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 
Fifth Generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5).
Produced 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 
higher spatial detail (9 km grid) to deliver richer land-surface information. 
Reanalysis merges numerical model output with global observations into a globally complete, physically consistent climate record; 
this “data assimilation” approach mirrors operational weather forecasting but is optimised for historical completeness rather than forecast timeliness.
Reanalysis datasets extend back several decades by sacrificing forecast deadlines, allowing additional time to gather observations and retrospectively ingest improved data, 
thereby enhancing data quality in earlier periods.
ERA5-Land uses atmospheric fields from ERA5—air temperature, humidity, pressure—as “forcing” inputs to drive its land-surface model, 
preventing rapid drift from reality that unconstrained simulations would suffer.
 Although observations do not enter the land model directly, they shape the atmospheric forcing through assimilation, giving ERA5-Land an indirect observational anchor.
 To reconcile ERA5’s coarser grid with ERA5-Land’s finer 9 km grid, a lapse-rate correction adjusts input temperatures, humidity, and pressures for altitude differences.
Like all numerical simulations, ERA5-Land carries uncertainty that generally grows backward in time as fewer observations were available to constrain the forcing.
 Users can combine ERA5-Land fields with the uncertainty estimates from equivalent ERA5 variables to assess confidence bounds.
The temporal resolution (hourly) and spatial detail (9 km) of ERA5-Land make it invaluable for land-surface applications such as flood and drought forecasting, 
agricultural monitoring, and hydrological studies.
The dataset presented here is a regridded subset of the full ERA5-Land archive, stored in an Analysis-Ready, 
Cloud-Optimised (ARCO) format specifically designed for retrieving long time-series for individual points. 
When a user’s requested location does not exactly match a grid point, the nearest grid point is automatically selected. This optimised data source ensures rapid response times.

ECMWF

ERA5 Land hourly time-series data from 1950 to present

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.
Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis.
Reanalysis 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.
ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities.
An uncertainty estimate is sampled by an underlying 10-member ensemble
at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience.
Such uncertainty estimates are closely related to the information content of the available observing system which
has evolved considerably over time. They also indicate flow-dependent sensitive areas.
To facilitate many climate applications, monthly-mean averages have been pre-calculated too,
though monthly means are not available for the ensemble mean and spread.
ERA5 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.
The 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.
An overview of all ERA5 datasets can be found in this article.
Information on access to ERA5 data on native resolution is provided in these guidelines.

ERA5 hourly time-series data on single levels from 1940 to present

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.
Data is available from 1940 onwards.
ERA5 replaces the ERA-Interim reanalysis.
Reanalysis 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.
This 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:

The daily aggregation statistic (daily mean, daily max, daily min, daily sum*)
The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours)
The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

*The daily sum is only available for the accumulated variables (see ERA5 documentation for more details).
Users 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.
For 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.

ERA5 post-processed daily statistics on single levels from 1940 to present

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.
Data is available from 1940 onwards.
ERA5 replaces the ERA-Interim reanalysis.
Reanalysis 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.
This 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:

The daily aggregation statistic (daily mean, daily max, daily min)
The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours)
The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

Users 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.
For 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.

ERA5 post-processed daily statistics on pressure levels from 1940 to present

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. 
ERA5-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'.    
The 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. 
The 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.

ERA5-Land hourly data from 1950 to present

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.
Data is available from 1940 onwards.
ERA5 replaces the ERA-Interim reanalysis.
Reanalysis 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.
ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities.
An uncertainty estimate is sampled by an underlying 10-member ensemble
at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience.
Such uncertainty estimates are closely related to the information content of the available observing system which
has evolved considerably over time. They also indicate flow-dependent sensitive areas.
To facilitate many climate applications, monthly-mean averages have been pre-calculated too,
though monthly means are not available for the ensemble mean and spread.
ERA5 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.
The data set presented here is a regridded subset of the full ERA5 data set on native resolution.
It is online on spinning disk, which should ensure fast and easy access.
It should satisfy the requirements for most common applications.
An overview of all ERA5 datasets can be found in this article.
Information on access to ERA5 data on native resolution is provided in these guidelines.
Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for
the uncertainty estimate (0.5 and 1 degree respectively for ocean waves).
There 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).
The present entry is "ERA5 hourly data on pressure levels from 1940 to present".

ERA5 hourly data on pressure levels from 1940 to present

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.
Data is available from 1940 onwards.
ERA5 replaces the ERA-Interim reanalysis.
Reanalysis 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.
ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities.
An uncertainty estimate is sampled by an underlying 10-member ensemble
at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience.
Such uncertainty estimates are closely related to the information content of the available observing system which
has evolved considerably over time. They also indicate flow-dependent sensitive areas.
To facilitate many climate applications, monthly-mean averages have been pre-calculated too,
though monthly means are not available for the ensemble mean and spread.
ERA5 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.
The data set presented here is a regridded subset of the full ERA5 data set on native resolution.
It is online on spinning disk, which should ensure fast and easy access.
It should satisfy the requirements for most common applications.
An overview of all ERA5 datasets can be found in this article.
Information on access to ERA5 data on native resolution is provided in these guidelines.
Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for
the uncertainty estimate (0.5 and 1 degree respectively for ocean waves).
There 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).
The present entry is "ERA5 hourly data on single levels from 1940 to present".

ERA5 hourly data on single levels from 1940 to present

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. 
ERA5-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'. 
This 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:

The daily aggregation statistic (daily mean, daily max, daily min)
The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours)
The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

Users 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.
For 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.

ERA5-Land post-processed daily statistics from 1950 to present

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.
The dataset is organised around two main variables:

the mean radiant temperature (MRT)
the universal thermal climate index (UTCI)

These variables describe how the human body experiences atmospheric conditions, specifically air temperature, humidity, ventilation and radiation.
The 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’s climate and its evolution in recent decades. ERA5 is regarded as a good proxy for observed atmospheric conditions.
The dataset currently covers 01/01/1940 to near real time and is regularly extended as ERA5 data become available.
The dataset is produced by the European Centre for Medium-range Weather Forecasts.

Thermal comfort indices derived from ERA5 reanalysis

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’s 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.
Sea ice edge and type are defined as follows:

Sea 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.
Sea 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 provided for both the Northern and Southern Hemispheres, and is limited to the extended winter months; October to April in the Northern Hemisphere, and February to August in the Southern Hemisphere. Sea ice type classification during summer is difficult due to the effect of melting at the ice surface which disturbs the passive microwave signature.

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. The CDRs of sea ice edge and type currently extend from 25 October 1978 to 31 December 2020 whereas the corresponding ICDRs extend to present (with a 16-day latency behind real time).
Versions 1.0, 2.0, 3.0 and the CDRs of 4.0 are based on observations 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. Following the deprecation of the SSMIS data stream in September 2025, Advanced Microwave Scanning Radiometer 2 (AMSR2) data is used as input for the version 4.0 ICDRs. The AMSR2-based ICDRs is produced independently from the historical SMMR–SSM/I–SSMIS CDRs and is not inter-calibrated with the existing CDRs. To support continuity assessment and facilitate user transition, the AMSR2 ICDRs are provided from 2019 onward, yielding an overlapperiod of approximately two years with the existing CDRs ending in 2020.
All data from versions 2.0, 3.0 and 4.0 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.
This 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).

Quality assessment of the dataset

Sea ice edge and type daily gridded data from 1978 to present derived from satellite observations

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.
Data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° 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.
The data was produced on behalf of the Copernicus Climate Change Service.

