0 spatial resolution. The correction to the 0.1\u00b0 grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1\u00b0 grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1\u00b0 resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model.\nThe data was produced on behalf of the Copernicus Climate Change Service.",
        "links": [
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        "keywords": [
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            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Variable domain: Land (biosphere)",
            "Provider: Copernicus C3S",
            "Sector: Agriculture"
        ],
        "license": "CC-BY-4.0",
        "providers": [
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                "name": "ECMWF"
            }
        ],
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            "temporal": {
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        "assets": {
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-land-surface-temperature",
        "title": "Land surface temperature monthly gridded data from 1995 to present derived from satellite observations",
        "description": "This dataset contains monthly daytime and nighttime clear-sky land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on polar orbiting satellites. Daytime and nighttime temperatures correspond to 10:00 and 22:00 local solar time (local solar time is defined as the time in a 24 hour day with 12 noon occurring when the sun is at its highest point at that longitude).\nLand surface temperature is a Global Climate Observing System (GCOS) Essential Climate Variable. This dataset provides a near 30-year time series of LST suitable for climate analysis, offering long\u2011term monthly data at relatively high spatial resolution for climate modellers and other users. The dataset is comprised of LSTs from the Along-Track Scanning Radiometer 2 (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR), Moderate Imaging Spectroradiometer on Earth Observation System - Terra (Terra MODIS), and the Sea and Land Surface Temperature Radiometer on Sentinel 3B (SLSTRB).\nThe inputs to the processing chain are Level 1B brightness temperatures from the ATSR-2, the AATSR, Terra MODIS, and SLSTRB. Data from the different instruments have been aligned by first intercalibrating all instruments to a common reference and then applying a correction for the 30 minute difference in equator crossing times between AATSR and SLSTRB (10:00) and ATSR-2 and Terra MODIS (10:30).\nLSTs are derived using a common calibration database and a single probabilistic cloud-clearing algorithm for all instruments. They are spatially gridded and then compiled into daily daytime and nighttime products. The daily time series are merged across instruments (Level 3S), and averaged to monthly values. Data are provided globally on a 0.01\u00b0 \u00d7 0.01\u00b0 equal-angle grid.\nThe dataset was produced by the University of Leicester as part of the European Space Agency (ESA) Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to GCOS standards.",
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        "keywords": [
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            "Spatial coverage: Global",
            "Variable domain: Land (biosphere)",
            "Product type: Satellite observations",
            "Provider: Copernicus C3S"
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        "license": "other",
        "providers": [
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                        "2024-12-31T00:00:00Z"
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        "summaries": "{}",
        "assets": {
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-upper-troposphere-humidity",
        "title": "Upper tropospheric humidity gridded data derived from satellite observations",
        "description": "Upper Tropospheric Humidity (UTH) is of key importance to the Earth\u2019s greenhouse effect and understanding of climate change. It is considered an Essential Climate Variable  (ECV) because it controls key atmospheric processes, including those involved in water vapour and cloud feedbacks, that can amplify the climate system\u2019s response to increases in other greenhouse gases. The Upper Tropospheric Humidity is defined as the integrated amount of Water Vapour in the atmospheric layer between ~500 hPa and ~200 hPa. \nThe Upper Tropospheric Humidity product consists of two editions: the UTH Thematic Climate Data Record (TCDR) and its extension in time Interim Climate Data Record (ICDR) Edition 1.0 on the one hand, the UTH Thematic Climate Data Record Edition 2.0 on the other hand.\nThe UTH TCDR and ICDR Edition 1.0 are derived from observations from the AMSU-B and MHS microwave humidity sounder instruments on board the NOAA- and MetOp- satellite series. Instantaneous satellite observations are used to derive a spatio-temporal averaged data record. The data are available as twice daily (one for the ascending and the other for the descending passes) averages on a regular latitude/longitude grid. Additionally, the daily mean UTH is provided as a weighted average of ascending and descending orbits for all the grid points with valid ascending and descending observations.\nThe UTH TCDR Edition 2.0 is a daily mean product computed from all available hourly observations for each grid cell. It relies on substantially improved retrievals and uncertainty estimates due to the inclusion of the additional sensors SSM/T-2 and ATMS on board the DMSP- satellite series and the S-NPP satellite respectively.\nUTH is part of the Water Vapour ECV inventory in the Climate Data Store (CDS) together with Total Column Water Vapour and Tropospheric Humidity Profiles. Both TCDR components are brokered to the CDS and were originally produced on behalf of EUMETSAT Satellite Application Facility on Climate Monitoring (CMSAF) by the UK Met Office. The ICDR component has been delivered directly to the CDS and was originally produced on behalf of the Copernicus Climate Change Service (C3S) by the UK Met Office.",
        "links": [
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        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Temporal coverage: Present",
            "Product type: Satellite observations",
            "Variable domain: Atmosphere (upper air)",
            "Provider: Copernicus C3S",
            "Provider: EUMETSAT SAF"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
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            "temporal": {
                "interval": [
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                        "1994-07-05T00:00:00Z",
                        "2021-02-28T00:00:00Z"
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        "summaries": "{}",
        "assets": {
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "reanalysis-oras5",
        "title": "ORAS5 global ocean reanalysis monthly data from 1958 to present",
        "description": "This dataset provides global ocean and sea-ice reanalysis (ORAS5: Ocean Reanalysis System 5) monthly mean data prepared by the  European Centre for Medium-Range Weather Forecasts (ECMWF) OCEAN5 ocean analysis-reanalysis system. This system comprises 5 ensemble members from which one member is published in this catalogue entry.\nReanalysis combines model data with observations from across the world into a globally complete and consistent dataset taking into account the laws of physics. The reanalysis provides information without temporal and spatial gaps, i.e. the data are continuous in time, and the assimilation system provides information on every model grid point independently of whether observations are available nearby or not.\nThe OCEAN5 reanalysis system uses the Nucleus for European Modelling of the Ocean (NEMO) ocean model and the NEMOVAR ocean assimilation system. NEMOVAR uses the so-called 3D-Var FGAT (First Guess at Appropriate Time) assimilation technique, which assimilates sub-surface temperature, salinity, sea-ice concentration and sea-level anomalies.\nThe ORAS5 data is forced by either global atmospheric reanalysis (for the consolidated product) or the ECMWF/IFS operational analysis (for the operational product) and is also constrained by observational data of sea surface temperature, sea surface salinity, sea-ice concentration, global-mean-sea-level trends and climatological variations of the ocean mass.\nThe consolidated product (referred to as \"Consolidated\" in the download form) uses reanalysis atmospheric forcing (ERA-40 until 1978 and ERA-Interim from 1979 to 2014) and re-processed observations. The near real-time (referred to as \"Operational\" in the download form) ORAS5 product is available from 2015 onwards and is updated on a monthly basis 15 days behind real time. It uses ECMWF operational atmospheric forcing and near real time observations. The consolidated data benefits from atmospheric forcing consistency. The operational data benefits from near real-time latency.\nORAS5 data are also available at the Copernicus Marine Environment Monitoring Service (CMEMS) and at the Integrated Climate Data Centre (ICDC), Hamburg University.  The present dataset, at the time of publication, provides more variables than the others and has regular updates with near real-time data. For the period from 2015 to the present, the operational ORAS5 data provided in the CDS is different from the dataset provided by CMEMS, because different atmospheric forcings and ocean observation data were used in the generation of the two products.\nThe ORAS5 dataset is produced by ECMWF and funded by the Copernicus Climate Change Service (C3S).",
        "links": [
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                "rel": "qa",
                "href": "https://cds.climate.copernicus.eu/datasets/reanalysis-oras5?tab=quality_assurance_tab",
                "title": "Quality assessment of the dataset",
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        ],
        "keywords": [
            "Product type: Reanalysis",
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Variable domain: Ocean (physics)"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "sis-energy-pecd",
        "title": "Climate and energy related variables from the Pan-European Climate Database derived from reanalysis and climate projections",
        "description": "The Pan-European Climate Database (PECD) provides information on climate and renewable energy variables for both historical and future time periods. For historical data, the ERA5 global reanalysis serves as the underlying climate data, while future projections are based on selected CMIP6 global climate models. The raw climate model data are further processed through downscaling to achieve higher spatial and temporal resolution and by applying bias adjustment. Each energy variable is derived from the underlying climate data, providing datasets for temperature, total precipitation, surface solar radiation downwards and wind speed, as well as energy-related variables such as wind and solar capacity factors, and hydropower inflows and generation.\nThe PECD dataset has been planned, designed, and produced by the Copernicus Climate Change Service (C3S) in collaboration with the European Network of Transmission System Operators for Electricity (ENTSO-E). This collaboration aims to increase the resilience of energy systems and optimize their performance in response to climate change. The PECD dataset existed prior to this collaboration between C3S and ENTSO-E but did not include future climate change signals. The current PECD dataset produced by C3S is the first to include climate change projections, providing energy analysts, planners, and decision-makers with essential tools for energy planning in the coming decades. The dataset is available in two formats: NetCDF for gridded indicators and CSV for area-averaged indicators.\nNote: The current dataset includes both PECDv4.1 (now deprecated) and PECDv4.2 versions. PECDv4.1 was not extended beyond 2021 as it was frozen leading up to the 2023 European Resource Adequacy Assessment (ERAA) in agreement with ENTSO-E. Additionally, PECDv4.1 version only includes data from three climate models and one emission scenario. In contrast, PECDv4.2 version includes data from six climate models and four emission scenarios, improving uncertainty representation. Best practice favours the use of larger ensembles of models to more thoroughly capture the range of uncertainties present in climate projections. The latter also includes new bias adjustments for wind speed, enhanced energy modelling, extended temporal coverage, and new spatial aggregation zones. It is therefore strongly recommended to use the version PECDv4.2.",
        "links": [
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/sis-energy-pecd"
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            {
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        "keywords": [
            "Product type: Reanalysis",
            "Temporal coverage: Past",
            "Spatial coverage: Europe",
            "Variable domain: Atmosphere (surface)",
            "Provider: Copernicus C3S",
            "Product type: Climate projections",
            "Temporal coverage: Future",
            "Product type: Climate indices",
            "Sector: Energy"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
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                        "2100-12-01T00:00:00Z"
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        "assets": {
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "sis-energy-derived-reanalysis",
        "title": "Climate and energy indicators for Europe from 1979 to present derived from reanalysis",
        "description": "The Copernicus climate change service (C3S) operational energy dataset provides climate and energy indicators for the European energy sector. The climate-relevant indicators for the energy sector considered are: air temperature, precipitation, incoming solar radiation, wind speed at 10 m and 100 m, and mean sea level air pressure. The energy indicators are electricity demand and power generation from various sources: wind (both onshore and offshore), solar and hydro (run-of-river and reservoir) power. Depending on the indicator, the data are available at the national, regional and grid (approximately 30x30 km) level for most European countries. The spatial aggregation of data over land uses the Eurostat NUTS0 & NUTS2 (Nomenclature des unit\u00e9s territoriales statistiques, 2016) regions. The offshore variables (e.g. offshore wind power) use the European maritime region definitions MAR0 and MAR1. Further information on the NUTS and MAR regions can be found in the documentation.\nThe C3S Energy operational service is composed of three main streams: historical (1979-present), seasonal forecasts and projections (typically covering the period 1970-2100). This historical dataset (1979-present) produces reference climate variables based on the ERA5 reanalysis. Energy variables are generated by transforming the climate variables using a combination of statistical models and physically based data. A comprehensive set of measured energy supply and demand data has been collected from various sources such as the European Network of Transmission System Operators (ENTSO-E). These data provide a crucial reference to assess the robustness of the models used to convert climate into electric energy variables.\nData is provided for the European domain, in a multi-variable, multi-timescale view of the climate and energy systems. This is beneficial in anticipating important climate-driven changes in the energy sector, through either long-term planning or medium-term operational activities. This is also used to investigate the role of temperature on electricity demand across Europe, as well as its interaction with the variability of renewable energy generation.",
        "links": [
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                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/sis-energy-derived-reanalysis"
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        "keywords": [
            "Product type: Reanalysis",
            "Temporal coverage: Past",
            "Spatial coverage: Europe",
            "Variable domain: Atmosphere (surface)",
            "Sector: Energy"
        ],
        "license": "CC-BY-4.0",
        "providers": [
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                "name": "ECMWF"
            }
        ],
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                "interval": [
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                        "1979-01-01T00:00:00Z",
                        "2026-01-01T00:00:00Z"
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
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                "type": "image/jpg",
                "roles": [
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            }
        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-greenland-ice-sheet-velocity",
        "title": "Ice sheet velocity for Antarctica and Greenland derived from satellite observations",
        "description": "This dataset provides annual ice velocity maps of the Greenland and Antarctic Ice Sheets derived from Sentinel-1 data. The product represents the current state-of-the-art in Europe for satellite-based ice velocity data records. It follows from the European Space Agency (ESA) Climate Change Initiative (CCI) and is guided by the Global Climate Observing System (GCOS) targets for the Ice Sheets Essential Climate Variable (ECV).\nMapping ice flow velocity and its temporal changes provides key information for investigating the dynamic response of glaciers and ice sheets to changing environmental conditions and the contribution to global sea level rise. Remote sensing techniques that utilise satellite data are the only feasible manner to derive accurate surface velocities of the Greenland and Antarctic Ice Sheets on a regular basis.\nThe ice velocity is derived by applying feature tracking techniques using repeat-pass Sentinel-1 Synthetic Aperture Radar (SAR) data acquired in Interferometric Wide (IW) swath mode. Ice velocity is provided at 250m (Greenland) or 200m (Antarctica) grid spacing in Polar Stereographic Projection. The horizontal velocity components are provided in meters per day, towards easting and northing direction of the grid. The vertical displacement is derived from a digital elevation model (DEM). Provided is a NetCDF file with gridded maps representing the horizontal and vertical velocity components, along with maps showing the magnitude of velocity, valid pixel count and uncertainty (standard deviation). The product combines multiple ice velocity maps, derived from 6- and 12-day repeat SAR data acquired over a full year, in an annually averaged mosaic running from October 1st to September 30th (Greenland) or April 1st to March 31st (Antarctica), roughly mimicking a glaciological year. The datasets are extended on an annual basis.\nThis data was produced on behalf of the Copernicus Climate Change Service.",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/satellite-greenland-ice-sheet-velocity"
            },
            {
                "rel": "parent",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            },
            {
                "rel": "qa",
                "href": "https://cds.climate.copernicus.eu/datasets/satellite-greenland-ice-sheet-velocity?tab=quality_assurance_tab",
                "title": "Quality assessment of the dataset",
                "type": "text/html"
            }
        ],
        "keywords": [
            "Temporal coverage: Past",
            "Temporal coverage: Present",
            "Variable domain: Land (cryosphere)",
            "Product type: Satellite observations",
            "Spatial coverage: Antarctic and Greenland"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
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            },
            "temporal": {
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                        "2017-10-01T00:00:00Z",
                        null
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                ]
            }
        },
        "summaries": "{}",
        "assets": {
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        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "sis-hydrology-variables-derived-seasonal-forecast",
        "title": "Multi-model seasonal forecasts of river discharge for Europe from January 2021 to present",
        "description": "This dataset provides hydrological seasonal forecasts of monthly mean river discharge across Europe. Two hydrological model ensembles are provided. The first is an E-HYPE multi-model system comprising eight model realisations using a catchment-based resolution. The second comprises the E-HYPEgrid, VIC-WUR and EFAS (LISFLOOD) hydrological models at a 5km gridded resolution.\nThe initialisation of the hydrological seasonal forecast uses the European Flood Awareness System (EFAS) daily gridded meteorological observations (EFAS-Meteo) up until the start of the forecast, and the subsequent integration of the meteorological seasonal forecasts using all 51 members of the ECMWF seasonal forecast system 5 (SEAS5) meteorological forecasts. A bias adjustment step using quantile mapping for temperature and precipitation was used for the E-HYPE and ViC-WUR models to minimize drift in the forecasts caused by biases in SEAS5 compared to EFAS-Meteo.\nThe final output is in the form of monthly mean river discharge for the coming seven months. The context of the forecasts is provided by upper and lower terciles of the historical EFAS-Meteo driven simulation for each month of the year.\nThis dataset is produced by the Swedish Meteorological and Hydrological Institute on behalf of the Copernicus Climate Change Service. The operational production is performed by C3S in collaboration with Copernicus Emergency Management Service (CEMS).",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/sis-hydrology-variables-derived-seasonal-forecast"
            },
            {
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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            {
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                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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        ],
        "keywords": [
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            "Spatial coverage: Europe",
            "Product type: Seasonal forecasts",
            "Variable domain: Land (hydrology)",
            "Temporal coverage: Present",
            "Provider: Copernicus C3S",
            "Temporal coverage: Future",
            "Sector: Water management"
        ],
        "license": "CC-BY-4.0",
        "providers": [
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                "name": "ECMWF"
            }
        ],
        "extent": {
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            "temporal": {
                "interval": [
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                        "2026-02-01T00:00:00Z"
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                ]
            }
        },
        "summaries": "{}",
        "assets": {
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                "type": "image/jpg",
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        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "projections-cmip6",
        "title": "CMIP6 climate projections",
        "description": "This catalogue entry provides daily and monthly global climate projections data from a large number of experiments, models and time periods computed in the framework of the sixth phase of the Coupled Model Intercomparison Project (CMIP6).\nCMIP6 data underpins the Intergovernmental Panel on Climate Change 6th Assessment Report. The use of these data is mostly aimed at:\n\naddressing outstanding scientific questions that arose as part of the IPCC reporting process;\nimproving the understanding of the climate system;\nproviding estimates of future climate change and related uncertainties;\nproviding input data for the adaptation to the climate change;\nexamining climate predictability and exploring the ability of models to predict climate on decadal time scales;\nevaluating how realistic the different models are in simulating the recent past.\n\nThe term \"experiments\" refers to the three main categories of CMIP6 simulations:\n\nHistorical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2014.\nClimate projection experiments following the combined pathways of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP). The SSP scenarios provide different pathways of the future climate forcing. The period covered is typically 2015-2100.\n\nThis catalogue entry provides both two- and three-dimensional data, along with an option to apply spatial and/or temporal subsetting to data requests. This is a new feature of the global climate projection dataset, which relies on compute processes run simultaneously in the ESGF nodes, where the data are originally located.\nThe data are produced by the participating institutes of the CMIP6 project.",
        "links": [
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                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/projections-cmip6"
            },
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                "type": "application/json",
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            },
            {
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            },
            {
                "rel": "qa",
                "href": "https://cds.climate.copernicus.eu/datasets/projections-cmip6?tab=quality_assurance_tab",
                "title": "Quality assessment of the dataset",
                "type": "text/html"
            }
        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Temporal coverage: Present",
            "Variable domain: Atmosphere (surface)",
            "Variable domain: Atmosphere (upper air)",
            "Product type: Climate projections",
            "Temporal coverage: Future"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
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            },
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                "interval": [
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                        "2300-12-31T00:00:00Z"
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
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                "type": "image/jpg",
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        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "derived-gridded-glacier-mass-change",
        "title": "Glacier mass change gridded data from 1976 to present derived from the Fluctuations of Glaciers Database",
        "description": "The dataset provides global annual glacier mass changes distributed on a global regular grid at 0.5\u00b0 resolution (latitude, longitude) based on the Fluctuations of Glaciers (FoG) database of the World Glacier Monitoring Service (WGMS). Glaciers play a fundamental role in the Earth\u2019s water cycles. They are one of the most important freshwater resources for societies and ecosystems and the recent increase in ice melt contributes directly to the rise of ocean levels. Due to this they have been declared as an Essential Climate Variable (ECV) by GCOS, the Global Climate Observing System. Within the Copernicus Services, the global gridded annual glacier mass change dataset provides information on changing glacier resources by combining glacier change observations from the Fluctuations of Glaciers (FoG) database that is brokered from World Glacier Monitoring Service (WGMS).\nInspired by previous methodological frameworks, a new approach was developed to combine the glacier mass balance and elevation change observations, providing a new and unique product of annual glacier mass change and related uncertainties for every hydrological year since 1975/76 distributed on a 0.5\u00b0 global regular grid. The present dataset bridges the gap regarding the spatio-temporal coverage of glacier change observations, providing for the first time in the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) an annually resolved glacier mass change product using the glacier elevation change sample as calibration. This goal has become feasible at the global scale thanks to a new globally near-complete (96% of the world glaciers) dataset of glacier elevation change observations ingested by the FoG database.\nTo develop the distributed glacier change product, the use of glacier outlines from the C3S Glacier Area product version 2 are used. A glacier is considered to belong to a grid-point when its geometric centroid lies within the grid point. The centroid is obtained from the glacier outlines from the C3S Glacier Area product version 2. The glacier changes in Gt correspond to the total mass of water lost/gained over the glacier surface during a given year. Note that to propagate to mm/cm/m of water column on the grid cell, the grid cell area needs to be considered. Note that hydrological year vary on the Southern Hemisphere (October to September next year) and Northern Hemispheres (April to March next year). The annual distributed glacier change dataset cannot resolve for this seasonal difference and is important for the user to account for them when using the datasets. This issue can only be resolved with a monthly distributed glacier change product.\nThis dataset has been produced by researchers at the WGMS on behalf of Copernicus Climate Change Service.",
        "links": [
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                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/derived-gridded-glacier-mass-change"
            },
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            },
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                "href": "https://cds.climate.copernicus.eu/datasets/derived-gridded-glacier-mass-change?tab=quality_assurance_tab",
                "title": "Quality assessment of the dataset",
                "type": "text/html"
            }
        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Variable domain: Land (cryosphere)",
            "Product type: Satellite observations",
            "Product type: In-situ observations"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
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                "bbox": [
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                        -89.75,
                        179.75,
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            },
            "temporal": {
                "interval": [
                    [
                        "1975-01-01T00:00:00Z",
                        null
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            }
        },
        "summaries": "{}",
        "assets": {
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "reanalysis-cerra-height-levels",
        "title": "CERRA sub-daily regional reanalysis data for Europe on height levels from 1984 to present",
        "description": "The Copernicus European Regional ReAnalysis (CERRA) datasets provide spatially and temporally consistent historical reconstructions of meteorological variables in the atmosphere and at the surface. There are four subsets: single levels (atmospheric and surface quantities), height levels (upper-air fields up to 500m), pressure levels (upper-air fields up to 1hPa) and model levels (native levels of the model). This entry provides reanalysis and forecast data on height levels for Europe from 1984 to present. One reason to provide atmospheric variables on height levels is for applications in the wind energy sector. Several atmospheric parameters are common to both reanalysis and forecast (e.g. temperature, wind), whilst others are produced only by the forecast model (e.g. specific rain water content).\nReanalysis combines model data with observations into a complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved, reprocessed versions of the original observations, which all benefit the quality of the reanalysis product. The CERRA dataset was produced using the HARMONIE-ALADIN limited-area numerical weather prediction and data assimilation system, hereafter referred to as the CERRA system. The CERRA system employs a 3-dimensional variational data assimilation scheme of the atmospheric state at every assimilation time. The reanalysis dataset is convenient owing to its provision of atmospheric estimates at each model domain grid point over Europe for each regular output time, over a long period, and always using the same data format.\nThe inputs to CERRA reanalysis are the observational data, lateral boundary conditions from ERA5 global reanalysis as prior estimates of the atmospheric state and physiographic datasets describing the surface characteristics of the model. The observing system has evolved over time, and although the data assimilation system can resolve data holes, the much sparser observational networks in the past periods (for example, a reduced amount of satellite data in the 1980s) can impact the quality of analyses leading to less accurate estimates. The uncertainty estimates for reanalysis variables are provided by the CERRA-EDA, a 10-member ensemble of data assimilation system.\nThe added value of the CERRA data with respect to the global reanalysis products is expected to come, for example, with the higher horizontal resolution that permits the usage of a better description of the model topography and physiographic data, and the assimilation of more surface observations.\nMore information about the CERRA dataset can be found in the Documentation section.",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/reanalysis-cerra-height-levels"
            },
            {
                "rel": "parent",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            },
            {
                "rel": "root",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            }
        ],
        "keywords": [
            "Product type: Reanalysis",
            "Temporal coverage: Past",
            "Spatial coverage: Europe",
            "Variable domain: Land (hydrology)",
            "Variable domain: Atmosphere (upper level)"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
                    [
                        17.0,
                        20.0,
                        35.0,
                        65.0
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            "temporal": {
                "interval": [
                    [
                        "1984-09-01T00:00:00Z",
                        "2025-11-30T00:00:00Z"
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
            "thumbnail": {
                "href": "https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-catalogue/resources/reanalysis-cerra-height-levels/overview_d0f0a9b88a4f4050fb25d42356fde10a516a3a9d3f872c39458814cb8a1e06c8.png",
                "type": "image/jpg",
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            }
        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "reanalysis-cerra-single-levels",
        "title": "CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present",
        "description": "The Copernicus European Regional ReAnalysis (CERRA) datasets provide spatially and temporally consistent historical reconstructions of meteorological variables in the atmosphere and at the surface. There are four subsets: single levels (atmospheric and surface quantities), height levels (upper-air fields up to 500m), pressure levels (upper-air fields up to 1hPa) and model levels (native levels of the model). This entry provides reanalysis and forecast data on single levels for Europe from 1984 to present. Several atmospheric parameters are common to both reanalysis and forecast (e.g. temperature, wind), whilst others are produced only by the forecast model (e.g. 10m wind gust, radiative fluxes).\nReanalysis combines model data with observations into a complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved, reprocessed, versions of the original observations, which all benefit the quality of the reanalysis product. The CERRA dataset was produced using the HARMONIE-ALADIN limited-area numerical weather prediction and data assimilation system, hereafter referred to as the CERRA system. The CERRA system employs a 3-dimensional variational data assimilation scheme of the atmospheric state at every assimilation time. The reanalysis dataset is convenient owing to its provision of atmospheric estimates at each model domain grid point over Europe for each regular output time, over a long period, and always using the same data format.\nThe inputs to CERRA reanalysis are the observational data, lateral boundary conditions from ERA5 global reanalysis as prior estimates of the atmospheric state and physiographic datasets describing the surface characteristics of the model. The observing system has evolved over time, and although the data assimilation system can resolve data holes, the much sparser observational networks in the past periods (for example a reduced amount of satellite data in the 1980s) can impact the quality of analyses leading to less accurate estimates. The uncertainty estimates for reanalysis variables are provided by the CERRA-EDA, a 10-member ensemble of data assimilation system.\nThe added value of the CERRA data with respect to the global reanalysis products is expected to come, for example, with the higher horizontal resolution that permits the usage of a better description of the model topography and physiographic data, and the assimilation of more surface observations.\nMore information about the CERRA dataset can be found in the Documentation section.",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/reanalysis-cerra-single-levels"
            },
            {
                "rel": "parent",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            },
            {
                "rel": "qa",
                "href": "https://cds.climate.copernicus.eu/datasets/reanalysis-cerra-single-levels?tab=quality_assurance_tab",
                "title": "Quality assessment of the dataset",
                "type": "text/html"
            }
        ],
        "keywords": [
            "Product type: Reanalysis",
            "Temporal coverage: Past",
            "Spatial coverage: Europe",
            "Variable domain: Land (hydrology)",
            "Variable domain: Atmosphere (surface)"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
                    [
                        17.0,
                        20.0,
                        35.0,
                        65.0
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                ]
            },
            "temporal": {
                "interval": [
                    [
                        "1984-09-01T00:00:00Z",
                        "2025-11-30T00:00:00Z"
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
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    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "reanalysis-cerra-pressure-levels",
        "title": "CERRA sub-daily regional reanalysis data for Europe on pressure levels from 1984 to present",
        "description": "The Copernicus European Regional ReAnalysis (CERRA) datasets provide spatially and temporally consistent historical reconstruction of meteorological variables in the atmosphere and at the surface. There are four subsets: single levels (atmospheric and surface quantities), height levels (upper-air fields up to 500m), pressure levels (upper-air fields up to 1hPa) and model levels (native levels of the model). This entry provides reanalysis and forecast data on pressure levels for Europe from 1984 to present. Several atmospheric parameters are common to both reanalysis and forecast (e.g. temperature, wind), whilst others are produced only by the forecast model (e.g. cloud cover).\nReanalysis combines model data with observations into a complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved, reprocessed versions of the original observations, which all benefit the quality of the reanalysis product. The CERRA dataset was produced using the HARMONIE-ALADIN limited-area numerical weather prediction and data assimilation system, hereafter referred to as the CERRA system. The CERRA system employs a 3-dimensional variational data assimilation scheme of the atmospheric state at every assimilation time. The reanalysis dataset is convenient owing to its provision of atmospheric estimates at each model domain grid point over Europe for each regular output time, over a long period, and always using the same data format.\nThe inputs to CERRA reanalysis are the observational data, lateral boundary conditions from ERA5 global reanalysis as prior estimates of the atmospheric state and physiographic datasets describing the surface characteristics of the model. The observing system has evolved over time, and although the data assimilation system can resolve data holes, the much sparser observational networks in the past periods (for example a reduced amount of satellite data in the 1980s) can impact the quality of analyses leading to less accurate estimates. The uncertainty estimates for reanalysis variables are provided by the CERRA-EDA, a 10-member ensemble of data assimilation system.\nThe added value of the CERRA data with respect to the global reanalysis products is expected to come, for example, with the higher horizontal resolution that permits the usage of a better description of the model topography and physiographic data, and the assimilation of more surface observations.\nMore information about the CERRA dataset can be found in the Documentation section.",
