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-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 single levels from 1940 to present".

ERA5 hourly 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

The Nordic Gridded Climate Dataset (NGCD) is a high resolution, observational, gridded dataset of daily minimum, maximum and mean temperatures and daily precipitation totals, covering Finland, Sweden and Norway. The time period covered begins in January 1961 and continues to the present. The dataset is regularly updated every 6 months, in March and in September. In addition, there are daily, provisional updates.
Spatial interpolation methods are applied to observational datasets to create gridded datasets. 
In general, there are three types of such methods: deterministic (type 1), stochastic (type 2) and pure mathematical (type 3).
NGCD applies both a deterministic kriging (type 1) interpolation approach and a stochastic Bayesian (type 2) interpolation approach to the same in-situ observational dataset collected by weather stations. For more details on the algorithms, users are advised to read the product user guide.
The input data is provided by the National Meteorological and Hydrological Services of Finland, Norway and Sweden. The time-series used for Finland and Sweden are the non-blended time-series from the station network of the European Climate Assessment & Dataset (ECA&D) project. For Norway, time-series are extracted from the climate database of the Norwegian Meteorological Institute.

Nordic gridded temperature and precipitation data from 1961 to present derived from in-situ observations

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 user-selected location timeseries of [ERA5 Land data](https://doi.org/10.24381/cds.e2161bac) for a limited set of variables. Its content may be undergo changes over time (e.g. file format, data file structure, deprecation etc) and is **not recommended for use in a production environment**. 

For users interested in large regions, the original ERA5 Land catalogue entry remains the more efficient option. 
We will make every effort to notify users of changes, either through this banner and/or the [Forum](https://forum.ecmwf.int/).

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.

ERA5 Land hourly time-series data 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 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 present and is updated daily. 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 (2012–2017). This product spans the 2002–2017 period and is not 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 with daily updates.
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 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 dataset is soon to be deprecated. An improved climate and energy indicators derived from reanalysis dataset at global level is currently in preparation for publication in the next few months. Watch our [Forum](https://forum.ecmwf.int/) for a future announcement!

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

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 by ENVEO IT GmbH.

Quality assessment of the dataset

Ice sheet velocity for Antarctica and Greenland derived from satellite observations

This dataset provides estimates of surface soil moisture over the globe from a large set of satellite sensors. It is based on the methodology developed in the ESA Climate Change Initiative for Soil Moisture and represents the current state-of-the-art for satellite-based soil moisture climate data record production, in line with the “Systematic observation requirements for satellite-based products for climate” as defined by GCOS (Global Climate Observing System). Data are on a regular latitude/longitude grid expectedly with gaps in space and time.
When dealing with satellite data it is common to encounter references to Climate Data Records (CDR) and interim-CDR (ICDR). For this dataset, both the ICDR and CDR parts of each product were generated using the same software and algorithms. The CDR is intended to have sufficient length, consistency, and continuity to detect climate variability and change. The ICDR provides a short-delay access to current data where consistency with the CDR baseline is expected but was not extensively checked. The dataset contains the following products: "active", "passive" and "combined". The "active" and "passive" products were  created by using  scatterometer and radiometer soil moisture products, respectively. The "Combined" product results from a blend based on both scatterometer and radiometer soil moisture products.
This dataset is produced on behalf of the Copernicus Climate Change Service (C3S).

Soil moisture gridded data from 1978 to present

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 currently only available for the Northern Hemisphere and limited to the extended boreal winter months (October through April). Sea ice type classification during summer is difficult due to the effect of melting at the ice surface which disturbs the passive microwave signature.

Both sea ice products are based on measurements from the series of Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSMIS) sensors and share the same algorithm baseline. However, sea ice edge makes use of two lower frequencies near 19 GHz and 37 GHz and a higher frequency near 90 GHz whereas sea ice type only uses the two lower frequencies. This dataset combines Climate Data Records (CDRs), which are intended to have sufficient length, consistency, and continuity to assess climate variability and change, and Interim Climate Data Records (ICDRs), which provide regular temporal extensions to the CDRs and where consistency with the CDRs is expected but not extensively checked. For this dataset, both the CDR and ICDR parts of each product were generated using the same software and algorithms.
The CDRs of sea ice edge and type currently extend from 25 October 1978 to 31 December 2020 whereas the corresponding ICDRs extend from January 2021 to present (with a 16-day latency behind real time). All data from the current release of the datasets (version 3.0) are Level-4 products, in which data gaps are filled by temporal and spatial interpolation. For product limitations and known issues, please consult the Product User Guide.
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).

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

The Burned Area products provide global information of total burned area (BA) at pixel and grid scale. The BA is identified with the date of first detection of the burned signal in the case of the pixel product, and with the total BA per grid cell in the case of the grid product. The products were obtained through the analysis of reflectance changes from medium resolution sensors (Terra MODIS, Sentinel-3 OLCI), supported by the use of MODIS thermal information. The burned area products also include information related to the land cover that has been burned, which has been extracted from the Copernicus Climate Change Service (C3S) land cover dataset, thus assuring consistency between the datasets.
The algorithms for BA retrieval were developed by the University of Alcala (Spain), and processed by Brockmann Consult GmbH (Germany).
Different product versions are available. FireCCI v5.0cds and FireCCI v5.1cds were developed as part of the Fire ECV Climate Change Initiative Project (Fire CCI) and brokered to C3S, offering the first global burned area time series at 250m spatial resolution. FireCCI v5.1cds used a more mature algorithm than the previous version. This algorithm was adapted to Sentinel-3 OLCI data to create the C3S v1.0 burned area product, extending the BA database to the present.
During July 2020, an error in some files in the version v5.1cds were identified, affecting the files of the grid product of January 2018, and the pixel and grid products of October, November and December 2019. These errors were fixed, and a new version, v5.1.1cds, was created for the whole time series, to replace version v5.1cds. The latter product has been deprecated, but it is temporally kept in the database for transparency and traceability reasons. Only version v5.1.1cds should be used.
The BA products are useful for researchers studying climate change, as they provide crucial information on burned biomass, which can be translated to greenhouse gases emissions amongst other contaminants. Burned area is also useful for land cover change studies, fire management and risk analysis.

Fire burned area from 2001 to present derived from satellite observations

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 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

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 as daily means (GPCP daily v1.3, now updated to GPCP daily v3.2), 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.
The dataset v1.3 and v2.3 are brokered from the GPCP. The dataset v3.2 is brokered from 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 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 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 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 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 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

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

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.
This catalogue entry comprises data from a number of origins summarised in the following sections.

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

The Essential Climate Variables for assessment of climate variability from 1979 to present dataset contains a selection of climatologies, monthly anomalies and monthly mean fields of Essential Climate Variables (ECVs) suitable for monitoring and assessment of climate variability and change. Selection criteria are based on accuracy and temporal consistency on monthly to decadal time scales. The ECV data products in this set have been estimated from climate reanalyses ERA-Interim, ERA5 and ERA5-Land, and, depending on the source, may have been adjusted to account for biases and other known deficiencies. Data sources and adjustment methods used are described in the Product User Guide, as are various particulars such as the baseline periods used to calculate monthly climatologies and the corresponding anomalies.
The statistics provided are monthly average fields, climatologies and anomalies, as well as 12-month running mean anomalies. Climatologies and anomalies are calculated with respect to two reference periods: 1981-2010 (ERA5, ERA5-Land and ERA-Int) and 1991-2020 (ERA5 and ERA5-Land ).
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 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

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 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 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 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 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

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 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 Atlas dataset (and viewer) have been updated to include new indices and data sources. Note that some of the available time periods have changed to include the latest data. Full details can be found in the documentation.

