This dataset provides historical hydrological simulations as well as hydrolog-ical seasonal reforecasts and forecasts for the period 1993 to present.
The data represent the current state-of-the-art in hydrological seasonal fore-casting, and uses multiple hydrological models and multiple meteorological inputs.
The hydrological seasonal forecasts provide impact model predictions based on the meteorological forecasts provided by C3S. The data set includes his-torical observation driven hydrological simulations (for initialization of the forecasts), reforecasts for a past period to allow assessment of skill and past performance, and monthly updated forecasts based on the two meteorologi-cal seasonal forecast products ECMWF-S51 and CMCC-S35.
The hydrological seasonal forecasts are driven by the meteorological season-al forecast ECMWF-s51 and CMCC-s35.
This dataset was produced on behalf of the Copernicus Climate Change Service.

C3S2-410-Lot2 Historical

C3S2-410-Lot2 Reforecast

This dataset provides historical hydrological simulations as well as operational seasonal hydrological forecasts and reforecasts for Europe from the Copernicus Climate Change Service (C3S) Water Service. It offers users freely available and timely information on past, present and future seasonal hydrological conditions. The dataset supports monitoring current European hydrological conditions and provides outlooks of how those conditions are expected to evolve over the next six months. 
The dataset includes both historical simulations and ensemble (re)forecasts generated using multiple hydrological models: E-HYPEcatch, E-HYPEgrid and VIC-WUR driven by two state-of-the-art seasonal climate forecast systems: ECMWF-SEAS5 and CMCC-SPS3.5. Historical simulations and forecasts are updated monthly and provide daily and monthly aggregations of key hydrological variables. The driving seasonal climate data have been bias-adjusted against observed precipitation and temperature and dynamically downscaled to match the 5 km × 5 km horizontal resolution of the hydrological grid (see the ‘Documentation’ tab for full details). Ensemble reforecasts (hindcasts) support forecast skill assessment and post-processing. A set of commonly used probabilistic skill score metrics, such as the Continuous Ranked Probability Skill Score (CRPSS), are provided for each month across the reforecast period to help users evaluate forecast skill and make informed decisions. 
Key variables — including river discharge, runoff water equivalent, soil wetness index, and snow depth water equivalent — are particularly valuable for operational water management, hydropower planning, and drought and flood early warning. The multi-model ensemble approach allows users to assess forecast uncertainty and skill, supporting probabilistic, risk-informed decision-making. Additionally, bias-adjusted, downscaled seasonal climate forcing data for precipitation and 2m temperature, used to drive the hydrological models, are also available and updated operationally each month. These forcings may also be used by others to drive additional hydrological or impact models. 
This dataset was produced on behalf of the Copernicus Climate Change Service by the Swedish Meteorological and Hydrological Institute (SMHI) and Wageningen University & Research (WUR).

C3S2-410-Lot2 Combined

C3S2-410-Lot2 Forecast

This dataset contains monthly and zonally averaged Upper Tropospheric and Lower Stratospheric (UTLS) temperature profiles derived from globally distributed GPS radio occultation (RO) measurements from EUMETSAT's Metop polar-orbiting satellites as well as other satellites carrying RO instruments. The atmospheric temperature distribution is an important characteristic for the Earth's climate system. Temperature changes are one of the most important indicators of global climate change, and any impacts on the temperature distribution may reveal the processes involved in those changes.
The dataset is produced by the EUMETSAT Radio Occultation Meteorology Satellite Application Facility (ROM SAF) and comprises the Climate Data Record (CDR, January 2002 to December 2023, ROM SAF product ID GRM-28-R2) and the Interim Climate Data Record (ICDR, January 2024 to present, ROM SAF product ID GRM-29-I2). The dry temperature profiles are retrieved using observed atmospheric refractivity profiles, and assuming hydrostatic equilibrium. Compared to other satellite observation techniques, RO observations have high vertical resolution, have a high long-term stability, and are not affected by clouds or the type of underlying surface (such as land and sea).
The retrieved dry temperature data are averaged in monthly and 5-degree latitude bands for altitudes below the mid-stratosphere. The variability (standard deviation) within such monthly bins is also provided, as well as associated quantities such as observation counts, sampling errors, and a mask (‘drymask’) indicating where the dry temperatures can safely be used as a measure of the physical temperature. In addition, dry temperature data are provided for collocated ECMWF reanalysis data (currently ERA5), sampled at the same times and locations as the observed data.