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            "Temporal coverage: Past",
            "Spatial coverage: Europe",
            "Variable domain: Land (hydrology)",
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        "id": "reanalysis-cerra-model-levels",
        "title": "CERRA sub-daily regional reanalysis data for Europe on model levels from 1984 to present",
        "description": "The Copernicus European Regional ReAnalysis (CERRA) datasets provide spatially and temporally consistent historical reconstructions of meteorological variables in the atmosphere and at the surface. There are four subsets: single levels (atmospheric and surface quantities), height levels (upper-air fields up to 500m), pressure levels (upper-air fields up to 1hPa) and model levels (native levels of the model). This entry provides reanalysis data on model levels for Europe from 1984 to present.\nReanalysis combines model data with observations into a complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved, reprocessed versions of the original observations, which all benefit the quality of the reanalysis product. The CERRA dataset was produced using the HARMONIE-ALADIN limited-area numerical weather prediction and data assimilation system, hereafter referred to as the CERRA system. The CERRA system employs a 3-dimensional variational data assimilation scheme of the atmospheric state at every assimilation time. The reanalysis dataset is convenient owing to its provision of atmospheric estimates at each model domain grid point over Europe for each regular output time, over a long period, and always using the same data format.\nThe inputs to CERRA reanalysis are the observational data, lateral boundary conditions from ERA5 global reanalysis as prior estimates of the atmospheric state and physiographic datasets describing the surface characteristics of the model. The observing system has evolved over time, and although the data assimilation system can resolve data holes, the much sparser observational networks in the past periods (for example a reduced amount of satellite data in the 1980s) can impact the quality of analyses leading to less accurate estimates. The uncertainty estimates for reanalysis variables are provided by the CERRA-EDA, a 10-member ensemble of data assimilation system.\nThe added value of the CERRA data with respect to the global reanalysis products is expected to come, for example, with the higher horizontal resolution that permits the usage of a better description of the model topography and physiographic data, and the assimilation of more surface observations.\nMore information about the CERRA dataset can be found in the Documentation section.",
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            "Variable domain: Atmosphere (upper level)"
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        "id": "reanalysis-era5-land-monthly-means",
        "title": "ERA5-Land monthly averaged data from 1950 to present",
        "description": "ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.\nERA5-Land provides a consistent view of the water and energy cycles at surface level during several decades.\nIt contains a detailed record from 1950 onwards, with a temporal resolution of 1 hour. The native spatial resolution of the ERA5-Land reanalysis dataset is 9km on a reduced Gaussian grid (TCo1279). The data in the CDS has been regridded to a regular lat-lon grid of 0.1x0.1 degrees.\nThe data presented here is a post-processed subset of the full ERA5-Land dataset. Monthly-mean averages have been pre-calculated to facilitate many applications requiring easy and fast access to the data, when sub-monthly fields are not required.",
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            "Spatial coverage: Global",
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            "Provider: Copernicus C3S",
            "Variable domain: Land (physics)"
        ],
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        "id": "reanalysis-era5-single-levels-monthly-means",
        "title": "ERA5 monthly averaged data on single levels from 1940 to present",
        "description": "ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.\nData is available from 1940 onwards.\nERA5 replaces the ERA-Interim reanalysis.\nReanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\nERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities.\nAn uncertainty estimate is sampled by an underlying 10-member ensemble\nat three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience.\nSuch uncertainty estimates are closely related to the information content of the available observing system which\nhas evolved considerably over time. They also indicate flow-dependent sensitive areas.\nTo facilitate many climate applications, monthly-mean averages have been pre-calculated too,\nthough monthly means are not available for the ensemble mean and spread.\nERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified.\nThe data set presented here is a regridded subset of the full ERA5 data set on native resolution.\nIt is online on spinning disk, which should ensure fast and easy access.\nIt should satisfy the requirements for most common applications.\nAn overview of all ERA5 datasets can be found in this article.\nInformation on access to ERA5 data on native resolution is provided in these guidelines.\nData has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for\nthe uncertainty estimate (0.5 and 1 degree respectively for ocean waves).\nThere are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities).\nThe present entry is \"ERA5 monthly mean data on single levels from 1940 to present\".",
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            "Spatial coverage: Global",
            "Variable domain: Atmosphere (surface)",
            "Variable domain: Atmosphere (upper air)",
            "Provider: Copernicus C3S"
        ],
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        "id": "reanalysis-era5-pressure-levels-monthly-means",
        "title": "ERA5 monthly averaged data on pressure levels from 1940 to present",
        "description": "ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.\nData is available from 1940 onwards.\nERA5 replaces the ERA-Interim reanalysis.\nReanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\nERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities.\nAn uncertainty estimate is sampled by an underlying 10-member ensemble\nat three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience.\nSuch uncertainty estimates are closely related to the information content of the available observing system which\nhas evolved considerably over time. They also indicate flow-dependent sensitive areas.\nTo facilitate many climate applications, monthly-mean averages have been pre-calculated too,\nthough monthly means are not available for the ensemble mean and spread.\nERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. So far this has only been the case for the month September 2021, while it will also be the case for October, November and December 2021. For months prior to September 2021 the final release has always been equal to ERA5T, and the goal is to align the two again after December 2021.\nERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified.\nThe data set presented here is a regridded subset of the full ERA5 data set on native resolution.\nIt is online on spinning disk, which should ensure fast and easy access.\nIt should satisfy the requirements for most common applications.\nAn overview of all ERA5 datasets can be found in this article.\nInformation on access to ERA5 data on native resolution is provided in these guidelines.\nData has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for\nthe uncertainty estimate (0.5 and 1 degree respectively for ocean waves).\nThere are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities).\nThe present entry is \"ERA5 monthly mean data on pressure levels from 1940 to present\".",
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        "title": "Earth's radiation budget from 1979 to present derived from satellite observations",
        "description": "The Earth\u2019s Radiation Budget (ERB) represents the overall balance between incoming and outgoing radiant energy at the Earth\u2019s top-of-the-atmosphere. This Essential Climate Variable is the primary forcing of the climate system and is therefore a fundamental quantity to be monitored to understand the Earth\u2019s climate and its variability. The Earth\u2019s Radiation Budget comprises of the quantification of the incoming radiation from the Sun and the outgoing reflected shortwave and emitted longwave radiation. \nThe Earth\u2019s Radiation Budget represents the balance between incoming, predominantly solar, radiation and outgoing radiation as either reflected solar radiation or thermal radiation emitted by the Earth system. The Earth is in a state of dynamic radiative balance, energy arriving from the Sun is balanced by outgoing radiation from the top-of-the-atmosphere. Changes in the Earth\u2019s surface and atmosphere (including cloud amount and properties, as well as concentrations of greenhouse gases) alter the radiative balance of the Earth\u2019 system and change the balance and distribution of outgoing thermal radiation and reflected solar radiation. Aside from the elevation (height) of the sun above the horizon, the largest factor controlling the variability in the Earth's radiation budget is cloud cover; the fraction of cloud cover, their height and opacity all impact on both the reflected solar and outgoing infrared radiation. Thus, knowledge of cloud properties, along with atmospheric composition and surface properties, allows computing the Earth\u2019s Radiation Budget. In addition to the spectrally integrated broadband incoming and outgoing fluxes, the monitoring of the solar spectral irradiance is also relevant given its importance in atmospheric chemistry processes.\nThis catalogue entry comprises data from a number of product families and origins summarised in the following sections.",
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            "Product type: Satellite observations",
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            "Provider: ESA CCI",
            "Provider: EUMETSAT SAF"
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        "id": "seasonal-monthly-ocean",
        "title": "Seasonal forecast monthly averages of ocean variables",
        "description": "This entry covers global ocean data aggregated to a monthly time resolution. The catalogue entry includes temperature and salinity characteristics of the upper oceans and complements the other seasonal forecast catalogue entries for the land and atmospheric variables. \nSeasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect distributions of outcomes.\nGiven the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated.\nTo this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The seasonal forecast data is grouped in several catalogue entries (CDS datasets), currently defined by the model component and type of variable: outputs from the ocean component or the atmospheric one (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment).\nThe data includes forecasts created in real-time each month starting from the publication of this entry and retrospective forecasts (hindcasts) initialised over periods in the past specified in the documentation for each origin and system.",
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            "Spatial coverage: Global",
            "Product type: Seasonal forecasts",
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            "Variable domain: Ocean (physics)",
            "Temporal coverage: Future"
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        "id": "seasonal-original-pressure-levels",
        "title": "Seasonal forecast subdaily data on pressure levels",
        "description": "This entry covers pressure-level data at the original time resolution (once every 12 hours). \nSeasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes.\nGiven the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time.\nWhile uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated.\nTo this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment).\nThe data includes forecasts created in real-time each month starting from the publication of this entry and retrospective forecasts (hindcasts) initialised over periods in the past specified in the documentation for each origin and system.",
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        "id": "seasonal-postprocessed-single-levels",
        "title": "Seasonal forecast anomalies on single levels",
        "description": "This entry covers single-level data post-processed for bias adjustment on a monthly time resolution. \nSeasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes.\nGiven the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time.\nWhile uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated.\nTo this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment).\nThe variables available in this data set are listed in the table below. The data includes forecasts created in real-time each month starting from the publication of this entry.",
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        "id": "seasonal-original-single-levels",
        "title": "Seasonal forecast daily and subdaily data on single levels",
        "description": "This entry covers single-level data and soil-level data at the original time resolution (once a day, or once every 6 hours, depending on the variable). \nSeasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes.\nGiven the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time.\nWhile uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated.\nTo this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment).\nThe data includes forecasts created in real-time each month starting from the publication of this entry and retrospective forecasts (hindcasts) initialised over periods in the past specified in the documentation for each origin and system.",
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        "id": "seasonal-monthly-pressure-levels",
        "title": "Seasonal forecast monthly statistics on pressure levels",
        "description": "This entry covers pressure-level data aggregated on a monthly time resolution. \nSeasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes.\nGiven the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time.\nWhile uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated.\nTo this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment).\nThe data includes forecasts created in real-time each month starting from the publication of this entry and retrospective forecasts (hindcasts) initialised over periods in the past specified in the documentation for each origin and system.",
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        "title": "Monthly drought indices from 1940 to present derived from ERA5 reanalysis",
        "description": "ERA5\u2013Drought is a global reconstruction of drought indices from 1940 to present.\nThe dataset comprises two standardised drought indices:\n- the Standardised Precipitation Index (SPI)\n- the Standardised Precipitation-Evapotranspiration Index (SPEI).\nThe SPI measures the precipitation deficit that accumulated over the preceding months and evaluates the deficit with respect to a reference period. The SPEI is an extension of the SPI and incorporates potential evapotranspiration to capture the impact of temperature on drought. SPI and SPEI values are in units of standard deviation from the standardised mean, i.e., negative values indicate drier-than-usual periods while positive values correspond to wetter-than-usual periods. Both indices can be used to identify the onset and the end of drought events as well as their severity.\nIn ERA5\u2013Drought, SPI and SPEI are calculated using precipitation and potential evapotranspiration from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses (ERA5). ERA5 combines model data with observations from across the world to provide a globally complete and consistent description of the atmosphere. Drought indices are calculated for a range of accumulation windows (1/3/6/12/24/36/48 months) using the reference period from 1991\u20132020. All data is regridded to a regular grid of 0.25 degrees, making it suitable for many common applications. SPI and SPEI are calculated using both the ERA5 reanalysis (single realisation from the monthly means of daily means(moda) stream) and the ensemble of the reanalysis (10 realisations from the monthly means of daily means for ensemble members (edmo) stream), enabling uncertainty assessment of drought occurrence and intensity. The quality of the derived indices is evaluated using significance testing. \nThe dataset currently covers 1940 to near-real time and is updated monthly. The consolidated data set is updated 2-3 months behind real time, while the intermediate data set is updated with 1 month of delay. New versions of the dataset are published as settings, such as the reference period, are updated or bug fixes are applied. Bug Fixes will be released using a minor revision (i.e. v1.1), while changes to the reference period will be released as major revisions (i.e. v2.0). Bug Fixes will be published to the Known Issues area on the Documentation tab.\nA more detailed description of the ERA5\u2013Drought dataset and comparisons to other drought indices can be found in the associated dataset paper (see Documentation Tab). Information on access and usage examples, e.g. to calculate the area in drought, are provided in these guidelines.\nThe dataset is produced by ECMWF.",
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        "id": "ecv-for-climate-change",
        "title": "Essential climate variables for assessment of climate variability from 1979 to present",
        "description": "The Essential Climate Variables for assessment of climate variability from 1979 to present dataset contains a selection of climatologies, monthly anomalies and monthly mean fields of Essential Climate Variables (ECVs) suitable for monitoring and assessment of climate variability and change. Selection criteria are based on accuracy and temporal consistency on monthly to decadal time scales. The ECV data products in this set have been estimated from climate reanalyses ERA-Interim, ERA5 and ERA5-Land, and, depending on the source, may have been adjusted to account for biases and other known deficiencies. Data sources and adjustment methods used are described in the Product User Guide, as are various particulars such as the baseline periods used to calculate monthly climatologies and the corresponding anomalies.\nThe statistics provided are monthly average fields, climatologies and anomalies, as well as 12-month running mean anomalies. Climatologies and anomalies are calculated with respect to two reference periods: 1981-2010 (ERA5, ERA5-Land and ERA-Int) and 1991-2020 (ERA5 and ERA5-Land ).\nThe C3S monthly climate bulletin (https://climate.copernicus.eu/climate-bulletins) provides an assessment of the monthly state of the climate with an emphasis on the European geographical domain. This data record is used as the basis for these monthly bulletins.",
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        "stac_version": "1.1.0",
        "id": "seasonal-postprocessed-pressure-levels",
        "title": "Seasonal forecast anomalies on pressure levels",
        "description": "This entry covers pressure-level data post-processed for bias adjustment on a monthly time resolution. \nSeasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes.\nGiven the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time.\nWhile uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated.\nTo this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment).\nThe variables available in this data set are listed in the table below. The data includes forecasts created in real-time each month starting from the publication of this entry.",
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        "id": "seasonal-monthly-single-levels",
        "title": "Seasonal forecast monthly statistics on single levels",
        "description": "This entry covers single-level data aggregated on a monthly time resolution. \nSeasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes.\nGiven the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time.\nWhile uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated.\nTo this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment).\nThe data includes forecasts created in real-time each month starting from the publication of this entry and retrospective forecasts (hindcasts) initialised over periods in the past specified in the documentation for each origin and system.",
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        "id": "satellite-sea-level-global",
        "title": "Sea level gridded data from satellite observations for the global ocean from 1993 to present",
        "description": "This dataset provides gridded daily and monthly mean global estimates of sea level anomaly based on satellite altimetry measurements. The rise in global mean sea level in recent decades has been one of the most important and well-known consequences of climate warming, putting a large fraction of the world population and economic infrastructure at greater risk of flooding. However, changes in the global average sea level mask regional variations that can be one order of magnitude larger. Therefore, it is essential to measure changes in sea level over the world\u2019s oceans as accurately as possible.\nSea level anomaly is defined as the height of water over the mean sea surface in a given time and region. In this dataset sea level anomalies are computed with respect to a twenty-year mean reference period (1993-2012) using up-to-date altimeter standards.\nIn the past, the altimeter sea level datasets were distributed on the CNES AVISO altimetry portal until their production was taken over by the Copernicus Marine Environment Monitoring Service (CMEMS) and the Copernicus Climate Change Service (C3S) in 2015 and 2016 respectively.\nThe sea level dataset provided here by C3S is climate-oriented, that is, dedicated to the monitoring of the long-term evolution of sea level and the analysis of the ocean/climate indicators, both requiring a homogeneous and stable sea level record. To achieve this, a steady two-satellite merged constellation is used at all time steps in the production system: one satellite serves as reference and ensures the long-term stability of the data record; the other satellite (which varies across the record) is used to improve accuracy, sample mesoscale processes and provide coverage at high latitudes. The C3S sea level dataset is used to produce Ocean Monitoring Indicators (e.g. global and regional mean sea level evolution), available in the CMEMS catalogue.\nThe CMEMS sea level dataset has a more operational focus as it is dedicated to the retrieval of mesoscale signals in the context of ocean modeling and analysis of the ocean circulation on a global or regional scale. Such applications require the most accurate sea level estimates at each time step with the best spatial sampling of the ocean with all satellites available, with less emphasis on long-term stability and homogeneity.\nThis dataset is updated three times a year with a delay of about 5 months relative to present time. This delay is mainly due to the timeliness of the input data, the centred processing temporal window and the validation process. However, these processing and validation steps are essential to enhance the stability and accuracy of the sea level products and make them suitable for climate applications.\nThis dataset includes estimates of sea level anomaly and absolute dynamic topography together with the corresponding geostrophic velocities, which provide an approximation of the ocean surface currents. More details about these variables, the sea level retrieval algorithms, additional filters, optimisation procedures, and the error estimation can be found in the documentation.",
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        "id": "sis-european-wind-storm-reanalysis",
        "title": "Windstorm tracks and footprints derived from reanalysis over Europe between 1940 to present",
        "description": "This dataset provides climate indicators of windstorms associated with extratropical cyclones, derived from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses (ERA5) over a pan-European domain. Developed as part of Copernicus Climate Change Service's (C3S) Enhanced Windstorm Service (EWS), and responding to requirements from users in the insurance sector, this dataset extends the previous C3S Windstorm Service (winter months only) to include the entire year. This dataset is updated on a monthly basis, in near real-time. The catalogue includes windstorms identified using cyclone tracking algorithms that pass the filtering criteria, including mean sea level pressure and 850hPa vorticity (please see the Product User Guide for more information). The dataset includes three main products:\n\n\nWindstorm track: a sequence of longitude\u2013latitude points which define the trajectory of an extra-tropical windstorm over time. A tracking algorithm is used to select the windstorm track. Two tracking algorithms are available in this dataset: TempestExtremes and Hodges algorithm (also known as TRACK).\n\n\nWindstorm footprint: the maximum 10m wind gust over a 72-hour time window. The time window is centred on the time step in which the tracking algorithm identifies the maximum 925 hPa wind speed related to the windstorm. Only windstorm footprints that make landfall are considered by the tracking algorithm. The windstorm footprints are identified on the original ERA5 grid (0.25\u00b0 x 0.25\u00b0) as well as statistically-downscaled high-resolution grid (0.016\u00b0 x 0.016\u00b0) to better represent orographic and wind shear effects. Two spatial configurations of the footprints are provided: i) the \"full domain\" and ii) \"windstorm footprint area\", also referred to as \"decontaminated\" (please see the Product User Guide for more information). \n\n\nWindstorm summary indicators: annual statistics derived from decontaminated windstorm footprints and both tracking algorithms. Four wind gust thresholds (0, 15.6, 20, and 25 m/s) are considered.\n\n\nThis dataset was produced on behalf of the Copernicus Climate Change Service.",
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        "id": "reanalysis-cerra-land",
        "title": "CERRA-Land sub-daily regional reanalysis data for Europe from 1984 to present",
        "description": "The Copernicus European Regional ReAnalysis Land (CERRA-Land) dataset provides spatially and temporally consistent historical reconstructions of surface and soil variables at the same horizontal resolution as the CERRA high-resolution reanalysis.\nThe need for precipitation and surface variables at an ever-increasing spatial and temporal resolution is a recurrent demand. These variables allow, among other things, to address water resource management issues and to carry out climate change impact studies. Regional surface reanalyses are a way to reconstruct these variables for past periods covering several decades using state-of-the-art models. Reanalysis combines model data with observations into a complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where a previous forecast is combined with  newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but usually at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved, reprocessed versions of the original observations, which all benefit the quality of the reanalysis product.\nThe dataset was produced using the CERRA-Land system which consists of a land surface modelling platform SURFEX (V8.1) and a daily (24-h) total accumulated precipitation assimilation system. Most of the data are forecasts generated based on the open-loop integration of the SURFEX. The observations are not directly used in their production but have an indirect influence through the atmospheric forcing  (e.g. 2m temperature) from the CERRA high-resolution reanalysis and precipitation reanalysis system used to integrate in time the SURFEX model. No downscaling method was used to build up the input forcing data because the CERRA-Land system has the same integration domain (e.g. grid spacing, orography) as the CERRA high-resolution atmospheric reanalysis. SURFEX was run offline, that is without feedback to the atmospheric analysis performed in the CERRA data assimilation cycles.\nTo solve both heat and water transfer equations in the soil, a discretisation of the soil into 14 layers was used. The surface precipitation analysis and the 12 snow layers model included in the CERRA-Land system significantly improve the representation of the snowpack over Europe in comparison with the CERRA dataset.\nThis dataset describes the evolution of soil moisture, soil temperature and snowpack in a consistent view over several decades at an enhanced resolution compared to ERA5 and ERA5-Land. The temporal and spatial resolutions of CERRA-Land data recommend this dataset, for example, for water resource management and climate change studies.\nThe added value of the CERRA-Land data with respect to the global reanalysis products is expected to come, for example, with the higher horizontal resolution that permits the usage of a better description of the model topography and physiographic data.\nMore information about the CERRA-Land dataset can be found in the Documentation section.",
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        "id": "insitu-observations-surface-land",
        "title": "Global land surface atmospheric variables from 1755 to present from comprehensive in-situ observations",
        "description": "This set of data holdings provides access to data collected from land surface meteorological observations across the globe. Data are available at the observational level and also at daily and monthly aggregations. Data have been collated and harmonised and quality control checks have been performed, but no attempt has been made to assess for potential biases. Data are provided for a range of commonly observed variables.\nSurface meteorological observations taken by a broad variety of organisations, including but not limited to National Meteorological Services, are being collated, reconciled, and harmonised. This work provides data that can underpin the development of reanalyses products as well as the production of gridded products and data services. \nWork is ongoing to prepare and analyse several hundred sources secured to date and new sources continue to accrue. Documentation on sources secured to date can be found in the inventory information provided in the documentation tab. Users with new sources are invited to upload these via the data deposition service for ingestion and consideration. Sources are available in a broad range of formats and frequently individual series have been shared multiple times. Steps have been undertaken to harmonise data formats and reconcile data sources to yield the longest possible individiual station records. \nStation period-of-record varies on a station-by-station basis with most stations starting after 1950, but many stations extending much further back in time. Daily and monthly aggregations extend further back in time than sub-daily records in general. Periodic updates and reissues shall refresh historical holdings availability and will also build on aspects such as quality checking and flagging of data. Each such release will be accompanied by a version number increment and a reissue of the product user guide. Updates for recent observations are in process of being provided at a latency of 1-2 days for those stations that are operational and sharing data across the WMO Information System, starting with updates to the daily holdings.\nData are available for several commonly observed variables as described in the main variables table. Note that which variables are available varies by the chosen timescale of data aggregation and by station. To aid users an inventory of stations by name, location, start and end date, and variables available is provided in the documentation page differentiated by timescale. Attributes are described in the related-variables tables. The datasets can be downloaded as NetCDF files (CDM-Obs-Core, see documentation) or as comma-separated values (CSV) files.\nUsers should ensure an appropriate assessment of long-term data quality prior to use in those applications which require consideration of such aspects.   \nMore details about the land meteorological station holdings are available in the product user guide, and details around data formatting can be found in the common data model documentation, both of which can be found in the documentation section. \nThis work is being completed on behalf of C3S in sustained collaboration with colleagues at NOAA's National Centres for Environmental Information who are the WMO designated World Data Centre for meteorology.",
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        "license": "other",
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        "id": "satellite-aerosol-properties",
        "title": "Aerosol properties gridded data from 1995 to present derived from satellite observations",
        "description": "This data set provides observational records of aerosol properties obtained from observations collected by various satellite instruments. Aerosols are minor constituents of the atmosphere by mass, but critical components in terms of impact on climate. Aerosols influence the global radiation balance directly by scattering and absorbing radiation, and indirectly through influencing cloud reflectivity, cloud cover and cloud lifetime.\nThe main variables provided by this dataset are: aerosol optical depth, fine mode aerosol optical depth, dust aerosol optical depth, single scattering albedo, aerosol layer height and aerosol extinction coefficient. These variables are derived from observations from several sensors using a set of different processing techniques. This provides the possibility to derive a large set of complementary aerosol properties needed to describe the complex nature of atmospheric aerosols. Furthermore, different algorithms have their specific strengths and weaknesses, meaning that datasets originating from the same sensor but processed by different algorithms provide a way to evaluate uncertainties (e.g. areas of good or bad agreement between them). Altogether, the aerosol properties dataset is very extensive and offers a choice of complementary options \u2013 which is appropriate depends on the intended application.\nSelected observational records in this dataset are extended in time on a semi-annual basis. At the moment of extending, these records are up-to-date until five months behind present time.   \nThis dataset is produced on behalf of the Copernicus Climate Change Service.",
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            "Variable domain: Atmosphere (composition)",
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            "Product type: Satellite observations"
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        "license": "CC-BY-4.0",
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        "id": "reanalysis-pan-carra-means",
        "title": "pan-Arctic regional reanalysis daily and monthly data from 1986 to present",
        "description": "The C3S Arctic Regional Reanalysis second generation (CARRA2) dataset contains daily and monthly meteorological variables at 2.5 km resolution. These variables are specified at single levels (including surface) and also at soil, height, pressure and model levels. These daily and monthly data are pre-calculated and have the following types depending on the variables: daily and monthly averages, extremes and totals. \nThe domain covers a full Arctic (pan-Arctic) region on a polar stereographic projection, reaching from south of Greenland to south of Alaska including the entire Arctic Ocean and a portion of Northern Europe.\nThe dataset has been produced with the use of the HARMONIE-AROME state-of-the-art regional numerical weather prediction model. High resolution reanalysis for the Arctic region is essential due to the more pronounced climate change in that region than elsewhere. This calls for a better description of this region providing additional details with respect to the global reanalyses (ERA5). The additional information is provided by the higher horizontal resolution, more local observations (from the Nordic countries and Greenland), better description of surface characteristics (high resolution satellite and physiographic data), high resolution dynamics and improved physical parameterisation of clouds and precipitation in particular. These details add value to the regional reanalysis with respect to its global counterpart. \nThe inputs to CARRA2 reanalysis are the observations, the ERA5 global reanalysis as lateral boundary conditions and the physiographic datasets describing the surface characteristics of the model. The observation values and information about their quality are used together to constrain the reanalysis where observations are available and provide information for the data assimilation system in areas in where less (or even no) observations are available.\nMore details about the reanalysis dataset and the extensive input data are given in the Documentation section.",
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            "Variable domain: Land (cryosphere)",
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            "Variable domain: Atmosphere (upper air)",
            "Spatial coverage: Arctic"
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        "id": "reanalysis-pan-carra",
        "title": "pan-Arctic regional reanalysis sub-daily data from 1986 to present",
        "description": "The C3S Arctic Regional Reanalysis second generation (CARRA2) dataset contains 3-hourly analyses at 2.5 km resolution. These variables are specified at single levels (including surface) and also at soil, height, pressure and model levels. Additionally, hourly forecasts are available between the analysis times and particularly forecasts up to 18 hours initialised from the analyses at 00 and 12 UTC.\nThe domain covers a full Arctic (pan-Arctic) region on a polar stereographic projection, reaching from south of Greenland to south of Alaska including the entire Arctic Ocean and a portion of Northern Europe.\nThe dataset has been produced with the use of the HARMONIE-AROME state-of-the-art regional numerical weather prediction model. High resolution reanalysis for the Arctic region is essential due to the more pronounced climate change in that region than elsewhere. This calls for a better description of this region providing additional details with respect to the global reanalyses (ERA5). The additional information is provided by the higher horizontal resolution, more local observations (from the Nordic countries and Greenland), better description of surface characteristics (high resolution satellite and physiographic data), high resolution dynamics and improved physical parameterisation of clouds and precipitation in particular. These details add value to the regional reanalysis with respect to its global counterpart. \nThe inputs to CARRA2 reanalysis are the observations, the ERA5 global reanalysis as lateral boundary conditions and the physiographic datasets describing the surface characteristics of the model. The observation values and information about their quality are used together to constrain the reanalysis where observations are available and provide information for the data assimilation system in areas in where less (or even no) observations are available.\nMore details about the reanalysis dataset and the extensive input data are given in the Documentation section.",
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            "Temporal coverage: Present",
            "Variable domain: Land (cryosphere)",
            "Variable domain: Atmosphere (surface)",
            "Variable domain: Atmosphere (upper air)",
            "Spatial coverage: Arctic"