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

This dataset provides global daily estimates of ocean surface chlorophyll-a concentration and remote sensing reflectance derived from multiple satellite sensors. These two products are part of the broader discipline of ocean colour remote sensing, which analyses ocean surface radiances measured from space to derive information about the optical properties and constituents of the upper ocean. This information plays an essential role in our ability to monitor the health and productivity of marine ecosystems, assess the role of the oceans in the global carbon cycle, and quantify the impacts of climate change. Satellite remote sensing is the only method for regular monitoring of ocean biology on a global scale.
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 composites of merged sensor products: SeaWiFS, MERIS, MODIS Aqua, VIIRS, and (from version 5.0 onward) OLCI. Note that Rrs and Chl-a data are only available over cloud- and ice-free areas. As a result, more complete spatial coverage (as shown in the map in the upper-right corner) can be achieved by aggregating data over longer time periods.
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 daily data from 1997 to present derived from satellite observations

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 comma-separated values (CSV) files organized in two different ways:

One row per report: for each report all variables are provided as individual columns in one row (referenced as csv-lev in the variable lists below).
One row per observable: each row provides information about one variable only (referenced as csv-obs in the variable lists below).



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 dataset provides estimates of water vapour derived from atmospheric delays in Global Navigation Satellite System
(GNSS) radio signals.
The initial data is collected from two in situ ground-based network of GNSS receivers – the International GNSS Service
(IGS) and EUREF Permanent Network (EPN). The IGS collects, archives, and freely distributes GNSS data from a 
cooperatively operated global network of more than 500 ground-based GNSS stations since 1994. The EPN is a European 
network of more than 300 continuously operating GNSS reference stations with precisely known coordinates.
The fundamental observable of ground-based GNSS is the Zenith Total Delay (ZTD). This observable represents the 
propagation delay induced by the atmosphere on GNSS signals between the GNSS satellites and a GNSS receiver.
Using in-situ measurements of ZTD as well as ancillary meteorological data at the site, the vertically-integrated total 
amount if water vapour in the air column can be retrieved. This integrated quantity is referred to as Total Column 
Water Vapour (TCWV), or Precipitable Water Column (PWC), Total Precipitable Water (TPW), 
or Integrated Precipitable Water (IPW), or also Integrated Water Vapour (IWV).
The TCWV values, with corresponding uncertainties, are available from the 2nd reprocessing campaign
of the EPN (EPN-repro2), the 3rd reprocessing campaign of the IGS (IGS-repro3), and the daily product of IGS. 
All networks provide hourly records of ZTD as part of the products derived from GNSS data processing.
The GNSS TCWV derived from these ZTD data is a new product, made available exclusively for the Climate Data Store (CDS).
The data user must consider that while the EPN-repro2 and IGS-repro3 consist of tropospheric parameters derived from consistently 
reprocessed data spanning multiple decades, the IGS daily product is a near real-time product which may contain jumps 
in the time-series when a new processing model is introduced. Therefore, IGS daily ZTD and TCWV should not be considered
as reference products. 
The method for retrieval of GNSS TCWV and details on the algorithms can be found in the documentation. For comparison, 
TCWV values from the ECMWF ERA5 reanalysis are also made available for the user.
The datasets can be downloaded as NetCDF files (CDM-Obs-Core, see documentation) or as comma-separated values (CSV) files.

In-situ observations of water vapour and atmospheric delay from the ground-based GNSS network from 1996 to present

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

Fraction of absorbed photosynthetically active radiation: FAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The FAPAR value results directly from the radiative transfer in the canopy which is instantaneous. It depends on canopy structure, vegetation element optical properties and illumination conditions. FAPAR is very useful as input to a number of primary productivity models based on simple efficiency considerations. FAPAR is relatively linearly related to reflectance values and is little sensitive to scaling issues. Note also that the FAPAR refers only to the green parts of the canopy.
Leaf area Index: LAI is defined as half the developed area of photosynthetically active elements of the vegetation per unit horizontal ground area. It determines the size of the interface for exchange of energy (including radiation) and mass between the canopy and the atmosphere. This is an intrinsic canopy primary variable that should not depend on observation conditions. LAI is strongly non-linearly related to reflectance. Therefore, its estimation from remote sensing observations is scale dependent. Note that vegetation LAI as estimated from remote sensing includes all the green contributors such as the understory when existing under forests canopies. However, except when using directional observations, LAI is not directly accessible from remote sensing observations due to the possible heterogeneity in leaf distribution within the canopy volume. Therefore, remote sensing observations are rather sensitive to the ‘effective’ leaf area index, i.e. the value that provides the same diffuse gap fraction while assuming a random distribution of leaves. The difference between the actual LAI and the effective LAI may be quantified by the clumping index that roughly varies between 0.5 (very clumped canopies) and 1.0 (randomly distributed leaves). Note that similarly to the other variables, the retrieved LAI is mainly corresponding to the green elements: the correct term to be used would be GAI (Green Area Index) although we propose to still use LAI for the sake of simplicity.
The following product versions are available:

Version v0.0 is a brokered dataset from the Copernicus Global Land Service (CGLS).
Version v1.0 is produced by the C3S service. In this version, the theoretical framework for the CDR LAI and FAPAR algorithm capitalizes on the past experience gained in the Copernicus Global Land products (Baret et al. 2013). It capitalizes itself on the development and validation of already existing products: CYCLOPES Version 3.1 and MODIS Collection 5, and the use of neural networks. Verger et al. (2008) demonstrated that this approach could be used efficiently to estimate LAI and FAPAR from existing products provided that a strong link exists between the inputs and the outputs in the training dataset. Error propagation was included in the version v1.0 (compared to v0.0).
Version v2.0 is produced by the C3S service. This version is retrieved by applying the Two-stream Inversion Package (TIP) to visible (VIS) and near-infrared (NIR) broadband albedos. TIP is based on the Two-stream Model developed by Pinty et al. 2006, which implements the two-stream approximation of radiative transfer for a homogeneous canopy (“1D-canopy”).
Version v3.0 is produced by the C3S service. This version builds on version 2 and adds a multi-sensor aspect to the LAI/FAPAR products delivered so far. The inversion of the BRDF coefficients was improved. A climatological BRDF based on SPOT-VGT is introduced as a priori information used for the inversion of the albedos which is the input for the TIP model.
Version v4.0 is produced by the C3S service and builds on version 3 using the Sentinel3 A/B SLSTR and OLCI dataset.

Please note that LAI version v0.0 and v1.0 represent the actual LAI. Starting from version v2.0, the LAI produced by the TIP (Two Inversion Package) model represents the "effective" LAI. The difference between the actual LAI and the effective LAI may be quantified by the clumping index that roughly varies between 0.5 (very clumped canopies) and 1.0 (randomly distributed leaves).