Agrometeorological indicators from 1979 to present derived from reanalysis

This catalogue entry provides the time series of the originally gridded Agrometeorological indicators from 1979 to present derived from reanalysis, also known as AgERA5. The main difference with the related entry is that the dataset is organised here to make time the primary dimension for analysis and extraction. For any user-defined location (point) or small area, the dataset returns the temporal evolution of the selected variables, enabling efficient time-series studies without requiring users to download and manage full global gridded files.
Variables included in the dataset are cloud cover, dewpoint temperature, precipitation flux, precipitation duration fraction (total, liquid, and solid), relative humidity at multiple times of day and derived 24-hour maximum and minimum, snow thickness (including liquid-water equivalent), solar radiation flux, 2m temperature statistics (mean, minimum, maximum for different time periods), vapour pressure, vapour pressure deficit at maximum temperature, reference evapotranspiration (Penman-Monteith FAO56), and wind speed.
AgERA5 provides daily surface meteorological data from 1979 to present as input for agriculture and agro-ecological studies. It is based on the hourly ECMWF ERA5 data at surface level, aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid is realised by applying grid- and variable-specific regression equations to the ERA5 dataset interpolated at 0.1°. The equations are trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution, so that the data is tuned to the finer topography, land-use pattern and land-sea delineation of the HRES model. The version used in this entry is AgERA5 v2.0.
Data Access
This time-series product is designed for efficient extraction of temporal data at specific locations or small areas. Users can extract a complete time series for a single geographic location (point query) or for all grid points within a small area (area query).
Output Formats
Data can be downloaded in NetCDF format, which preserves full metadata and coordinate information, or in CSV format, which is convenient for direct import into spreadsheets and analysis tools. Both formats are available for point and small-area queries.
Programmatic Access
This dataset is well suited to programmatic access via the CDS API. Users can request time series for individual points or small areas without transferring complete global gridded files. This "time-first" access pattern reduces data handling overhead for workflows focused on location-specific analyses and local-to-regional studies.

Agrometeorological indicators time-series from 1979 to present derived from reanalysis

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.
The 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.
The 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.
The 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.
More details about the reanalysis dataset and the extensive input data are given in the Documentation section.

Arctic regional reanalysis on pressure levels from 1991 to present

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.
The 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.
The 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.
The 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.
More details about the reanalysis dataset and the extensive input data are given in the Documentation section.

Arctic regional reanalysis on single levels from 1991 to present

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.
The 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.
The 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.
The 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.
More details about the reanalysis dataset and the extensive input data are given in the Documentation section.

Arctic regional reanalysis on height levels from 1991 to present

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.
The 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.
The 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.
The 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.
More details about the reanalysis dataset and the extensive input data are given in the Documentation section.

Arctic regional reanalysis on model levels from 1991 to present

We are currently experiencing data access issues that may cause some download requests to fail. Older versions of the data are unavailable [more details](https://www.ceda.ac.uk/news/updates/2025/2025-11-28-incident-data-list/). We anticipate that access will restored in the near future.

This dataset provides daily and monthly global estimates of sea surface temperature (SST) based on observations from multiple satellite sensors since July 1979 (for the daily product) and January 1980 respectivly (for the monthly product). 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’s climate.
The 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).
The 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.
Unlike 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.
The 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).

Global sea surface temperature derived from satellite observations

This dataset provides atmospheric CO₂ observations as derived from satellite radiances. It consists of two types of products each covering a different part of the atmosphere: (i) The average CO₂ mixing ratio of the entire atmosphere from the Earth’s surface to the top of the atmosphere. These column-averaged mixing ratio products are referred to as “XCO2” products. (ii) Mid-tropospheric (MT) CO₂ 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 CO₂. This is important as CO₂ is the most important anthropogenic so-called greenhouse gas driving global warming and associated climate change. CO₂ time series show an annual cycle primarily due to uptake and release of CO₂ 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 CO₂ shows significant inter-annual variations, due to variations of the (e.g., weather related) uptake and release of CO₂ by the natural CO₂ 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 CO₂ 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 CO₂ growth rate, etc.
The 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 CO₂ 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 CO₂ variations in the mid-troposphere and only limited sensitivity to CO₂ changes near the ground. The two types of datasets are therefore highly complementary as they contain different information on atmospheric CO₂.
The dataset consists of 2 types of products: 

column-averaged mixing ratios of CO₂, denoted XCO2
mid-tropospheric CO₂ columns, denoted MTCO2.  

The XCO2 products have been retrieved from SCIAMACHY/ENVISAT, TANSO-FTS/GOSAT, TANSO-FTS2/GOSAT2 and OCO-2 and more recently, OCO-3. The mid-tropospheric CO₂ 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. In the latest update (v5.1), the product is provided with an increased spatial resolution of 1°, which was possible mainly due to the inclusion of OCO-3 data. 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.
This 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).

Carbon dioxide data from 2002 to present derived from satellite observations

The next Intermediate Climate Data Record (ICDR) update for the surface soil moisture, root-zone soil moisture and freeze/thaw classification products is expected to be slightly delayed. The data for the dekad covering 1–10 August 2026 should be available around 22 August 2026. The delay is caused by the late availability of one of the input data streams.

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–atmosphere interactions, global biogeochemical cycles, ecology, hydrological and land surface modelling, drought monitoring, and meteorology. 
All 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–longitude grid, with potential gaps in space and time due to missing satellite overpasses. 
In 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. 
Available Products: 
- 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.
- 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 (0–10, 10–40, 40–100, and 0–100 cm). RZSM is commonly preferred over SSM for drought and large-scale agricultural applications.
- Freeze/Thaw (F/T) provides a binary classification of the surface soil moisture state (frozen or unfrozen) based on K-band radiometer brightness temperatures.
This dataset is produced on behalf of the Copernicus Climate Change Service (C3S) by TU Wien, Planet, and EODC GmbH.

Soil moisture gridded data from 1978 to present

Upper Tropospheric Humidity (UTH) is of key importance to the Earth’s 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’s 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. 
The 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.
The 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.
The 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.
UTH 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.

Upper tropospheric humidity gridded data derived from satellite observations

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).
Mapping 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.
The 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.
This data was produced on behalf of the Copernicus Climate Change Service.

Ice sheet velocity for Antarctica and Greenland derived from satellite observations

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.
Reanalysis 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.
The 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.
The 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.
The 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.
ORAS5 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.
The ORAS5 dataset is produced by ECMWF and funded by the Copernicus Climate Change Service (C3S).

ORAS5 global ocean reanalysis monthly data from 1958 to present

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).
CMIP6 data underpins the Intergovernmental Panel on Climate Change 6th Assessment Report. The use of these data is mostly aimed at:

addressing outstanding scientific questions that arose as part of the IPCC reporting process;
improving the understanding of the climate system;
providing estimates of future climate change and related uncertainties;
providing input data for the adaptation to the climate change;
examining climate predictability and exploring the ability of models to predict climate on decadal time scales;
evaluating how realistic the different models are in simulating the recent past.

The term "experiments" refers to the three main categories of CMIP6 simulations:

Historical 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.
Climate 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.

This 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.
The data are produced by the participating institutes of the CMIP6 project.