        ],
        "license": "CC-BY-4.0",
        "providers": [
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "insitu-comprehensive-upper-air-observation-network",
        "title": "Comprehensive Upper-air Observation Network from 1901 to present",
        "description": "The Comprehensive Upper-air Observation Network (CUON) dataset is designed \nto contain all available observations from balloons ascents through the atmosphere. \nThe main geophysical variables are temperature, humidity, and wind speed and wind direction,\nas functions of atmospheric pressure levels. The observation platforms include pilot balloons, radiosondes, and ozonesondes.\nThe main observation data sources are:\n- the NOAA Integrated Global Radiosonde Archive (IGRA version 2),\n- the NCAR Upper-Air Database (UADB), and\n- the observation feedback of the ERA5 global reanalysis, which assimilated observations acquired or rescued by the EU FP7 ERA-CLIM and ERA-CLIM2 projects.\nAdditional sources of observations collected by balloon-borne sensors are described in the Product User Guide (PUG, available from the documentation tab). They include, for example, the World Ozone and Ultraviolet Radiation Data Centre WOUDC and the NCEP/NCAR Arctic Marine Rawinsonde Archive.\nThe Algorithm Theoretical Basis Document (ATBD, available from the documentation tab) presents more details about this dataset, and how it is assembled.\nKey processing steps are as follows. \nThe data sources are first subject to quality controls. \nThis step helps to inform the choice to be made when duplicates are found. \nThe resulting merged dataset is homogenized in order to reduce systematic errors or biases. \nThe homogenization method is mainly based on analysing background departure time series from reanalyses, \nas documented in peer-reviewed literature (see ATBD).\nAccounting for these biases helps to reduce the overall observation uncertainty. \nEstimates of observation random uncertainty are produced when background and analysis departure information \nfrom a prior reanalysis are available.\nFor balloon ascents featuring wind observations, the balloon track is reconstructed, with\nobservation coordinates (latitude, longitude) that slowly vary as the balloon ascends and \ndrifts away from its launch position.\nThe times of launches are also refined as far as possible from the nominal times of reporting, \nusing available metadata.\nUsers can download the data, as well as the suggested homogenization bias adjustments \nand estimated uncertainties associated with the main geophysical variables. \nThe present dataset aims to support climate applications, including global and regional reanalyses. \nUsers should be aware that uncertainties are generally larger for data in the 20th century (especially first half) than in the 21st century. In addition, post-2010 balloon ascents can contain sometimes up to more than one thousand levels, when initial soundings may only feature only a few levels.\nThe dataset can be downloaded as NetCDF files (CDM-Obs-Core, see documentation tab) or as comma-separated values (CSV) files.",
        "links": [
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                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/insitu-comprehensive-upper-air-observation-network"
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            "Spatial coverage: Global",
            "Product type: In-situ observations",
            "Variable domain: Atmosphere (upper air)",
            "Provider: Copernicus C3S"
        ],
        "license": "CC-BY-4.0",
        "providers": [
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            "temporal": {
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        "stac_version": "1.1.0",
        "id": "satellite-ozone-v1",
        "title": "Ozone monthly gridded data from 1970 to present derived from satellite observations",
        "description": "This dataset provides monthly averaged ozone values derived from a large set of satellite sensors with global coverage.  Ozone is naturally formed in the atmosphere through the interaction between molecular oxygen and solar radiation. In the stratosphere, it acts as a shield protecting us from the UV radiation emitted by the Sun. At ground level however, it is a human health irritant and a component of smog. Ozone and climate change are strongly related. \nThe ozone abundance in the atmosphere can be measured from space using a number of different remote-sensing techniques relying on ozone absorption at UV, visible, infrared and millimeter wavelengths. Instruments use the solar light or the thermal radiation emitted by the Earth to derive the vertical distribution of ozone in nadir, limb and solar occultation observation geometries. In the measured signal, molecular absorption features characteristic of ozone are detected using appropriate retrieval algorithms and are used to quantify its abundance. \nData are available as Level-3 and/or Level-4 data products. Level-3 data correspond to measurements provided on a regular latitude/longitude after re-gridding of level-2 data (satellite orbit tracks) and averaging in time (over a month). Level-4 products are generated from Level-2 data after assimilation in a three-dimensional chemistry-transport model.\nWe distinguish between Climate Data Record (CDR) and intermediate-CDR (ICDR). For ozone datasets distributed here, both ICDR and CDR products are generated using common software and algorithms. The CDRs cover past time periods and include the sensors that are no longer in operation. They are meant to have sufficient length, consistency, and continuity to detect climate variability and change. The ICDRs are generated by satellite instruments still in operation, and are therefore regularly updated. Recent ICDR data are provided with a short-delay. They are expected to be consistent with the CDR baseline but have not undergone any extensive checking. Users are invited to read the documentation in order to determine the time coverage of each product.\nZonal averages and combined products obtained by merging data from different satellites are also provided. Nadir column data products from individual sensors are updated on a quarterly basis. All other products are updated once or twice a year.\nThis dataset is produced on behalf of C3S based on algorithm developments performed as part of the European Space Agency (ESA) Ozone Climate Change Initiative (CCI) project.",
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            "Variable domain: Atmosphere (composition)",
            "Spatial coverage: Global",
            "Product type: Satellite observations"
        ],
        "license": "CC-BY-4.0",
        "providers": [
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        "id": "satellite-methane",
        "title": "Methane data from 2002 to present derived from satellite observations",
        "description": "This dataset provides atmospheric CH\u2084 observations as derived from satellite radiances. It consists of two types of products each covering a different part of the atmosphere: (i) The average CH\u2084 mixing ratio of the entire atmosphere from the Earth\u2019s surface to the top of the atmosphere. These column-averaged mixing ratio products are referred to as \u201cXCH4\u201d products. (ii) Mid-tropospheric (MT) CH\u2084 mixing ratio products. These products are referred to as \"MTCH4\" products. These products can be used to monitor the spatial distribution and temporal evolution of CH\u2084. This is important as CH\u2084 is after CO\u2082 the most important anthropogenic so-called greenhouse gas enhancing global warming and associated climate change. Methane has many natural and anthropogenic sources. CH\u2084 time series show an annual cycle, e.g., due seasonal wetland emissions. Currently atmospheric methane also shows and increasing trend. The dataset can be used for several climate related applications: It can be used in combination with appropriate modelling (e.g., inverse modelling) to obtain information on CH\u2084 emission sources, to assess the quality of climate models, to generate (e.g., forcing) input data as used by climate models, to determine the atmospheric CH\u2084 growth rate, etc.\nThe XCH4 and MTCH4 satellite-derived data products complement networks of surface observations. Typically, surface observations are more accurate, but they are also sparse whereas the satellite observations cover large parts of the world not observed from the ground. The XCH4 and MTCH4 datasets are generated by applying so called retrieval algorithms to satellite radiance measurements. The XCH4 products are based on measurements of reflected solar radiation in the short-wave near-infrared (SWIR) spectral region. These measurements are sensitive to CH\u2084 changes in the entire atmospheric vertical column down to the Earth surface. This implies that the observations are limited to essentially cloud-free conditions. Strict quality filtering is applied to the data products to achieve highest possible data quality. Whereas generation of the XCH4 products requires reflected solar radiation, i.e., daytime observation conditions, the MT products have been generated from radiance measurements in the thermal infrared (TIR) spectral region. These measurements can be conducted day and night and therefore have a much better spatio-temporal coverage compared to the XCH4 data. These measurements have maximum sensitivity for CH\u2084 variations in the mid-troposphere and only limited sensitivity to CH\u2084 changes near the ground. The two types of datasets are therefore highly complementary as they contain different information on atmospheric CH\u2084.\nThe dataset consists of 2 types of products: \n\ncolumn-averaged mixing ratios of CH\u2084, denoted XCH4 \nmid-tropospheric CH\u2084 columns, denoted MTCH4.\n\nThe XCH4 products are available as Level 2 (L2) products (satellite orbit tracks) and as Level 3 (L3) product (gridded). The XCH4 L2 products are available as individual sensor products (SCIAMACHY: WFMD and IMAP algorithms; GOSAT: OCFP, OCPR, SRFP and SRPR algorithms; GOSAT-2: SRFP and SRPR algorithms) and as a multi-sensor merged product (EMMA algorithm). The L3 XCH4 product is generated by gridding the corresponding EMMA Level 2 product and is provided in OBS4MIPS format. The IASI Level 2 products are generated with the NLIS algorithm. The IASI Level 3 products are generated by merging and gridding the corresponding Level 2 products and are available in OBS4MIPS format. The XCH4 L3 dataset will be updated on a yearly basis, with each update cycle adding a new data version for the entire period, up to one year behind real time. The MTCH4 L3 dataset will be updated every 6 months. The L2 data end in 2022.\nThis dataset is produced on behalf of C3S with the exception of the SCIAMACHY L2 products that were generated in the framework of the GHG-CCI project of the European Space Agency (ESA) Climate Change Initiative (CCI).",
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            "Variable domain: Atmosphere (composition)",
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        "stac_version": "1.1.0",
        "id": "satellite-albedo",
        "title": "Surface albedo 10-daily gridded data from 1981 to present",
        "description": "This catalogue entry provides satellite-derived estimates of surface albedo, an Essential Climate Variable (ECV) as defined by the Global Climate Observing System (GCOS). Surface albedo is critical for understanding the Earth\u2019s climate, its variability, and trends, as it quantifies the fraction of solar irradiance reflected by the Earth\u2019s surface. This variable plays a vital role in the global radiation budget, influencing surface temperatures, water balance, and climate feedback mechanisms, such as the ice-albedo feedback in polar regions.\nSurface albedo, specifically the directional-hemispherical reflectance (also known as black-sky albedo), represents the proportion of incoming solar radiation reflected by the surface under direct illumination conditions, computed for local solar noon. It depends on surface properties such as land cover, snow and ice extent, soil moisture, and vegetation characteristics. Surface albedo is essential for climate and weather studies, as it governs the amount of solar energy absorbed by the Earth, impacting energy exchange between the surface and atmosphere.\nSince version 3, surface albedo datasets have been produced using data from sensors onboard the Sentinel-3 satellites, namely the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR), at a spatial resolution of 300m. These estimates are derived at a 10-day frequency using a 20-day moving temporal window of daily surface reflectance data. The processing algorithm normalises reflectances through inversion of a semi-empirical, kernel-driven reflectance model, prioritising recent observations. Directional albedo is computed per spectral band by integrating over the viewing hemisphere, with broadband albedo derived via a linear relationship from spectral albedo. Earlier versions of the dataset utilised data from NOAA/AVHRR (4km spatial resolution), SPOT-VGT, and PROBA-V (1km resolution), with products available from 1981 to the present. For detailed information on versioning, please refer to the Documentation.\nThese datasets have been produced on behalf of the Copernicus Climate Change Service (C3S), with some earlier versions brokered from the Copernicus Global Land Service (CGLS).",
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        "id": "satellite-lai-fapar",
        "title": "Leaf area index and fraction absorbed of photosynthetically active radiation 10-daily gridded data from 1981 to present",
        "description": "This catalogue entry provides satellite-derived estimates of two related variables: Leaf Area Index (LAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR). LAI and fAPAR are both Essential Climate Variables (ECVs) as defined by the Global Climate Observing System (GCOS), meaning they have been designated as essential for contributing to a comprehensive view of Earth\u2019s climate, its variability and trends. Combined, LAI and fAPAR are considered vital for monitoring vegetation dynamics, assessing vegetation growth and productivity, and understanding vegetation-atmosphere exchanges. \nLAI determines the size of the interface for the exchange of energy (including radiation) and mass between the canopy and the atmosphere. fAPAR is the fraction of photosynthetically active radiation absorbed by the canopy. It depends on canopy structure, vegetation element optical properties and illumination conditions. \nSince V4, these datasets have been produced using data obtained from sensors onboard the Sentinel-3 satellites, specifically the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR). V4 data are available at a spatial resolution of 300m. These estimates for LAI and fAPAR are derived from broadband white-sky albedo in the Visible and Near-Infrared regions using a 1D radiative transfer model - the Two-Stream Inversion Package (TIP). Previous versions of the datasets make use of NOAA (4km spatial resolution), PROBA and SPOT satellites (1km resolution). Please note that LAI version v0.0 and v1.0 represent the actual LAI. Starting from version v2.0, the LAI produced by the TIP model represents the \"effective\" LAI. For more information on versioning please refer to the Documentation.\nThese datasets have been produced on behalf of C3S, with the exception of V0.0 which has been brokered from the Copernicus Global Land Service (CGLS).",
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        "stac_version": "1.1.0",
        "id": "satellite-carbon-dioxide",
        "title": "Carbon dioxide data from 2002 to present derived from satellite observations",
        "description": "This dataset provides atmospheric CO\u2082 observations as derived from satellite radiances. It consists of two types of products each covering a different part of the atmosphere: (i) The average CO\u2082 mixing ratio of the entire atmosphere from the Earth\u2019s surface to the top of the atmosphere. These column-averaged mixing ratio products are referred to as \u201cXCO2\u201d products. (ii) Mid-tropospheric (MT) CO\u2082 mixing ratio products. These products are referred to as \"MTCO2\" products. These products can be used to monitor the spatial distribution and temporal evolution of CO\u2082. This is important as CO\u2082 is the most important anthropogenic so-called greenhouse gas driving global warming and associated climate change. CO\u2082 time series show an annual cycle primarily due to uptake and release of CO\u2082 by the terrestrial biosphere (but also due to ocean uptake and release) and a general increase due to burning of fossil fuels (oil, gas, coal). The annual increase of CO\u2082 shows significant inter-annual variations, due to variations of the (e.g., weather related) uptake and release of CO\u2082 by the natural CO\u2082 sources and sinks. The dataset can be used for a number of climate related applications: It can be used in combination with appropriate modelling (e.g., inverse modelling) to obtain information on CO\u2082 sources and sinks, to assess the quality of climate models, to generate (e.g., forcing) input data as used by climate models, to determine the atmospheric CO\u2082 growth rate, etc.\nThe XCO2 and MTCO2 satellite-derived data products complement networks of surface observations. Typically, surface observations are more accurate, but they are also sparse whereas the satellite observations cover large parts of the world not observed from the ground. The XCO2 and MTCO2 datasets are generated by applying so called retrieval algorithms to satellite radiance measurements. The XCO2 products are based on measurements of reflected solar radiation in the short-wave near-infrared (SWIR) spectral region. These measurements are sensitive to CO\u2082 changes in the entire atmospheric vertical column down to the Earth surface. This implies that the observations are limited to essentially cloud-free conditions. Strict quality filtering is applied to the data products to achieve highest possible data quality. Whereas generation of the XCO2 products requires reflected solar radiation, i.e., daytime observation conditions, the MT products have been generated from radiance measurements in the thermal infrared (TIR) spectral region. These measurements can be conducted day and night and therefore have a much better spatio-temporal coverage compared to the XCO2 data. These measurements have maximum sensitivity for CO\u2082 variations in the mid-troposphere and only limited sensitivity to CO\u2082 changes near the ground. The two types of datasets are therefore highly complementary as they contain different information on atmospheric CO\u2082.\nThe dataset consists of 2 types of products: \n\ncolumn-averaged mixing ratios of CO\u2082, denoted XCO2\nmid-tropospheric CO\u2082 columns, denoted MTCO2.  \n\nThe XCO2 products have been retrieved from SCIAMACHY/ENVISAT, TANSO-FTS/GOSAT, TANSO-FTS2/GOSAT2 and OCO-2. The mid-tropospheric CO\u2082 product has been retrieved from the IASI instruments on-board the Metop satellite series and from AIRS. The XCO2 products are available as Level 2 (L2) products (satellite orbit tracks) and as Level 3 (L3) product (gridded). The XCO2 L2 products are available as individual sensor products (SCIAMACHY: BESD and WFMD algorithms; GOSAT: OCFP and SRFP algorithms; GOSAT-2: SRFP algorithm) and as a multi-sensor merged product (EMMA algorithm). The L3 XCO2 product is generated by gridding the corresponding EMMA Level 2 product and is provided in OBS4MIPS format. The IASI and AIRS Level 2 products are generated with the NLIS algorithm. The IASI Level 3 products are generated by merging and gridding the corresponding Level 2 products and are available in OBS4MIPS format. The XCO2 L3 dataset will be updated on a yearly basis, with each update cycle adding a new data version for the entire period, up to one year behind real time. The MTCO2 L3 dataset will be updated every 6 months. The L2 data ends in 2022.\nThis dataset is produced on behalf of C3S with the exception of the SCIAMACHY and AIRS L2 products that were generated in the framework of the GHG-CCI project of the European Space Agency (ESA) Climate Change Initiative (CCI).",
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        "stac_version": "1.1.0",
        "id": "satellite-sea-surface-temperature",
        "title": "Global sea surface temperature derived from satellite observations",
        "description": "This dataset provides daily global estimates of sea surface temperature (SST) based on observations from multiple satellite sensors since July 1979. SST is a key driver of global weather and climate patterns and plays an important role in the exchanges of energy, momentum, moisture and gases between the ocean and atmosphere. Accurate SST data are therefore essential for understanding and assessing variability and long-term changes in the Earth\u2019s climate.\nThe SST data provided here are based on measurements made by three series of thermal infrared sensors flown onboard multiple polar-orbiting satellites: the Advanced Very High Resolution Radiometers (AVHRRs), the Along Track Scanning Radiometers (ATSRs), and the Sea and Land Surface Temperature Radiometers (SLSTRs); and two microwave sensors: the Advanced Microwave Scanning Radiometers (AMSR).\nThe dataset provides SST products of two different processing levels: Level-3 Collated (L3C) and Level-4 (L4). The L3C data product consists of SST observations from a single instrument, mapped onto a regular latitude-longitude grid. Each file typically includes all observations collected during a 24-hour period. Because no spatial interpolation is applied, the product may contain gaps in coverage, for example due to cloud cover. The L4 data product is a spatially complete, gridded product generated by combining SST observations from multiple instruments using an analysis method, such as optimal interpolation, which estimates values in areas without direct observations.\nUnlike near-real-time SST products (such as those distributed by the Copernicus Marine Service), this dataset is designed specifically for climate applications, providing the length, consistency, and continuity needed to assess long-term climate variability and trends. Its temporal consistency is ensured by using a subset of well-calibrated satellites, which maximises data stability, and by avoiding the use of in-situ measurements from ships and buoys (also important for independent validation of the satellite record). The dataset forms what is known as a Climate Data Record (CDR), a long, homogeneous, and quality-controlled time series suitable for studying climate change. Between major releases of the CDR, an Interim Climate Data Record (ICDR) provides regular updates to extend the record forward in time.\nThe SST algorithms were developed as part of the ESA SST Climate Change Initiative (CCI) project, which also funded the production of the CDR. The Copernicus Climate Change Service (C3S) funded the production of the ICDR for 2022. For 2023 and 2024, the production of the ICDR was funded by the UK Earth Observation Climate Information Service (EOCIS) and UK Marine and Climate Advisory Service (UKMCAS).",
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        "keywords": [
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            "Spatial coverage: Global",
            "Temporal coverage: Present",
            "Product type: Satellite observations",
            "Variable domain: Ocean (physics)"
        ],
        "license": "other",
        "providers": [
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        "stac_version": "1.1.0",
        "id": "reanalysis-carra-means",
        "title": "Arctic regional reanalysis daily and monthly data from 1991 to present",
        "description": "The daily and monthly data of the C3S Arctic Regional Reanalysis (CARRA) dataset contains daily and monthly meteorological variables at 2.5 km resolution. This includes fields at the single levels (including surface) and on pressure, height, soil and model levels.\nThese daily and monthly data are pre-calculated and have the following types depending on the variables: daily and monthly averages, extremes and totals. \nThe dataset includes two domains. The West domain covers Greenland, the Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, the Greenland Sea and Svalbard. The East domain covers Svalbard, Jan Mayen, Franz Josef Land, Novaya Zemlya, the Barents Sea, and the northern parts of the Norwegian Sea and Scandinavia.\nThe dataset has been produced with the use of the HARMONIE-AROME state-of-the-art non-hydrostatic regional numerical weather prediction model. High resolution reanalysis for the Arctic region is particularly important because the climate change is more pronounced in the Arctic region than elsewhere in the Earth. This fact calls for a better description of this region providing additional details with respect to the global reanalyses (ERA5 for instance). The additional information is provided by the higher horizontal resolution, more local observations (from the Nordic countries and Greenland), better description of surface characteristics (high resolution satellite and physiographic data), high resolution non-hydrostatic dynamics and improved physical parameterisation of clouds and precipitation in particular.\nThe inputs to CARRA reanalysis are the observations, the ERA5 global reanalysis as lateral boundary conditions and the physiographic datasets describing the surface characteristics of the model. The observation values and information about their quality are used together to constrain the reanalysis where observations are available and provide information for the data assimilation system in areas in where less observations are available.\nMore details about the reanalysis dataset and the extensive input data are given in the Documentation section.",
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            "Variable domain: Land (cryosphere)",
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            "Variable domain: Atmosphere (upper air)",
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        "id": "satellite-precipitation",
        "title": "Precipitation monthly and daily gridded data from 1979 to present derived from satellite measurements",
        "description": "Within the hydrological cycle, precipitation is the main component of water transport from the atmosphere to the Earth\u2019s surface. Precipitation varies strongly, depending on geographical location, season, synopsis, and other meteorological factors. The supply of freshwater through precipitation is vital for many subsystems of the climate and the environment, but there are also hazards related to extensive precipitation or the lack of precipitation.\nThe analysis of the Global Precipitation Climatology Project (GPCP) provides global estimates of precipitation as monthly means (GPCP monthly v2.3, now updated to GPCP monthly v3.2 and v3.3) and as daily means (GPCP daily v1.3, now updated to GPCP daily v3.2 and v3.3), based on estimates using microwave imagers on polar-orbiting satellites and infrared imagers on geostationary satellites. The updated monthly and daily products incorporate advances in data processing and calibration techniques, ensuring improved accuracy and consistency. The monthly product also includes information from rain-gauge observations analyzed by the Global Precipitation Climatology Centre (GPCC). The GPCP daily product is tied to GPCC indirectly via its calibration with the GPCP monthly product. Improvements in GPCP V3.3 over V3.2 include: upgraded input datasets, new adjustments to ensure a homogeneous record and the absolute bias error as a new output field.\nThe datasets v1.3 and v2.3 are provided on behalf of GPCP. The datasets v3.2 and v3.3 are provided on behalf of the Goddard Earth Sciences Data and Information Services Center (GES DISC): see licence texts on the right.",
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        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "insitu-observations-igra-baseline-network",
        "title": "In situ observations of meteorological variables from the Integrated Global Radiosounding Archive and the Radiosounding Harmonization dataset from 1978 onward",
        "description": "This catalogue entry provides access to vertical profiles of standard meteorological variables. It includes two archives. \nThe first is version 2 of the Integrated Global Radiosounding Archive (IGRA) from 1978 which incorporates global \nradiosounding profiles of temperature, humidity and wind from a large number of data sources,\nwhich is 30% larger than the previous version 1. IGRA v2 is the result of quality assurance procedures applied to the\nradiosoundings data which can be grouped into eight categories: fundamental \u201csanity\u201d checks, checks on the plausibility\nand temporal consistency of surface elevation, internal consistency checks, checks for the repetition of values, \nchecks for gross position errors in ship tracks, climatology-based checks, checks on the vertical and temporal \nconsistency of temperature, and data completeness checks. No uncertainty estimation for the IGRA data is available.\nThe second is the Radiosounding HARMonization (RHARM) dataset where values of temperature,\nrelative humidity and wind are homogenized to remove systematic effects (such as change in the measurement sensors,\nsolar radiation biases, sonde time-lag, calibration drifts, station relocation etc.) at 700 IGRA radiosounding stations\nand radiosoundings from ships.\nRHARM includes twice daily (0000 and 1200 UTC) radiosonde data at the mandatory levels (listed below).\nAt levels with pressure lower than 10 hPa, homogenized temperatures are not provided because of the paucity of available \nobservations and the issues affecting the measurements at those levels.\nRelative humidity homogenization and data provision are limited to 250 hPa owing to pervasive sensor \nperformance issues at greater altitudes. \nMoreover, an estimation of the measurement uncertainty is provided for each value of the time series. \nThe radiosounding significance levels are also homogenized per interpolation along with the corresponding uncertainties. \nThe RHARM dataset thus inherits the IGRA quality assurance procedures, and additional quality checks are then applied,\nperforming tests on physical plausibility, accuracy of the homogenization (bias adjustment), presence of outliers, and coherency check\nfor the homogenization applied at the significant levels. \nThe IGRA dataset is provided by NOAA's National Centers for Environmental Information (NCEI) also through the IGRA data portal.\nThe RHARM dataset has been specifically developed for the Copernicus Climate Change Service (C3S). \nThe dataset can be downloaded as NetCDF files (CDM-Obs-Core, see documentation) or as comma-separated values (CSV) files.",
        "links": [
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/insitu-observations-igra-baseline-network"
            },
            {
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            },
            {
                "rel": "root",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            }
        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Product type: In-situ observations",
            "Variable domain: Atmosphere (upper air)",
            "Provider: Copernicus C3S"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
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                        -90.0,
                        360.0,
                        90.0
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                ]
            },
            "temporal": {
                "interval": [
                    [
                        "1978-01-01T00:00:00Z",
                        null
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
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    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "insitu-observations-woudc-ozone-total-column-and-profiles",
        "title": "In situ total column ozone and ozone soundings from 1924 to present from the World Ozone and Ultraviolet Radiation Data Centre",
        "description": "This dataset provides access to two types of ozone observations: total column ozone estimates and vertical profiles of ozone concentration.\nThe total ozone estimates are based on solar UV radiation measurements made by ground-based spectrophotometers (Dobson or Brewer type spectrophotometers).\nThe vertical profiles of ozone concentration are estimated primarily using ozonesonde observations.\nData are available for 159 Dobson stations, 109 Brewer stations and 135 ozonesondes stations.\nThe ozone data are included in the quadrennial Scientific Assessments of Ozone Depletion as part of the \u201cVienna convention for the protection of the ozone layer\u201d and are used extensively for a range of studies and applications.",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/insitu-observations-woudc-ozone-total-column-and-profiles"
            },
            {
                "rel": "parent",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            },
            {
                "rel": "root",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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        ],
        "keywords": [
            "Temporal coverage: Past",
            "Variable domain: Atmosphere (composition)",
            "Spatial coverage: Global",
            "Product type: In-situ observations",
            "Variable domain: Atmosphere (surface)",
            "Variable domain: Atmosphere (upper air)",
            "Provider: Copernicus C3S"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
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                "bbox": [
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            "temporal": {
                "interval": [
                    [
                        "1924-01-01T00:00:00Z",
                        null
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
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                "roles": [
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        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "insitu-observations-gnss",
        "title": "In-situ observations of water vapour and atmospheric delay from the ground-based GNSS network from 1996 to present",
        "description": "This dataset provides estimates of water vapour derived from atmospheric delays in Global Navigation Satellite System\n(GNSS) radio signals.\nThe initial data is collected from two in situ ground-based network of GNSS receivers \u2013 the International GNSS Service\n(IGS) and EUREF Permanent Network (EPN). The IGS collects, archives, and freely distributes GNSS data from a \ncooperatively operated global network of more than 500 ground-based GNSS stations since 1994. The EPN is a European \nnetwork of more than 300 continuously operating GNSS reference stations with precisely known coordinates.\nThe fundamental observable of ground-based GNSS is the Zenith Total Delay (ZTD). This observable represents the \npropagation delay induced by the atmosphere on GNSS signals between the GNSS satellites and a GNSS receiver.\nUsing in-situ measurements of ZTD as well as ancillary meteorological data at the site, the vertically-integrated total \namount if water vapour in the air column can be retrieved. This integrated quantity is referred to as Total Column \nWater Vapour (TCWV), or Precipitable Water Column (PWC), Total Precipitable Water (TPW), \nor Integrated Precipitable Water (IPW), or also Integrated Water Vapour (IWV).\nThe TCWV values, with corresponding uncertainties, are available from the 2nd reprocessing campaign\nof the EPN (EPN-repro2), the 3rd reprocessing campaign of the IGS (IGS-repro3), and the daily product of IGS. \nAll networks provide hourly records of ZTD as part of the products derived from GNSS data processing.\nThe GNSS TCWV derived from these ZTD data is a new product, made available exclusively for the Climate Data Store (CDS).\nThe data user must consider that while the EPN-repro2 and IGS-repro3 consist of tropospheric parameters derived from consistently \nreprocessed data spanning multiple decades, the IGS daily product is a near real-time product which may contain jumps \nin the time-series when a new processing model is introduced. Therefore, IGS daily ZTD and TCWV should not be considered\nas reference products. \nThe method for retrieval of GNSS TCWV and details on the algorithms can be found in the documentation. For comparison, \nTCWV values from the ECMWF ERA5 reanalysis are also made available for the user.\nThe datasets can be downloaded as NetCDF files (CDM-Obs-Core, see documentation) or as comma-separated values (CSV) files.",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/insitu-observations-gnss"
            },
            {
                "rel": "parent",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            },
            {
                "rel": "root",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Temporal coverage: Present",
            "Product type: In-situ observations",
            "Variable domain: Atmosphere (upper air)",
            "Provider: Copernicus C3S"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
                    [
                        0.0,
                        -89.0,
                        360.0,
                        89.0
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                ]
            },
            "temporal": {
                "interval": [
                    [
                        "1996-01-01T00:00:00Z",
                        null
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
            "thumbnail": {
                "href": "https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-catalogue/resources/insitu-observations-gnss/overview_0ea25401fe1219b6bd5b5aa7768e5f32d01cab84d3351afc9509d6f4a85e242f.png",
                "type": "image/jpg",
                "roles": [
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        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "insitu-observations-gruan-reference-network",
        "title": "In situ temperature, relative humidity and wind profiles from 2006 to March 2020 from the GRUAN reference network",
        "description": "The Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) is an international reference observing network, established in 2006, of sites measuring essential climate variables above Earth's surface, designed to fill an important gap in the current global observing system. GRUAN measurements are providing high-quality climate data records from the surface, through the troposphere, and into the stratosphere. These are being used to determine trends, constrain and calibrate data from more spatially comprehensive observing systems (including satellites and current radiosonde networks), and provide appropriate data for studying atmospheric processes. GRUAN is envisaged as a global network of eventually 30-40 sites that, to the extent possible, builds on existing observational networks and capabilities.\nGRUAN observation processing is designed to adjust systematic errors in radiosonde measurements of pressure, temperature, humidity, and wind, and to quantify how the uncertainties related to these error sources are derived. \nThis characterization ensures the traceability to SI units or accepted standards, providing extensive metadata and full documentation of measurements and algorithms.\nThis allows the full reprocessing from the raw data.\nThe Climate Data Store provides access to the C3S GRUAN Data Product (GDP) version 2 (V2), for Vaisala RS92 and for Meisei RS-11G sondes. Data products for the new Vaisala RS41 sondes will be added at a later point. Specific GRUAN data products for other sonde types are also under development. \nThe current dataset provides radiosoundings from 17 stations.\nThe GRUAN dataset provides the vertical aggregation of quantities listed below in the main-variables table. \nAttributes are described in the related-variables tables.\nThe dataset can be downloaded as NetCDF files (CDM-Obs-Core, see documentation) or as comma-separated values (CSV) files.\nMore details about the the GRUAN dataset are given in the Documentation section, and\ngeneral information and background can be obtained from the GRUAN website (www.gruan.org).",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/insitu-observations-gruan-reference-network"
            },
            {
                "rel": "parent",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            },
            {
                "rel": "root",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Product type: In-situ observations",
            "Variable domain: Atmosphere (upper air)",
            "Provider: Copernicus C3S"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
                    [
                        0.0,
                        -89.0,