Leaf area index and fraction absorbed of photosynthetically active radiation 10-daily gridded data from 1981 to present

This dataset provides global Earth surface albedo products. The albedo has an impact on the amount of energy that Earth absorbs from the Sun and, therefore, is an important variable in climate and weather studies.
The surface albedo quantifies the fraction of irradiance reflected by the surface of the Earth. It provides information on the radiative basis, thus on the temperature and water balance. The directional albedo or directional-hemispherical reflectance (also called black-sky albedo) is defined as the integration of the bi-directional reflectance over the viewing hemisphere. It assumes all energy is coming from a direct radiation from the sun and is computed for the local solar noon.
The current algorithm is applied at a 10-day frequency on daily surface reflectances acquired during a moving temporal window of 20 days. It first normalizes the reflectances through inversion of a semi-empiric, kernel-driven reflectance model and favouring the most recent observations. Directional albedo is then computed, per spectral band, by integration over the viewing hemisphere and for local solar noon. Broadband albedo is then calculated from the spectral albedo by a linear relationship.
Multiple platform/sensors were used offering various spatial resolution and temporal coverage (1 km from SPOT-VGT and PROBA-V, 4 km from AVHRR). Products are available from 1981 to present.

Surface albedo 10-daily gridded data from 1981 to present

This dataset provides monthly and zonally averaged tropospheric humidity profiles derived from globally distributed GPS radio occultation (RO) measurements from EUMETSAT's Metop polar-orbiting satellites. Humidity plays an important role in the Earth's climate system, due to the strong greenhouse effect of water vapour but also for its role in the global energy transport, vertically in the atmosphere and horizontally between different geographical regions. It is central to the formation of clouds and precipitation, and determines the fundamental conditions for the biosphere, including distribution of rainfall and droughts. 
The dataset is produced by the EUMETSAT Radio Occultation Meteorology Satellite Application Facility (ROM SAF) and comprises the Climate Data Record (CDR, December 2006 to December 2016, ROM SAF product ID GRM-29-R1) and the Interim Climate Data Record (ICDR, January 2017 to present, ROM SAF product ID GRM-29-I1). Tropospheric humidity profiles are retrieved using 1D variational assimilation of observed atmospheric refractivity profiles and background model information from the ECMWF reanalysis. Compared to other satellite observation techniques, RO observations have high vertical resolution and are not affected by clouds or the type of underlying surface (such as land and sea).
The RO retrievals are averaged in monthly and 5-degree latitude bands for altitudes below 12km which roughly covers the troposphere. The variability (standard deviation) within such monthly bins are also provided, as well as associated quantities such as observation counts, sampling errors and an estimate of the fraction of model information in the humidity retrievals. In addition, mean and variability are provided for humidity for the a priori ECMWF reanalysis data, sampled (i.e. collocated) similarly at the satellite observation locations. 
Please note that the terms humidity and water vapour are used interchangeably on this page and in the documentation.

Tropospheric humidity profiles averaged monthly and zonally from 2006 to present derived from satellite 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

Cloud properties directly influence the state of the Earth´s upper-air atmosphere. Clouds have an effect on weather and climate through their contribution to the Earth’s water cycle and impact on the Earth’s energy budget. They impact atmospheric motions on a range of spatial and temporal scales and contribute to changes in the atmospheric composition. Water vapour originating from surface evaporation and transpiration condensates to form clouds, eventually leading to precipitation, which constitutes a fundamental mechanism of water redistribution around the globe.  Therefore, knowledge of cloud properties allows for a deeper understanding of the  feedback mechanisms within the hydrological cycle, directly in terms of precipitation, but also indirectly on the Earth´s energy budget through the changes in radiative fluxes. According to the 6th Intergovernmental Panel on Climate Change (IPCC) assessment report, clouds (together with aerosols) are among the largest sources of uncertainty of the estimates of the Earth´s energy budget, as well as of the potential feedback mechanisms and responses to climate change.
The Cloud Properties ECV contains four main variables, which can be separated  into averaged cloudiness, cloud height products, and cloud physical properties for both the ice and liquid water phases. These variables are provided in  two “product families”, according to the data provenance. The "CCI product family" originates from the European Space Agency (ESA) Cloud_cci project, which produced a Thematic Climate Data Record (TCDR) based on ATSR2 and AATSR (Origin: “ESA”), and extensions using SLSTR produced specifically for the Copernicus Climate Change Service (Origin: “C3S”). The “CLARA (CM SAF cLoud, Albedo and surface Radiation) product family” originates in the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF), who produced a TCDR based on AVHRR observations. Both product families provide two complementary  data records, differing in algorithms, input data, temporal and horizontal resolution. These TCDR timeseries are intended to have sufficient length, consistency, and continuity to detect climate variability and change. They are frequently updated with Interim Climate Data Records (ICDR) or simply extensions, generated using the same software and algorithms to cover more recent periods. Further details on algorithms, data description, and extensive validation results are given in the Documentation section.

Cloud properties global gridded monthly and daily data from 1979 to present derived from satellite observations

The Surface Radiation Budget (SRB) represents the balance between the heating of the Earth’s surface through absorption of incoming solar radiation and cooling through the emission of infra-red radiation, which is a fundamental component of the surface energy budget. Small changes in the surface radiation budget can lead to large climatological responses, which makes a permanent and precise monitoring indispensable. Accurate understanding of the surface radiation budget at regional and global scales can improve the understanding of the Earth’s climate system and supports research on current and anticipated climate change, as well as the evaluation of radiative processes in climate models. Better knowledge of the global and regional surface radiation is also required for a successful utilisation of renewable energy sources, such as solar power plants.
The dataset contains seven variables, which can be separated into solar (shortwave) and thermal (longwave) surface radiation components. Both components include incoming (downwelling), outgoing (upwelling), and the net radiation fluxes. These variables are provided from two “product families”, according to the data provenance. The "CCI product family" originated from the European Space Agency (ESA) Cloud_cci project, which produced a Thematic Climate Data Record (TCDR) based on ATSR2 and AATSR (“Origin”:“ESA”) and extensions using SLSTR produced specifically for Copernicus Climate Change Service (“Origin”: “C3S”). The “CLARA (CM SAF cLoud, Albedo and surface Radiation) product family” originates in the EUMETSAT´s Satellite Application Facility on Climate Monitoring (CM SAF), who produced a TCDR based on AVHRR observations. The two product families follow somewhat different nomenclatures. Therefore, a table relating the names used in the download form with the names used within the files and in other datasets distributed by the Climate Data Store is added at the end of this overview. 
The “CLARA product family” and “CCI product family” provide complementary data records, differing in temporal and horizontal resolution. These TCDR timeseries are intended to have sufficient length, consistency, and continuity to detect climate variability and change. They are frequently updated with Interim Climate Data Records (ICDRs) or simply extensions, generated using the same software and algorithms to cover more recent periods. Further details on algorithms, data description, and extensive validation results are given in the Documentation section.