CMIP6 climate projections

This dataset provides atmospheric CH₄ observations as derived from satellite radiances. It consists of two types of products each covering a different part of the atmosphere: (i) The average CH₄ mixing ratio of the entire atmosphere from the Earth’s surface to the top of the atmosphere. These column-averaged mixing ratio products are referred to as “XCH4” products. (ii) Mid-tropospheric (MT) CH₄ 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 CH₄. This is important as CH₄ is after CO₂ the most important anthropogenic so-called greenhouse gas enhancing global warming and associated climate change. Methane has many natural and anthropogenic sources. CH₄ 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 CH₄ 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 CH₄ growth rate, etc.
The 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 CH₄ 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 CH₄ variations in the mid-troposphere and only limited sensitivity to CH₄ changes near the ground. The two types of datasets are therefore highly complementary as they contain different information on atmospheric CH₄.
The dataset consists of 2 types of products: 

column-averaged mixing ratios of CH₄, denoted XCH4 
mid-tropospheric CH₄ columns, denoted MTCH4.

The 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.
This 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).

Methane data from 2002 to present derived from satellite observations

This set of holdings provides access to surface marine meteorological weather reports and observations made by merchant 
and naval ships, drifting buoys and other platforms and vessels over 
the global ocean. Data have been collated and harmonised and quality control checks have been performed, but no attempt
has been made to assess or correct for potential biases. 
Data are provided for a range of commonly observed variables.
The weather reports contain instantaneous, or point, observations of a number of key Essential Climate Variables (ECVs) 
and other parameters as denoted in the main variables table, made at routine intervals (typically 1, 3, or 6 hourly).
Note that not all reports will contain information for all variables. 
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.
The data available is released in the CDS as version 2 and consist primarily in a reprocessing of the International Comprehensive Ocean-Atmosphere Data Set (ICOADS), Release 3.0 with major improvements made to the quality control and duplicate flagging of observations and
improved availability of metadata. In addition, this release includes drifting buoy data reprocessed for Copernicus In Situ by the Copernicus Reprocessing of Argos and Iridium Drifters (C-RAID) project.
Additional data and flags are planned to be added in subsequent releases. 
Users should ensure an appropriate assessment of long-term data quality prior to use in those applications which require
consideration of such aspects. Users should be aware that data availability is spatio-temporally incomplete and varies 
significantly throughout the period of record.
More details about the marine data 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.
This 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.

Global marine surface meteorological variables from 1850 to present from comprehensive in-situ observations

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.
The 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.
The 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.
To 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.
This 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.
The 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.
More information about the CERRA-Land dataset can be found in the Documentation section.

CERRA-Land sub-daily regional reanalysis data for Europe from 1984 to present

This dataset is no longer supported and access to the data will be closed in due course. Users are requested to switch to a new CDS dataset that continues to be supported: [Multi-model hydrological seasonal forecasts and historical simulations for Europe](https://cds.climate.copernicus.eu/datasets/sis-hydrology-europe-seasonal)

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.
The 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.
The 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.
This 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).

Multi-model seasonal forecasts of river discharge for Europe from January 2021 to present

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’s 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).
Sea ice thickness is estimated from satellite radar altimetry, which measures the sea ice freeboard—the 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.
This dataset provides both a Level-3 (L3) product with spatial gaps based on a combination of altimetry measurements from European Remote Sensing Satellite-2 (ERS-2), Envisat, CryoSat-2 and Sentinel-3, and a Level-4 (L4) product (gap-filled) based on a combination of altimetry measurements from CryoSat-2, Sentinel-3 and L-band radiometry measurements from Soil Moisture and Ocean Salinity (SMOS) mission.
The L4 product offers improved accuracy for thin ice, higher temporal resolution, and complete spatial coverage with a higher spatial resolution. 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.
Both products are limited to the Arctic and the winter months (October–April), 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.

Sea ice thickness gridded data for the Arctic derived from satellite observations

ERA5–Drought is a global reconstruction of drought indices from 1940 to present.
The dataset comprises two standardised drought indices:
- the Standardised Precipitation Index (SPI)
- the Standardised Precipitation-Evapotranspiration Index (SPEI).
The 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.
In ERA5–Drought, 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 1991–2020. 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. 
The 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.
A more detailed description of the ERA5–Drought 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.
The dataset is produced by ECMWF.

Monthly drought indices from 1940 to present derived from ERA5 reanalysis

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.
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 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.
The 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.
The 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.
More information about the CERRA dataset can be found in the Documentation section.

CERRA sub-daily regional reanalysis data for Europe on model levels from 1984 to present

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).
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 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.
The 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.
The 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.
More information about the CERRA dataset can be found in the Documentation section.

CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present

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).
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 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.
The 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.
The 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.
More information about the CERRA dataset can be found in the Documentation section.

CERRA sub-daily regional reanalysis data for Europe on height levels from 1984 to present

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).
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 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.
The 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.
The 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.
More information about the CERRA dataset can be found in the Documentation section.

CERRA sub-daily regional reanalysis data for Europe on pressure levels from 1984 to present

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 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.
The statistics provided are monthly average fields, climatologies and anomalies, as well as 12-month running mean and seasonal averages fields. Climatologies and anomalies are calculated with respect to two reference periods: 1981-2010 and 1991-2020.
The 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.

Essential climate variables for assessment of climate variability from 1979 to present

This dataset provides global information on the timing and location of Active Fires (AF) burning on Earth's surface during satellite overpasses, and also records their Fire Radiative Power (FRP) as a measure of strength. FRP relates to a fire's combustion rate, and the rate at which smoke containing greenhouse gases, reactive gases and particular matter is released into the atmosphere. The vast majority of detected hotspots are related to landscape fires, however other high temperature targets such as active volcanoes and gas flares are also present in the data. This dataset also provides a dedicated gas flare product to contribute to tracking the changing use and intensity of global industrial gas flaring. The AF & FRP products are primarily for use investigating links between biomass burning, climate change and variability, land-atmosphere interactions, global biogeochemical cycles, atmospheric composition and air quality. 
The dataset is 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 (NTC) Sentinel-3 SLSTR Level 2 AF detection and FRP product issued by the European Space Agency (ESA). 
The C3S AF & FRP products include a Level 2 summary product that provides a text-based summary of all Level 2 AF detections and FRP data collected over a one-month period. This information is reported separately for each Sentinel-3 satellite, and further separated by the observing type and time in V1.2, these are: daytime, night-time, and night-time gas flares.
In addition, three Level 3 ‘synthesis products’ for each of the daytime, night-time and gas flare datasets are also provided. The daytime and night-time synthesis products each grids the Level 2 AF and the FRP data at various spatial and temporal resolutions, along with making some adjustments for cloud cover that blocks the fires from the satellite sensors view. The gas flare product is only produced from night-time observations and for hotspots identified as being likely industrial gas flares. The following Level 3 ‘synthesis products’ are available for V1.2 (January 2020 to present):
 - Daily global Level 3 AF & FRP products are generated on a global 0.1-degree resolution 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.
 - 27-day 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 observations. This period matches the Sentinel-3 satellites standard orbital repeat cycle.
 - 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 resolution matches the MODIS Climate Modelling Grid active fire product. 
The AF and FRP and Gas Flare products were produced on behalf of the C3S and the monthly product has been designed to be broadly comparable with NASA's MODIS climate modelling grid active fire product, since they are likely to be used in combination to provide the longest-term AF & FRP time-series.