                        360.0,
                        89.0
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                ]
            },
            "temporal": {
                "interval": [
                    [
                        "1978-01-01T00:00:00Z",
                        "2018-10-17T00:00:00Z"
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
            "thumbnail": {
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                "type": "image/jpg",
                "roles": [
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                ]
            }
        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-sea-ice-thickness",
        "title": "Sea ice thickness gridded data for the Arctic derived from satellite observations",
        "description": "This dataset provides gridded sea ice thickness data for the Arctic, derived from satellite observations. Sea ice plays a vital role in the climate system, influencing air-sea interactions, the Earth\u2019s energy balance, and marine ecosystems. It is recognised by the Global Climate Observing System (GCOS) as an Essential Climate Variable (ECV). Sea ice thickness is a key characteristic used alongside other parameters such as concentration, edge, and type, all available in the Climate Data Store (CDS).\nSea ice thickness is estimated from satellite radar altimetry, which measures the sea ice freeboard\u2014the height difference between the ice surface and open water in leads (areas of open water within the sea ice). Since most sea ice floats below the surface, total thickness is roughly 10 times the freeboard. Snow cover, however, complicates this relationship, requiring additional data on snow depth and density for accurate estimates.\nThis dataset provides both a Level-3 (L3) product (with spatial gaps and from a single satellite) based on altimetry measurements from Envisat and CryoSat-2, and a Level-4 (L4) product (gap-filled) based on a combination of altimetry measurements from CryoSat-2 and L-band radiometry measurements from Soil Moisture and Ocean Salinity (SMOS) mission. \nThe L4 product offers improved accuracy for thin ice, higher temporal resolution, and complete spatial coverage. However, its shorter time span makes it less suitable for assessing long-term climate trends compared to L3. More details about each product are provided below.\nBoth products are limited to the Arctic and the winter months (October\u2013April), as melt conditions in other months introduce biases. There is currently no sea ice thickness data of sufficient quality available for the southern hemisphere. The Level-3 algorithm was developed through the European Space Agency (ESA) Climate Change Initiative (CCI) on sea ice.",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/satellite-sea-ice-thickness"
            },
            {
                "rel": "parent",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            },
            {
                "rel": "root",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            },
            {
                "rel": "qa",
                "href": "https://cds.climate.copernicus.eu/datasets/satellite-sea-ice-thickness?tab=quality_assurance_tab",
                "title": "Quality assessment of the dataset",
                "type": "text/html"
            }
        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Product type: Satellite observations",
            "Provider: Copernicus C3S",
            "Variable domain: Ocean (physics)"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
                    [
                        0.0,
                        -89.0,
                        360.0,
                        89.0
                    ]
                ]
            },
            "temporal": {
                "interval": [
                    [
                        "2002-10-01T00:00:00Z",
                        "2024-12-01T00:00:00Z"
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
            "thumbnail": {
                "href": "https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-catalogue/resources/satellite-sea-ice-thickness/overview_c3885a4bad303271233f2a62a1af915decc6f9d331d4137ab8a8800007a5ed72.png",
                "type": "image/jpg",
                "roles": [
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            }
        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-sea-ice-drift",
        "title": "Sea ice drift daily gridded data derived from satellite observations",
        "description": "This dataset provides daily gridded fields of sea ice drift vectors for both hemispheres derived from satellite passive microwave brightness temperatures and atmospheric reanalysis data. Sea ice is an important component of our climate system and a sensitive indicator of climate change. Its presence or retreat significantly affects ocean-atmosphere interactions, the Earth\u2019s energy budget as well as marine ecosystems. It is classified as an Essential Climate Variable by the Global Climate Observing System.\nSea ice drift refers to the movement of ice, represented by vectors showing how far and in which direction the ice travels over a set time period (here 24 hours). It is one of the parameters commonly used to characterise sea ice. This dataset is of relatively coarse resolution and based on data from several satellite sensors:\n\nthe Special Sensor Microwave/Imager (SSM/I; 1991\u20132006)\nthe Special Sensor Microwave Imager/Sounder (SSMIS; 2005 onward)\nthe Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sensor (2002\u20132011) and its successor, AMSR2 (2012\u20132017)\n\nWind data from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses (ERA5) is also used during the summer melt season. The underlying algorithm uses satellite data from the near-90GHz channel on SSM/I and SSMIS, and from the 37GHz channel on AMSR-E and AMSR2. The satellite images are processed to enhance key features, making it easier to track the movement of sea ice. Data from different satellites are combined to create a single product. During summer and when satellite data is unavailable, missing data is filled using a free-drift model based on ERA5 wind data and trained on AMSR-E and AMSR2 drift data. The product is gap-filled with spatial interpolation where needed.\nThis dataset is brokered on behalf of the Copernicus Climate Change Service by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF).",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/satellite-sea-ice-drift"
            },
            {
                "rel": "parent",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            },
            {
                "rel": "root",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Product type: Satellite observations",
            "Variable domain: Ocean (physics)"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
                    [
                        0.0,
                        -90.0,
                        360.0,
                        90.0
                    ]
                ]
            },
            "temporal": {
                "interval": [
                    [
                        "1991-01-01T00:00:00Z",
                        "2020-12-31T00:00:00Z"
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
            "thumbnail": {
                "href": "https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-catalogue/resources/satellite-sea-ice-drift/overview_184d387bf42f71294a08123be66f3360476c9dd11f835f398efcb79abbbe04ae.png",
                "type": "image/jpg",
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        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "derived-era5-land-daily-statistics",
        "title": "ERA5-Land post-processed daily statistics from 1950 to present",
        "description": "ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. \nERA5-Land uses ERA5 atmospheric variables, such as air temperature and air humidity, as input to control the simulated land fields. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'. \nThis catalogue entry provides post-processed ERA5-land hourly data aggregated to daily time steps. Note that the accumulated variables are omitted (e.g. total precipitation, runoff, etc - please refer to table 3 in the ERA5-Land online documentation for a full list of accumulated variables). In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:\n\nThe daily aggregation statistic (daily mean, daily max, daily min)\nThe sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours)\nThe option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)\n\nUsers should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code and advice on how to return daily statistics for the accumulated variables, please see the documentation.\nFor more details on the hourly data used to calculate the daily statistics, please refer to the ERA5-land hourly data catalogue entry and the documentation found therein.",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/derived-era5-land-daily-statistics"
            },
            {
                "rel": "parent",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            },
            {
                "rel": "root",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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        ],
        "keywords": [
            "Product type: Reanalysis",
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Variable domain: Land (biosphere)",
            "Variable domain: Land (hydrology)",
            "Provider: Copernicus C3S",
            "Variable domain: Land (physics)"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
                    [
                        0.0,
                        -89.0,
                        360.0,
                        89.0
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                ]
            },
            "temporal": {
                "interval": [
                    [
                        "1950-01-01T00:00:00Z",
                        "2025-10-22T00:00:00Z"
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
            "thumbnail": {
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                "type": "image/jpg",
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        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-ice-sheet-mass-balance",
        "title": "Gravimetric mass balance data for the Antarctic and Greenland ice sheets derived from satellite observations",
        "description": "This dataset provides a complete archive of monthly mass change timeseries for the drainage basins of the Antarctic and Greenland Ice Sheets from NASA's Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) mission, which have operated from 2002 to current data.\nIt is guided by the Global Climate Observing System targets for the Ice Sheets Mass Change Essential Climate Variable. The GRACE twin satellites measured changes in the Earth\u2019s gravitational field over time and inferred from them mass changes over the ice sheets, using the regional integration approach. A timeseries of mass change is provided for each ice sheet, and also for each drainage basin separately.\nThe dataset complements ice change measurements made by other methods, such as the surface elevation change rate detailed in the Climate Data Store (CDS) dataset: 'Ice sheet surface elevation change rate for Greenland and Antarctica from 1992 to present from derived satellite observations', which covers the same areas and includes the GRACE mission timespan.\nThe data was brokered from ESA\u2019s Greenland and ESA's Antarctic Ice Sheet Climate Change Initiative projects.",
        "links": [
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/satellite-ice-sheet-mass-balance"
            },
            {
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                "type": "application/json",
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            {
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                "title": "Quality assessment of the dataset",
                "type": "text/html"
            }
        ],
        "keywords": [
            "Temporal coverage: Past",
            "Temporal coverage: Present",
            "Variable domain: Land (cryosphere)",
            "Product type: Satellite observations",
            "Spatial coverage: Antarctic and Greenland"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
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                "bbox": [
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                        -90,
                        180,
                        90
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                ]
            },
            "temporal": {
                "interval": [
                    [
                        "2003-01-01T00:00:00Z",
                        "2022-12-31T00:00:00Z"
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-sea-ice-edge-type",
        "title": "Sea ice edge and type daily gridded data from 1978 to present derived from satellite observations",
        "description": "This dataset provides daily gridded data of sea ice edge and sea ice type derived from brightness temperatures measured by satellite passive microwave radiometers. Sea ice is an important component of our climate system and a sensitive indicator of climate change. Its presence or its retreat has a strong impact on air-sea interactions, the Earth\u2019s energy budget as well as marine ecosystems. It is recognized by the Global Climate Observing System as an Essential Climate Variable. Sea ice edge and type are some of the parameters used to characterise sea ice. Other parameters include sea ice concentration and sea ice thickness, also available in the Climate Data Store.\nSea ice edge and type are defined as follows:\n\nSea ice edge classifies the sea surface into open water, open ice, and closed ice depending on the amount of sea ice present in each grid cell. This variable is provided for both the Northern and Southern Hemispheres. Note that a sea ice concentration threshold of 30% is used to distinguish between open water and open ice, which differs from the 15% threshold commonly used for other sea ice products such as sea ice extent.\nSea ice type classifies ice-covered areas into two categories based on the age of the sea ice: multiyear ice versus seasonal first-year ice. This variable is currently only available for the Northern Hemisphere and limited to the extended boreal winter months (October through April). Sea ice type classification during summer is difficult due to the effect of melting at the ice surface which disturbs the passive microwave signature.\n\nBoth sea ice products are based on measurements from the series of Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSMIS) sensors and share the same algorithm baseline. However, sea ice edge makes use of two lower frequencies near 19 GHz and 37 GHz and a higher frequency near 90 GHz whereas sea ice type only uses the two lower frequencies. This dataset combines Climate Data Records (CDRs), which are intended to have sufficient length, consistency, and continuity to assess climate variability and change, and Interim Climate Data Records (ICDRs), which provide regular temporal extensions to the CDRs and where consistency with the CDRs is expected but not extensively checked. For this dataset, both the CDR and ICDR parts of each product were generated using the same software and algorithms.\nThe CDRs of sea ice edge and type currently extend from 25 October 1978 to 31 December 2020 whereas the corresponding ICDRs extend from January 2021 to present (with a 16-day latency behind real time). All data from the current release of the datasets (version 3.0) are Level-4 products, in which data gaps are filled by temporal and spatial interpolation. For product limitations and known issues, please consult the Product User Guide.\nThis dataset is produced on behalf of Copernicus Climate Change Service (C3S), with heritage from the operational products generated by EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF).",
        "links": [
            {
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/satellite-sea-ice-edge-type"
            },
            {
                "rel": "parent",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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            {
                "rel": "root",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            },
            {
                "rel": "qa",
                "href": "https://cds.climate.copernicus.eu/datasets/satellite-sea-ice-edge-type?tab=quality_assurance_tab",
                "title": "Quality assessment of the dataset",
                "type": "text/html"
            }
        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Product type: Satellite observations",
            "Variable domain: Ocean (physics)"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
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                        89.0
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            },
            "temporal": {
                "interval": [
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                        "1978-10-25T00:00:00Z",
                        "2025-09-24T00:00:00Z"
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                ]
            }
        },
        "summaries": "{}",
        "assets": {
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "sis-biodiversity-era5-regional",
        "title": "Downscaled bioclimatic indicators for selected regions from 1979 to 2018 derived from reanalysis",
        "description": "This dataset provides a historical reconstruction of bioclimatic indicators at 1  x 1 km resolution for Europe, Northern Brazil and Central Africa. These bioclimate indicators describe how the climate affects ecosystems, the services they deliver, and nature\u2019s biodiversity. They are specifically relevant for applications within the biodiversity and ecosystem services community.\nThe 76 indicators cover bioclimatic variables for the land environment, characterising surface energy, drought, soil moisture and the (near-) surface climate including wind as well as Essential Climate Variables (ECV) relevant to the biodiversity community and are based on hourly or monthly ERA5 (ERA5-Land) reanalysis data. The indicators have been downscaled to 1 x 1 km resolution using a statistical downscaling methodology that takes into account the relationship between orography and a climate state variable.\nThe bioclimatic indicators are widely used within the biodiversity community and have been chosen based on user requirements and consultation with stakeholders, in order to facilitate the direct use of climate information in screening analyses or in diverse downstream applications. The temporal resolution differs depending on the indicator, varying between; monthly, annual, and multi-annual averages.\nThis dataset was produced on behalf of the Copernicus Climate Change Service.",
        "links": [
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/sis-biodiversity-era5-regional"
            },
            {
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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            {
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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        ],
        "keywords": [
            "Product type: Reanalysis",
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Variable domain: Land (biosphere)",
            "Provider: Copernicus C3S",
            "Product type: Climate indices",
            "Sector: Biodiversity"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
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                        -90.0,
                        180.0,
                        90.0
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            "temporal": {
                "interval": [
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                        "1979-01-01T00:00:00Z",
                        "2018-12-31T00:00:00Z"
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            }
        },
        "summaries": "{}",
        "assets": {
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-fire-radiative-power",
        "title": "Fire radiative power and active fire pixels from 2020 to present derived from satellite observations",
        "description": "The Copernicus Climate Change Service AF & FRP dataset provides active fire (AF) and fire radiative power (FRP) information identified as essential climate variables for fire and derived from observations made by the Sea and Land Surface Temperature Radiometer (SLSTR) operating concurrently onboard the European Sentinel-3A and -3B satellites. The AF & FRP products are generated from the non-time critical Sentinel-3A and 3B SLSTR Level 2 AF detection and FRP product from European Space Agency (ESA). \nThe AF & FRP products encompass a Level 2 summary product which provides a text-based summary of the Level 2 AF detection and FRP data collected over the period of one month across the globe but separated for the different sentinel satellites Sentinel-3A and 3B and the observing time and type \u2013 nighttime or nighttime gas flare or daytime in v1.2. In addition, three Level 3 \u2018synthesis products\u2019 are also provided, each one grids the Level 2 AF and the FRP data at various spatial and temporal resolutions along with some adjustments for cloud cover that blocks the fires from the satellite sensors view. Following Level 3 \u2018synthesis products\u2019 are available:\n\n\nV1.0 (February 2020 to February 2021):\n\ndaily global Level 3 FRP product generated on a global 0.1 degree resolution grid. AF data coming from observations made by both Sentinel-3A (S3A) and -3B (S3B) have separate layers in this product. Only night-time land observations are included at this moment, the full daytime will be provided in the future once the corresponding NTC Level 2 FRP data are available.\n27 day global Level 3 FRP product also derived at 0.1 degree grid cell resolution but summarising the FRP data from both Sentinel-3A and -3B. This time period matches the Sentinel-3 satellites standard orbital repeat cycle.\nmonthly global Level 3 FRP product which provides global AF detection and FRP data at a grid cell size of 0.25 degrees. This resolution matches the MODIS Climate Model-ling Grid (CMG) active fire product.\n\n\n\nV1.2 (January 2021 \u2013 to present)\n\nthe daily global Level 3 AF & FRP products are generated on a global 0.1-degree reso-lution grid. Based on the Level 2 AF & FRP data, the daily global Level 3 AF & FRP products are separated by the observations made by Sentinel-3A or -3B and the night-time and daytime land observations.\nthe 27day global Level 3 AF & FRP products are also derived at 0.1-degree grid cell resolution but summarise the AF & FRP data over 27 days but also separated by the observations made by Sentinel-3A or -3B and the night-time and daytime land obser-vations. This time period matches the Sentinel-3 satellites standard orbital repeat cy-cle.\nthe monthly global Level 3 AF & FRP product provides global AF detection and FRP data at a grid cell size of 0.25 degrees but also separated by the observations made by Sentinel-3A or -3B and the night-time and daytime land observations. This resolu-tion matches the MODIS Climate Modelling Grid active fire product. \n\n\n\nThe purpose of these AF and FRP products is to provide a summarised and 'easy to use' version of the Level 2 AF and FRP products, as well as providing the AF and FRP information in a consistently gridded format. The latter in particular is designed to be suitable for use in global modelling, trend analysis and model evaluation.\nThe AF and  FRP were produced on behalf of the Copernicus Climate Change Service and have been designed to be broadly comparable with the equivalent Fire Burnt Area product, since they are likely to be used in combination with them to provide a long-term FRP time-series.",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/satellite-fire-radiative-power"
            },
            {
                "rel": "parent",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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            {
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                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Variable domain: Land (biosphere)",
            "Temporal coverage: Present",
            "Product type: Satellite observations"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
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        "extent": {
            "spatial": {
                "bbox": [
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                        90.0
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            "temporal": {
                "interval": [
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                        "2020-01-01T00:00:00Z",
                        "2025-02-28T00:00:00Z"
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        "summaries": "{}",
        "assets": {
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    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "insitu-gridded-observations-nordic",
        "title": "Nordic gridded temperature and precipitation data from 1961 to present derived from in-situ observations",
        "description": "The Nordic Gridded Climate Dataset (NGCD) is a high resolution, observational, gridded dataset of daily minimum, maximum and mean temperatures and daily precipitation totals, covering Finland, Sweden and Norway. The time period covered begins in January 1961 and continues to the present.\nSpatial interpolation methods are applied to observational datasets to create gridded datasets. \nIn general, there are three types of such methods: deterministic (type 1), stochastic (type 2) and pure mathematical (type 3).\nNGCD applies both a deterministic kriging (type 1) interpolation approach and a stochastic Bayesian (type 2) interpolation approach to the same in-situ observational dataset collected by weather stations. For more details on the algorithms, users are advised to read the product user guide.\nThe input data is provided by the National Meteorological and Hydrological Services of Finland, Norway and Sweden. The time-series used for Finland and Sweden are the non-blended time-series from the station network of the European Climate Assessment & Dataset (ECA&D) project. For Norway, time-series are extracted from the climate database of the Norwegian Meteorological Institute.",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/insitu-gridded-observations-nordic"
            },
            {
                "rel": "parent",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            },
            {
                "rel": "root",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            }
        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Europe",
            "Product type: In-situ observations",
            "Variable domain: Atmosphere (surface)",
            "Provider: Copernicus C3S"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
                    [
                        4.0,
                        43.0,
                        17.5,
                        49.0
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                ]
            },
            "temporal": {
                "interval": [
                    [
                        "1961-01-01T00:00:00Z",
                        "2025-09-18T00:00:00Z"
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
            "thumbnail": {
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            }
        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-humidity-profiles",
        "title": "Tropospheric humidity profiles averaged monthly and zonally from 2006 to present derived from satellite observations",
        "description": "This dataset provides monthly and zonally averaged tropospheric humidity profiles derived from globally distributed GPS radio occultation (RO) measurements from EUMETSAT's Metop polar-orbiting satellites. Humidity plays an important role in the Earth's climate system, due to the strong greenhouse effect of water vapour but also for its role in the global energy transport, vertically in the atmosphere and horizontally between different geographical regions. It is central to the formation of clouds and precipitation, and determines the fundamental conditions for the biosphere, including distribution of rainfall and droughts. \nThe dataset is produced by the EUMETSAT Radio Occultation Meteorology Satellite Application Facility (ROM SAF) and comprises the Climate Data Record (CDR, December 2006 to December 2016, ROM SAF product ID GRM-29-R1) and the Interim Climate Data Record (ICDR, January 2017 to present, ROM SAF product ID GRM-29-I1). Tropospheric humidity profiles are retrieved using 1D variational assimilation of observed atmospheric refractivity profiles and background model information from the ECMWF reanalysis. Compared to other satellite observation techniques, RO observations have high vertical resolution and are not affected by clouds or the type of underlying surface (such as land and sea).\nThe RO retrievals are averaged in monthly and 5-degree latitude bands for altitudes below 12km which roughly covers the troposphere. The variability (standard deviation) within such monthly bins are also provided, as well as associated quantities such as observation counts, sampling errors and an estimate of the fraction of model information in the humidity retrievals. In addition, mean and variability are provided for humidity for the a priori ECMWF reanalysis data, sampled (i.e. collocated) similarly at the satellite observation locations. \nPlease note that the terms humidity and water vapour are used interchangeably on this page and in the documentation.",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/satellite-humidity-profiles"
            },
            {
                "rel": "parent",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            }
        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Temporal coverage: Present",
            "Product type: Satellite observations",
            "Variable domain: Atmosphere (upper air)",
            "Provider: EUMETSAT SAF"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
                    [
                        0.0,
                        -90.0,
                        360.0,
                        90.0
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            "temporal": {
                "interval": [
                    [
                        "2006-12-01T00:00:00Z",
                        "2025-06-01T00:00:00Z"
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
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    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "sis-water-level-change-timeseries-cmip6",
        "title": "Global sea level change time series from 1950 to 2050 derived from reanalysis and high resolution CMIP6 climate projections",
        "description": "This dataset provides time series of global sea level related variables including tides, storm surges and sea level rise from 1950 to 2050 based on hydrodynamic modelling. The dataset provides a basis for studies, for instance, aiming to evaluate sea level variability, coastal flooding, coastal erosion, and accessibility of ports.\nThe time series are computed using the Deltares Global Tide and Surge Model (GTSM) version 3.0, a hydrodynamic model that dynamically simulates water levels at 10-minute intervals and capable of using input forcing from reanalysis and climate models. The dataset is based on climate forcing from ERA5 global reanalysis and 5 Global Climate Models (GCMs) of the high resolution Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate projection dataset from the High Resolution Model Intercomparison Project (HighResMIP) multi-model ensemble. By making use of the HighResMIP multi-model ensemble, it is possible to quantify the uncertainties associated with the input climate forcing in this dataset.\nThe simulations for the historical period may be selected from either ERA5 reanalysis or historical simulations from the GCMs. The future period simulations are from the GCMs and are based on global climate projections using the high-emissions scenario SSP5-8.5 (Shared Socioeconomic Pathways). An exception to this is the tidal elevation variable that is not based on any future climate scenarios, since the tide-generating forces considered in GTSM are not related to any climatological nor meteorological conditions.\nThis dataset was produced on behalf of the Copernicus Climate Change Service.",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/sis-water-level-change-timeseries-cmip6"
            },
            {
                "rel": "parent",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            },
            {
                "rel": "root",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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        ],
        "keywords": [
            "Product type: Reanalysis",
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Provider: Copernicus C3S",
            "Variable domain: Ocean (physics)",
            "Product type: Climate projections",
            "Temporal coverage: Future"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
                    [
                        -180.0,
                        -90.0,
                        180.0,
                        90.0
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                ]
            },
            "temporal": {
                "interval": [
                    [
                        "1950-01-01T00:00:00Z",
                        "2050-12-31T00:00:00Z"
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
            "thumbnail": {
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        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "insitu-observations-near-surface-temperature-us-climate-reference-network",
        "title": "In situ observations of meteorological and soil variables from the US Climate Reference Network near the surface from 2006 to present",
        "description": "This catalogue entry provides access to a continuous series of near-surface climate observations collected in-situ at \nUnited States Climate Reference Network (USCRN) stations. \nThere are over 130 USCRN stations over the conterminous United States (U.S.), Alaska, and Hawaii. \nThe USCRN stations are managed and maintained by the U.S. National Oceanic and Atmospheric Administration (NOAA).\nThe USCRN observations include air temperature, humidity, wind speed, precipitation, solar radiation, \nas well as\u00a0surface skin temperature, soil temperature, and soil moisture. \nThis dataset provides reference observations, with confidence, for \nthe monitoring of climate change and variability or the validation of satellite observations or models.\nThe confidence is provided in the form of detailed uncertainties, due to environmental variability and measurement limitations. \nThe Product User Guide, accessible in the documentation, explains the origins of these uncertainties and how \nthey were quantified. The documentation presents also the source data and metadata.\nThe USCRN stations collect data at a high temporal resolution, with some variables recorded as frequently as every two seconds and others on an hourly or daily basis. \nThis temporal resolution offers a granular insight into changing weather patterns and climate variability. \nData are accumulated to provide daily and monthly readings, making it easier to discern long-term trends and patterns.\nSeveral variables are also available at hourly and sub-hourly (5-minute) resolutions.\nThe USCRN dataset is also made available through the USCRN data portal, \nprovided by the NOAA National Centers for Environmental Information (NCEI).\nMore information about this dataset can be found in the Tables below and in the documentation provided under the Documentation tab.\nThe dataset can be downloaded as NetCDF files (CDM-Obs-Core, see documentation) or as comma-separated values (CSV) files.\n",
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        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Product type: In-situ observations",
            "Variable domain: Atmosphere (surface)"
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        "license": "other",
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    {
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        "stac_version": "1.1.0",
        "id": "satellite-lake-water-level",
        "title": "Lake water levels from 1992 to present derived from satellite observations",
        "description": "This dataset provides lake water levels for 311 selected lakes on four continents derived from satellite radar altimetry. Lake water level information is traditionally obtained via ground-based observation systems and networks that suffer from well-known inherent problems: high cost, sparse coverage (often limited to political local/national instead of geographical/hydrological boundaries), slow dissemination of data, heterogeneous temporal coverage, destruction of the stations during floods, absence of stations in remote areas, absence of management strategy, etc. \nAlthough originally conceived to study open ocean processes, the radar altimeter satellites have nevertheless acquired numerous useful measurements over lakes. With this technique, the Lake Water Level is defined as the height, in meters above the geoid (the shape that the surface would take under the influence of the gravity and rotation of Earth) of the reflecting surface. It is observed by space radar altimeters that measure the time it takes for radar pulses to reach the ground targets, directly below the spacecraft (nadir position), and return. Hence, only lakes located along the satellite's ground tracks can be monitored, with a quality of measurement that not only depends of the size of the lake, but also on the reflecting targets in its surroundings such as topography or vegetation. For each of the key lakes monitored in the C3S Lakes production system, the Level-3 Water Level is provided in separate file along with the corresponding uncertainty.\nThis altimetric technique, that can complement ground-based observations systems, potentially provides a major improvement in the field of continental hydrology, due to the global coverage (however limited to Earth\u2019s portion at nadir of the orbital ground tracks), regular temporal sampling and short delivery delays. \nThe Lake Water Level is a recognized Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). Lake Water Level products are generated by CLS on behalf of the Copernicus Climate Change Services, the Earth Observation Programme of the European Commission.",
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            "Spatial coverage: Global",
            "Variable domain: Land (hydrology)",
            "Temporal coverage: Present",
            "Product type: Satellite observations"
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        "summaries": "{}",
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    {
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        "stac_version": "1.1.0",
        "id": "satellite-fire-burned-area",
        "title": "Fire burned area from 2001 to present derived from satellite observations",
        "description": "The Burned Area products provide global information of total burned area (BA) at pixel and grid scale. The BA is identified with the date of first detection of the burned signal in the case of the pixel product, and with the total BA per grid cell in the case of the grid product. The products were obtained through the analysis of reflectance changes from medium resolution sensors (Terra MODIS, Sentinel-3 OLCI), supported by the use of MODIS thermal information. The burned area products also include information related to the land cover that has been burned, which has been extracted from the Copernicus Climate Change Service (C3S) land cover dataset, thus assuring consistency between the datasets.\nThe algorithms for BA retrieval were developed by the University of Alcala (Spain), and processed by Brockmann Consult GmbH (Germany).\nDifferent product versions are available. FireCCI v5.0cds and FireCCI v5.1cds were developed as part of the Fire ECV Climate Change Initiative Project (Fire CCI) and brokered to C3S, offering the first global burned area time series at 250m spatial resolution. FireCCI v5.1cds used a more mature algorithm than the previous version. This algorithm was adapted to Sentinel-3 OLCI data to create the C3S v1.0 burned area product, extending the BA database to the present.\nDuring July 2020, an error in some files in the version v5.1cds were identified, affecting the files of the grid product of January 2018, and the pixel and grid products of October, November and December 2019. These errors were fixed, and a new version, v5.1.1cds, was created for the whole time series, to replace version v5.1cds. The latter product has been deprecated, but it is temporally kept in the database for transparency and traceability reasons. Only version v5.1.1cds should be used.\nThe BA products are useful for researchers studying climate change, as they provide crucial information on burned biomass, which can be translated to greenhouse gases emissions amongst other contaminants. Burned area is also useful for land cover change studies, fire management and risk analysis.",