Surface radiation budget from 1979 to present derived from satellite observations

This dataset provides global maps describing the land surface into 22 classes, which have been defined using the United Nations Food and Agriculture Organization’s (UN FAO) Land Cover Classification System (LCCS). In addition to the land cover  (LC) maps, four quality flags are produced to document the reliability of the classification and change detection. 
In order to ensure continuity, these land cover maps are consistent with the series of global annual LC maps from the 1990s to 2015 produced by the European Space Agency (ESA) Climate Change Initiative (CCI), which are also available on the ESA CCI LC viewer.
To produce this dataset, the entire  Medium Resolution Imaging Spectrometer (MERIS) Full and Reduced Resolution  archive from 2003 to 2012 was first classified into a unique 10-year baseline LC map. This is then back- and up-dated using change detected from (i) Advanced Very-High-Resolution Radiometer (AVHRR) time series from 1992 to 1999, (ii) SPOT-Vegetation (SPOT-VGT) time series from 1998 to 2012 and (iii) PROBA-Vegetation (PROBA-V), Sentinel-3 OLCI (S3 OLCI) and Sentinel-3 SLSTR (S3 SLSTR) time series from 2013.
Beyond the climate-modelling communities, this dataset’s long-term consistency, yearly updates, and high thematic detail on a global scale have made it attractive for a multitude of applications such as land accounting, forest monitoring and desertification, in addition to scientific research.

Land cover classification gridded maps from 1992 to present derived from satellite observations

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as  GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below.
These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events.
The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate.
The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset.
The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique.
The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring.
The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. 
The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location.
The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations.
The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation.
This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. 
These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

Temperature and precipitation gridded data for global and regional domains derived from in-situ and satellite observations

Please be aware that the generation of this dataset is using an alternative source for the ERA5 data and may be subject to changes over time (e.g. file format, data file structure, deprecation etc). This dataset should therefore be regarded as “experimental” and is **not recommended for use in a production environment**. 

Notification of changes via this catalogue entry banner and/or in the [Forum](https://forum.ecmwf.int/) will be provided on best efforts.

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

This dataset provides monthly gridded data of sea ice thickness for the Arctic region based on satellite radar altimetry observations. 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 thickness is one of the parameters commonly used to characterise sea ice, alongside sea ice concentration, sea ice edge, and sea ice type, also available in the Climate Data Store.
Satellite radar altimeters provide measurements of the sea ice freeboard, which is the difference between the height of the surface of sea ice and the surface of water in open leads (areas of open water within the sea ice). Because of the buoyancy of ice in water, typically about 90% of the ice thickness remains under water and thus the total ice thickness is about 10 times the freeboard. However, snow on top of sea ice changes this ratio and complicates the estimation of the ice thickness, requiring the use of auxiliary information about snow depth and density. The retrieval of ice thickness uses the narrow radar swath at the nadir of the satellite at full resolution of approximately 1-10 km and a point spacing of 300 meters. This Level-2 sea-ice thickness products (not provided here) is then gridded for a period of a month to obtain full coverage of a north polar grid at a resolution of 25 km. The algorithm used was developed as part of the European Space Agency Climate Change Initiative (ESA CCI) on Sea Ice.
The data provided here are Level-3 Collated (L3C) products: they contain monthly gridded values from orbit data from a single platform (Envisat or CryoSat-2) without interpolation or any other form of gap filling. The files also contain estimates of the algorithm uncertainty as well as a quality status flag indicating potential issues with the retrieval not captured in the algorithm uncertainty. Sources of uncertainty in the algorithm are related to the auxiliary data and to the use of different radar altimeter concepts in Envisat (pulse-limited) and CryoSat-2 (synthetic aperture radar).
This dataset combines a Climate Data Record (CDR), which has sufficient length, consistency, and continuity to be used to assess climate variability and change, and an Interim Climate Data Record (ICDR), which provides regular temporal extensions to the CDR and where consistency with the CDR is expected but not extensively checked. Here, the CDR is based on measurements from the RA-2 altimeter on Envisat (October 2002  to October 2010) and the SIRAL altimeter on CryoSat-2 (November 2010 to April 2020). The ICDR is based on observations from CryoSat-2 only (from April 2015 onward) and is updated monthly with a one-month delay behind real time. Users should note that the quality and accuracy of the data record are higher during the CryoSat-2 period than during the Envisat period. As a result, care should be taken when combining the two missions to assess long-term changes and trends. More information can be found in the Product User Guide and Product Quality Assessment Report.
This dataset is currently limited spatially to the Arctic region and temporally to the winter months of October through April due to unresolved bias originating from melting snow or open melt ponds in the remaining five months. For a similar reason, no sea-ice thickness data with sufficient quality exist for the Southern Hemisphere. The extension of the CDR/ICDR to other periods, regions, and radar altimeter missions is under development in the extension of the ESA CCI Sea Ice project (ESA CCI+).
This dataset is produced on behalf of the Copernicus Climate Change Service (C3S).

Sea ice thickness monthly gridded data for the Arctic from 2002 to present derived from satellite observations

This dataset provides daily estimates of global sea surface temperature (SST) based on observations from multiple satellite sensors since September 1981. SST is known to be a significant driver of global weather and climate patterns and to play important roles in the exchanges of energy, momentum, moisture and gases between the ocean and atmosphere. As such, its knowledge is essential to understand and assess variability and long-term changes in the Earth’s climate.
The SST data provided here are based on measurements carried out by the following infrared sensors flown onboard multiple polar-orbiting satellites: the series of Advanced Very High Resolution Radiometers (AVHRRs), the series of Along Track Scanning Radiometers (ATSRs), and the Sea and Land Surface Temperature Radiometer (SLSTR).
The dataset provides SST products of different processing levels. Only Level-3 Collated and Level-4 and served through this entry in the Catalogue. Due to the large number of files at Level-2 Pre-processed and Level-3 Collated these products are served through the Climate Data Store API. For more information on how to access these levels consult the documentation.
The four types of products are:

Level-2 Pre-processed (L2P): SST data on the native satellite swath grid and derived from single-sensor measurements.
Level-3 Uncollated (L3U): SST product generated by regridding L2P data onto a global latitude-longitude grid.
Level-3 Collated (L3C): global daily (day and night) single-sensor SST product based on collated L3U data.
Level-4 (L4): spatially complete global SST product based on data from multiple sensors.

These products are available as Climate Data Records (CDRs), which have sufficient length, consistency, and continuity to be used to assess climate variability and changes. These SST CDRs are identical to those produced as part of the European Space Agency (ESA) SST Climate Change Initiative (CCI) project. Interim CDRs (ICDRs) are produced at levels L3C and L4 on behalf of the Copernicus Climate Change Service (C3S) to extend the baseline CDRs. Both SST CDRs and ICDRs are generated using software and algorithms developed as part of the ESA SST CCI.
Users should use the most recent version of the dataset whenever possible. Data from the previous version are also made available but cover shorter periods.