Fire radiative power and active fire pixels from 2020 to present derived from satellite observations

This dataset provides global climate and energy indicators from 1950 to the present at hourly and coarser temporal resolutions, offering a long-term, harmonised data foundation for energy-sector applications. It enables users to analyse energy indicators for the past climate, supporting energy planning, system design, risk assessments, climate impact modelling and model benchmarking. Based on ECMWF reanalysis v5 (ERA5), it includes key climate variables, such as temperature, precipitation, surface solar radiation and wind speed, that have been pre-processed to enhance consistency and usability for energy modelling. These pre-processing steps include bias adjustment (for wind speed) and spatial aggregation at country and sub-country levels. 
Energy indicators are computed using a combination of physics-based and statistical models, selected according to data availability and the specific characteristics of each energy subsector. 
- Wind power indicators (onshore and offshore) are calculated by applying turbine power curves to wind speeds extrapolated to the wind turbine hub height.
- The photovoltaic (PV) indicator combines statistical relationships with physics-based representations of PV system performance incorporating solar irradiance, temperature, and standard PV configurations.
- Hydropower indicators are produced using statistical models calibrated with observed generation data for the European domain, where such data are available, and a proxy approach based on climate variables for global coverage.
- Electricity demand is modelled explicitly for Europe using a climate-sensitive statistical model calibrated on measured load data.
- Energy demand at the global scale is represented through the Energy Degree Days (EDD) indicator, which captures heating- and cooling-related demand driven by temperature in regions where load data are not available.
This dataset was produced on behalf of the Copernicus Climate Change Service (C3S).

Global climate and energy indicators from 1950 to present derived from reanalysis

This catalogue entry provides access to time-series data for a selection of ERA5 variables on a subset of pressure levels. For any user-defined location or small area, the retrieved data are the temporal evolution of the selected variables, thus enabling efficient time-series studies without requiring users to download and manage full global gridded files.
ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather, providing hourly analysis estimates from 1940 onwards. Reanalysis 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 a previous forecast is combined with newly available observations to produce a new best estimate of the state of the atmosphere.
The dataset presented here is a regridded subset of the full ERA5 data set on native resolution that provides a selection of variables on pressure levels, stored in a format designed for retrieving long time-series for a single point or a small area. The data corresponds to ERA5 analyses only. When the requested location does not match the exact location of a grid point then the nearest grid point is used instead.
ERA5 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.
For access to the complete ERA5 pressure levels dataset with all variables, product types, and full spatial coverage, see: ERA5 hourly data on pressure levels from 1940 to present. An overview of all ERA5 datasets can be found in The family of ERA5 datasets.

ERA5 hourly time-series data on pressure levels from 1940 to present

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).
Land 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‑term 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).
The 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).
LSTs 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° × 0.01° equal-angle grid.
The 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.

Land surface temperature monthly gridded data from 1995 to present derived from satellite observations

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.
Data is available from 1940 onwards.
ERA5 replaces the ERA-Interim reanalysis.
Reanalysis 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.
ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities.
An uncertainty estimate is sampled by an underlying 10-member ensemble
at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience.
Such uncertainty estimates are closely related to the information content of the available observing system which
has evolved considerably over time. They also indicate flow-dependent sensitive areas.
To facilitate many climate applications, monthly-mean averages have been pre-calculated too,
though monthly means are not available for the ensemble mean and spread.
ERA5 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.
ERA5 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.
The data set presented here is a regridded subset of the full ERA5 data set on native resolution.
It is online on spinning disk, which should ensure fast and easy access.
It should satisfy the requirements for most common applications.
An overview of all ERA5 datasets can be found in this article.
Information on access to ERA5 data on native resolution is provided in these guidelines.
Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for
the uncertainty estimate (0.5 and 1 degree respectively for ocean waves).
There 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).
The present entry is "ERA5 monthly mean data on pressure levels from 1940 to present".

ERA5 monthly averaged data on pressure levels from 1940 to present

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.
Data is available from 1940 onwards.
ERA5 replaces the ERA-Interim reanalysis.
Reanalysis 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.
ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities.
An uncertainty estimate is sampled by an underlying 10-member ensemble
at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience.
Such uncertainty estimates are closely related to the information content of the available observing system which
has evolved considerably over time. They also indicate flow-dependent sensitive areas.
To facilitate many climate applications, monthly-mean averages have been pre-calculated too,
though monthly means are not available for the ensemble mean and spread.
ERA5 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.
The data set presented here is a regridded subset of the full ERA5 data set on native resolution.
It is online on spinning disk, which should ensure fast and easy access.
It should satisfy the requirements for most common applications.
An overview of all ERA5 datasets can be found in this article.
Information on access to ERA5 data on native resolution is provided in these guidelines.
Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for
the uncertainty estimate (0.5 and 1 degree respectively for ocean waves).
There 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).
The present entry is "ERA5 monthly mean data on single levels from 1940 to present".

ERA5 monthly averaged data on single levels from 1940 to present

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.
ERA5-Land provides a consistent view of the water and energy cycles at surface level during several decades.
It 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.
The 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.

ERA5-Land monthly averaged data from 1950 to present

This entry covers pressure-level data aggregated on a monthly time resolution. 
Seasonal 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.
Given 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.
To 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).
The 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.

Seasonal forecast monthly statistics on pressure levels

This entry covers single-level data aggregated on a monthly time resolution. 
Seasonal 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.
Given 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.
To 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).
The 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.

Seasonal forecast monthly statistics on single levels

This entry covers pressure-level data post-processed for bias adjustment on a monthly time resolution. 
Seasonal 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.
Given 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.
To 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).
The 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.

Seasonal forecast anomalies on pressure levels

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). 
Seasonal 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.
Given 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.
To 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).
The 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.

Seasonal forecast daily and subdaily data on single levels

The Earth’s Radiation Budget (ERB) represents the overall balance between incoming and outgoing radiant energy at the Earth’s 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’s climate and its variability. The Earth’s Radiation Budget comprises of the quantification of the incoming radiation from the Sun and the outgoing reflected shortwave and emitted longwave radiation. 
The Earth’s 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’s surface and atmosphere (including cloud amount and properties, as well as concentrations of greenhouse gases) alter the radiative balance of the Earth’ 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’s 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.
This catalogue entry comprises data from a number of product families and origins summarised in the following sections.

Earth's radiation budget from 1979 to present derived from satellite observations

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. 
Seasonal 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.
Given 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.
To 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).
The 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.

Seasonal forecast monthly averages of ocean variables

This entry covers pressure-level data at the original time resolution (once every 12 hours). 
Seasonal 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.
Given 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.
To 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).
The 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.

Seasonal forecast subdaily data on pressure levels

This entry covers single-level data post-processed for bias adjustment on a monthly time resolution. 
Seasonal 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.
Given 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.
To 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).
The 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.