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        "keywords": [
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            "Spatial coverage: Global",
            "Variable domain: Land (biosphere)",
            "Product type: Satellite observations"
        ],
        "license": "other",
        "providers": [
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            }
        ],
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                        "2022-04-01T00:00:00Z"
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        "assets": {
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        "stac_version": "1.1.0",
        "id": "satellite-lake-water-temperature",
        "title": "Lake surface water temperature from 1995 to present derived from satellite observations",
        "description": "This dataset provides daily mid-morning measurements for Lake Surface Water Temperature (LSWT) derived from satellite observations. LSWT, together with five other variables (lake ice cover and thickness, water leaving reflectance, water level and extent) is recognised as the Essential Climate Variable (ECV) \"Lakes\" by the Global Climate Observing System (GCOS). LSWT is a key parameter in determining lake ecological conditions as it influences physical, chemical and biological processes. Lake water temperatures are also important for understanding the hydrological cycle as they determine air-water heat and moisture exchange. This dataset supports the investigation of large-scale lake-climate interaction on inter-annual time scales, and represents the current state-of-the-art for LSWT record production.\nThe satellite observations are from the Along Track Scanning Radiometer (ATSR), Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), and Sea and Land Surface Temperature Radiometer (SLSTR) sensors. To ensure consistency across sensors, the retrieved temperature data have been bias-adjusted using periods of sensor overlap. As the dataset contains only observations, gaps in time and space may be present if data could not be reliably retrieved. The data are provided on a regular spatial grid, and for each LSWT value there is an associated uncertainty estimate and a quality level based on statistical confidence.\nA companion dataset, also available through the Copernicus Climate Data Store (CDS), is the Lake Water Levels dataset. Used together they can provide a more complete picture of how lakes respond to and influence climate.\nMost of the LSWT data in this dataset have been developed within the United Kingdom (UK) Natural Environment Research Council (NERC) GloboLakes project and within the European Space Agency (ESA) Climate Change Initiative (CCI) for lakes. Future improved versions will use developments within the ESA CCI for lakes.",
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        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Variable domain: Land (hydrology)",
            "Product type: Satellite observations"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
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                "interval": [
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        },
        "summaries": "{}",
        "assets": {
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-total-column-water-vapour-land-ocean",
        "title": "Global monthly and daily high-spatial resolution of total column water vapour from 2002 to 2017 derived from satellite observations",
        "description": "Atmospheric water vapor, quantified here as Total Column Water Vapor (TCWV), holds paramount importance in monitoring and understanding Earth's climate dynamics. Its role as an Essential Climate Variable is pivotal, serving as the foundation for cloud formation, cloud physics, and significantly impacting the Earth's heat budget through its proficient absorption of both long and short-wave radiation.\nThis catalogue entry presents two extensive, high-quality observational datasets of TCWV, specifically tailored for climate analysis and applications.\nThe dataset combines Medium Resolution Imaging Spectrometer (MERIS) retrievals in the near-infrared (NIR) across terrestrial and coastal regions with Special Sensor Microwave Imager (SSM/I) TCWV retrievals in the microwave spectrum over ocean surfaces. Additionally, an associated dataset accessible in the Related Data section, offers TCWV specifics over oceanic areas, computed using distinct sensors and algorithms but with higher temporal resolution.\nThe SSM/I, a well-established instrument, adeptly retrieves TCWV over open ocean surfaces, and provides a long time series and global coverage at a reasonable resolution. The MERIS TCWV dataset provides a continuous data record spanning from May 2002 to March 2012, aligning with the TCWV requirements for the World Meteorological Organization's (WMO) Observing Systems Capability Analysis and Review Tool (OSCAR). \nUtilizing a differential absorption technique centered at 940 nm in the NIR spectrum, MERIS ensures a robust retrieval methodology proven reliable for sensors within the water vapor absorption band. Covering nearly a decade, this MERIS dataset delivers products of global coverage with a reasonable resolution and good uncertainty estimate. Although not necessarily needed as input for the retrieval, ERA-Interim reanalysis TCWV serves as a priori information. These parameters are not provided in the final dataset.\nSSM/I and MERIS retrievals were merged in order to have global coverage with each instrument used to its full potential.\nWithin the ESA Water Vapour Climate Change Initiative Project (WV_cci) the NIR based TCWV retrievals over land, coasts and sea-ice were improved and in addition to MERIS data from the Moderate-resolution Imaging Spectrometer (MODIS) and the Ocean and Land Colour Instrument (OLCI) were used as input data over land. \nThe NIR data were combined with TCWV data from EUMETSAT's Satellite Application Facility (CM SAF) Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) over open ocean. This is the total column water vapour COMBI product. The COMBI dataset is an evolution of the \u201cMonthly total column water vapour over land and ocean from 2002 to 2012 derived from satellite observations\u201d developed in the predecessor project in Phase I.\nTCWV from 2002 to 2012\nThe total column water vapour is based on a combination of MERIS (NIR) over land surfaces and coastal areas with SSM/I data over ocean surfaces available from 2002 to 2012 on a monthly basis.\nTCWV COMBI from 2002 to 2017\nFor the total column water vapour COMBI product, SSM/I observations were used to generate TCWV data over the global ice-free ocean (EUMETSAT CM SAF HOAPS) while MERIS, MODIS and OLCI observations were used over land. These NIR data were combined with TCWV data from EUMETSAT CM SAF HOAPS and is available from 2002\u20132017 on both daily and monthly basis.\nThis dataset is produced by EUMETSAT CM SAF on behalf of the Copernicus Climate Change Service (C3S).",
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        "keywords": [
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            "Spatial coverage: Global",
            "Temporal coverage: Present",
            "Product type: Satellite observations",
            "Variable domain: Atmosphere (upper air)",
            "Provider: Copernicus C3S"
        ],
        "license": "other",
        "providers": [
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-sea-ice-concentration",
        "title": "Sea ice concentration from 1978 to present derived from satellite observations",
        "description": "This dataset provides daily gridded data of sea ice concentration for both hemispheres derived from satellite passive microwave brightness temperatures. Sea ice is an important component of our climate system and a sensitive indicator of climate change. Its presence or its retreat has a strong impact on air-sea interactions, the Earth\u2019s energy budget as well as marine ecosystems. It is recognised by the Global Climate Observing System as an Essential Climate Variable. Sea ice concentration is defined as the fraction of a pixel or grid cell in a satellite image or other gridded product that is covered with sea ice. It is one of the parameters commonly used to characterise sea ice. Other sea ice parameters include sea ice thickness, sea ice edge, and sea ice type, also available in the Climate Data Store.\nThe dataset consists of two products:\n\nThe Global Sea Ice Concentration Climate Data Record produced by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF). This is a coarse-resolution product based on measurements from the following sensors: Scanning Multichannel Microwave Radiometer (SMMR; 1979\u20131987), Special Sensor Microwave/Imager (SSM/I; 1987\u20132006), and Special Sensor Microwave Imager/Sounder (SSMIS; 2005 onward). This product spans the period from 1979 to 2025. In the following, it is referred to as the SSMIS product.\nThe Global Sea Ice Concentration Climate Data Record produced by the European Space Agency Climate Change Initiative Phase 2 project (ESA CCI). This is a medium-resolution product based on measurements from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sensor (2002\u20132011) and its successor, AMSR2 (2012\u20132020). This product spans the 2002\u20132020 period and is not updated. In the following, it is referred to as the AMSR product.\n\nBoth products are provided on the same polar projection with a grid resolution of 25 km. However, the AMSR product has a true spatial resolution (as resolved by the sensor) of about 15\u201325 km versus 30\u201360 km for the SSMIS product. Therefore, the AMSR product provides a much more detailed view of the sea ice cover than the SSMIS product, especially along the marginal ice zone, the transitional zone between open water and the dense sea ice pack. On the other hand, the clear strength of the SSMIS product is its more than 40-year long and consistent record.\nAlthough originating from different projects, the two products share the same algorithm baseline, which is both a continuation of the EUMETSAT OSI SAF approach and a series of innovations contributed mostly by ESA CCI activities. For both products, the underlying algorithm makes use of a combination of the same three temperature channels near 19 GHz and 37 GHz. The data also share a common data format so that interested users can revert some of the filtering steps and access the raw output of the SIC algorithms. Both are level-4 products in the sense that gaps are filled by temporal and spatial interpolation. However, gap filling is not applied to fill in days when no input satellite data are available.\nFurther details about each product can be found below as well as in the Documentation section.",
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        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Product type: Satellite observations",
            "Variable domain: Ocean (physics)"
        ],
        "license": "other",
        "providers": [
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            "temporal": {
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                        "2025-09-29T00:00:00Z"
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        },
        "summaries": "{}",
        "assets": {
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "multi-origin-c3s-atlas",
        "title": "Gridded dataset underpinning the Copernicus Interactive Climate Atlas",
        "description": "This catalogue entry provides the gridded climate data (monthly/annual timeseries) used for the Copernicus Climate Change Service Atlas (C3S Atlas). The gridded datasets consist of in-situ and satellite observation-based datasets, reanalyses (CERRA, ERA5, ERA5-Land, and ORAS5) and global (CMIP5 and CMIP6) and regional (CORDEX) climate projections for the variables and indices included in the C3S Atlas. This dataset complements the Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas (IPCC Atlas dataset hereafter), including new datasets, variables and indices. The variables and indices describe various types of climatic impact characteristics: heat and cold, wet and dry, snow and ice, wind and radiation, ocean, circulation and drought characteristics of the climate system. All data sources included in this entry are available in the Climate Data Store (CDS, see \u201cRelated data\u201d in the sidebar). Contrary to the frozen IPCC Atlas dataset, this entry will update adding new data on a regular basis.\nThis dataset includes gridded information with monthly/annual temporal resolution for observations/reanalyses of the recent past and climate projections for the 35 variables and indices computed from daily/monthly data across the different datasets. The climate projections are based on Representative Concentration Pathways (RCP) / Shared Socioeconomic Pathways (SSP) scenarios. The datasets are harmonised using regular latitude-longitude grids. Bias correction is available for threshold-based indices. Two methods are available, depending on the variable; linear scaling and the ISIMIP method. \nThis dataset allows the reproduction, expansion and customisation of the climate change products provided interactively by the Copernicus Interactive Climate Atlas. This is an interactive web application displaying global/regional maps of observed trends and climate changes for future periods across scenarios or for global warming levels, and regionally aggregated time series, seasonal cycle plots and climate stripes.",
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            "Product type: Reanalysis",
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Temporal coverage: Present",
            "Product type: In-situ observations",
            "Variable domain: Atmosphere (surface)",
            "Variable domain: Atmosphere (upper air)",
            "Product type: Climate projections",
            "Temporal coverage: Future"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
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                        0.0,
                        -89.0,
                        360.0,
                        89.0
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                ]
            },
            "temporal": {
                "interval": [
                    [
                        "1860-01-01T00:00:00Z",
                        "2300-12-31T00:00:00Z"
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-ocean-colour",
        "title": "Ocean colour daily data from 1997 to present derived from satellite observations",
        "description": "This dataset provides global daily estimates of ocean surface chlorophyll-a concentration and remote sensing reflectance derived from multiple satellite sensors. These two products are part of the broader discipline of ocean colour remote sensing, which analyses ocean surface radiances measured from space to derive information about the optical properties and constituents of the upper ocean. This information plays an essential role in our ability to monitor the health and productivity of marine ecosystems, assess the role of the oceans in the global carbon cycle, and quantify the impacts of climate change. Satellite remote sensing is the only method for regular monitoring of ocean biology on a global scale.\nRemote-sensing reflectance (or Rrs) is defined as the ratio of water-leaving radiance to downwelling irradiance and serves as the main input to algorithms used to derive other ocean colour products. Chlorophyll-a (Chl-a) is the main photosynthetic pigment found in phytoplankton, which form the base of the marine food-web and are responsible for approximately half of global photosynthesis. Chl-a can be estimated from Rrs data using different algorithms (see details in the Documentation). Here, we provide a blended Chl-a estimate from multiple algorithms, where blending is based on the suitability of each candidate algorithm to the optical typology of a given pixel. This approach provides the best estimates of global Chl-a across a range of water types.\nThe files from this dataset contain global daily composites of merged sensor products: SeaWiFS, MERIS, MODIS Aqua, VIIRS, and (from version 5.0 onward) OLCI. Note that Rrs and Chl-a data are only available over cloud- and ice-free areas. As a result, more complete spatial coverage (as shown in the map in the upper-right corner) can be achieved by aggregating data over longer time periods.\nThis dataset is produced using the processing chain software developed by the Ocean Colour component of the European Space Agency Climate Change Initiative project (ESA OC-CCI). Version 5.0 of the dataset is produced by the C3S service whereas previous versions are brokered from ESA OC-CCI.",
        "links": [
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/satellite-ocean-colour"
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            {
                "rel": "qa",
                "href": "https://cds.climate.copernicus.eu/datasets/satellite-ocean-colour?tab=quality_assurance_tab",
                "title": "Quality assessment of the dataset",
                "type": "text/html"
            }
        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Product type: Satellite observations",
            "Variable domain: Ocean (biochemistry)"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
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                "bbox": [
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                        360.0,
                        90.0
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            },
            "temporal": {
                "interval": [
                    [
                        "1997-09-04T00:00:00Z",
                        "2024-12-31T00:00:00Z"
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                ]
            }
        },
        "summaries": "{}",
        "assets": {
            "thumbnail": {
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    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "sis-energy-derived-projections",
        "title": "Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections",
        "description": "This dataset provides climate and energy indicators for the Europe as part of the Copernicus climate change service (C3S) Energy operational service. The climate-relevant indicators for the energy sector considered are: air temperature, precipitation, incoming solar radiation, wind speed at 10 m and 100 m, and mean sea level air pressure. The energy indicators are electricity demand and power generation from various sources: wind (both onshore and offshore), solar and hydro (run-of-river and reservoir) power. Depending on the indicator, the data are available at the national, regional and grid (approximately 30x30 km) level for most European countries. The spatial aggregation of data over land uses the Eurostat NUTS0 & NUTS2 (Nomenclature des unit\u00e9s territoriales statistiques, 2016) regions. The offshore variables (e.g. offshore wind power) use the European maritime region definitions MAR0 and MAR1. Further information on the NUTS and MAR regions can be found in the documentation.\nData is provided for the European domain, in a multi-variable, multi-timescale view of the climate and energy systems. This is beneficial in anticipating important climate-driven changes in the energy sector, through either long-term planning or medium-term operational activities. This is also used to investigate the role of temperature on electricity demand across Europe, as well as its interaction with the variability of renewable energy generation.\nThe C3S Energy operational service is composed of three main streams: historical (1979-present), seasonal forecasts and projections (typically covering the period 2005-2100). This projections dataset (2005-2100) produces reference climate variables based on the European regional climate model experiment, CORDEX. Energy variables are generated by transforming the climate variables using a combination of statistical models and physically based data. A comprehensive set of measured energy supply and demand data has been collected from various sources such as the European Network of Transmission System Operators (ENTSO-E). These data provide a crucial reference to assess the robustness of the models used to convert climate into electric energy variables.",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/sis-energy-derived-projections"
            },
            {
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            {
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        ],
        "keywords": [
            "Spatial coverage: Europe",
            "Temporal coverage: Present",
            "Variable domain: Atmosphere (surface)",
            "Product type: Climate projections",
            "Temporal coverage: Future",
            "Sector: Energy"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
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                        27.0,
                        45.0,
                        72.0
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            },
            "temporal": {
                "interval": [
                    [
                        "1970-01-01T00:00:00Z",
                        "2100-01-01T00:00:00Z"
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                ]
            }
        },
        "summaries": "{}",
        "assets": {
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        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-land-cover",
        "title": "Land cover classification gridded maps from 1992 to present derived from satellite observations",
        "description": "This dataset provides global maps describing the land surface into 22 classes, which have been defined using the United Nations Food and Agriculture Organization\u2019s (UN FAO) Land Cover Classification System (LCCS). In addition to the land cover  (LC) maps, four quality flags are produced to document the reliability of the classification and change detection. \nIn order to ensure continuity, these land cover maps are consistent with the series of global annual LC maps from the 1990s to 2015 produced by the European Space Agency (ESA) Climate Change Initiative (CCI), which are also available on the ESA CCI LC viewer.\nTo produce this dataset, the entire  Medium Resolution Imaging Spectrometer (MERIS) Full and Reduced Resolution  archive from 2003 to 2012 was first classified into a unique 10-year baseline LC map. This is then back- and up-dated using change detected from (i) Advanced Very-High-Resolution Radiometer (AVHRR) time series from 1992 to 1999, (ii) SPOT-Vegetation (SPOT-VGT) time series from 1998 to 2012 and (iii) PROBA-Vegetation (PROBA-V), Sentinel-3 OLCI (S3 OLCI) and Sentinel-3 SLSTR (S3 SLSTR) time series from 2013.\nBeyond the climate-modelling communities, this dataset\u2019s long-term consistency, yearly updates, and high thematic detail on a global scale have made it attractive for a multitude of applications such as land accounting, forest monitoring and desertification, in addition to scientific research.",
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                "title": "Quality assessment of the dataset",
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        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Variable domain: Land (biosphere)",
            "Product type: Satellite observations"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
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                        360.0,
                        90.0
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                        "2022-01-01T00:00:00Z"
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            }
        },
        "summaries": "{}",
        "assets": {
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        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "insitu-gridded-observations-global-and-regional",
        "title": "Temperature and precipitation gridded data for global and regional domains derived from in-situ and satellite observations",
        "description": "This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as  GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below.\nThese data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events.\nThe NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate.\nThe Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset.\nThe NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique.\nThe Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05\u00b0 resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring.\nThe Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate \u201call'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. \nThe Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location.\nThe Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations.\nThe Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation.\nThis catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. \nThese data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.",
        "links": [
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            },
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        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Temporal coverage: Present",
            "Product type: Satellite observations",
            "Product type: In-situ observations",
            "Variable domain: Atmosphere (surface)"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
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        "extent": {
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                        90.0
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            "temporal": {
                "interval": [
                    [
                        "1750-01-01T00:00:00Z",
                        "2021-01-01T00:00:00Z"
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                ]
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        },
        "summaries": "{}",
        "assets": {
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-ice-sheet-elevation-change",
        "title": "Ice sheet surface elevation change rate for Greenland and Antarctica from 1992 to present derived from satellite observations",
        "description": "This dataset provides estimates of surface elevation change over the Greenland and Antarctic ice sheets since 1992, utilizing satellite radar altimetry from five missions: ERS-1, ERS-2, ENVISAT, CryoSat-2, and Sentinel-3A. The surface elevation change is modelled over successive, overlapping periods and reported monthly. The dataset production method is an evolution of those employed by the European Space Agency (ESA)'s Greenland and Antarctic Ice Sheet Climate Change Initiatives and is guided by the Global Climate Observing System targets for the Ice Sheets Essential Climate Variable.\nAn annual Climate Data Record (CDR), and monthly intermediate CDRs (ICDRs) are issued. Each monthly record includes all previous data, from 1992 onwards, as well as that month's update.\nThis product is designed to provide data stability, so changes in the historic data, eg. if a satellite's elevation data is reprocessed or if inter-satellite cross-calibration is revised, are only introduced in the annual CDR. Each annual CDR is given a version number.\nThe differences in the geographical location of the two sheets result in site-specific processing:\nGreenland: Data consists of surface elevation change rate and its uncertainty in a five-year (for the early satellites: ERS-1, ERS-2, and ENVISat) or three-year (for CryoSat-2 and Sentinel-3) moving window. The moving window is advanced at one-month steps. Elevation measurements from satellite radar altimetry are used to build time-series of elevation change by the most optimal combination of the crossover-, repeat-track- and plane-fitting methods. The timeseries is derived for each cell on a 25 km by 25 km polar stereographic grid, covering the main Greenland ice sheet, and not including peripheral glaciers and ice caps. Data gaps have been filled using a gaussian process interpolation method.\nAntarctica: Data consists of surface elevation change rate over a five-year moving window that advances in one-month steps. It covers the Antarctic ice sheet, ice shelves and associated ice rises and islands on a 25 km by 25 km polar stereographic grid. Elevation measurements from five satellite radar altimetry missions, ERS1, ERS2, EnviSat, CryoSat-2 and Sentinel-3, are used to produce timeseries of surface elevation change by the crossover method for each grid cell. The mission timeseries are cross-calibrated into a consistent record, which is used to derive surface elevation change rates and their uncertainty estimates in each cell and time-window. Data gaps are flagged but not filled.",
        "links": [
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/satellite-ice-sheet-elevation-change"
            },
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                "type": "application/json",
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                "href": "https://cds.climate.copernicus.eu/datasets/satellite-ice-sheet-elevation-change?tab=quality_assurance_tab",
                "title": "Quality assessment of the dataset",
                "type": "text/html"
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        "keywords": [
            "Temporal coverage: Past",
            "Temporal coverage: Present",
            "Variable domain: Land (cryosphere)",
            "Product type: Satellite observations",
            "Spatial coverage: Antarctic and Greenland"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
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                        90.0
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            "temporal": {
                "interval": [
                    [
                        "1992-01-01T00:00:00Z",
                        null
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        "summaries": "{}",
        "assets": {
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    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "insitu-gridded-observations-europe",
        "title": "E-OBS daily gridded meteorological data for Europe from 1950 to present derived from in-situ observations",
        "description": "E-OBS is a daily gridded land-only observational dataset over Europe. The blended time series from the station network of the European Climate Assessment & Dataset (ECA&D) project form the basis for the E-OBS gridded dataset. All station data are sourced directly from the European National Meteorological and Hydrological Services (NMHSs) or other data holding institutions. For a considerable number of countries the number of stations used is the complete national network and therefore much more dense than the station network that is routinely shared among NMHSs (which is the basis of other gridded datasets). The density of stations gradually increases through collaborations with NMHSs within European research contracts. \nInitially, in 2008, this gridded dataset was developed to provide validation for the suite of Europe-wide climate model simulations produced as part of the European Union ENSEMBLES project. While E-OBS remains an important dataset for model validation, it is also used more generally for monitoring the climate across Europe, particularly with regard to the assessment of the magnitude and frequency of daily extremes.\nThe position of E-OBS is unique in Europe because of the relatively high spatial horizontal grid spacing, the daily resolution of the dataset, the provision of multiple variables and the length of the dataset. Finally, the station data on which E-OBS is based are available through the ECA&D webpages (where the owner of the data has given permission to do so). In these respects it contrasts with other datasets.\nThe dataset is daily, meaning the observations cover 24 hours per time step. The exact 24-hour period can be different per region. The reason for this is that some data providers measure between midnight to midnight while others might measure from morning to morning. Since E-OBS is an observational dataset, no attempts have been made to adjust time series for this 24-hour offset. It is made sure, where known, that the largest part of the measured 24-hour period corresponds to the day attached to the time step in E-OBS (and ECA&D).",
        "links": [
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/insitu-gridded-observations-europe"
            },
            {
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                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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                "title": "Quality assessment of the dataset",
                "type": "text/html"
            }
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        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Europe",
            "Product type: In-situ observations",
            "Variable domain: Atmosphere (surface)"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
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            "spatial": {
                "bbox": [
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                        -40.0,
                        25.0,
                        75.0,
                        75.0
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            },
            "temporal": {
                "interval": [
                    [
                        "1950-01-01T00:00:00Z",
                        null
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        "summaries": "{}",
        "assets": {
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    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "sis-hydrology-meteorology-derived-projections",
        "title": "Temperature and precipitation climate impact indicators from 1970 to 2100 derived from European climate projections",
        "description": "This dataset provides precipitation and near surface air temperature for Europe as Essential Climate Variables (ECVs) and as a set of Climate Impact Indicators (CIIs) based on the ECVs. \nECV datasets provide the empirical evidence needed to understand the current climate and predict future changes. \nCIIs contain condensed climate information which facilitate relatively quick and efficient subsequent analysis. Therefore, CIIs make climate information accessible to application focussed users within a sector.\nThe ECVs and CIIs provided here were derived within the water management sectoral information service to address questions specific to the water sector. However, the products are provided in a generic form and are relevant for a range of sectors, for example agriculture and energy.\nThe data represent the current state-of-the-art in Europe for regional climate modelling and indicator production. Data from eight model simulations included in the Coordinated Regional Climate Downscaling Experiment (CORDEX) were used to calculate a total of two ECVs and five CIIs at a spatial resolution of 0.11\u00b0 x 0.11\u00b0 and 5km x 5km.\nThe ECV data meet the technical specification set by the Global Climate Observing System (GCOS), as such they are provided on a daily time step. They are bias adjusted using the EFAS gridded observations as a reference dataset. Note these are model output data, not observation data as is the general case for ECVs.\nThe CIIs are provided as mean values over a 30-year time period. For the reference period (1971-2000) data is provided as absolute values, for the future periods the data is provided as absolute values and as the relative or absolute change from the reference period. The future periods cover 3 fixed time periods (2011-2040, 2041-2070 and 2071-2100) and 3 \"degree scenario\" periods defined by when global warming exceeds a given threshold (1.5 \u00b0C, 2.0 \u00b0C or 3.0 \u00b0C). The global warming is calculated from the global climate model (GCM) used, therefore the actual time period of the degree scenarios will be different for each GCM.\nThis dataset is produced and quality assured by the Swedish Meteorological and Hydrological Institute on behalf of the Copernicus Climate Change Service.",
        "links": [
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        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Europe",
            "Temporal coverage: Present",
            "Variable domain: Atmosphere (surface)",
            "Provider: Copernicus C3S",
            "Product type: Climate projections",
            "Temporal coverage: Future",
            "Sector: Water management"
        ],
        "license": "CC-BY-4.0",
        "providers": [
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            }
        ],
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        "id": "sis-agroclimatic-indicators",
        "title": "Agroclimatic indicators from 1951 to 2099 derived from climate projections",
        "description": "This dataset provides agroclimatic indicators used to characterise plant-climate interactions for global agriculture. Agroclimatic indicators are useful in conveying climate variability and change in the terms that are meaningful to the agricultural sector. The objective of this dataset is to provide these indicators at a global scale in an easily accessible and usable format for further downstream analysis and the forcing of agricultural impact models.\nERA-interim reanalysis and bias-corrected climate datasets have been used to generate the agroclimatic indicators for historical and future time periods. The input data was provided through the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), of which the ISIMIP Fast Track product was used. This product contains daily, biased-corrected, climate data from 5 CMIP5 General Circulation Models covering the period 1951-2099 (historical run up to 2005). The agroclimatic indicators were also generated using the WFDEI (Watch Forcing Data methodology applied to ERA-Interim) for the 1981-2010 climatological period. A total of 26 indicators are provided in this dataset at a spatial resolution of 0.5\u00b0x0.5\u00b0 on a lat-lon grid. The temporal resolution of the variables differs depending on the indicator - they are available at 10 consecutive days (10-day), seasonal or annual resolution.\nAgroclimatic indicators are often used in species distribution modelling to study phenological developments of plants under varying climate conditions. For many users in the agricultural community, assessments of crop development for the current or future cropping seasons are particularly important. This is especially true for the agro-policy and the agro-business communities, as early indications of production anomalies are of paramount importance for tax/subsidies and price volatility. The provision of pre-computed agroclimatic indicators make them readily available to the user and will facilitate the use of climate data by the agricultural community.\nThe data was produced on behalf of the Copernicus Climate Change Service.",
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        "keywords": [
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            "Spatial coverage: Global",
            "Variable domain: Land (biosphere)",
            "Temporal coverage: Present",
            "Product type: Climate projections",
            "Temporal coverage: Future",
            "Sector: Agriculture"
        ],
        "license": "CC-BY-4.0",
        "providers": [
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                        null
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        "stac_version": "1.1.0",
        "id": "sis-biodiversity-cmip5-global",
        "title": "Global bioclimatic indicators from 1950 to 2100 derived from climate projections",