Sea surface temperature daily data from 1981 to present derived from satellite observations

This dataset provides monthly averaged ozone values derived from a large set of satellite sensors with global coverage.  Ozone is naturally formed in the atmosphere through the interaction between molecular oxygen and solar radiation. In the stratosphere, it acts as a shield protecting us from the UV radiation emitted by the Sun. At ground level however, it is a human health irritant and a component of smog. Ozone and climate change are strongly related. 
The ozone abundance in the atmosphere can be measured from space using a number of different remote-sensing techniques relying on ozone absorption at UV, visible, infrared and millimeter wavelengths. Instruments use the solar light or the thermal radiation emitted by the Earth to derive the vertical distribution of ozone in nadir, limb and solar occultation observation geometries. In the measured signal, molecular absorption features characteristic of ozone are detected using appropriate retrieval algorithms and are used to quantify its abundance. 
Data are available as Level-3 and/or Level-4 data products. Level-3 data correspond to measurements provided on a regular latitude/longitude after re-gridding of level-2 data (satellite orbit tracks) and averaging in time (over a month). Level-4 products are generated from Level-2 data after assimilation in a three-dimensional chemistry-transport model.
We distinguish between Climate Data Record (CDR) and intermediate-CDR (ICDR). For ozone datasets distributed here, both ICDR and CDR products are generated using common software and algorithms. The CDRs cover past time periods and include the sensors that are no longer in operation. They are meant to have sufficient length, consistency, and continuity to detect climate variability and change. The ICDRs are generated by satellite instruments still in operation, and are therefore regularly updated. Recent ICDR data are provided with a short-delay. They are expected to be consistent with the CDR baseline but have not undergone any extensive checking. Users are invited to read the documentation in order to determine the time coverage of each product.
Zonal averages and combined products obtained by merging data from different satellites are also provided. Nadir column data products from individual sensors are updated on a quarterly basis. All other products are updated once or twice a year.
This dataset is produced on behalf of C3S based on algorithm developments performed as part of the European Space Agency (ESA) Ozone Climate Change Initiative (CCI) project.

Ozone monthly gridded data from 1970 to present derived from satellite observations

This dataset provides estimates of surface elevation change over the Greenland and Antarctic ice sheets since 1992, utilizing satellite radar altimetry from five missions: ERS-1, ERS-2, ENVISAT, CryoSat-2, and Sentinel-3A. The surface elevation change is modelled over successive, overlapping periods and reported monthly. The dataset production method is an evolution of those employed by the European Space Agency (ESA)'s Greenland and Antarctic Ice Sheet Climate Change Initiatives and is guided by the Global Climate Observing System targets for the Ice Sheets Essential Climate Variable.
An annual Climate Data Record (CDR), and monthly intermediate CDRs (ICDRs) are issued. Each monthly record includes all previous data, from 1992 onwards, as well as that month's update.
This product is designed to provide data stability, so changes in the historic data, eg. if a satellite's elevation data is reprocessed or if inter-satellite cross-calibration is revised, are only introduced in the annual CDR. Each annual CDR is given a version number.
The differences in the geographical location of the two sheets result in site-specific processing:
Greenland: Data consists of surface elevation change rate and its uncertainty in a five-year (for the early satellites: ERS-1, ERS-2, and ENVISat) or three-year (for CryoSat-2 and Sentinel-3A) moving window. The moving window is advanced at one-month steps. Elevation measurements from satellite radar altimetry are used to build time-series of elevation change by the most optimal combination of the crossover-, repeat-track- and plane-fitting methods. The timeseries is derived for each cell on a 25km by 25km polar stereographic grid, covering the main Greenland ice sheet, and not including peripheral glaciers and ice caps. Data gaps have been filled using an ordinary Kriging interpolation method, and the distance to the nearest observational point is provided as utility information. The distance can be used to flag filled data.
Antarctica: Data consists of surface elevation change rate over a five-year moving window that advances in one-month steps. It covers the Antarctic ice sheet, ice shelves and associated ice rises and islands on a 25km by 25km polar stereographic grid. Elevation measurements from five satellite radar altimetry missions, ERS1, ERS2, EnviSat, CryoSat-2 and Sentinel-3A, are used to produce timeseries of surface elevation change by the crossover method for each grid cell. The mission timeseries are cross-calibrated into a consistent record, which is used to derive surface elevation change rates and their uncertainty estimates in each cell and time-window. Data gaps are flagged but not filled.

Ice sheet surface elevation change rate for Greenland and Antarctica from 1992 to present derived from satellite observations

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

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 because it controls key atmospheric processes, including water vapour and cloud feedbacks that can amplify the climate system’s response to increases in other greenhouse gases.
The UTH Thematic Climate Data Record (TCDR) and Interim Climate Data Record (ICDR) 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.
UTH is the Jacobian weighted relative humidity in the upper troposphere. Its natural logarithm can be expressed as a linear function of the radiance emanated from water vapour emissions in the upper troposphere, expressed as brightness temperature. Various threshold masks are applied in order to identify and remove measurements contaminated by clouds, for example due to scattering by large ice particles in the presence of deep convection, and measurements contaminated by the surface. A viewing angle dependent analytical equation is used to convert off-nadir brightness temperatures into nadir equivalents, thereby providing a correction for limb darkening effects.
UTH is part of the Water Vapour ECV inventory in the CDS together with Total Column Water Vapour and Tropospheric Humidity Profiles. The TCDR component of this dataset is brokered to the CDS and was originally produced on behalf of EUMETSAT Satellite Application Facility on Climate Monitoring (CMSAF) by the Met Office. The ICDR component has been delivered directly to the CDS and was originally produced on behalf of the C3S by the Met Office.

Upper tropospheric humidity gridded data from 1999 to present derived from satellite observations

This dataset provides observations of atmospheric methane (CH₄) amounts obtained from observations collected by several current and historical satellite instruments. 
Methane is a naturally occurring Greenhouse Gas (GHG), but one whose abundance has been increased substantially above its pre-industrial value of some 720 ppb by human activities, primarily because of agricultural emissions (e.g., rice production, ruminants) and fossil fuel production and use. A clear annual cycle is largely due to seasonal wetland emissions.
Atmospheric methane abundance is indirectly observed by various satellite instruments. 
These instruments measure spectrally resolved near-infrared and infrared radiation reflected or emitted by the Earth and its atmosphere. 
In the measured signal, molecular absorption signatures from methane and constituent gasses can be identified. 
It is through analysis of those absorption lines in these radiance observations that the averaged methane abundance in the sampled atmospheric column can be determined.
The software used to analyse the absorption lines and determine the methane concentration in the sampled atmospheric column is referred to as the retrieval algorithm. 
For this dataset, methane abundances have been determined by applying several algorithms to different satellite instruments.
The data set consists of 2 types of products: 

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

The XCH4 products have been retrieved from SCIAMACHY/ENVISAT, TANSO-FTS/GOSA Tand TANSO-FTS-2/GOSAT-2. 
The mid-tropospheric CH4 product has been retrieved from the IASI instruments onboard the Metop satellite series. 
The XCH4 products are available as Level 2 (L2) products (satellite orbit tracks) and as Level 3 (L3) product (gridded). 
The L2 products are available as individual sensor products (SCIAMACHY: WFMD and IMAP algorithms; GOSAT: OCFP, OCPR, SRFP and SRPR algorithms) and as a multi-sensor merged product (EMMA algorithm). 
The L3 XCH4 product is provided in OBS4MIPS format. 
The IASI products are available as L2 products generated with the NLIS algorithm.
This data set is updated on a yearly basis, with each update cycle adding (if required) a new data version for the entire period, up to one year behind real time.
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 data set provides observational records of aerosol properties obtained from observations collected by various satellite instruments. Aerosols are minor constituents of the atmosphere by mass, but critical components in terms of impact on climate. Aerosols influence the global radiation balance directly by scattering and absorbing radiation, and indirectly through influencing cloud reflectivity, cloud cover and cloud lifetime.
The main variables provided by this dataset are: aerosol optical depth, fine mode aerosol optical depth, dust aerosol optical depth, single scattering albedo, aerosol layer height and aerosol extinction coefficient. These variables are derived from observations from several sensors using a set of different processing techniques. This provides the possibility to derive a large set of complementary aerosol properties needed to describe the complex nature of atmospheric aerosols. Furthermore, different algorithms have their specific strengths and weaknesses, meaning that datasets originating from the same sensor but processed by different algorithms provide a way to evaluate uncertainties (e.g. areas of good or bad agreement between them). Altogether, the aerosol properties dataset is very extensive and offers a choice of complementary options – which is appropriate depends on the intended application.
Selected observational records in this dataset are extended in time on a semi-annual basis. At the moment of extending, these records are up-to-date until five months behind present time.   
This dataset is produced on behalf of the Copernicus Climate Change Service.