Seasonal forecast anomalies on single levels

This catalogue entry provides monthly mean time series from the global ocean and sea-ice reanalysis ORAS5 (Ocean Reanalysis System 5), with data organised to prioritise temporal access at grid point locations rather than full spatial fields. The dataset enables users to efficiently retrieve long, continuous time series for individual ocean points, supporting analyses of ocean variability and change without the need to download and process large global gridded datasets.
The data originate from the European Centre for Medium-range Weather Forecasts (ECMWF) OCEAN5 ocean analysis–reanalysis system and represent one ensemble member from a five-member ensemble. Reanalysis combines numerical model output with a wide range of observations using data assimilation techniques to produce a temporally and spatially consistent record that respects physical laws. ORAS5 provides gap-free monthly information over the global ocean and sea-ice domain.
The underlying reanalysis is produced using the Nucleus for European Modelling of the Ocean (NEMO) ocean model coupled with the NEMOVAR data assimilation system, which applies a 3D-Var First Guess at Appropriate Time (FGAT) approach. The assimilation incorporates observations of subsurface temperature and salinity, sea-ice concentration, sea-level anomalies, sea surface temperature, and sea surface salinity, ensuring physically consistent estimates of the ocean state through time.
Two production streams are included. The consolidated product uses reanalysis atmospheric forcing (ERA-40 up to 1978 and ERA-Interim from 1979 to 2014) together with reprocessed observations, providing a temporally consistent historical record. The operational product, available from 2015 onward, is updated monthly with a latency of approximately 15 days and uses ECMWF operational atmospheric forcing and near-real-time observations. These two streams balance long-term consistency and near-real-time availability.
The dataset presented here is a regridded subset of ORAS5 (see related datasets) stored in an Analysis-Ready, Cloud-Optimised (ARCO) format, specifically designed for rapid extraction of long time series at individual points. When a requested location does not coincide exactly with a model grid point, the nearest grid point is automatically selected. This design ensures fast response times and minimal data handling overhead, making the dataset well suited for workflows focused on temporal variability, trend analysis, and location-specific diagnostics.
This time-series-oriented access is particularly valuable for users interested in ocean climate indicators, long-term monitoring at fixed locations (e.g. moorings, transects, or regions of interest), and applications where temporal evolution is more important than full spatial coverage, such as validation studies, climate services, and operational monitoring.
ORAS5 is produced by ECMWF and funded by the Copernicus Climate Change Service (C3S).

ORAS5 global ocean reanalysis monthly time-series data from 1958 to present

This dataset provides daily and monthly fields of global sea surface temperature (SST) and sea ice surface temperature (IST) from 1982 to the present, using observations from multiple infrared and passive microwave satellite sensors. SST is a key driver of global weather and climate, affecting ocean-atmosphere interactions and driving climate variability.
Where sea ice is present, satellites cannot measure SST directly. As a result, temperature under sea ice is typically estimated using assumptions about the freezing point of seawater. A unique feature of this dataset is the inclusion of IST, which represents the temperature at the very top, or "skin", of sea ice. IST helps track how sea ice interacts with the ocean and atmosphere through processes like radiation, conduction, freezing, and melting, making it an important indicator of climate change in polar regions. It is one of the Sea Ice Essential Climate Variables defined by the Global Climate Observing System (GCOS).
Each data file contains two temperature fields: 
1. SST-only field: Combines observed SST in ice-free waters with estimated SST under sea ice to produce the spatially complete temperature fields for the global ocean.

Surface temperature field: Combines SST in ice-free waters with IST where sea ice is present. A sea-ice mask distinguishes ice-covered and ice-free regions.

This dataset is part of the Copernicus Climate Data Store (CDS) SST family. It is closely related to the global SST version 3.0 dataset produced by the European Space Agency Climate Change Initiative (ESA CCI) project (see related datasets), as both use the same satellite observations. A key difference is that ESA CCI dataset assigns a fixed value of -1.8 °C under sea ice, while this dataset uses a salinity-dependent freezing point. Another related dataset in the CDS focuses only on polar IST and SST derived from a single satellite sensor, without combining it with global SST fields.
This dataset is produced by the Danish Meteorological Institute (DMI) on behalf of the Copernicus Climate Change Service.

Combined sea surface and sea ice surface temperature data derived from satellite observations

This dataset provides ensemble reforecasts of hydrological simulations for Europe from the Copernicus Climate Change Service (C3S) Water Service. Ensemble reforecasts facilitate the assessment of forecast skill and post-processing techniques and support the Water Service's operational hydrological seasonal forecasts available in the Climate Data Store.
The dataset includes ensemble reforecasts generated using multiple hydrological models: E-HYPEcatch (European setup of the Hydrological Predictions for the Environment model, catchment resolution), E-HYPEgrid (European setup of the Hydrological Predictions for the Environment model, 5km grid resolution) and VIC-WUR (Variable Infiltration Capacity hydrological model, Wageningen University & Research) driven by two state-of-the-art seasonal climate forecast systems: ECMWF-SEAS5 (European Centre for Medium-range Weather Forecasts SEAsonal forecasting System 5) and CMCC-SPS3.5 (Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici- Global Seasonal Ensemble Prediction System). The driving seasonal climate data have been bias-adjusted against observed precipitation and temperature and dynamically downscaled to match the 5 km × 5 km horizontal resolution of the hydrological grid.  A set of commonly used probabilistic skill score metrics, such as the Continuous Ranked Probability Skill Score (CRPSS), are provided for each month across the reforecast period to help users evaluate forecast skill and make informed decisions. 
The multi-model ensemble approach allows users to assess forecast uncertainty and skill, supporting probabilistic, risk-informed decision-making. Additionally, bias-adjusted, downscaled seasonal climate forcing data for precipitation and 2m temperature, used to drive the hydrological models, are also available and updated operationally each month. These forcings may also be used by others to drive additional hydrological or impact models. 
This dataset was produced on behalf of the Copernicus Climate Change Service by the Swedish Meteorological and Hydrological Institute (SMHI) and Wageningen University & Research (WUR).

Multi-model hydrological seasonal reforecasts for Europe

This dataset provides operational seasonal hydrological forecasts as well as historical simulations and meteorological forcings for Europe from the Copernicus Climate Change Service (C3S) Water Service. It offers users freely available and timely information on past, present and future seasonal hydrological conditions. The dataset supports monitoring of current European hydrological conditions and provides outlooks of how those conditions are expected to evolve over the next six months. 
The data are generated using multiple hydrological models: E-HYPEcatch (European setup of the Hydrological Predictions for the Environment model, catchment resolution), E-HYPEgrid (European setup of the Hydrological Predictions for the Environment model, 5km grid resolution) and VIC-WUR (Variable Infiltration Capacity hydrological model, Wageningen University & Research) driven by two state-of-the-art seasonal climate forecast systems: ECMWF-SEAS5 (European Centre for Medium-range Weather Forecasts Seasonal forecasting system 5) and CMCC-SPS3.5 (Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici- Global Seasonal Ensemble Prediction System). Historical simulations are also generated by forcing each hydrological model with meteorological observations since 1992, and are updated each month. They can be used for long-term monitoring of hydrological conditions and provide initial conditions for the seasonal hydrological forecasts and seasonal hydrological reforecasts.
Both the historical simulations and seasonal hydrological forecasts are updated monthly and provide daily and monthly aggregations of key hydrological variables. The driving seasonal climate data have been bias-adjusted against observed precipitation and temperature and dynamically downscaled to match the 5 km × 5 km horizontal resolution of the hydrological grid. Ensemble reforecasts are also available from the Climate Data Store (see related datasets). The multi-model ensemble approach allows users to assess forecast uncertainty and skill, supporting probabilistic, risk-informed decision-making. Additionally, bias-adjusted, downscaled seasonal climate forcing data for precipitation and 2m temperature, used to drive the hydrological models, are also available and updated operationally each month. These forcings may also be used to drive additional hydrological models, impact models or ensemble reforecasts. 
This dataset was produced on behalf of the Copernicus Climate Change Service by the Swedish Meteorological and Hydrological Institute (SMHI) and Wageningen University & Research (WUR).

Multi-model hydrological seasonal forecasts and historical simulations for Europe

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. 
Initially, 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.
The 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.
The 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).

E-OBS daily gridded meteorological data for Europe from 1950 to present derived from in-situ observations

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 unités 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.
Data 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.
The 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.

Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections

This catalogue entry provides access to a continuous series of near-surface climate observations collected in-situ at 
United States Climate Reference Network (USCRN) stations. 
There are over 130 USCRN stations over the conterminous United States (U.S.), Alaska, and Hawaii. 
The USCRN stations are managed and maintained by the U.S. National Oceanic and Atmospheric Administration (NOAA).
The USCRN observations include air temperature, humidity, wind speed, precipitation, solar radiation, 
as well as surface skin temperature, soil temperature, and soil moisture. 
This dataset provides reference observations, with confidence, for 
the monitoring of climate change and variability or the validation of satellite observations or models.
The confidence is provided in the form of detailed uncertainties, due to environmental variability and measurement limitations. 
The Product User Guide, accessible in the documentation, explains the origins of these uncertainties and how 
they were quantified. The documentation presents also the source data and metadata.
The 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. 
This temporal resolution offers a granular insight into changing weather patterns and climate variability. 
Data are accumulated to provide daily and monthly readings, making it easier to discern long-term trends and patterns.
Several variables are also available at hourly and sub-hourly (5-minute) resolutions.
The USCRN dataset is also made available through the USCRN data portal, 
provided by the NOAA National Centers for Environmental Information (NCEI).
More information about this dataset can be found in the Tables below and in the documentation provided under the Documentation tab.
The dataset can be downloaded as NetCDF files (CDM-Obs-Core, see documentation) or as comma-separated values (CSV) files.


In situ observations of meteorological and soil variables from the US Climate Reference Network near the surface from 2006 to present

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.
GRUAN 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. 
This characterization ensures the traceability to SI units or accepted standards, providing extensive metadata and full documentation of measurements and algorithms.
This allows the full reprocessing from the raw data.
The 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. 
The current dataset provides radiosoundings from 17 stations.
The GRUAN dataset provides the vertical aggregation of quantities listed below in the main-variables table. 
Attributes are described in the related-variables tables.
The dataset can be downloaded as NetCDF files (CDM-Obs-Core, see documentation) or as comma-separated values (CSV) files.
More details about the the GRUAN dataset are given in the Documentation section, and
general information and background can be obtained from the GRUAN website (www.gruan.org).

In situ temperature, relative humidity and wind profiles from 2006 to March 2020 from the GRUAN reference network

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.
Surface 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. 
Work 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. 
Station 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 often extend further back in time than sub-daily records. Periodic updates and reissues refresh historical holdings availability and also build on aspects such as quality checking and flagging of data. Each such release is accompanied by a version number increment and a reissue of the product user guide. The latest release (served as version 3) extends the temporal coverage until February 2026. 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.
Data 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 (Common Data Model for observations (CDM-Obs-Core), see documentation) or as comma-separated values (CSV) files.
Users should ensure an appropriate assessment of long-term data quality prior to use in those applications which require consideration of such aspects.   
More 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. 
This work is being completed on behalf of C3S in sustained collaboration with colleagues at NOAA's National Centres for Environmental Information (NCEI) who are the WMO designated World Data Centre for meteorology.

Global land surface atmospheric variables from 1718 to present from comprehensive in-situ observations

This catalogue entry provides access to vertical profiles of standard meteorological variables. It includes two archives. 
The first is version 2 of the Integrated Global Radiosounding Archive (IGRA) from 1978 which incorporates global 
radiosounding profiles of temperature, humidity and wind from a large number of data sources,
which is 30% larger than the previous version 1. IGRA v2 is the result of quality assurance procedures applied to the
radiosoundings data which can be grouped into eight categories: fundamental “sanity” checks, checks on the plausibility
and temporal consistency of surface elevation, internal consistency checks, checks for the repetition of values, 
checks for gross position errors in ship tracks, climatology-based checks, checks on the vertical and temporal 
consistency of temperature, and data completeness checks. No uncertainty estimation for the IGRA data is available.
The second is the Radiosounding HARMonization (RHARM) dataset where values of temperature,
relative humidity and wind are homogenized to remove systematic effects (such as change in the measurement sensors,
solar radiation biases, sonde time-lag, calibration drifts, station relocation etc.) at 700 IGRA radiosounding stations
and radiosoundings from ships.
RHARM includes twice daily (0000 and 1200 UTC) radiosonde data at the mandatory levels (listed below).
At levels with pressure lower than 10 hPa, homogenized temperatures are not provided because of the paucity of available 
observations and the issues affecting the measurements at those levels.
Relative humidity homogenization and data provision are limited to 250 hPa owing to pervasive sensor 
performance issues at greater altitudes. 
Moreover, an estimation of the measurement uncertainty is provided for each value of the time series. 
The radiosounding significance levels are also homogenized per interpolation along with the corresponding uncertainties. 
The RHARM dataset thus inherits the IGRA quality assurance procedures, and additional quality checks are then applied,
performing tests on physical plausibility, accuracy of the homogenization (bias adjustment), presence of outliers, and coherency check
for the homogenization applied at the significant levels. 
The IGRA dataset is provided by NOAA's National Centers for Environmental Information (NCEI) also through the IGRA data portal.
The RHARM dataset has been specifically developed for the Copernicus Climate Change Service (C3S). 
The dataset can be downloaded as NetCDF files (CDM-Obs-Core, see documentation) or as comma-separated values (CSV) files.

In situ observations of meteorological variables from the Integrated Global Radiosounding Archive and the Radiosounding Harmonization dataset from 1978 onward

This dataset provides access to two types of ozone observations: total column ozone estimates and vertical profiles of ozone concentration.
The total ozone estimates are based on solar UV radiation measurements made by ground-based spectrophotometers (Dobson or Brewer type spectrophotometers).
The vertical profiles of ozone concentration are estimated primarily using ozonesonde observations.
Data are available for 159 Dobson stations, 109 Brewer stations and 135 ozonesondes stations.
The ozone data are included in the quadrennial Scientific Assessments of Ozone Depletion as part of the “Vienna convention for the protection of the ozone layer” and are used extensively for a range of studies and applications.

In situ total column ozone and ozone soundings from 1924 to present from the World Ozone and Ultraviolet Radiation Data Centre

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’s 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.
The dataset consists of two products:

The 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; 1979–1987), Special Sensor Microwave/Imager (SSM/I; 1987–2006), and Special Sensor Microwave Imager/Sounder (SSMIS; 2005 onward). This product spans the period from 1979 to 2025 and is not updated. In the following, it is referred to as the SSMIS product.
The 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 (2002–2011) and its successor, AMSR2 (2012 onward). This product spans the 2002 to present (i.e. operationally updated). In the following, it is referred to as the AMSR product.

Both 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 15–25 km versus 30–60 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.
Although 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.
Further details about each product can be found below as well as in the Documentation section.

Sea ice concentration from 1978 to present derived from satellite observations

This dataset, commonly known as the Randolph Glacier Inventory (RGI), provides global glacier outlines compiled from maps, aerial photographs and satellite images. The dataset is a snapshot (one outline per glacier) constructed from source data that were mostly acquired in the period 1998-2010.
The 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, including new outlines created within the Copernicus Climate Change Service 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. 
The provided RGI is brokered from the NSIDC and consists of three datasets providing:

glacier outlines with attribute information in shape file format
tabular data in csv format describing the area of each glacier in a specific elevation interval, i.e. its hypsometry
a raster field in netCDF format containing the percentage of glacier cover in each grid cell

The RGI data available from NSIDC (shape and csv files) are arranged in 19 first order regions that cover all glacierized regions in the world. The datasets available here include all glaciers in one file. Coordinates are in longitude and latitude with the World Geodetic System 1984 (WGS84) datum.
The RGI glacier outlines provided here are widely used by the glacier community to spatially constrain calculations of the geodetic glacier mass balance - a dataset provided by the World Glacier Monitoring Service (WGMS) and made available in the Copernicus Climate Data Store.