        "description": "This dataset provides global bioclimatic indicators derived from CMIP5 climate projections. These bioclimatic indicators describe how the climate affects ecosystems, the services ecosystems deliver and nature\u2019s biodiversity. They are specifically relevant for applications within the biodiversity and ecosystem community. \nThe 78 indicators cover bioclimatic variables for both land and marine environments characterising surface energy, drought, soil moisture and the (near-)surface climate including wind as well as Essential Climate Variables (ECV). The selection of indicators is based on user requirements and consultation with stakeholders, in order to facilitate the direct use of climate information in screening analyses or in diverse downstream applications.\nThe indicators calculated based on daily CMIP5 climate projections from 10 Global Circulation Models for two future climate scenarios, Representative Concentration Pathway (RCP) 4.5 & RCP 8.5. The data have been additionally bias-adjusted against ERA5 reanalysis data. The temporal resolution differs depending on the indicator, varying between monthly, annual and multi-annual averages.\nThis dataset was produced on behalf of the Copernicus Climate Change Service.",
        "links": [
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/sis-biodiversity-cmip5-global"
            },
            {
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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        "keywords": [
            "Spatial coverage: Global",
            "Variable domain: Land (biosphere)",
            "Provider: Copernicus C3S",
            "Product type: Climate projections",
            "Temporal coverage: Future",
            "Product type: Climate indices",
            "Sector: Biodiversity"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
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                        180.0,
                        90.0
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            },
            "temporal": {
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                    [
                        "1950-01-01T00:00:00Z",
                        "2099-12-31T00:00:00Z"
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            }
        },
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        "assets": {
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    {
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        "stac_version": "1.1.0",
        "id": "sis-biodiversity-cmip5-regional",
        "title": "Downscaled bioclimatic indicators for selected regions from 1950 to 2100 derived from climate projections",
        "description": "The dataset provides bioclimatic indicators derived from CMIP5 climate projections at 1 km x 1 km resolution for selected regions; Europe, Northern Brazil and Central Africa. This comprehensive set of bioclimatic indicators is specifically relevant for applications within the biodiversity and ecosystem services community.\nThe 76 indicators cover bioclimatic variables for the land environment, characterising surface energy, drought, soil moisture and the (near-) surface climate including wind as well as Essential Climate Variables (ECV). The selection of indicators is based upon user requirements and consultation with stakeholders in order to facilitate the direct use of climate information in screening analyses or in diverse downstream applications.\nThe indicators are calculated based on daily CMIP5 climate projections from 10 Global Circulation Models for two future climate scenarios, Representative Concentration Pathway (RCP) 4.5 & RCP 8.5. The indicators have been downscaled to 1 x 1 km resolution using a statistical downscaling methodology that takes into account the relationship between orography and a climate state variable. The data have been additionally bias-adjusted against ERA5 reanalysis data.\nThis dataset was produced on behalf of the Copernicus Climate Change Service.",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/sis-biodiversity-cmip5-regional"
            },
            {
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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            {
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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        "keywords": [
            "Spatial coverage: Global",
            "Variable domain: Land (biosphere)",
            "Provider: Copernicus C3S",
            "Product type: Climate projections",
            "Temporal coverage: Future",
            "Product type: Climate indices",
            "Sector: Biodiversity"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
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                        180.0,
                        90.0
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                "interval": [
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                        "2099-12-31T00:00:00Z"
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        },
        "summaries": "{}",
        "assets": {
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "sis-biodiversity-era5-global",
        "title": "Global bioclimatic indicators from 1979 to 2018 derived from reanalysis",
        "description": "This dataset provides a historical global reconstruction of bioclimatic indicators derived from ERA5 reanalysis on a latitude-longitude grid. These bioclimate indicators describe how the climate affects ecosystems, the services they deliver, and nature\u2019s biodiversity. They are specifically relevant for applications within the biodiversity and ecosystem services community.\nThe 78 indicators cover bioclimatic variables from both land and marine environments characterising surface energy, drought, soil moisture and the (near-)surface climate including wind as well as Essential Climate Variables (ECV) relevant to the biodiversity community and are based on hourly or monthly ERA5 reanalysis data.\nThe bioclimatic indicators are widely used within the biodiversity community and have been chosen based on user requirements and consultation with stakeholders, in order to facilitate the direct use of climate information in screening analyses or in diverse downstream applications. The temporal resolution differs depending on the indicator varying between monthly, annual, and multi-annual averages.\nThis dataset was produced on behalf of the Copernicus Climate Change Service.",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/sis-biodiversity-era5-global"
            },
            {
                "rel": "parent",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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            {
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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        "keywords": [
            "Product type: Reanalysis",
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Variable domain: Land (biosphere)",
            "Provider: Copernicus C3S",
            "Product type: Climate indices",
            "Sector: Biodiversity"
        ],
        "license": "CC-BY-4.0",
        "providers": [
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            }
        ],
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                        83.8
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                        "1979-01-01T00:00:00Z",
                        "2018-12-31T00:00:00Z"
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        "assets": {
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "sis-ecde-climate-indicators",
        "title": "Climate indicators for Europe from 1940 to 2100 derived from reanalysis and climate projections",
        "description": "This dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the Copernicus Climate Data Store (CDS). These indicators describe how climate variability and change of essential climate variables can impact sectors such as health, agriculture, forestry, energy, tourism, or water and coastal management.\nThose indices are relevant for adaptation planning at the European and national level and their development was driven by the European Environment Agency (EEA) to address informational needs of climate change adaptation national initiatives across the EU and partner countries as expressed by user requirements and stakeholder consultation. The indices cover the hazard categories introduced by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They are also made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA\u2019s Climate-adapt site.\nThe indices are either downloaded from the CDS where available, or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specification (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as ideally the indices should come from the same dataset with identical specifications.\nThe indices related to temperature, precipitation and wind (20 out of 30) were calculated from atmospheric variables in the same datasets: 'Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections', and 'ERA5 hourly data on single levels from 1940 to present'. The other indices are directly available from CDS datasets generated by specific theme projects. More information about this dataset can be found in the documentation.\nThe underlying datasets hosted on the CDS are:\n\nERA5 hourly data on single levels from 1940 to present - used to calculate most of the temperature, precipitation and wind speed indicators as it provides the historical and observation based baseline used to monitor the indicators.\nClimate and energy indicators for Europe from 2005 to 2100 derived from climate projections - used to calculate most of the temperature, precipitation and wind speed indicators as it provides bias-corrected sub-daily data. It is used for all the indicators except those specified in the following datasets below.\nFire danger indicators for Europe from 1970 to 2098 derived from climate projections - provides the high fire danger days and fire weather indicators.\nHydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections - provides the river flood, river discharge, aridity actual, and mean soil moisture indicators.\nMountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections - provides the snowfall amount index.\nWater level change indicators for the European coast from 1977 to 2100 derived from climate projections - provides the relative sea level rise and extreme sea level indicators.\n\nThis dataset was produced on behalf of the Copernicus Climate Change Service.",
        "links": [
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                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/sis-ecde-climate-indicators"
            },
            {
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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        ],
        "keywords": [
            "Product type: Reanalysis",
            "Temporal coverage: Past",
            "Spatial coverage: Europe",
            "Variable domain: Land (biosphere)",
            "Variable domain: Atmosphere (surface)",
            "Provider: Copernicus C3S",
            "Product type: Climate projections",
            "Temporal coverage: Future",
            "Product type: Climate indices"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
                    [
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                        35.0,
                        45.0,
                        72.0
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            "temporal": {
                "interval": [
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                        "1940-01-01T00:00:00Z",
                        "2100-12-31T00:00:00Z"
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        "assets": {
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        "stac_version": "1.1.0",
        "id": "sis-ecv-cmip5-bias-corrected",
        "title": "Essential climate variables for water sector applications derived from climate projections",
        "description": "This dataset contains 4 Essential Climate Variables (ECV) for the 18 bias adjusted Global Climate Models (GCM) from CMIP5: daily precipitation rate, and daily mean, maximum and minimum temperatures. \nThe data are bias adjusted using the Distribution Based Scaling (DBS) method  versus the global reference dataset HydroGFD2.0, both bias adjustment method and global reference dataset developed by the Swedish Meteorological and Hydrological Institute (SMHI).\nThe DBS method is a parametric quantile-mapping variant. \nThis type of methods fit a statistical distribution to the cumulative distribution function and use those fitted distributions to conduct the quantile-mapping. \nHere, we used a double-gamma distribution (i.e. separate gamma distributions for the bulk and the high tail) for precipitation and the normal distribution for all temperature variables. \nTemperature corrections were done conditional on the wet/dry state of the corresponding precipitation time series.\nThe seasonal variations in the biases were represented by monthly parameter windows for precipitation and a smoothed seasonal cycle for the temperature distribution parameters. \nThe smoothing was done using twelve harmonic components.\nThere is some post-processing in place for the data set to be suitable for hydrological impact modeling. \nBias-adjustment of daily mean, maximum and minimum temperature using quantile mapping can in some cases lead to inconsistencies. \nFor instance, maximum (minimum) temperature could be lower (higher) than mean temperature. \nIf such inconsistencies occur, daily minimum and maximum temperatures are adjusted in such a way that the anomalies with respect to the daily mean temperature are in line with the climatological anomalies for the particular day in the seasonal cycle. \nThis means, for example, that an inconsistency occurring on June 25 will be adjusted using the climatological anomalies for June 25, estimated by a moving window. \nAlso, the adjustment is done conditional on the wet/dry state of the corresponding precipitation series. \nThe climatology of the anomalies was derived from the HydroGFD2.0 dataset.\nIn addition, DBS\u2019s limitations lead to some data not being bias-adjusted or values beyond physically plausible ranges.\nIn those cases, DBS gives missing values as output. \nGrid cells where the DBS method resulted in such missing values have been interpolated in time or space. \nIf interpolation was not possible, full time series from nearest grid cell was copied to relating grid point.",
        "links": [
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                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/sis-ecv-cmip5-bias-corrected"
            },
            {
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                "type": "application/json",
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        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Temporal coverage: Present",
            "Variable domain: Atmosphere (surface)",
            "Provider: Copernicus C3S",
            "Product type: Climate projections",
            "Temporal coverage: Future"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
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        "extent": {
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                        -89.0,
                        360.0,
                        89.0
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            },
            "temporal": {
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                    [
                        "2000-10-12T00:00:00Z",
                        "2018-10-18T00:00:00Z"
                    ]
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        "summaries": "{}",
        "assets": {
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        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "sis-european-risk-extreme-precipitation-indicators",
        "title": "Extreme precipitation risk indicators for Europe and European cities from 1950 to 2019",
        "description": "The dataset presents climate impact indicators related to extreme precipitation in Europe under current climate conditions. The suite of indicators include recent historic records, recurrence intervals, and other relevant statistical measures to evaluate the magnitude and frequency of extreme precipitation events. These are provided as gridded products, with one product covering the whole of Europe, and the other higher resolution product focused on 20 European cities that were identified as vulnerable to urban pluvial flooding based on stakeholder surveys.\nThis dataset makes use of precipitation data available in the Climate Data Store (i.e. E-OBS gridded land-only observational dataset and ERA5 reanalysis) combined with additional datasets capable of improving the spatial and temporal resolution of the precipitation data, making it suitable for pluvial flood analysis at city scales. These are derived from i) the network of meteorological stations included in the European Climate Assessment & Dataset (ECA&D) programme and ii) dynamically downscaled ERA5 reanalysis at 2 km x 2 km (ERA5-2km) using the regional climate model COSMO-CLM and accounting for urban parameterization, specifically performed for the 20 European cities identified as vulnerable to urban pluvial flooding.\nAt the European scale, E-OBS and ERA5 precipitation data are used to compute indicators at different temporal resolutions (i.e. daily, monthly, yearly, and 30-year) according to the type of indicator. The precipitation amounts at fixed return periods are also computed for point observations from meteorological stations using the ECA&D network and are then interpolated onto the E-OBS grid. At the city scale, a dynamically downscaled ERA5-2km precipitation data are instead used to derive daily indicators, allowing city stakeholders to detect and rank local extreme precipitation events and evaluate their magnitude.\nThis dataset was produced on behalf of the Copernicus Climate Change Service.",
        "links": [
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            "Spatial coverage: Europe",
            "Variable domain: Land (hydrology)",
            "Provider: Copernicus C3S",
            "Product type: Climate indices"
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        "license": "CC-BY-4.0",
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        "id": "sis-european-wind-storm-indicators",
        "title": "Winter windstorm indicators for Europe from 1979 to 2021 derived from reanalysis",
        "description": "This dataset provides climatological indicators on European winter windstorms and their economic impact derived from ERA5 reanalysis. Also provided are risk indicators from a synthetically derived set of physically realistic windstorm events based on modelled climatic conditions. The primary users include the insurance sector, reinsurers and insurance industry service providers in response to their requirements for a catalogue of historic windstorm events within Europe. It is important for this sector to be able to characterise the temporal and geographic distribution of potentially destructive windstorm events over Europe. These data can also support sectors such as energy, transport, civil engineering and government.\nThe indicators available in this dataset are categorised as follows: \n\n\nWindstorm tracks \u2013 the spatial track centred on the location of maximum relative vorticity above a threshold defining a windstorm as it moves over the North Atlantic Ocean and Europe. \n\n\nWindstorm footprints \u2013 the maximum wind speed of gusts for each grid point of the domain during the passage of the storm over a period of 72-hours since the selected storm is first identified.\n\n\nSummary indicators (Tier 1) \u2013 uses the windstorm tracks and maximum 10m wind speed to produce annual and decadal summary statistics for the land regions in Europe.\n\n\nLoss and risk indicators (Tier 3) \u2013 describing the socio-economic impact of windstorms for various land cover and building types in Europe.\n\n\nThis dataset was produced on behalf of the Copernicus Climate Change Service.",
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            "Temporal coverage: Past",
            "Spatial coverage: Europe",
            "Variable domain: Atmosphere (surface)",
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        "license": "other",
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        "id": "sis-european-wind-storm-synthetic-events",
        "title": "Synthetic windstorm events for Europe from 1986 to 2011",
        "description": "This dataset contains a set of synthetic windstorm events consisting of 22,980 individual storm footprints over Europe. These are a physically realistic set of plausible windstorm events based on the modelled climatic conditions. It is not designed to reproduce actual historical observations but as a comparator for the stochastic event sets generally used for windstorm risk analysis in the insurance industry. This is because there is no data assimilation process used to align the model output to historical observations. The dataset aims to capture the windstorms' risk profile over the winter months and consider the characteristics of windstorms in a warming climate. The dataset may be downloaded by selecting the required years and months from which daily files are produced. These are there as mere reference; the associated storms bare no resembelence to what was actually observed and recorded at the time.\nThe windstorms are identified from the maximum 3 second wind gusts derived from the 10m wind speed from the Met Office HadGEM3 model using the Global Atmosphere 3 and Global Land 3 configurations. The dataset covers the northern hemisphere winter months between September and May only. It includes three individual iterations of the event sets, whose methodology and inputs are slightly different:\n\n\nSynthetic set 1.2: recalibrated using four historical windstorm events; Xynthia, Kyrill, Daria and 87J.\n\n\nSynthetic set 2: A storm severity index combining maximum wind gust speed above a threshold of 25 m s\u207b\u00b9 and land area was used to select the strongest six events from the Met Office historical events database.\n\n\nSynthetic set 3: downscaling was based on station observations from the four named storms used in Synthetic event set v1.2; Xynthia, Kyrill, Daria and 87J.\n\n\nFive simulation ensembles initialized from one of five consecutive days and allowed to evolve independently were used to increase the number of events compared to the input data and to account for the uncertainty in forecasted climatic conditions. The three sets and all simulation ensembles may be combined and used together in further analysis. This synthetic event set is complementary to the catalogue of actual windstorms in Europe from 1979 to 2021 also available in the Climate Data Store.\nThis dataset was produced on behalf of the Copernicus Climate Change Service.",
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            "Spatial coverage: Europe",
            "Variable domain: Atmosphere (surface)",
            "Provider: Copernicus C3S",
            "Product type: Climate projections"
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        "id": "sis-health-vector",
        "title": "Climatic suitability for the presence and seasonal activity of the Aedes albopictus mosquito for Europe derived from climate projections",
        "description": "This dataset contains climatic suitability indicators for the Aedes albopictus (tiger mosquito) for Europe produced within the C3S European Health service. The provided indicators are the climatic suitability for the presence of Aedes albopictus and the season length of presence of Aedes albopictus. This mosquito transmits vector-borne diseases, such as dengue and chikungunya.\nEnvironmental factors such as temperature and rainfall impact the survival chance and seasonal activity of Aedes albopictus. In Europe, the environmental conditions become more favorable for the establishment of Aedes albopictus, which is a serious threat for human health in Europe.\nThe temperature statistics are calculated, either for the season winter and summer or for the whole year, based on a bias-adjusted EURO-CORDEX dataset. Then, the statistics are averaged for 30 years as a smoothed average from 1971 to 2100. This results in a timeseries covering the period from 1986 to 2085. Finally, the timeseries are averaged for the model ensemble and the standard deviation to this ensemble mean is provided.",
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            "Variable domain: Land (biosphere)",
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            "Provider: Copernicus C3S",
            "Product type: Climate projections",
            "Temporal coverage: Future",
            "Sector: Health"
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        "stac_version": "1.1.0",
        "id": "sis-hydrology-variables-derived-projections",
        "title": "Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections",
        "description": "This dataset provides water variables and indicators based on hydrological impact modelling, forced by bias adjusted regional climate simulations from the European Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX). The dataset contains Essential Climate Variable (ECV) data in the form of daily mean river discharge and a set of climate impact indicators (CIIs) for both water quantity and quality. \nECV datasets provide the empirical evidence needed to understand the current climate and predict future changes. \nCIIs contain condensed climate information which facilitate relatively quick and efficient subsequent analysis. Therefore, CIIs make climate information accessible to application focussed users within a sector.\nThe ECVs and CIIs provided here were derived within the water management sectoral information service to address questions specific to the water sector. However, the products are provided in a generic form and are relevant for a range of sectors, for example agriculture and energy.\nThe data represent the current state-of-the-art in Europe for regional climate and hydrological modelling and indicator production. Eight bias adjusted model simulations from the EURO-CORDEX EUR-11 were used to force a multi-model setup of the hydrological model E-HYPEcatch at a pan-European domain. A total of 18 water quality and quantity CIIs and 1 water ECV are provided in this dataset at catchment scale and on a 5km x 5km grid. \nThe CIIs are provided as mean values over a 30-year time period. For the reference period (1971-2000) data is provided as absolute values, for the future periods the data is provided as absolute values and as the relative or absolute change from the reference period. The future periods cover 3 fixed time periods (2011-2040, 2041-2070 and 2071-2100) and 3 \"degree scenario\" periods defined by when global warming exceeds a given threshold (1.5 \u00b0C, 2.0 \u00b0C and 3.0 \u00b0C). The global warming is calculated from the global climate model (GCM) used, therefore the actual time period of the degree scenarios will be different for each GCM.\nThe river discharge ECV data meet the technical specification set by the Global Climate Observing System (GCOS), as such they are provided on a daily time step. Note these are model output data, not observation data as is the general case for ECVs.\nThis dataset is produced and quality assured by the Swedish Meteorological and Hydrological Institute on behalf of the Copernicus Climate Change Service.",
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            "Spatial coverage: Europe",
            "Temporal coverage: Present",
            "Variable domain: Atmosphere (surface)",
            "Provider: Copernicus C3S",
            "Product type: Climate projections",
            "Temporal coverage: Future",
            "Sector: Water management"
        ],
        "license": "CC-BY-4.0",
        "providers": [
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        "stac_version": "1.1.0",
        "id": "sis-hydrology-variables-derived-seasonal-reforecast",
        "title": "Multi-model seasonal reforecasts of river discharge for Europe",
        "description": "This dataset provides hydrological seasonal reforecasts of monthly mean river discharge across Europe for the period 1993 to 2016. The first is an E-HYPE multi-model system comprising eight model realisations using a catchment-based resolution. The second comprises the E-HYPEgrid, VIC-WUR and LISFLOOD-EFAS hydrological models at a 5km gridded resolution.\nThe initialisation of the hydrological seasonal forecast uses the European Flood Awareness System (EFAS) daily gridded meteorological observations (EFAS-Meteo) up until the start of the reforecast, and the subsequent integration of the meteorological seasonal reforecasts using all 25 members of the ECMWF seasonal forecast system 5 (SEAS5) meteorological reforecasts for the period January 1993 to December 2016. Seasonal reforecasts are produced to past dates but using the same model as the operaional forecast. A bias adjustment step using quantile mapping for temperature and precipitation was used for the E-HYPE and ViC-WUR models to minimize drift in the forecasts caused by biases in SEAS5 compared to EFAS-Meteo.\nThe final output is in the form of monthly mean river discharge with a lead time of seven months for each reforcast starting date. The context of the forecasts is provided by upper and lower terciles of the historical EFAS-Meteo driven simulation for each month of the year.\nThis dataset is produced by the Swedish Meteorological and Hydrological Institute on behalf of the Copernicus Climate Change Service in collaboration with the Copernicus Emergency Management Service (CEMS).",
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            "Spatial coverage: Europe",
            "Product type: Seasonal forecasts",
            "Variable domain: Land (hydrology)",
            "Provider: Copernicus C3S",
            "Sector: Water management"
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        "license": "CC-BY-4.0",
        "providers": [
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        "stac_version": "1.1.0",
        "id": "sis-heat-and-cold-spells",
        "title": "Heat waves and cold spells in  Europe derived from climate projections",
        "description": "The dataset contains the number of hot and cold spell days using different European-wide and national/regional definitions developed within the C3S European Health service.\n These heat wave and cold spell days are available for different future time periods and use different climate change scenarios.\nA heat wave or cold spell is a prolonged period of extremely high or extremely low temperature for a particular region. However, there is a lack of rigorous definitions for heat waves and cold spells. This dataset combines multiple definitions and allows the user to compare European-wide definitions with national/regional definitions.\nFirst, the temperature statistics are calculated, either for the season winter and summer or for the whole year, based on a bias-adjusted EURO-CORDEX dataset. Then, the statistics are averaged for 30 years as a smoothed average from 1971 to 2100. This results in a timeseries covering the period from 1986 to 2085. Finally, the timeseries are averaged for the model ensemble and the standard deviation to this ensemble mean is provided.",
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            "Spatial coverage: Europe",
            "Temporal coverage: Present",
            "Variable domain: Atmosphere (surface)",
            "Provider: Copernicus C3S",
            "Product type: Climate projections",
            "Temporal coverage: Future",
            "Sector: Health"
        ],
        "license": "CC-BY-4.0",
        "providers": [
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        "stac_version": "1.1.0",
        "id": "sis-marine-properties",
        "title": "Marine biogeochemistry data for the Northwest European Shelf and Mediterranean Sea from 2006 up to 2100 derived from climate projections",
        "description": "The dataset contains model projections of changes in marine physics and biogeochemistry and the lower trophic levels of the marine food web across the Northwest European Shelf and Mediterranean Sea out to the year 2100. The dataset has been produced using the marine ecosystem model, ERSEM v15.06 (European Regional Seas Ecosystem Model), coupled to the regional ocean circulation models, POLCOMS (the Proudman Oceanographic Laboratory Coastal Ocean Modelling System) and NEMO (Nucleus for European Modelling of the Ocean) using the FABM (Framework for Aquatic Biogeochemical Models) coupler. ERSEM is designed to simulate the cycles of carbon and the major nutrient elements nitrogen, phosphorous and silicon within the marine environment. Organisms at the base of the marine food web \u2013 the microscopic aquatic plants known as phytoplankton \u2013 play an integral role in these cycles and are explicitly represented in the model. So are the microscopic marine organisms which feed upon them and provide a vital link to commercially exploited species of fish and shell fish higher up the food chain.\nPOLCOMS and NEMO are well-established physical models with the ability to simulate regions that include both the deep ocean and the continental shelf. They track the movement of water and transfer of energy and momentum in three dimensions, enabling the water temperature, salinity (the salt content of sea water) and currents to be modelled. The coupled POLCOMS/NEMO-ERSEM system makes it possible to include the effects of physical transport and mixing processes on the spatiotemporal distribution of nutrients, phytoplankton, and other components of the marine ecosystem. The dataset includes physical variables, such as horizontal velocity components, temperature and salinity; and a range of biogeochemical variables, including the concentration of dissolved oxygen and the concentration of different nutrients. These variables were simulated under two future scenarios, based on different Representative Concentration Pathways (RCP) for future greenhouse gas emissions: the intermediate scenario, RCP4.5, in which greenhouse gas emissions peak around 2040 before declining; and the business as usual scenario, RCP8.5, in which greenhouse gas emissions continue to rise throughout the century. The hydrodynamic biogeochemical models were each driven by a global climate model generated for the Coupled Model Inter-comparison Project Phase 5 (CMIP5) at the open ocean boundaries, in combination with downscaled atmospheric data generated using the Swedish Meteorological and Hydrological Institute (SMHI) Rossby Centre Regional Atmospheric Model (RCA4).\nThis dataset was produced on behalf of the Copernicus Climate Change Service.",
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                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/sis-marine-properties"
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            "Temporal coverage: Present",
            "Provider: Copernicus C3S",
            "Product type: Climate projections",
            "Temporal coverage: Future",
            "Variable domain: Ocean (biology)",
            "Sector: Coastal regions"
        ],
        "license": "CC-BY-4.0",
        "providers": [
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        "id": "sis-ocean-wave-indicators",
        "title": "Ocean surface wave indicators for the European coast from 1977 to 2100 derived from climate projections",
        "description": "The dataset presents wave climate indicators based upon ocean surface wave parameters computed for a European-wide domain. This dataset provides an understanding of the European wave climate under the impact of climate change. It provides added value for various coastal sectors and studies such as port, shipping, and coastal management.\nThe ocean surface wave field are computed using the ECMWF's Wave Model (Stand Alone WAM, SAW) forced by surface wind and accounting for ice coverage in polar latitudes. The wave climate is defined by means of wave spectral parameters such as the significant wave height and the peak wave period. In order to assess the impact of climate change on the ocean's surface wave field, the SAW model is run for three different climate scenarios: the present climate (labelled 'historical'), and two Representative Concentration Pathway (RCP) scenarios that correspond to an optimistic emission scenario where emissions start declining beyond 2040 (RCP4.5) and a pessimistic scenario where emissions continue to rise throughout the century often called the business-as-usual scenario (RCP8.5). The wave climate in these scenarios are simulated using wind forcing from a member of the EURO-CORDEX climate model ensemble - the HIRHAM5 regional climate model downscaled from the global climate model EC-EARTH. In addition to the three climate scenarios, the indicators are also computed using ERA5 reanlysis wind forcing. This provides recent historical wave climate indicators that may be used, for example, to look at specific events in the past.\nThis dataset was produced on behalf of the Copernicus Climate Change Service.",
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            "Spatial coverage: Europe",
            "Temporal coverage: Present",
            "Variable domain: Ocean (physics)",
            "Product type: Climate projections",
            "Temporal coverage: Future",
            "Sector: Coastal regions"
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        "license": "CC-BY-4.0",
        "providers": [
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                        "2100-12-31T00:00:00Z"
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    {
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        "id": "sis-ocean-wave-timeseries",
        "title": "Ocean surface wave time series for the European coast from 1976 to 2100 derived from climate projections",
        "description": "The dataset presents time series of the coastal wave climate based upon ocean surface wave parameters computed for a European-wide domain. This dataset provides an understanding of the wave climate under the impact of climate change for the Northwest European Shelf and Mediterranean Sea. It provides added value for various coastal sectors and studies such as port, shipping, and coastal management.\nThe ocean surface wave fields are computed using the ECMWF's Wave Model (SAW) forced by surface wind and accounting for ice coverage in polar latitudes. The wave climate is defined by means of the integrated wave spectral parameters such as the significant wave height and the peak wave period. In order to assess the impact of climate change on the ocean's surface wave field, the SAW model is run for three different climate scenarios: the current climate (also termed historical), and two Representative Concentration Pathway (RCP) scenarios that correspond to an optimistic emission scenario where emissions start declining beyond 2040 (RCP4.5) and a pessimistic scenario where emissions continue to rise throughout the century often called the business-as-usual scenario (RCP8.5). The wave climate in these scenarios are simulated using wind forcing from a member of the EURO-CORDEX climate model ensemble - the HIRHAM5 regional climate model downscaled from the global climate model EC-EARTH. Given that the projections of these climate scenarios are based on a single combination of the regional and global climate models, users of these data should take in consideration that there is an inevitable underestimation of the uncertainty associated with this dataset. In addition to the three climate scenarios, the time series are also computed using ERA5 reanlysis wind forcing. This provides the recent historical wave climate that may be used, for example, to look at specific events in the past.\nThis dataset was produced on behalf of the Copernicus Climate Change Service.",
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        "keywords": [
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            "Spatial coverage: Europe",
            "Temporal coverage: Present",
            "Variable domain: Ocean (physics)",
            "Product type: Climate projections",
            "Temporal coverage: Future",
            "Sector: Coastal regions"
        ],
        "license": "CC-BY-4.0",
        "providers": [
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        "stac_version": "1.1.0",
        "id": "sis-temperature-statistics",
        "title": "Temperature statistics for Europe derived from climate projections",
        "description": "This dataset contains temperature exposure statistics for Europe (e.g. percentiles) derived from the daily 2 metre mean, minimum and maximum air temperature for the entire year, winter (DJF: December-January-February) and summer (JJA: June-July-August). These statistics were derived within the C3S European Health service and are available for different future time periods and using different climate change scenarios.\nTemperature percentiles are typically used in epidemiology and public health when defining health risk estimates and when looking at current and future health impacts, and they allow to identify a common threshold and comparison between different cities/areas. \nThe temperature statistics are calculated, either for the season winter and summer or for the whole year, based on a bias-adjusted EURO-CORDEX dataset. The statistics are averaged for 30 years as a smoothed average from 1971 to 2100. This results in a timeseries covering the period from 1986 to 2085. Finally, the timeseries are averaged for the model ensemble and the standard deviation to this ensemble mean is provided.",