Aerosol properties gridded data from 1995 to present derived from satellite observations

This dataset provides observations of atmospheric carbon dioxide (CO₂) amounts obtained from observations collected by several current and historical satellite instruments. 
Carbon dioxide is a naturally occurring Greenhouse Gas (GHG), but one whose abundance has been increased substantially above its pre-industrial value of some 280 ppm by human activities, primarily because of emissions from combustion of fossil fuels, deforestation and other land-use change. 
The annual cycle (especially in the northern hemisphere) is primarily due to seasonal uptake and release of atmospheric CO2 by terrestrial vegetation.
Atmospheric carbon dioxide abundance is indirectly observed by various satellite instruments.
These instruments measure spectrally resolved near-infrared and/or infrared radiation reflected or emitted by the Earth and its atmosphere. 
In the measured signal, molecular absorption signatures from carbon dioxide and other constituent gasses can be identified. 
It is through analysis of those absorption lines in these radiance observations that the averaged carbon dioxide abundance in the sampled atmospheric column can be determined.
The software used to analyse the absorption lines and determine the carbon dioxide concentration in the sampled atmospheric column is referred to as the retrieval algorithm. 
For this dataset, carbon dioxide abundances have been determined by applying several algorithms to different satellite instruments. 
Typically, different algorithms have different strengths and weaknesses and therefore, which product to use for a given application typically depends on the application.
The data set consists of 2 types of products: 

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

The XCO2 products have been retrieved from SCIAMACHY/ENVISAT, TANSO-FTS/GOSAT, TANSO-FTS2/GOSAT2 and OCO-2.
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 L2 products are available as individual sensor products (SCIAMACHY: BESD and WFMD algorithms; GOSAT: OCFP and SRFP algorithms) and as a multi-sensor merged product (EMMA algorithm). 
The L3 XCO2 product is provided in OBS4MIPS format. 
The IASI and AIRS products are available as L2 products generated with the NLIS algorithm.
This data set is updated on a yearly basis, with each update cycle adding (if required) a new data version for the entire period, up to one year behind real time.
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

This dataset provides  active fire (AF) and fire radiative power (FRP) products derived from observations made by the Sea and Land Surface Temperature Radiometer (SLSTR) operating concurrently onboard the European Sentinel-3A and -3B satellites.  These products are generated from  the non-time critical (NTC) Level 2 AF detection and FRP product from European Space Agency (ESA). The AF, FRP and the Burnt Areas are Essential Climate Variables (ECV) from the Global Observing System for Climate (GCOS). Burnt Area is provided through a different catalogue entry.
The AF & FRP products encompass a Level 2 summary product which provides a text-based summary of the Level 2 AF detection and FRP data collected over the period of one month across the globe but separated for the different sentinel satellites Sentinel-3A and 3B and the observing time – nighttime or daytime in v1.2. In addition, three Level 3 ‘synthesis products’ are also provided, each one grids the Level 2 AF and the FRP data at various spatial and temporal resolutions along with some adjustments for cloud cover that blocks the fires from the satellite sensors view. Following Level 3 ‘synthesis products’ are available:


V1.0 (February 2020 to February 2021):

daily global Level 3 FRP product generated on a global 0.1 degree resolution grid. AF data coming from observations made by both Sentinel-3A (S3A) and -3B (S3B) have separate layers in this product. Only night-time land observations are included at this moment, the full daytime will be provided in the future once the corresponding NTC Level 2 FRP data are available.
27 day global Level 3 FRP product also derived at 0.1 degree grid cell resolution but summarising the FRP data from both Sentinel-3A and -3B. This time period matches the Sentinel-3 satellites standard orbital repeat cycle.
monthly global Level 3 FRP product which provides global AF detection and FRP data at a grid cell size of 0.25 degrees. This resolution matches the MODIS Climate Model-ling Grid (CMG) active fire product.



V1.2 (January 2021 – to present)

the daily global Level 3 AF & FRP products are generated on a global 0.1-degree reso-lution grid. Based on the Level 2 AF & FRP data, the daily global Level 3 AF & FRP products are separated by the observations made by Sentinel-3A or -3B and the night-time and daytime land observations.
the 27day global Level 3 AF & FRP products are also derived at 0.1-degree grid cell resolution but summarise the AF & FRP data over 27 days but also separated by the observations made by Sentinel-3A or -3B and the night-time and daytime land obser-vations. This time period matches the Sentinel-3 satellites standard orbital repeat cy-cle.
the monthly global Level 3 AF & FRP product provides global AF detection and FRP data at a grid cell size of 0.25 degrees but also separated by the observations made by Sentinel-3A or -3B and the night-time and daytime land observations. This resolu-tion matches the MODIS Climate Modelling Grid active fire product. 



The purpose of these AF and FRP products is to provide a summarised and 'easy to use' version of the Level 2 AF and FRP products, as well as providing the AF and FRP information in a consistently gridded format. The latter in particular is designed to be suitable for use in global modelling, trend analysis and model evaluation.
The AF and  FRP were produced on behalf of the Copernicus Climate Change Service and have been designed to be broadly comparable with the equivalent Fire Burnt Area product, since they are likely to be used in combination with them to provide a long-term FRP time-series.

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

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

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 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

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 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). They are also made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site.
The indices are either downloaded from the CDS where available, or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specification (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as ideally the indices should come from the same dataset with identical specifications.
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.

Climate indicators for Europe from 1940 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 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 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 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

This dataset provides water variables and indicators based on hydrological impact modelling, forced by bias adjusted regional climate simulations from the European Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX). The dataset contains Essential Climate Variable (ECV) data in the form of daily mean river discharge and a set of climate impact indicators (CIIs) for both water quantity and quality. 
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 and hydrological modelling and indicator production. Eight bias adjusted model simulations from the EURO-CORDEX EUR-11 were used to force a multi-model setup of the hydrological model E-HYPEcatch at a pan-European domain. A total of 18 water quality and quantity CIIs and 1 water ECV are provided in this dataset at catchment scale and on a 5km x 5km grid. 
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 and 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.
The river discharge ECV data meet the technical specification set by the Global Climate Observing System (GCOS), as such they are provided on a daily time step. Note these are model output data, not observation data as is the general case for ECVs.
This dataset is produced and quality assured by the Swedish Meteorological and Hydrological Institute on behalf of the Copernicus Climate Change Service.

Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections

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

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

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

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

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 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 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 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 contains a set of synthetic windstorm events consisting of 22,980 individual storm footprints over Europe. These are a physically realistic set of plausible windstorm events based on the modelled climatic conditions. It is not designed to reproduce actual historical observations but as a comparator for the stochastic event sets generally used for windstorm risk analysis in the insurance industry. This is because there is no data assimilation process used to align the model output to historical observations. The dataset aims to capture the windstorms' risk profile over the winter months and consider the characteristics of windstorms in a warming climate. The dataset may be downloaded by selecting the required years and months from which daily files are produced. These are there as mere reference; the associated storms bare no resembelence to what was actually observed and recorded at the time.
The windstorms are identified from the maximum 3 second wind gusts derived from the 10m wind speed from the Met Office HadGEM3 model using the Global Atmosphere 3 and Global Land 3 configurations. The dataset covers the northern hemisphere winter months between September and May only. It includes three individual iterations of the event sets, whose methodology and inputs are slightly different:


Synthetic set 1.2: recalibrated using four historical windstorm events; Xynthia, Kyrill, Daria and 87J.


Synthetic set 2: A storm severity index combining maximum wind gust speed above a threshold of 25 m s⁻¹ and land area was used to select the strongest six events from the Met Office historical events database.


Synthetic set 3: downscaling was based on station observations from the four named storms used in Synthetic event set v1.2; Xynthia, Kyrill, Daria and 87J.


Five simulation ensembles initialized from one of five consecutive days and allowed to evolve independently were used to increase the number of events compared to the input data and to account for the uncertainty in forecasted climatic conditions. The three sets and all simulation ensembles may be combined and used together in further analysis. This synthetic event set is complementary to the catalogue of actual windstorms in Europe from 1979 to 2021 also available in the Climate Data Store.
This dataset was produced on behalf of the Copernicus Climate Change Service.

Synthetic windstorm events for Europe from 1986 to 2011

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

The Pan-European Climate Database (PECD) provides information on climate and renewable energy variables for both historical and future time periods. For historical data, the ERA5 global reanalysis serves as the underlying climate data, while future projections are based on selected CMIP6 global climate models. The raw climate model data are further processed through downscaling to achieve higher spatial and temporal resolution and by applying bias adjustment. Each energy variable is derived from the underlying climate data, providing datasets for temperature, total precipitation, surface solar radiation downwards and wind speed, as well as energy-related variables such as wind, solar, and hydropower.
The PECD dataset has been planned, designed, and produced by the Copernicus Climate Change Service (C3S) in collaboration with the European Network of Transmission System Operators for Electricity (ENTSO-E). This collaboration aims to increase the resilience of energy systems and optimize their performance in response to climate change. The PECD dataset existed prior to this collaboration between C3S and ENTSO-E but did not include future climate change signals. The current PECD dataset produced by C3S is the first to include climate change projections, providing energy analysts, planners, and decision-makers with essential tools for energy planning in the coming decades. The dataset is available in two formats: NetCDF for gridded indicators and CSV for area-averaged indicators.
Note: The current PECDv4.1 only includes data from three climate models and one emission scenario. However, it is important to note that the use of a larger set of models is essential to adequately capture the uncertainty inherent in climate projections. The next PECDv4.2, which will be available early next year, will include a wider range of models and scenarios to improve the representation of uncertainty.

Climate and energy related variables from the Pan-European Climate Database derived from reanalysis and 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

This dataset provides global estimates of daily accumulated and monthly means of precipitation. The precipitation estimates are based on a merge of passive microwave observations from two different radiometer classes operating on multiple Low Earth Orbit (LEO) satellites.
Spaceborne passive microwave (MW) provides the most effective measurements for the remote sensing of precipitation because the MW upwelling radiation is directly responsive to the cloud microphysical structure and, in particular, to the emission and scattering properties of precipitation-size hydrometeors (solid and liquid). However, they are available at low spatial and temporal resolution, due to the limited number of passes per day (depending on latitude and number of platforms) at each location.
 On the other hand, infrared (IR) sensors, available also on geostationary platforms, provide measurements that mostly respond to upper-level cloud structure, but at much higher temporal and spatial resolution. Since precipitation is not directly sensed in the infrared, these observations are often merged with microwave-based precipitation estimates and rain gauges. A precipitation product merging IR and MW is also available on the Climate Data Store: GPCP precipitation dataset.
The two different radiometer classes used in the present  Copernicus micrOwave-based gloBal pRecipitAtion (COBRA) dataset are: 
i) Conically scanning MW imagers; observations obtained by applying methodologies of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) in the Satellite Application Facility on Climate Monitoring (CM SAF).
ii) Cross-track scanning MW sounders; observations obtained through the dedicated Passive microwave Neural network Precipitation Retrieval for Climate Applications (PNPR-CLIM) algorithm.
This datset is independent of IR imagery and rain-gauge observations. 
A pure passive MW-based precipitation dataset overcomes the challenges and limitations of precipitation estimates based on IR observations, and the issues related to the inadequacy of the rain gauge networks in some regions and their almost complete absence over the ocean. 
The main limitations, however, are linked to the varying (in time and space) revisiting time of the LEO satellites and low temporal sampling compared to geostanionary IR observations.
This dataset is produced by the Copernicus Climate Change Service (C3S).

Precipitation monthly and daily gridded data from 2000 to 2017 derived from satellite microwave observations

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 extend further back in time than sub-daily records in general. Periodic updates and reissues shall refresh historical holdings availability and will also build on aspects such as quality checking and flagging of data. Each such release will be accompanied by a version number increment and a reissue of the product user guide. Updates for recent observations are in process of being provided at a latency of 1-2 days for those stations that are operational and sharing data across the WMO Information System, starting with updates to the daily holdings.
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 and users can choose whether to receive all attributes or solely essential attributes. Data are downloaded as comma-seperated values (CSV) files organised as one observations of one variable per row.
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 who are the WMO designated World Data Centre for meteorology.

Global land surface atmospheric variables from 1755 to 2020 from comprehensive in-situ observations

Atmospheric water vapor, quantified here as Total Column Water Vapor (TCWV), holds paramount importance in monitoring and understanding Earth's climate dynamics. Its role as an Essential Climate Variable is pivotal, serving as the foundation for cloud formation, cloud physics, and significantly impacting the Earth's heat budget through its proficient absorption of both long and short-wave radiation.
This catalogue entry presents two extensive, high-quality observational datasets of TCWV, specifically tailored for climate analysis and applications.
The dataset combines Medium Resolution Imaging Spectrometer (MERIS) retrievals in the near-infrared (NIR) across terrestrial and coastal regions with Special Sensor Microwave Imager (SSM/I) TCWV retrievals in the microwave spectrum over ocean surfaces. Additionally, an associated dataset accessible in the Related Data section, offers TCWV specifics over oceanic areas, computed using distinct sensors and algorithms but with higher temporal resolution.
The SSM/I, a well-established instrument, adeptly retrieves TCWV over open ocean surfaces, and provides a long time series and global coverage at a reasonable resolution. The MERIS TCWV dataset provides a continuous data record spanning from May 2002 to March 2012, aligning with the TCWV requirements for the World Meteorological Organization's (WMO) Observing Systems Capability Analysis and Review Tool (OSCAR). 
Utilizing a differential absorption technique centered at 940 nm in the NIR spectrum, MERIS ensures a robust retrieval methodology proven reliable for sensors within the water vapor absorption band. Covering nearly a decade, this MERIS dataset delivers products of global coverage with a reasonable resolution and good uncertainty estimate. Although not necessarily needed as input for the retrieval, ERA-Interim reanalysis TCWV serves as a priori information. These parameters are not provided in the final dataset.
SSM/I and MERIS retrievals were merged in order to have global coverage with each instrument used to its full potential.
Within the ESA Water Vapour Climate Change Initiative Project (WV_cci) the NIR based TCWV retrievals over land, coasts and sea-ice were improved and in addition to MERIS data from the Moderate-resolution Imaging Spectrometer (MODIS) and the Ocean and Land Colour Instrument (OLCI) were used as input data over land. 
The NIR data were combined with TCWV data from EUMETSAT's Satellite Application Facility (CM SAF) Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) over open ocean. This is the total column water vapour COMBI product. The COMBI dataset is an evolution of the “Monthly total column water vapour over land and ocean from 2002 to 2012 derived from satellite observations” developed in the predecessor project in Phase I.
TCWV from 2002 to 2012
The total column water vapour is based on a combination of MERIS (NIR) over land surfaces and coastal areas with SSM/I data over ocean surfaces available from 2002 to 2012 on a monthly basis.
TCWV COMBI from 2002 to 2017
For the total column water vapour COMBI product, SSM/I observations were used to generate TCWV data over the global ice-free ocean (EUMETSAT CM SAF HOAPS) while MERIS, MODIS and OLCI observations were used over land. These NIR data were combined with TCWV data from EUMETSAT CM SAF HOAPS and is available from 2002–2017 on both daily and monthly basis.
This dataset is produced by EUMETSAT CM SAF on behalf of the Copernicus Climate Change Service (C3S).