Glaciers distribution data from the Randolph Glacier Inventory for year 2000

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:


Windstorm track: a sequence of longitude–latitude 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).


Windstorm 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° x 0.25°) as well as statistically-downscaled high-resolution grid (0.016° x 0.016°) 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). 


Windstorm 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.


This dataset was produced on behalf of the Copernicus Climate Change Service.

Windstorm tracks and footprints derived from reanalysis over Europe between 1940 to present

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.
High-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.
The 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:

what will future climate forcing be?
what will be the response of the climate system to changes in forcing?
what is the uncertainty related to natural variability of the climate system?

The term "experiment" in the CDS form refers to three main categories:

Evaluation: 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;
Historical: 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;
Scenario: 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.

In CORDEX, the same experiments were done using different RCMs (labelled as “Regional Climate Model” in the CDS form).
In 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 “Global Climate Model” in the form and are also available in the CDS.
Additionally, 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.
The 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.
The 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.

CORDEX regional climate model data on single levels

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’s 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.
The data represent the current state-of-the-art for satellite-based precipitation climate data record production in Europe, which is in line with the “Systematic observation requirements for satellite-based products for climate” 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.
This precipitation data record and its processing chain are called Global Interpolated RAinFall Estimate (GIRAFE). GIRAFE provides a global 1° gridded daily accumulated precipitation amount together with uncertainty estimates coming from the sampling, and a global 1° 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.
This dataset has been produced by the EUMETSAT Satellite Application Facility on Climate Monitoring.

Daily and monthly Global Interpolated RAinFall Estimation (GIRAFE) data derived from satellite observations

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.
The 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.
A 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.
Most 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.

Lake surface water temperature from 1995 to present derived from satellite observations

This dataset provides global daily and monthly 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.
Remote-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.
The files from this dataset contain global daily and monthly 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.
This 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.

Ocean colour from 1997 to present derived from satellite observations

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’s oceans as accurately as possible.
Sea 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.
In 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.
The 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.
The 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.
This 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.
This 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.

Sea level gridded data from satellite observations for the global ocean from 1993 to present

This catalogue entry provides a time series of thermal comfort indices, organised to make time the primary dimension for analysis and extraction. For any user-defined location (point) or small area, the dataset returns the temporal evolution of the selected variables, enabling efficient time-series studies without requiring users to download and manage full global gridded files.
The dataset provides two main variables. Mean radiant temperature describes how the human body experiences radiation in a given environment, defined as the uniform temperature of a fictive black-body radiation enclosure that would result in the same net radiation energy exchange with the subject. Universal thermal climate index is defined as the air temperature of a reference outdoor environment that would elicit the same physiological response in the human body as the actual environment.
The dataset covers from January 1940 to near real time and is derived from ERA5 reanalysis from the European Centre for Medium-Range Forecasts (ECMWF).
Data Access
This time-series product is designed for efficient extraction of temporal data at specific locations or small areas. Users can extract a complete time series for a single geographic location (point query) or for all grid points within a small area (area query, maximum 3x3 degrees).
Output Formats
Data can be downloaded in NetCDF format, which is available for both area and point-based queries and preserves full metadata and coordinate information. CSV format is available for point-based queries only and is convenient for direct import into spreadsheets and analysis tools.
Programmatic Access
This dataset is well suited to programmatic access via the CDS API. Users can request time series for individual points or small areas without transferring complete global gridded files. This "time-first" access pattern reduces data handling overhead for workflows focused on location-specific analyses and local-to-regional studies.
Related Resources
For access to the original gridded files with full spatial coverage and additional statistics (daily, monthly, seasonal, yearly), see the related dataset: Thermal comfort indices derived from ERA5 reanalysis

Thermal comfort indices time-series derived from ERA5 reanalysis

This dataset is superseded by the [Global climate and energy indicators from 1950 to present derived from reanalysis](https://cds.climate.copernicus.eu/datasets/sis-energy-global-reanalysis?tab=overview).
From 01 July 2026, this dataset will no longer be updated, it will not be supported by the data providers and data and documentation will be provided as is. This dataset will subsequently be deprecated in December 2026.
Users are encouraged to use our [Forum](https://forum.ecmwf.int) to raise any item of discussion with respect to this dataset.

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 unités 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.
The 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.
Data 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.

Climate and energy indicators for Europe from 1979 to present derived from reanalysis

Within the hydrological cycle, precipitation is the main component of water transport from the atmosphere to the Earth’s 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.
The 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.
The 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.

Precipitation monthly and daily gridded data from 1979 to present derived from satellite measurements

This dataset provides daily estimates of snow-covered land areas obtained from observations collected by multiple optical satellite instruments. As snow cover plays a crucial role in weather and climate, this data is particularly valuable for monitoring, estimating and assessing relevant changes.
It provides Level 3 collated (L3C) processed products, namely global daily single-sensor Snow cover fraction viewable (SCFV) and Snow cover fraction on ground (SCFG) products, both of which are based on optical satellite data.
The retrieval method is developed in the European Space Agency (ESA) Climate Change Initiative for Snow. The data is in line with the “Systematic observation requirements for satellite-based products for climate” as defined by Global Climate Observing System (GCOS).
The dataset provides two snow cover extent products, which are homogenized in non-forested areas, but provide different thematic snow information in forested areas:
- SCFV provides the snow cover as observable from the satellite sensor in forested areas.
- SCFG provides an estimate of the snow cover fraction on the ground in forested areas by applying a correction for the masking effect of the forest canopy. 
These products are available as Climate Data Records (CDRs), which have sufficient length, consistency and continuity to detect climate variability and change. Interim CDRs (ICDRs) are produced on behalf of the Copernicus Climate Change Service (C3S) with the same software and algorithms to extend the baseline CDRs. 
This dataset is produced on behalf of C3S by ENVEO IT GmbH, except for Moderate Resolution Imaging Spectroradiometer (MODIS) based snow cover extent products which were produced in the ESA Climate Change Initiative Snow project by ENVEO IT GmbH.

Snow cover extent from 1982 to present derived from satellite observations

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 “Related data” in the sidebar). Contrary to the frozen IPCC Atlas dataset, this entry will update adding new data on a regular basis.
This 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. 
This 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.

Gridded dataset underpinning the Copernicus Interactive Climate Atlas

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):

very low: <5.2
low: 5.2 - 11.2
moderate: 11.2 - 21.3
high: 21.3 - 38.0
very high: 38.0 - 50
extreme: >=50.0 

This 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.
The 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.
This dataset was produced on behalf of the Copernicus Climate Change Service.

Fire danger indicators for Europe from 1970 to 2098 derived from climate projections

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 – the microscopic aquatic plants known as phytoplankton – 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.
POLCOMS 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).
This dataset was produced on behalf of the Copernicus Climate Change Service.

Marine biogeochemistry data for the Northwest European Shelf and Mediterranean Sea from 2006 up to 2100 derived from climate projections

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.
The 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.
This dataset was produced on behalf of the Copernicus Climate Change Service.

Ocean surface wave indicators for the European coast from 1977 to 2100 derived from climate projections

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.
ERA-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°x0.5° 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.
Agroclimatic 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.
The data was produced on behalf of the Copernicus Climate Change Service.

Agroclimatic indicators from 1951 to 2099 derived from climate projections

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’s biodiversity. They are specifically relevant for applications within the biodiversity and ecosystem community. 
The 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.
The 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.
This dataset was produced on behalf of the Copernicus Climate Change Service.