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/sis-temperature-statistics"
            },
            {
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                "type": "application/json",
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            },
            {
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                "type": "application/json",
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        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Europe",
            "Temporal coverage: Present",
            "Variable domain: Atmosphere (surface)",
            "Provider: Copernicus C3S",
            "Product type: Climate projections",
            "Temporal coverage: Future"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
                    [
                        -180,
                        -90,
                        180,
                        90
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            },
            "temporal": {
                "interval": [
                    [
                        "1986-01-01T00:00:00Z",
                        "2085-12-31T00:00:00Z"
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "sis-tourism-fire-danger-indicators",
        "title": "Fire danger indicators for Europe from 1970 to 2098 derived from climate projections",
        "description": "The dataset presents projections of fire danger indicators for Europe based upon the Canadian Fire Weather Index System (FWI) under future climate conditions. The FWI is a meteorologically based index used worldwide to estimate the fire danger and is implemented in the Global ECMWF Fire Forecasting model (GEFF). In this dataset, daily FWI values, seasonal FWI values, and other FWI derived, threshold-specific, indicators were modelled using the GEFF model to estimate the fire danger in future climate scenarios. These indicators include the number of days with moderate, high, or very high fire danger conditions as classified by the European Forest Fire Information System (EFFIS) during the northern hemisphere's fire season (June-September):\n\nvery low: <5.2\nlow: 5.2 - 11.2\nmoderate: 11.2 - 21.3\nhigh: 21.3 - 38.0\nvery high: 38.0 - 50\nextreme: >=50.0 \n\nThis dataset may serve to assess future fire danger conditions for regions across Europe and support the development of a long-term tourism strategy to reduce the risk of forest fires on nature-based tourism infrastructure.\nThe FWI is a meteorologically based index that accounts for the effect of fuel moisture and weather conditions on fire behaviour. Daily noon values of air temperature, relative humidity, wind speed and 24-h accumulated precipitation are required for the calculation of the index. In order to attain the meteorological variables, projections from multiple global climate models downscaled to a regional climate model were used as input to the GEFF model. The climate models were developed within the EURO-CORDEX initiative, providing high-resolution and comparable model output centered on the European domain. In order to assess the impact of climate change, the GEFF model is run for four different climate scenarios: the present climate (labelled 'historical'), and three Representative Concentration Pathway (RCP) scenarios consistent with an optimistic emission scenario where emissions start declining beyond 2020 (RCP2.6), a scenario where emissions start declining beyond 2040 (RCP4.5) and a pessimistic scenario where emissions continue to rise throughout the century (RCP8.5). Historical simulations, for the period 1970-2005, are included to provide a reference for the FWI projections. An estimate of the statistical uncertainty associated with climate projections may be derived with the use of multiple climate model outcomes. This may be achieved by the user both implicitly or explicitly by selecting from a choice of mean, best case, or worst case multi-model outcomes. It should be noted, however, that the multi-model approach may improve the robustness of the outcomes but does not take into account all possible aspects of uncertainty associated with modelling a future climate.\nThis dataset was produced on behalf of the Copernicus Climate Change Service.",
        "links": [
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/sis-tourism-fire-danger-indicators"
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        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Europe",
            "Variable domain: Land (biosphere)",
            "Product type: Climate projections",
            "Temporal coverage: Future"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
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                "bbox": [
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                        "2098-12-31T00:00:00Z"
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        },
        "summaries": "{}",
        "assets": {
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    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "sis-tourism-snow-indicators",
        "title": "Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections",
        "description": "This dataset provides meteorological and snow indicators for Europe, characterizing operating conditions of winter ski resorts under past and future climate scenarios. The dataset consists of 39 indicators of atmospheric and snow conditions computed in a similar manner for all mountain regions in Europe at the scale of NUTS-3 regions (Nomenclature of Territorial Units for Statistics) and by steps of 100 m elevation. The snow indicators are generated using the Crocus snowpack model, a multi-layer snowpack model embedded in the land surface model, SURFEX (Surface Externalis\u00e9e). In order to assess the impact of climate change, the model is run for four different climate scenarios: the present climate (labelled 'historical'), and three Representative Concentration Pathway (RCP) scenarios that correspond to an optimistic emission scenario where emissions start declining beyond 2020 (RCP2.6), a further optimistic emission scenario where emissions start declining beyond 2040 (RCP4.5) and a pessimistic scenario where emissions continue to rise throughout the century, often called the high emission scenario (RCP8.5). In order to simulate these climate scenarios the SURFEX model is forced with atmospheric fields provided by adjusted EURO-CORDEX ensemble climate projections (European branch of the Coordinated Downscaling Experiment). Regional climate models downscaled from global climate models are used to provide the high resolution, pan-European, indicators required to assess the snow reliability for all mountainous regions across Europe. In addition to the climate scenarios, a reanalysis dataset is computed using UERRA reanalysis.\nA total of 39 indicators are made available in this dataset, divided into seven distinct groups:\n\nStart and end date of snow season\nAnnual amount of machine made snow produced\nPrecipitation\nSnow depth\nSnow water equivalent\nAir temperature\nPotential snow making hours\n\nThe Crocus model makes it possible to account for both snow grooming and mechanical snow-making based upon the physical representation of these snow management practices, adding further value to the end-user by providing indicated snow management requirements under future climate conditions. However, it is not designed to replace higher resolution products available in some European regions that provide a more detailed view of ski conditions; for example, accounting for slope, local meteorological phenomena and local snow management practices. Instead this dataset presents a homogenous product at a pan-European level and hence its main goal is to compare the main features of past and future snow conditions across Europe or to compare distant destinations; for example, Scandinavia and Eastern Europe (for a given elevation and time horizon). \nThis dataset was produced on behalf of the Copernicus Climate Change Service.",
        "links": [
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/sis-tourism-snow-indicators"
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            {
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        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Europe",
            "Variable domain: Land (hydrology)",
            "Product type: Climate projections",
            "Temporal coverage: Future",
            "Sector: Tourism"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
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                        27.7,
                        44.0,
                        70.0
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                        "2100-01-01T00:00:00Z"
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        "assets": {
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "sis-water-level-change-indicators-cmip6",
        "title": "Global sea level change indicators from 1950 to 2050 derived from reanalysis and high resolution CMIP6 climate projections",
        "description": "This dataset provides statistical indicators of tides, storm surges and sea level that can be used to characterize global sea level in present-day conditions and also to assess changes under climate change. The indicators calculated include extreme-value indicators (e.g. return periods including confidence bounds for total water levels and surge levels), probability indicators (e.g. percentile for total water levels and surge levels). They provide a basis for studies aiming to evaluate sea level variability, coastal flooding, coastal erosion, and accessibility of ports at a global scale. The extreme value statistics for different return periods can be used to assess the frequency of an event and form the basis of risk assessments. The global coverage allows for world-wide assessments that are particularly useful for the data scarce regions where detailed modelling studies are currently lacking.\nThe indicators are computed from time series data available in a related dataset in the Climate Data Store named Global sea level change time series from 1950 to 2050 derived from reanalysis and high resolution CMIP6 climate projections (see Related data), where further details of the modelling are provided. The indicators are produced for three different 30-year periods corresponding to historical, present, and future climate conditions (1951-1980, 1985-2014, and 2021-2050). The future period is based on global climate projections using the high-emission scenario SSP5-8.5. The dataset is based on climate forcing from ERA5 global reanalysis and 4 Global Climate Models (GCMs) of the high resolution Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate projection dataset from the High Resolution Model Intercomparison Project (HighResMIP) multi-model ensemble. An estimate of the uncertainties associated with the climate forcing has been obtained through the use of a multi-model ensemble. Each of the indicators provides ensemble statistics computed across the 4 members of the HighResMIP ensemble (e.g. median, mean, standard deviation, range). Absolute and relative changes for the future period (2015-2050) relative to the present-day (1985-2014) are provided to assess climate change impacts on water levels.\nThis dataset was produced on behalf of the Copernicus Climate Change Service.",
        "links": [
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/sis-water-level-change-indicators-cmip6"
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                "type": "application/json",
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        ],
        "keywords": [
            "Product type: Reanalysis",
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Provider: Copernicus C3S",
            "Variable domain: Ocean (physics)",
            "Product type: Climate projections",
            "Temporal coverage: Future"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
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            }
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                        90.0
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                        "2050-12-31T00:00:00Z"
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        "assets": {
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        "stac_version": "1.1.0",
        "id": "sis-extreme-indices-cmip6",
        "title": "Climate extreme indices and heat stress indicators derived from CMIP6 global climate projections",
        "description": "The dataset provides climate extreme indices related to temperature and precipitation as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), as well as selected heat stress indicators (HSI). The indices are provided for historical and future climate projections (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) included in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and used in the 6th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). This dataset provides a comprehensive source of pre-calculated and consistent ETCCDI and heat stress indicators commonly used by the climate science and impact communities. The majority of models used in this catalogue entry are now available in the Climate Data Store though the indices offered in this entry additionally include ensemble members obtained from the Earth System Grid Federation.\nThe indices are calculated from CMIP6 models that have the necessary daily resolved data for both historical and at least two of the future projections. In addition, four of the chosen models contained a large number of ensemble members in order to enable the estimation of the associated uncertainty in the spread of model outcomes when calculating the ETCCDI indices (CanESM5, EC-Earth3, MIROC6 and MPI-ESM1-2-LR). All the ETCCDI indices in this dataset are calculated using the climdex.pcic R package, which was developed, evaluated and approved by the ETCCDI.\nTo facilitate the usage of heat stress indicators in combination with thresholds on absolute values, this dataset additionally provides bias-adjusted heat stress indicators. Bias adjustment is carried out using the ISIMIP3b bias-adjustment method and employs the WATCH Forcing Data methodology applied to ERA5 (WFDE5) dataset as reference. Providing both pre-calculated bias-adjusted data and data without bias adjustment is of great value for climate and impact studies since the calculation of these datasets also are computationally expensive. The WFDE5 dataset is also available in the Climate Data Store.\nThe heat stress indicators combine near-surface air temperature, near-surface specific humidity, and surface air pressure to give indications of adverse effects of heat on human health. Other variables like wind or solar radiation are not considered, and the selected heat stress indicators thus represent indoor conditions or calm conditions in the shade.\nThis dataset was produced on behalf of the Copernicus Climate Change Service.",
        "links": [
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/sis-extreme-indices-cmip6"
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            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Variable domain: Land (biosphere)",
            "Product type: Climate projections",
            "Temporal coverage: Future",
            "Product type: Climate indices"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
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                        null,
                        null
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    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-total-column-water-vapour-ocean",
        "title": "Monthly and 6-hourly total column water vapour over ocean from 1988 to 2020 derived from satellite observations",
        "description": "Water vapour is has been recognised as an  Essential Climate Variable as it provides the basis for all cloud formation and physics and furthermore influences the Earth's heat budget due to its high absorbance of long and short-wave radiation. Total column water vapour (TCWV) is a measure of the integrated water vapour content of the atmosphere.\nThis catalogue entry provides the TCWV data product usually called Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS).\nThe HOAPS product uses a 1D-Var retrieval concept for atmospheric water vapour content from the measurements of the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) sensors in the microwave spectra over ice-free ocean surfaces. \nThe dataset provides a continuous data record composed of a Thematic Climate Data Record (TCDR) from 1988 to 2014 and of an Interim Climate Data Record (ICDR) from 2015 onward. It was produced following the World Meteorological Organisation's Observing Systems Capability Analysis and Review Tool (OSCAR)  requirements for TCWV.\nThe SSM/I and SSMIS are well-established instruments for TCWV retrieval over ice-free open ocean surfaces and provide a long time series (1987 to present) and global coverage at a reasonable resolution.\nThe TCDR HOAPS dataset is produced on behalf of EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) and the ICDR is produced within the Copernicus Climate Change Serice (C3S) project. Both products are based on the HOAPS 4.0 algorithm at DWD (Deutscher Wetterdienst).  A related dataset providing TCWV data over both land and ocean, but only at monthly  time resolution, computed using a different set of sensors and algorithms  is also available (the link to that dataset is under the Related data section).",
        "links": [
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                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/satellite-total-column-water-vapour-ocean"
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                "title": "Quality assessment of the dataset",
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        ],
        "keywords": [
            "Temporal coverage: Past",
            "Temporal coverage: Present",
            "Product type: Satellite observations",
            "Variable domain: Atmosphere (upper air)",
            "Provider: Copernicus C3S",
            "Provider: EUMETSAT SAF"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
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            "temporal": {
                "interval": [
                    [
                        "2002-05-01T00:00:00Z",
                        "2012-03-01T00:00:00Z"
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
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    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-precipitation-microwave",
        "title": "Precipitation monthly and daily gridded data from 2000 to 2017 derived from satellite microwave observations",
        "description": "This dataset provides global estimates of daily accumulated and monthly means of precipitation. The precipitation estimates are based on a merge of passive microwave observations from two different radiometer classes operating on multiple Low Earth Orbit (LEO) satellites.\nSpaceborne passive microwave (MW) provides the most effective measurements for the remote sensing of precipitation because the MW upwelling radiation is directly responsive to the cloud microphysical structure and, in particular, to the emission and scattering properties of precipitation-size hydrometeors (solid and liquid). However, they are available at low spatial and temporal resolution, due to the limited number of passes per day (depending on latitude and number of platforms) at each location.\n On the other hand, infrared (IR) sensors, available also on geostationary platforms, provide measurements that mostly respond to upper-level cloud structure, but at much higher temporal and spatial resolution. Since precipitation is not directly sensed in the infrared, these observations are often merged with microwave-based precipitation estimates and rain gauges. A precipitation product merging IR and MW is also available on the Climate Data Store: GPCP precipitation dataset.\nThe two different radiometer classes used in the present  Copernicus micrOwave-based gloBal pRecipitAtion (COBRA) dataset are: \ni) Conically scanning MW imagers; observations obtained by applying methodologies of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) in the Satellite Application Facility on Climate Monitoring (CM SAF).\nii) Cross-track scanning MW sounders; observations obtained through the dedicated Passive microwave Neural network Precipitation Retrieval for Climate Applications (PNPR-CLIM) algorithm.\nThis datset is independent of IR imagery and rain-gauge observations. \nA pure passive MW-based precipitation dataset overcomes the challenges and limitations of precipitation estimates based on IR observations, and the issues related to the inadequacy of the rain gauge networks in some regions and their almost complete absence over the ocean. \nThe main limitations, however, are linked to the varying (in time and space) revisiting time of the LEO satellites and low temporal sampling compared to geostanionary IR observations.\nThis dataset is produced by the Copernicus Climate Change Service (C3S).",
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                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/satellite-precipitation-microwave"
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                "title": "Quality assessment of the dataset",
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        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Variable domain: Land (hydrology)",
            "Product type: Satellite observations",
            "Variable domain: Atmosphere (surface)",
            "Provider: Copernicus C3S"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
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                "bbox": [
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                        89.0
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                ]
            },
            "temporal": {
                "interval": [
                    [
                        "2000-01-01T00:00:00Z",
                        "2017-12-31T00:00:00Z"
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                ]
            }
        },
        "summaries": "{}",
        "assets": {
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    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-sea-surface-temperature-ensemble-product",
        "title": "Sea surface temperature daily gridded data from 1981 to 2016 derived from a multi-product satellite-based ensemble",
        "description": "This dataset provides global daily sea surface temperature (SST) data from the Group for High Resolution Sea Surface Temperature (GHRSST) multi-product ensemble (GMPE) produced by the European Space Agency SST Climate Change Initiative (ESA SST CCI). The GMPE system was designed to allow users to compare the outputs from different SST analysis systems and understand their similarities and differences. Although originally intended for comparison of near real time data, it has also been used to compare long historical datasets. Note that the dataset provided here is the climate version of the GMPE dataset. An operational version, with different input products and time coverage, also exists but is not distributed by the CDS.\nThe SST analyses ingested into the GMPE system come from the following seven SST products and providers:\n\nESA SST CCI Analysis version 2.0\nESA SST CCI Analysis version 1.1\nCopernicus Marine Environment Monitoring Service (CMEMS) Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) Reprocessing\nNational Centers for Environmental Information (NCEI) Advanced Very High Resolution Radiometer (AVHRR) Optimal Interpolation (OI) Global Blended SST Analysis\nCanada Meteorological Center (CMC) 0.2-degree Global Foundation SST Analysis\nHadley Centre Sea Ice and Sea Surface Temperature (HadISST) Analysis version 2.2.0.0\nJapan Meteorological Agency (JMA) Merged satellite and in-situ Data Global Daily SST (MGDSST) Analysis\n\nThese products are all spatially complete (through use of infilling or reconstruction techniques) but were originally produced for different purposes and with different user requirements in mind. Therefore, each producer has made different choices on aspects of data production such as which input observations to use and what type of SST to represent. For example, the CMEMS OSTIA, CMC, and MGDSST analyses attempt to represent the foundation SST (water temperature free of diurnal temperature variability) while the ESA SST CCI and HadISST analyses estimate the SST at a standard depth of 20 cm. The AVHRR OI product, on the other hand, is bias-corrected to in situ observations and hence will be representative of their depths.\nThe GMPE dataset provides the median and standard deviation of the input SST products, the differences between each input product and the median, and the horizontal gradients in each of the input SST products as well as the final ensemble product. The HadISST product consists of 10 different realisations, therefore the median and standard deviation are calculated for an ensemble of 16 input fields. All fields are provided on a common 0.25 degree regular latitude-longitude grid and extend from 1 September 1981 to 31 December 2016, although some of the individual input products cover shorter periods. The dataset will not be extended beyond 2016.",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/satellite-sea-surface-temperature-ensemble-product"
            },
            {
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                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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            {
                "rel": "qa",
                "href": "https://cds.climate.copernicus.eu/datasets/satellite-sea-surface-temperature-ensemble-product?tab=quality_assurance_tab",
                "title": "Quality assessment of the dataset",
                "type": "text/html"
            }
        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Product type: Satellite observations",
            "Variable domain: Ocean (physics)"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
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                "bbox": [
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            },
            "temporal": {
                "interval": [
                    [
                        "1981-09-01T00:00:00Z",
                        "2016-12-31T00:00:00Z"
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                ]
            }
        },
        "summaries": "{}",
        "assets": {
            "thumbnail": {
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                "type": "image/jpg",
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        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "insitu-observations-surface-marine",
        "title": "Global marine surface meteorological variables from 1851 to 2010 from comprehensive in-situ observations",
        "description": "This set of holdings provides access to surface marine meteorological weather reports and observations made by merchant \nand naval ships, drifting buoys and other platforms and vessels over \nthe global ocean. Data have been collated and harmonised and quality control checks have been performed, but no attempt\nhas been made to assess or correct for potential biases. \nData are provided for a range of commonly observed variables.\nThe weather reports contain instantaneous, or point, observations of a number of key Essential Climate Variables (ECVs) \nand other parameters as denoted in the main variables table, made at routine intervals (typically 1, 3, or 6 hourly).\nNote that not all reports will contain information for all variables. \nAttributes are described in the related-variables table and users can choose whether to receive all\nattributes or solely essential attributes. Data are downloaded as comma-seperated values (CSV) files organised as one \nobservation of one variable per row.\nThe data available is released in the CDS  as version 1 and consist in a reprocessing of the International Comprehensive Ocean-Atmosphere Data\nSet (ICOADS), Release 3.0 with major improvements made to the quality control and duplicate flagging of observations and\nimproved availability of metadata. The current release represents a preliminary reprocessing of the period 1851-2010,\nwith additional data and flags planned to be added in subsequent releases. \nUsers should ensure an appropriate assessment of long-term data quality prior to use in those applications which require\nconsideration of such aspects. Users should be aware that data availability is spatio-temporally incomplete and varies \nsignificantly throughout the period of record.\nMore details about the marine data holdings are available in the product user guide, and details around data formatting \ncan be found in the common data model documentation, both of which can be found in the documentation section.\nThis work is being completed on behalf of Copernicus Climate Change Services in sustained collaboration with colleagues\nat NOAA's National Centres for Environmental Information who are the WMO designated World Data Centre for meteorology.",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/insitu-observations-surface-marine"
            },
            {
                "rel": "parent",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
            },
            {
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Product type: In-situ observations",
            "Variable domain: Atmosphere (surface)",
            "Spatial coverage: Point data",
            "Variable domain: Ocean (surface)"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
                    [
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                        -90.0,
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                        90.0
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                ]
            },
            "temporal": {
                "interval": [
                    [
                        "1851-01-01T00:00:00Z",
                        null
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                ]
            }
        },
        "summaries": "{}",
        "assets": {
            "thumbnail": {
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                "type": "image/jpg",
                "roles": [
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            }
        }
    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "satellite-precipitation-microwave-infrared",
        "title": "Daily and monthly Global Interpolated RAinFall Estimation (GIRAFE) data derived from satellite observations",
        "description": "This dataset provides global estimates of precipitation based on satellite observations. Precipitation is the main component of water transport from the atmosphere to the Earth\u2019s surface within the hydrological cycle. It varies strongly, depending on geographical location, season, synopsis, and other meteorological factors. The supply with freshwater through precipitation is vital for many subsystems of the climate and the environment, but there are also hazards related to extensive precipitation or the lack of precipitation. The present dataset allows the investigation and quantification of these aspects of precipitation, possibly in conjunction with other datasets covering components of the hydrological cycle.\nThe data represent the current state-of-the-art for satellite-based precipitation climate data record production in Europe, which is in line with the \u201cSystematic observation requirements for satellite-based products for climate\u201d as defined by GCOS (Global Climate Observing System). Spaceborne passive microwave (MW) imagers and sounders, available on different Low Earth Orbit (LEO) platforms, provide the most effective measurements for the remote sensing of precipitation because of the sensitivity of the MW upwelling radiation to the cloud microphysical properties, especially the emission and scattering of precipitation-size hydrometeors (solid and liquid). However, they are available at low spatial and temporal resolution, due to the limited number of overpasses per day (depending on latitude and number of platforms) at each location. A further ECV Precipitation product only based on MW observations, COBRA, is also available in the CDS. On the other hand, infrared (IR) sensors onboard geostationary (GEO) platforms, provide only information on the upper-level cloud structure, but at much higher temporal and spatial resolution, for example improving the representative sampling of intermittent precipitation. Since precipitation is not directly observed in the infrared, these measurements are often merged with microwave-based precipitation estimates.\nThis precipitation data record and its processing chain are called Global Interpolated RAinFall Estimate (GIRAFE). GIRAFE provides a global 1\u00b0 gridded daily accumulated precipitation amount together with uncertainty estimates coming from the sampling, and a global 1\u00b0 gridded monthly mean of daily accumulation. In the above sense, GIRAFE optimizes the sampling of precipitation by merging observations by LEO MW imagers and sounders (Level-2 data) with GEO-Ring IR brightness temperatures (Level-1 data). The daily accumulated precipitation is also aggregated to monthly mean precipitation.\nThis dataset has been produced by the EUMETSAT Satellite Application Facility on Climate Monitoring.",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/satellite-precipitation-microwave-infrared"
            },
            {
                "rel": "parent",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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            {
                "rel": "root",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/"
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        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Variable domain: Land (hydrology)",
            "Product type: Satellite observations",
            "Variable domain: Atmosphere (surface)",
            "Provider: EUMETSAT SAF"
        ],
        "license": "other",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
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                        0.0,
                        -89.0,
                        360.0,
                        89.0
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            },
            "temporal": {
                "interval": [
                    [
                        "2002-01-01T00:00:00Z",
                        "2022-12-31T00:00:00Z"
                    ]
                ]
            }
        },
        "summaries": "{}",
        "assets": {
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    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "insitu-gridded-observations-alpine-precipitation",
        "title": "Alpine gridded monthly precipitation data since 1871 derived from in-situ observations",
        "description": "This dataset, also known as the Long-term Alpine Precipitation Reconstruction (LAPrec), provides gridded fields of monthly precipitation for the Alpine region (eight countries). The dataset is derived from station observations and is provided in two issues: \n\nLAPrec1871 starts in 1871 and is based on data from 85 input series;\nLAPrec1901 starts in 1901 and is based on data from 165 input series. \n\nThis allows user flexibility in terms of requirements defined by temporal extent or spatial accuracy. LAPrec was constructed to satisfy high climatological standards, such as temporal consistency and the realistic reproduction of spatial patterns in complex terrain. As the dataset covers over one-hundred years in temporal extent, it is a qualified basis for historical climate analysis in a mountain region that is highly affected by climate change.\nThe production of LAPrec combines two data sources: \n\n\nHISTALP (Historical Instrumental Climatological Surface Time Series of the Greater Alpine Region) offers homogenised station series of monthly precipitation reaching back into the 19th century.\n\n\nAPGD (Alpine Precipitation Grid Dataset) provides daily precipitation gridded data for the period 1971\u20132008 built from more than 8500 rain gauges. \n\n\nThe adopted reconstruction method, Reduced Space Optimal Interpolation (RSOI), establishes a linear model between station and grid data, calibrated over the period when both are available. RSOI involves a Principal Component Analysis (PCA) of the high-resolution grid data, followed by an Optimal Interpolation (OI) using the long-term station data.\nThe LAPrec dataset is updated on a two-year basis, by no later than the end of February each second year. The latest version of the dataset will extend until the end of the year before its release date.\nLAPrec has been developed in the framework of the Copernicus Climate Change Service in a collaboration between the national meteorological services of Switzerland (MeteoSwiss, Federal Office of Meteorology and Climatology) and Austria (ZAMG, Zentralanstalt f\u00fcr Meteorologie und Geodynamik).\nFor more information on input data, methodical construction, applicability, versioning and data access, see the product user guide in the Documentation tab.\nThe latest version of the dataset will temporally extend until the end of the year before its release date.",
        "links": [
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                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/insitu-gridded-observations-alpine-precipitation"
            },
            {
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                "type": "application/json",
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        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Europe",
            "Product type: In-situ observations",
            "Variable domain: Atmosphere (surface)"
        ],
        "license": "CC-BY-4.0",
        "providers": [
            {
                "name": "ECMWF"
            }
        ],
        "extent": {
            "spatial": {
                "bbox": [
                    [
                        4.0,
                        43.0,
                        17.5,
                        49.0
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            "temporal": {
                "interval": [
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                        "1871-01-01T00:00:00Z",
                        null
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        },
        "summaries": "{}",
        "assets": {
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    },
    {
        "type": "Collection",
        "stac_version": "1.1.0",
        "id": "insitu-glaciers-extent",
        "title": "Glaciers distribution data from the Randolph Glacier Inventory for year 2000",
        "description": "This dataset, commonly known as the Randolph Glacier Inventory (RGI), provides global glacier outlines compiled from maps, aerial photographs and satellite images. The data is provided as a \"snapshot\" (single time-slice) constructed from images that were mostly acquired in the period 2000-2010.\nThe dataset represents the latest globally complete and homogenized state-of-the-art glacier outline collection. The regional datasets have been created by scientists from all over the world and were provided to the Global Land Ice Measurements from Space (GLIMS) glacier database at the National Snow and Ice Data Center (NSIDC). Among others, the dataset plays a key role in determining the contribution of glacier melt to global sea-level rise and regional hydrology, as well as modelling of past and future glacier evolution. \nThe provided RGI is brokered from the NSIDC and consists of three datasets providing:\n\nglacier outlines with attribute information in shape file format\ntabular data describing the area of each glacier in a specific elevation interval, i.e. its hypsometry\na netCDF file containing the percentage of glacier cover in each grid cell\n\nFor easier access, the shape and csv files are arranged in 19 first order regions that cover all glacierized regions in the world. The netCDF file includes all glaciers in one file. Coordinates are in longitude and latitude with the World Geodetic System 1984 (WGS84) datum.\nThe RGI glacier outlines provided here are widely used by the glacier community to spatially constrain calculations of the geodetic glacier mass balance, which are also available in the Climate Data Store when forwarded to the World Glacier Monitoring Service (WGMS).",
        "links": [
            {