Global monthly and daily high-spatial resolution of total column water vapour from 2002 to 2017 derived from satellite observations

This dataset provides  mid-morning daily values for lake surface water temperature (LSWT) retrieved from satellite over a regular grid together with associated LSWT uncertainty and quality levels based on statistical confidence levels. The data represent current state-of-the-art for LSWT record production.
The satellite data are from the Along Track Scanning Radiometer (ATSR) and Advanced Very High Resolution Radiometer (AVHRR) sensors. The retrieved temperatures have been bias adjusted for consistency between sensors using overlap periods. The dataset contains only observations and therefore gaps in time and space may be present.
LSWT is a recognised Essential Climate Variable (ECV) of the Global Climate Observing System (GCOS). It is one of the key parameters determining the ecological conditions within a lake, since it influences physical, chemical and biological processes. Lake water temperatures also determine air-water heat and moisture exchange and therefore they are important for understanding the hydrological cycle. This dataset supports the investigation of large-scale lake-climate interaction on inter-annual time scales.
Most of the LSWT data in this dataset have been developed within the United Kingdom (UK) Natural Environment Research Council (NERC)  GloboLakes project and future improved versions will use developments within the European Space Agency (ESA) Climate Change Initiative (CCI) for lakes.

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

This dataset provides global daily sea surface temperature (SST) data from the Group for High Resolution Sea Surface Temperature (GHRSST) multi-product ensemble (GMPE) produced by the European Space Agency SST Climate Change Initiative (ESA SST CCI). The GMPE system was designed to allow users to compare the outputs from different SST analysis systems and understand their similarities and differences. Although originally intended for comparison of near real time data, it has also been used to compare long historical datasets. Note that the dataset provided here is the climate version of the GMPE dataset. An operational version, with different input products and time coverage, also exists but is not distributed by the CDS.
The SST analyses ingested into the GMPE system come from the following seven SST products and providers:

ESA SST CCI Analysis version 2.0
ESA SST CCI Analysis version 1.1
Copernicus Marine Environment Monitoring Service (CMEMS) Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) Reprocessing
National Centers for Environmental Information (NCEI) Advanced Very High Resolution Radiometer (AVHRR) Optimal Interpolation (OI) Global Blended SST Analysis
Canada Meteorological Center (CMC) 0.2-degree Global Foundation SST Analysis
Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) Analysis version 2.2.0.0
Japan Meteorological Agency (JMA) Merged satellite and in-situ Data Global Daily SST (MGDSST) Analysis

These products are all spatially complete (through use of infilling or reconstruction techniques) but were originally produced for different purposes and with different user requirements in mind. Therefore, each producer has made different choices on aspects of data production such as which input observations to use and what type of SST to represent. For example, the CMEMS OSTIA, CMC, and MGDSST analyses attempt to represent the foundation SST (water temperature free of diurnal temperature variability) while the ESA SST CCI and HadISST analyses estimate the SST at a standard depth of 20 cm. The AVHRR OI product, on the other hand, is bias-corrected to in situ observations and hence will be representative of their depths.
The GMPE dataset provides the median and standard deviation of the input SST products, the differences between each input product and the median, and the horizontal gradients in each of the input SST products as well as the final ensemble product. The HadISST product consists of 10 different realisations, therefore the median and standard deviation are calculated for an ensemble of 16 input fields. All fields are provided on a common 0.25 degree regular latitude-longitude grid and extend from 1 September 1981 to 31 December 2016, although some of the individual input products cover shorter periods. The dataset will not be extended beyond 2016.

Sea surface temperature daily gridded data from 1981 to 2016 derived from a multi-product satellite-based ensemble

Water vapour is has been recognised as an  Essential Climate Variable as it provides the basis for all cloud formation and physics and furthermore influences the Earth's heat budget due to its high absorbance of long and short-wave radiation. Total column water vapour (TCWV) is a measure of the integrated water vapour content of the atmosphere.
This catalogue entry provides the TCWV data product usually called Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS).
The HOAPS product uses a 1D-Var retrieval concept for atmospheric water vapour content from the measurements of the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) sensors in the microwave spectra over ice-free ocean surfaces. 
The dataset provides a continuous data record composed of a Thematic Climate Data Record (TCDR) from 1988 to 2014 and of an Interim Climate Data Record (ICDR) from 2015 onward. It was produced following the World Meteorological Organisation's Observing Systems Capability Analysis and Review Tool (OSCAR)  requirements for TCWV.
The SSM/I and SSMIS are well-established instruments for TCWV retrieval over ice-free open ocean surfaces and provide a long time series (1987 to present) and global coverage at a reasonable resolution.
The TCDR HOAPS dataset is produced on behalf of EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) and the ICDR is produced within the Copernicus Climate Change Serice (C3S) project. Both products are based on the HOAPS 4.0 algorithm at DWD (Deutscher Wetterdienst).  A related dataset providing TCWV data over both land and ocean, but only at monthly  time resolution, computed using a different set of sensors and algorithms  is also available (the link to that dataset is under the Related data section).

Hours	Number of requests	Number of users	Downloaded data
Last 1H	22279	1142	6186 GB
Last 2H	43813	1547	11180 GB
Last 3H	63374	1860	14968 GB
Last 6H	125481	2521	27684 GB
Last 12H	260801	3376	59019 GB
Last 24H	476640	4724	105470 GB

	Catalogue entry	Number of users	Downloaded data
1	reanalysis-era5-pressure-levels	1328	57492 GB
2	reanalysis-era5-single-levels	2302	20662 GB
3	reanalysis-era5-complete	197	9645 GB
4	reanalysis-era5-land	513	8733 GB
5	reanalysis-oras5	32	2268 GB
6	reanalysis-cerra-single-levels	15	1864 GB

	Country	Number of requests	Number of users	Downloaded data
1	GB	15891	211	15384 GB
2	DE	12523	156	11630 GB
3	FR	21190	204	4853 GB
4	IT	6736	133	1855 GB
5	ES	12316	100	1178 GB
6	NL	1959	57	1079 GB

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