Global bioclimatic indicators from 1950 to 2100 derived from climate projections

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’s biodiversity. They are specifically relevant for applications within the biodiversity and ecosystem services community.
The 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.
The 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.
This dataset was produced on behalf of the Copernicus Climate Change Service.

Global bioclimatic indicators from 1979 to 2018 derived from reanalysis

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.
This 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.
At 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.
This dataset was produced on behalf of the Copernicus Climate Change Service.

Extreme precipitation risk indicators for Europe and European cities from 1950 to 2019

Maximum and minimum temperature, surface runoff and wind gust are missing from parts of this dataset. We are working on back-archiving the data. More details in the [known issues document](https://confluence.ecmwf.int/display/CKB/Copernicus+pan-Arctic+Regional+Reanalysis+%28CARRA2%29%3A+known+issues).

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. 
The 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.
The 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. 
The 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.
More details about the reanalysis dataset and the extensive input data are given in the Documentation section.

pan-Arctic regional reanalysis daily and monthly data from 1986 to present

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 Externalisée). 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.
A total of 39 indicators are made available in this dataset, divided into seven distinct groups:

Start and end date of snow season
Annual amount of machine made snow produced
Precipitation
Snow depth
Snow water equivalent
Air temperature
Potential snow making hours

The 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). 
This dataset was produced on behalf of the Copernicus Climate Change Service.

Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections

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.
The indicators available in this dataset are categorised as follows: 


Windstorm tracks – 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. 


Windstorm footprints – 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.


Summary indicators (Tier 1) – uses the windstorm tracks and maximum 10m wind speed to produce annual and decadal summary statistics for the land regions in Europe.


Loss and risk indicators (Tier 3) – describing the socio-economic impact of windstorms for various land cover and building types in Europe.


This dataset was produced on behalf of the Copernicus Climate Change Service.

Winter windstorm indicators for Europe from 1979 to 2021 derived from reanalysis

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.
The 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.
The 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. 
The 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.
More details about the reanalysis dataset and the extensive input data are given in the Documentation section.

pan-Arctic regional reanalysis sub-daily data from 1986 to present

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.
The 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.
To 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.
The 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.
This dataset was produced on behalf of the Copernicus Climate Change Service.

Climate extreme indices and heat stress indicators derived from CMIP6 global climate projections

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. 
ECV datasets provide the empirical evidence needed to understand the current climate and predict future changes. 
CIIs 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.
The 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.
The 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° x 0.11° and 5km x 5km.
The 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.
The 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 °C, 2.0 °C or 3.0 °C). 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.
This dataset is produced and quality assured by the Swedish Meteorological and Hydrological Institute on behalf of the Copernicus Climate Change Service.

Temperature and precipitation climate impact indicators from 1970 to 2100 derived from European climate projections

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.
The 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.
This dataset was produced on behalf of the Copernicus Climate Change Service.

Ocean surface wave time series for the European coast from 1976 to 2100 derived from climate projections

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.
Temperature 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. 
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. 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.

Temperature statistics for Europe derived from climate projections

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.
The 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.
This dataset was produced on behalf of the Copernicus Climate Change Service.

Global sea level change indicators from 1950 to 2050 derived from reanalysis and high resolution CMIP6 climate projections

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.
 These heat wave and cold spell days are available for different future time periods and use different climate change scenarios.
A 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.
First, 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.

Heat waves and cold spells in  Europe derived from climate projections

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.
The 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.
The 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.
This dataset was produced on behalf of the Copernicus Climate Change Service.

Downscaled bioclimatic indicators for selected regions from 1950 to 2100 derived from climate projections

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.
The 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.
The 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.
This dataset was produced on behalf of the Copernicus Climate Change Service.

Global sea level change time series from 1950 to 2050 derived from reanalysis and high resolution CMIP6 climate projections

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. 
The 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).
The DBS method is a parametric quantile-mapping variant. 
This type of methods fit a statistical distribution to the cumulative distribution function and use those fitted distributions to conduct the quantile-mapping. 
Here, 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. 
Temperature corrections were done conditional on the wet/dry state of the corresponding precipitation time series.
The seasonal variations in the biases were represented by monthly parameter windows for precipitation and a smoothed seasonal cycle for the temperature distribution parameters. 
The smoothing was done using twelve harmonic components.
There is some post-processing in place for the data set to be suitable for hydrological impact modeling. 
Bias-adjustment of daily mean, maximum and minimum temperature using quantile mapping can in some cases lead to inconsistencies. 
For instance, maximum (minimum) temperature could be lower (higher) than mean temperature. 
If 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. 
This 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. 
Also, the adjustment is done conditional on the wet/dry state of the corresponding precipitation series. 
The climatology of the anomalies was derived from the HydroGFD2.0 dataset.
In addition, DBS’s limitations lead to some data not being bias-adjusted or values beyond physically plausible ranges.
In those cases, DBS gives missing values as output. 
Grid cells where the DBS method resulted in such missing values have been interpolated in time or space. 
If interpolation was not possible, full time series from nearest grid cell was copied to relating grid point.

Essential climate variables for water sector applications derived from climate projections

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.
Environmental 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.
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.

Climatic suitability for the presence and seasonal activity of the Aedes albopictus mosquito for Europe derived from climate projections

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’s biodiversity. They are specifically relevant for applications within the biodiversity and ecosystem services community.
The 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.
The 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.
This dataset was produced on behalf of the Copernicus Climate Change Service.

Downscaled bioclimatic indicators for selected regions from 1979 to 2018 derived from reanalysis

This dataset is no longer supported and access to the data will be closed in due course. Users are requested to switch to a new CDS dataset that continues to be supported: [Multi-model hydrological seasonal reforecasts for Europe](https://cds.climate.copernicus.eu/datasets/sis-hydrology-europe-seasonal-reforecast)

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.
The 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.
The 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.
This 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).

Multi-model seasonal reforecasts of river discharge for Europe

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.
Those 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).
The indices have either been calculated through a specific workflow using CDS dataset in input or directly retrieved From the CDS when already available. 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.
The 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.
The underlying datasets hosted on the CDS are:

ERA5 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.
Climate 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.
Fire danger indicators for Europe from 1970 to 2098 derived from climate projections - provides the high fire danger days and fire weather indicators.
Hydrology-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.
Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections - provides the snowfall amount index.
Water 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.

This dataset was produced on behalf of the Copernicus Climate Change Service.

Hours	Number of requests	Number of users	Downloaded data
Last 1H	20183	2188	3690 GB
Last 2H	40993	2865	7363 GB
Last 3H	61507	3339	11301 GB
Last 6H	127463	4190	23309 GB
Last 12H	262583	5429	49867 GB
Last 24H	504911	7413	100418 GB

	Catalogue entry	Number of users	Downloaded data
1	reanalysis-era5-pressure-levels	1946	42308 GB
2	reanalysis-era5-single-levels	3224	22537 GB
3	reanalysis-era5-land	1117	12405 GB
4	reanalysis-era5-complete	225	4916 GB
5	seasonal-original-single-levels	93	3260 GB
6	reanalysis-cerra-pressure-levels	9	2621 GB

	Country	Number of requests	Number of users	Downloaded data
1	DE	16328	208	5405 GB
2	GB	23838	315	5003 GB
3	FR	12114	225	4350 GB
4	IT	13315	152	3356 GB
5	ES	17526	169	2615 GB
6	PL	5822	46	1988 GB

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