                "rel": "self",
                "type": "application/json",
                "href": "https://cds.climate.copernicus.eu/api/catalogue/v1/collections/insitu-glaciers-extent"
            },
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                "title": "Quality assessment of the dataset",
                "type": "text/html"
            }
        ],
        "keywords": [
            "Temporal coverage: Past",
            "Spatial coverage: Global",
            "Variable domain: Land (cryosphere)",
            "Product type: Satellite observations",
            "Provider: Copernicus C3S"
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        "license": "other",
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        "id": "reanalysis-era5-complete",
        "title": "Complete ERA5 global atmospheric reanalysis",
        "description": "ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate covering the period from January 1940 to present. It is produced by the Copernicus Climate Change Service (C3S) at ECMWF and provides hourly estimates of a large number of atmospheric, land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes an ensemble component at half the resolution to provide information on synoptic uncertainty of its products.\nERA5.1 is a dedicated product with the same horizontal and vertical resolution that was produced for the years 2000 to 2006 inclusive to significantly improve a discontinuity in global-mean temperature in the stratosphere and uppermost troposphere that ERA5 suffers from during that period. Users that are interested in this part of the atmosphere in this era are advised to access ERA5.1 rather than ERA5.\nERA5 and ERA5.1 use a state-of-the-art numerical weather prediction model to assimilate a variety of observations, including satellite and ground-based measurements, and produces a comprehensive and consistent view of the Earth's atmosphere. These products are  widely used by researchers and practitioners in various fields, including climate science, weather forecasting, energy production and machine learning among others, to understand and analyse past and current weather and climate conditions.",
        "links": [
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            "Spatial coverage: Global",
            "Temporal coverage: Present",
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            "Provider: Copernicus C3S"
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        "stac_version": "1.1.0",
        "id": "projections-climate-atlas",
        "title": "Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas",
        "description": "This catalogue entry provides gridded data from global (CMIP5 and CMIP6) and regional (CORDEX) projections for the set of 22 variables and indices included in the IPCC Interactive Atlas, a novel contribution from Working Group I (WGI) to the IPCC Sixth Assessment Report (AR6). These variables and indices are relevant for the climatic impact-drivers used in the regional assessments conducted in AR6 (Chapters 10, 11, 12 and Atlas), related to heat and cold, wet and dry, snow and ice, and wind. This dataset is particularly intended for Climate Data Store (CDS) users who want to develop customised products not directly available from the IPCC Interactive Atlas (e.g. regional information at national or subnational scales). \nThis dataset includes gridded information with monthly/annual temporal resolution for historical experiments and climate projections based on Representative Concentration Pathways (RCP) / Shared Socioeconomic Pathways (SSP) scenarios for CMIP5/6 and CORDEX multi-model ensembles for the 22 variables and indices (computed from daily data). The ensembles are harmonised using regular grids with horizontal resolutions of 2\u00b0 (CMIP5), 1\u00b0 (CMIP6), 0.5\u00b0 (CORDEX), and 0.25\u00b0 (European CORDEX domain); details on the particular ensembles for each dataset are included in the documentation links. \nThis dataset allows the reproduction, expansion and customisation of the climate change products displayed in the IPCC Interactive Atlas. This  includes the global/continental maps of CMIP/CORDEX climate changes (for future periods across scenarios or for global warming levels, e.g. +2\u00b0C), and the regionally-aggregated time series, scatter plots, or global warming level plots. \nRelated datasets, also available through the CDS, include the CMIP5/6 global climate projections and the CORDEX regional climate projections. The original CMIP and CORDEX data was produced by the institutions and modelling centres participating in these initiatives, as described in AR6 WGI Annex II, with partial support from different programmes, including support from Copernicus for some of the EURO-CORDEX runs and for data curation and publication of world-wide CORDEX datasets. As a result, the dataset is fully reproducible from the CDS for CORDEX, but not for CMIP (some models and versions are different in the CDS and the Atlas ensembles).  \nThis dataset is distributed as part of the IPCC-DDC Atlas products under a Creative Commons Attribution 4.0 International License (CC-BY 4.0) and Copernicus has supported the standardisation and technical curation.",
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        "id": "derived-reanalysis-energy-moisture-budget",
        "title": "Mass-consistent atmospheric energy and moisture budget monthly data from 1979 to present derived from ERA5 reanalysis",
        "description": "This dataset provides monthly means of mass-consistent, vertically integrated, atmospheric energy and moisture budget quantities derived from 1-hourly ERA5 reanalysis data. \nThe vertically integrated budget diagnostics include the tendencies and lateral fluxes of total energy, water vapour, and latent heat (with the latent heat of vaporization varying with temperature). In addition, the divergences of the lateral fluxes are provided. \nThese divergences and  fluxes are also available in the ERA5 archive, but do not employ mass-consistent horizontal wind fields and are contaminated with numerical noise. In the present dataset, mass consistency is achieved by iteratively adjusting the horizontal wind field at every time step. \nThe divergence and tendency terms from this dataset can be combined to indirectly estimate mass-consistent surface flux terms of the atmospheric energy and moisture budget. In the energy budget, horizontal and vertical enthalpy fluxes associated with water and snow are neglected. This framework allows for unambiguous estimation of net surface heat fluxes (sum of radiative and turbulent heat fluxes) when combined with net radiative fluxes  at the top of the atmosphere (not included in this dataset).  The divergence and tendency term of the atmospheric moisture budget can be combined to indirectly estimate surface freshwater fluxes (P+E) which have an unbiased global mean.\nIn addition,  this dataset allows the computation of the following:\n\n\nThe divergence of total energy flux with temperature-independent latent heat of vaporization is obtained by subtracting the divergence of latent heat flux and add the divergence of water vapour flux multiplied by L\u1d65 = 2.5008 J kg\u207b\u00b9 instead (this works analogously for tendency and transport terms).\n\n\nDivergence fields with full spectral resolution can be derived from corresponding east- and northward transports (divergence terms in this dataset are truncated at wave number 180 to reduce artificial noise over high topography).  \n\n\nThis dataset is produced on behalf of Copernicus Climate Change Service by the Department of Meteorology and Geophysics, University of Vienna.",
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        "id": "projections-cmip6-decadal-prototype",
        "title": "CMIP6 predictions underpinning the C3S decadal prediction prototypes",
        "description": "This catalogue entry provides daily and monthly global climate model data from Decadal Climate Predictions Project (DCPP) experiments, part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The decadal data in the Climate Data Store (CDS) are a quality-controlled subset of the full DCPP.\nCMIP6-DCPP data addresses the ability of the climate system to be predicted on annual, multi-annual and decadal timescales. The information generated by the DCPP can provide a basis for socially relevant operational climate predictions on annual to decadal timescales. The use of these data is mostly aimed at:\n\nassessing historical decadal prediction skill (hindcasts);\nexploring the potential for operational forecast production (forecasts).\n\nThe term \"experiments\" in the context of DCPP refers to two categories of simulations:\n\nhindcast experiments which cover the recent past from 1960 and are constrained by climate observations.\nforecast experiments that are initialised with observations from 2019 onwards and are constrained by conditions of the ssp245 illustrative climate scenario.\n\nFor DCPP experiments, each model is used to produce ensemble of simulations that are initialised annually. The ensemble members provide uncertainty information and they are concatenated into one data file for each model.\nThis catalogue entry provides daily and monthly near surface air temperature, precipitation, sea level pressure, daily minimum/maximum near surface air temperature and 500 hPa geopotential height data. The data is accompanied with an option to apply spatial and/or temporal subsetting to data requests.\nThe data have been produced by the institutes participating in the sectoral decadal demonstrator service developed by the Copernicus Climate Change Service (C3S) in 2021. This demonstrator provided decadal prediction products tailored to specific users from the agriculture, energy, infrastructure and insurance sectors. The data were used in these demonstrators following processing procedures necessary to extract valid information (e.g., bias adjustment); details on this processing are available in the technical appendix. Any application - similar to or different from these examples - needs to consider and apply the required data processing with care.",
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            "Temporal coverage: Present",
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            "Variable domain: Atmosphere (upper air)",
            "Product type: Climate projections",
            "Temporal coverage: Future"
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        "license": "other",
        "providers": [
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        "id": "derived-near-surface-meteorological-variables",
        "title": "Near surface meteorological variables from 1979 to 2019 derived from bias-corrected reanalysis",
        "description": "This dataset provides bias-corrected reconstruction of near-surface meteorological variables derived from the fifth generation of the European Centre for Medium-Range Weather Forecasts  (ECMWF) atmospheric reanalyses (ERA5). It is intended to be used as a meteorological forcing dataset for land surface and hydrological models. \nThe dataset has been obtained using the same methodology used to derive the widely used water, energy and climate change (WATCH) forcing data, and is thus also referred to as WATCH Forcing Data methodology applied to ERA5 (WFDE5). The data are derived from the ERA5 reanalysis product that have been re-gridded to a half-degree resolution. Data have been adjusted using an elevation correction and monthly-scale bias corrections based on Climatic Research Unit (CRU) data (for temperature, diurnal temperature range, cloud-cover, wet days number and precipitation fields) and Global Precipitation Climatology Centre (GPCC) data (for precipitation fields only). Additional corrections are included for varying atmospheric aerosol-loading and separate precipitation gauge observations. For full details please refer to the product user-guide.\nThis dataset was produced on behalf of Copernicus Climate Change Service (C3S).",
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            "Spatial coverage: Global",
            "Product type: Derived reanalysis",
            "Variable domain: Atmosphere (surface)"
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        "providers": [
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        "assets": {
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        "id": "projections-cmip5-daily-single-levels",
        "title": "CMIP5 daily data on single levels",
        "description": "This catalogue entry provides daily climate projections on single levels from a large number of experiments, models, members and time periods computed in the framework of the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The term \"single levels\" is used to express that the variables are computed at one vertical level which can be surface (or a level close to the surface) or a dedicated pressure level in the atmosphere. Multiple vertical levels are excluded from this catalogue entry.\nCMIP5 data are used extensively in the Intergovernmental Panel on Climate Change Assessment Reports (the latest one is IPCC AR5, which was published in 2014). The use of these data is mostly aimed at:\n\naddressing outstanding scientific questions that arose as part of the IPCC reporting process;\nimproving the understanding of the climate system;\nproviding estimates of future climate change and related uncertainties;\nproviding input data for the adaptation to the climate change;\nexamining climate predictability and exploring the ability of models to predict climate on decadal time scales;\nevaluating how realistic the different models are in simulating the recent past.\n\nThe term \"experiments\" refers to the three main categories of CMIP5 simulations:\n\nHistorical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2005.;\nEnsemble of experiments from the Atmospheric Model Intercomparison Project (AMIP), which prescribes the oceanic variables for all models and during all period of the experiment. This configuration removes the added complexity of ocean-atmosphere feedbacks in the climate system. The period covered is typically 1950-2005.\nEnsemble of climate projection experiments following the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. The RCP scenarios provide different pathways of the future climate forcing. The period covered is typically, 2006-2100 some extended RCP experimental data is available from 2100-2300.\n\nIn CMIP5, the same experiments were run using different GCMs. In addition, for each model, the same experiment was repeatedly done using slightly different conditions (like initial conditions or different physical parameterisations for instance) producing in that way an ensemble of experiments closely related. Note that CMIP5 GCM data can be also used as lateral boundary conditions for Regional Climate Models (RCMs). RCMs are also available in the CDS (see CORDEX datasets). \nThe data are produced by the participating institutes of the CMIP5 project. The latest CMIP GCM experiments will form the CMIP6 dataset, which will be published in the CDS in a later stage.",
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            "Temporal coverage: Present",
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            "Variable domain: Atmosphere (upper air)",
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            "Temporal coverage: Future"
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        "id": "reanalysis-uerra-europe-complete",
        "title": "Complete UERRA regional reanalysis for Europe from 1961 to 2019",
        "description": "The UERRA datasets contain reanalysis data of the atmosphere, the surface and near-surface as well as for the soil covering Europe. Essential climate variables are generated with the UERRA-HARMONIE and the MESCAN-SURFEX systems. UERRA-HARMONIE is a 3-dimensional variational data assimilation system, while MESCAN-SURFEX is a complementary surface analysis system. Using the Optimal Interpolation method, MESCAN provides the best estimate of daily accumulated precipitation and six-hourly air temperature and relative humidity at 2 meters above the model topography.\nThe land surface platform SURFEX is forced with downscaled forecast fields from UERRA-HARMONIE as well as MESCAN analyses. It is run offline, i.e. without feedback to the atmospheric analysis performed in MESCAN or the UERRA-HARMONIE data assimilation cycles. Using SURFEX offline allows taking full benefit of precipitation analysis and to use the more advanced physics options to better represent surface variables such as surface temperature and surface fluxes, and soil processes related to water and heat transfer in the soil and snow.\nIn general, reanalysis combines model data with observations into a complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (6 hours in the UERRA-HARMONIE system) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\nThe assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The laws of physics allow for estimates at locations where data coverage is low. The provision of estimates at each grid point in Europe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with. \nThe observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates. UERRA-HARMONIE data is available from 1961 and is updated once a month with a delay to real-time of about 4 months. The system provides four analyses per day \u2013 at 0 UTC, 6 UTC, 12 UTC, and 18 UTC. Between the analyses, forecasts of the system are available with hourly resolution. Hence, estimates of the status of the atmosphere are available for every hour since 1961. Moreover, forecasts up to 30 hours are available from the analyses initialised at 0 UTC and 12 UTC.\nIn addition to observations in the model domain, a regional reanalysis needs information at its lateral boundaries. For the UERRA-HARMONIE system, this information is taken from the global reanalyses ERA40 (until the end of 1978) and ERA-interim (from 1979). The improvement over global products comes with the higher horizontal resolution that allows incorporating more regional details, e.g. topography. Moreover, it enables the system even to consider more observations at places with dense observation networks. The UERRA-HARMONIE regional reanalysis is produced at a horizontal resolution of 11km and MESCAN SURFEX provides data at a resolution of 5.5km.\nFor the UERRA-HARMONIE system, variables are produced at the surface and on model levels (65 levels) but are also interpolated to two other level types: pressure levels (24 levels between 1000-10hPa), and height levels (11 levels between 15m-500m). The output of height levels were introduced with special focus on the wind energy sector and their needs. Soil data is available on 14 levels from the surface to a depth of 12m. The number of available parameters varies between the different level types.\nIn order to make data access more manageable, the UERRA-HARMONIE and MESCAN-SURFEX dataset has been split into four records. Analysis time steps are available via the CDS whereas forecasts are available only through CDSAPI.",
        "links": [
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        "id": "projections-cordex-domains-single-levels",
        "title": "CORDEX regional climate model data on single levels",
        "description": "This catalogue entry provides Regional Climate Model (RCM) data on single levels from a number of experiments, models, domains, resolutions, ensemble members, time frequencies and periods computed over several regional domains all over the World in the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX). The term \"single levels\" is used to express that the variables are 2D-matrices computed on one vertical level which can be surface (or a level close to the surface) or a dedicated pressure level in the atmosphere. Multiple vertical levels are excluded from this catalogue entry.\nHigh-resolution Regional Climate Models (RCMs) can provide climate change information on regional and local scales in relatively fine detail, which cannot be obtained from coarse scale Global Climate Models (GCMs). This is manifested in better description of small-scale regional climate characteristics and also in more accurate representation of extreme events. Consequently, outputs of such RCMs are indispensable in supporting regional and local climate impact studies and adaptation decisions. RCMs are not independent from the GCMs, since the GCMs provide lateral and lower boundary conditions to the regional models. In that sense RCMs can be viewed as magnifying glasses of the GCMs.\nThe CORDEX experiments consist of RCM simulations representing different future socio-economic scenarios (forcings), different combinations of GCMs and RCMs and different ensemble members of the same GCM-RCM combinations. This experiment design through the ensemble members allows for studies addressing questions related to the key uncertainties in future climate change. These uncertainties come from differences in the scenarios of future socio-economic development, the imperfection of regional and global models used and the internal (natural) variability of the climate system. This experiment design allows for studies addressing questions related to the key uncertainties in future climate change:\n\nwhat will future climate forcing be?\nwhat will be the response of the climate system to changes in forcing?\nwhat is the uncertainty related to natural variability of the climate system?\n\nThe term \"experiment\" in the CDS form refers to three main categories:\n\nEvaluation: CORDEX experiment driven by ECMWF ERA-Interim reanalysis for a past period. These experiments can be used to evaluate the quality of the RCMs using perfect boundary conditions as provided by a reanalysis system. The period covered is typically 1980-2010;\nHistorical: CORDEX experiment which covers a period for which modern climate observations exist. Boundary conditions are provided by GCMs. These experiments, that follow the observed changes in climate forcing, show how the RCMs perform for the past climate when forced by GCMs and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1950-2005;\nScenario: Ensemble of CORDEX climate projection experiments using RCP (Representative Concentration Pathways) forcing scenarios. These scenarios are the RCP 2.6, 4.5 and 8.5 scenarios providing different pathways of the future climate forcing. Boundary conditions are provided by GCMs. The period covered is typically 2006-2100.\n\nIn CORDEX, the same experiments were done using different RCMs (labelled as \u201cRegional Climate Model\u201d in the CDS form).\nIn addition, for each RCM, there is a variety of GCMs, which can be used as lateral boundary conditions. The GCMs used are coming from the CMIP5 (5th phase of the Coupled Model Intercomparison Project) archive. These GCM boundary conditions are labelled as \u201cGlobal Climate Model\u201d in the form and are also available in the CDS.\nAdditionally, the uncertainty related to internal variability of the climate system is sampled by running several simulations with the same RCM-GCM combination. On the forms, these are indexed as separate ensemble members (the naming convention for ensemble members is available in the documentation). For each GCM, the same experiment was repeatedly done using slightly different conditions (like initial conditions or different physical parameterisations for instance) producing in that way an ensemble of experiments closely related. More details behind these sequential ensemble numbers is available in the detailed documentation.\nThe data are produced by the institutes and modelling centres participating in the different CORDEX domains with partial support from different international and national contributions including support from COPERNICUS for some of the EURO-CORDEX runs.\nThe data can be used for commercial purposes (unrestricted use) with the exception of the simulations from the following RCMs: BOUN-RegCM4-3 model (for Central Asia and Middle East and North Africa domains) and RU-CORE-RegCM4-3 model (for South-East Asia domain). Precise terms of use are provided in the CORDEX licence.",
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            "Variable domain: Atmosphere (surface)",
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        "title": "UERRA regional reanalysis for Europe on height levels from 1961 to 2019",
        "description": "The present UERRA dataset contains analyses of atmospheric variables on height levels, from 1961 to 2019.\nThe data have been generated using the UERRA-HARMONIE system by combining model data with observations into a complete and consistent dataset using the laws of physics. This principle is called data assimilation. UERRA-HARMONIE employs a 3-dimensional variational data assimilation method. The assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The laws of physics allow for estimates at locations where data coverage is low. The provision of estimates at each grid point in Europe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with.\nThe observing system has evolved drastically over time, and although the assimilation system can resolve data holes, the quality of analyses varies throughout the period, with less accurate estimates in 1960s due to sparse observational networks. In addition to observations in the model domain, a regional reanalysis needs model data which provide a first estimate of the atmospheric state. For the UERRA-HARMONIE system, this information is taken from the global reanalyses ERA40 (until the end of 1978) and ERA-interim (from 1979 onwards). The improvement of regional reanalyses over global products comes with the higher horizontal resolution that allows incorporating more regional details (e.g. topography). Moreover, it enables the system even to ingest more observations at places with dense observation networks.",
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        "title": "UERRA regional reanalysis for Europe on single levels from 1961 to 2019",
        "description": "This UERRA dataset contains analyses of surface and near-surface essential climate variables from\nUERRA-HARMONIE and MESCAN-SURFEX systems. Forecasts up to 30 hours initialised\nfrom the analyses at 00 and 12 UTC are available only through the CDS-API (see Documentation).\nUERRA-HARMONIE is a 3-dimensional variational data assimilation system,\nwhile MESCAN-SURFEX is a complementary surface analysis system.\nUsing the Optimal Interpolation method, MESCAN provides the best estimate of daily accumulated precipitation\nand six-hourly air temperature and relative humidity at 2 meters above the model topography.\nThe land surface platform SURFEX is forced with downscaled forecast fields from UERRA-HARMONIE as well as MESCAN analyses.\nIt is run offline, i.e. without feedback to the atmospheric analysis performed in MESCAN or the UERRA-HARMONIE data\nassimilation cycles. Using SURFEX offline allows to take full benefit of precipitation analysis and to use the more\nadvanced physics options to better represent surface variables such as surface temperature and\nsurface fluxes, and soil processes related to water and heat transfer in the soil and snow.\nIn general, the assimilation systems are able to estimate biases between observations and to sift good-quality\ndata from poor data. The laws of physics allow for estimates at locations where data coverage is low. The provision of\nestimates at each grid point in Europe for each regular output time, over a long period, always using the same format,\nmakes reanalysis a very convenient and popular dataset to work with.\nThe observing system has changed drastically over time, and although the assimilation system\ncan resolve data holes, the much sparser observational networks, e.g. in 1960s,\nwill have an impact on the quality of analyses leading to less accurate estimates.\nThe improvement over global reanalysis products comes with the higher horizontal resolution\nthat allows incorporating more regional details (e.g. topography). Moreover, it enables\nthe system even to use more observations at places with dense observation networks.",
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        "id": "reanalysis-uerra-europe-soil-levels",
        "title": "UERRA regional reanalysis for Europe on soil levels from 1961 to 2019",
        "description": "The UERRA dataset provides estimations of the climate in Europe based on model data\ncombined with observations using the UERRA-HARMONIE system and MESCAN-SURFEX system.\nUERRA-HARMONIE is a 3-dimensional data assimilation system, whereas\nMESCAN-SURFEX is a complementary surface analysis system. In general, the\nassimilation systems are able to estimate biases between observations and to\nsift good-quality data from poor data. The laws of physics allow for estimates\nat locations where data coverage is low. The provision of estimates at each\ngrid point in Europe for each regular output time, over a long period, always\nusing the same format, makes reanalysis a very convenient and popular dataset\nto work with.\nThe land surface platform SURFEX is forced with downscaled forecast fields from\nUERRA-HARMONIE as well as MESCAN analyses. It is run offline, i.e. without\nfeedback to the atmospheric analysis performed in MESCAN or the UERRA-HARMONIE\ndata assimilation cycles.\nThe observing system has changed drastically over time, and although the\nassimilation system can resolve data holes, the initially much sparser networks\nwill have an impact on the quality of analyses leading to less accurate\nestimates. The improvement over global reanalysis products comes with the\nhigher horizontal resolution that allows incorporating more regional details (e.g. topography).\nMoreover, it enables the system even to use more observations at places with dense observation networks.",
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        "id": "projections-cmip5-monthly-pressure-levels",
        "title": "CMIP5 monthly data on pressure levels",
        "description": "This catalogue entry provides monthly climate projections on pressure levels from a large number of experiments, models, members and time periods computed in the framework of the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The term \"pressure levels\" is used to express that the variables were computed at multiple vertical levels, which may differ in number and location among the different models.\nCMIP5 data are used extensively in the Intergovernmental Panel on Climate Change Assessment Reports (the latest one is IPCC AR5, which was published in 2014). The use of these data is mostly aimed at:\n\naddressing outstanding scientific questions that arose as part of the IPCC reporting process;\nimproving the understanding of the climate system;\nproviding estimates of future climate change and related uncertainties;\nproviding input data for the adaptation to the climate change;\nexamining climate predictability and exploring the ability of models to predict climate on decadal time scales;\nevaluating how realistic the different models are in simulating the recent past.\n\nThe term \"experiments\" refers to the three main categories of CMIP5 simulations:\n\nHistorical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2005.;\nEnsemble of experiments from the Atmospheric Model Intercomparison Project (AMIP), which prescribes the oceanic variables for all models and during all period of the experiment. This configuration removes the added complexity of ocean-atmosphere feedbacks in the climate system. The period covered is typically 1950-2005.\nEnsemble of climate projection experiments following the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. The RCP scenarios provide different pathways of the future climate forcing. The period covered is typically 2006-2100, some extended RCP experimental data is available from 2100-2300.\n\nIn CMIP5, the same experiments were run using different GCMs. In addition, for each model, the same experiment was repeatedly done using slightly different conditions (like initial conditions or different physical parameterisations for instance) producing in that way an ensemble of experiments closely related. Note that CMIP5 GCM data can be also used as lateral boundary conditions for Regional Climate Models (RCMs). RCMs are also available in the CDS (see CORDEX datasets). \nThe data are produced by the participating institutes of the CMIP5 project. The latest CMIP GCM experiments will form the CMIP6 dataset, which will be published in the CDS in a later stage.",
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            "Spatial coverage: Global",
            "Temporal coverage: Present",
            "Variable domain: Atmosphere (surface)",
            "Variable domain: Atmosphere (upper air)",
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        "id": "projections-cmip5-monthly-single-levels",
        "title": "CMIP5 monthly data on single levels",
        "description": "This catalogue entry provides monthly climate projections on single levels from a large number of experiments, models, members and time periods computed in the framework of fifth phase of the Coupled Model Intercomparison Project (CMIP5). The term \"single levels\" is used to express that the variables are computed at one vertical level which can be surface (or a level close to the surface) or a dedicated pressure level in the atmosphere. Multiple vertical levels are excluded from this catalogue entry.\nCMIP5 data are used extensively in the Intergovernmental Panel on Climate Change Assessment Reports (the latest one is IPCC AR5, which was published in 2014). The use of these data is mostly aimed at:\n\naddressing outstanding scientific questions that arose as part of the IPCC reporting process;\nimproving the understanding of the climate system;\nproviding estimates of future climate change and related uncertainties;\nproviding input data for the adaptation to the climate change;\nexamining climate predictability and exploring the ability of models to predict climate on decadal time scales;\nevaluating how realistic the different models are in simulating the recent past.\n\nThe term \"experiments\" refers to the three main categories of CMIP5 simulations:\n\nHistorical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2005.\nEnsemble of experiments from the Atmospheric Model Intercomparison Project (AMIP), which prescribes the oceanic variables for all models and during all period of the experiment. This configuration removes the added complexity of ocean-atmosphere feedbacks in the climate system. The period covered is typically 1950-2005.\nEnsemble of climate projection experiments following the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. The RCP scenarios provide different pathways of the future climate forcing. The period covered is typically 2006-2100, some extended RCP experimental data is available from 2100-2300.\n\nIn CMIP5, the same experiments were run using different GCMs. In addition, for each model, the same experiment was repeatedly done using slightly different conditions (like initial conditions or different physical parameterisations for instance) producing in that way an ensemble of experiments closely related. Note that CMIP5 GCM data can be also used as lateral boundary conditions for Regional Climate Models (RCMs). RCMs are also available in the CDS (see CORDEX datasets). \nThe data are produced by the participating institutes of the CMIP5 project. The latest CMIP GCM experiments will form the CMIP6 dataset, which will be published in the CDS in a later stage.",
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            "Spatial coverage: Global",
            "Temporal coverage: Present",
            "Variable domain: Atmosphere (surface)",
            "Variable domain: Atmosphere (upper air)",
            "Product type: Climate projections",
            "Temporal coverage: Future"
        ],
        "license": "other",
        "providers": [
            {
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        ],
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    {
        "type": "Collection",
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        "id": "projections-cmip5-daily-pressure-levels",
        "title": "CMIP5 daily data on pressure levels",
        "description": "This catalogue entry provides daily climate projections on pressure levels from a large number of experiments, models, members and time periods computed in the framework of fifth phase of the Coupled Model Intercomparison Project (CMIP5). The term \"pressure levels\" is used to express that the variables were computed at multiple vertical levels, which may differ in number and location among the different models.\nCMIP5 data are used extensively in the Intergovernmental Panel on Climate Change Assessment Reports (the latest one is IPCC AR5, which was published in 2014). The use of these data is mostly aimed at:\n\naddressing outstanding scientific questions that arose as part of the IPCC reporting process;\nimproving the understanding of the climate system;\nproviding estimates of future climate change and related uncertainties;\nproviding input data for the adaptation to the climate change;\nexamining climate predictability and exploring the ability of models to predict climate on decadal time scales;\nevaluating how realistic the different models are in simulating the recent past.\n\nThe term \"experiments\" refers to the three main categories of CMIP5 simulations:\n\nHistorical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2005.;\nEnsemble of experiments from the Atmospheric Model Intercomparison Project (AMIP), which prescribes the oceanic variables for all models and during all period of the experiment. This configuration removes the added complexity of ocean-atmosphere feedbacks in the climate system. The period covered is typically 1950-2005.\nEnsemble of climate projection experiments following the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. The RCP scenarios provide different pathways of the future climate forcing. The period covered is typically 2006-2100, some extended RCP experimental data is available from 2100-2300.\n\nIn CMIP5, the same experiments were run using different GCMs. In addition, for each model, the same experiment was repeatedly done using slightly different conditions (like initial conditions or different physical parameterisations for instance) producing in that way an ensemble of experiments closely related. Note that CMIP5 GCM data can be also used as lateral boundary conditions for Regional Climate Models (RCMs). RCMs are also available in the CDS (see CORDEX datasets). \nThe data are produced by the participating institutes of the CMIP5 project. The latest CMIP GCM experiments will form the CMIP6 dataset, which will be published in the CDS in a later stage.",
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            "Spatial coverage: Global",
            "Temporal coverage: Present",
            "Variable domain: Atmosphere (surface)",
            "Variable domain: Atmosphere (upper air)",
            "Product type: Climate projections",
            "Temporal coverage: Future"
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        "license": "other",
        "providers": [
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    {
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        "stac_version": "1.1.0",
        "id": "provider-c3s-data-rescue-without",
        "title": "Data Rescue Service from the Copernicus Climate Change Service",
        "description": "The present Catalogue entry introduces the Data Rescue Service online portal which is run by a contract team coordinated by the Copernicus Climate Change Service (C3S). \nRegistration with, and login to, the C3S Data Rescue Service portal is independent of the present C3S Climate Data Store catalogue. \nData rescue is the discovery, preservation, quality control, digitisation and consolidation of past measurements of weather conditions.\nThe C3S Data Rescue Service does not provide rescued data - that data is sent to international repositories.\nThe C3S Data Rescue Service facilitates and coordinates the rescue of weather and climate data from around the world. \nThe service collects and shares information on past, current and planned data rescue projects, feeds data into international repositories and promotes tools, best practice and standards for all aspects of the data rescue process.",
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                "type": "application/json",
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            },
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        "license": "other",
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                        180.0,
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                ]
            },
            "temporal": {
                "interval": [
                    [
                        null,
                        null
                    ]
                ]
            }
        },
        "summaries": "{}",
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]