Upper Air Temperature Profiles averaged monthly and zonally from 2002 to present derived from satellite observations

This dataset provides lake water levels for 251 selected lakes on four continents derived from satellite radar altimetry. Lake water level information is traditionally obtained via ground-based observation systems and networks that suffer from well-known inherent problems: high cost, sparse coverage (often limited to political local/national instead of geographical/hydrological boundaries), slow dissemination of data, heterogeneous temporal coverage, destruction of the stations during floods, absence of stations in remote areas, absence of management strategy, etc. 
Although originally conceived to study open ocean processes, the radar altimeter satellites have nevertheless acquired numerous useful measurements over lakes. With this technique, the Lake Water Level is defined as the height, in meters above the geoid (the shape that the surface would take under the influence of the gravity and rotation of Earth) of the reflecting surface. It is observed by space radar altimeters that measure the time it takes for radar pulses to reach the ground targets, directly below the spacecraft (nadir position), and return. Hence, only lakes located along the satellite's ground tracks can be monitored, with a quality of measurement that not only depends of the size of the lake, but also on the reflecting targets in its surroundings such as topography or vegetation. For each of the key lakes monitored in the C3S Lakes production system, the Level-3 Water Level is provided in separate file along with the corresponding uncertainty.
This altimetric technique, that can complement ground-based observations systems, potentially provides a major improvement in the field of continental hydrology, due to the global coverage (however limited to Earth’s portion at nadir of the orbital ground tracks), regular temporal sampling and short delivery delays. 
The Lake Water Level is a recognized Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). Lake Water Level products are generated by CLS on behalf of the Copernicus Climate Change Services, the Earth Observation Programme of the European Commission.

Quality assessment of the dataset

Lake water levels from 1992 to present derived from satellite observations

This is a test dataset for data hosted by EUM, available via EUMDAC. Upper Tropospheric Humidity (UTH)

EUMDAC UTH

The Pan-European Climate Database (PECD) provides information on climate and renewable energy variables for both historical and future time periods. For historical data, the ERA5 global reanalysis serves as the underlying climate data, while future projections are based on selected CMIP6 global climate models. The raw climate model data are further processed through downscaling to achieve higher spatial and temporal resolution and by applying bias adjustment. Each energy variable is derived from the underlying climate data, providing datasets for temperature, total precipitation, surface solar radiation downwards and wind speed, as well as energy-related variables such as wind and solar capacity factors, and hydropower inflows and generation.
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 dataset includes both PECDv4.1 and PECDv4.2 versions. PECDv4.1 was not extended beyond 2021 as it was frozen ahead of the 2023 European Resource Adequacy Assessment (ERAA) in agreement with ENTSO-E. Additionally, PECDv4.1 version only includes data from three climate models and one emission scenario. In contrast, PECDv4.2 version includes data from six climate models and four emission scenarios, improving uncertainty representation. Best practice favours the use of larger ensembles of models to more thoroughly capture the range of uncertainties present in climate projections. The latter also includes new bias adjustments for wind speed, enhanced energy modelling, extended temporal coverage, and new spatial aggregation zones. It is therefore strongly recommended to use the version PECDv4.2.

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

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.

Pan-Arctic regional reanalysis daily and monthly means from 1985 to present

Pan-Arctic regional reanalysis sub-daily data from 1985 to present

This is a sandbox dataset demonstarting the various options in a catalogue entry.
The overview image here is different to the one displayed in the search results. The image in the search results is always stored as overview.png. For the image in the catalogue entry, the gecko default behvaiour is to first look for an image called overview-detail.png, if this is not found then it will use overview.png. Note: it is possible to specify any filename for the image used in the catalgoue entry in the overview.yaml configuration, but this is discouraged.
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Sandbox Dataset Layout

Buggy dataset

Daily and monthly means of the Arctic regional reanalysis from 1991 to present

This dataset provides a complete archive of monthly mass change timeseries for the drainage basins of the Antarctic and Greenland Ice Sheets from NASA's Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) mission, which have operated from 2002 to current data.
It is guided by the Global Climate Observing System targets for the Ice Sheets Mass Change Essential Climate Variable. The GRACE twin satellites measured changes in the Earth’s gravitational field over time and inferred from them mass changes over the ice sheets, using the regional integration approach. A timeseries of mass change is provided for each ice sheet, and also for each drainage basin separately.
The dataset complements ice change measurements made by other methods, such as the surface elevation change rate detailed in the Climate Data Store (CDS) dataset: 'Ice sheet surface elevation change rate for Greenland and Antarctica from 1992 to present from derived satellite observations', which covers the same areas and includes the GRACE mission timespan.
The data was brokered from ESA’s Greenland and ESA's Antarctic Ice Sheet Climate Change Initiative projects.

Gravimetric mass balance data for the Antarctic and Greenland ice sheets derived from satellite observations

The text should be written in a manner that is accessible to a non-specialist user and aims to describe what the dataset contains. The user will want to know whether the dataset would be useful to them. Typically, the overview text is two, three, or four paragraphs in length. Please see other CDS catalogue entries, especially those in the same sectoral theme, for examples.
Paragraph 1 (required)
Provides a high level introduction to the dataset and explain its relevance to climate science. 
- Recommend the first sentence begins “This dataset provides…”
- Describe in high level terms what this dataset provides to the user. 
- How does this dataset contribute to C3S’s mission to provide freely available data about the Earth’s past, present and future climate? What is the knowledge gap and how does this dataset address it?
Paragraph 2 (recommended)
Touch upon the technical details of the dataset. 
- Elaborate on the added value of the dataset over the input data. 
- Mention the input data and processing chain.
- Describe how was this data processed so users may decide whether it is fit to use for their purpose (i.e. the use of downscaling, interpolation, bias-adjustments techniques, etc.).
Paragraph 3 (recommended)
Describe the variables should you think they require particular attention:
- Maybe these variables address a particular big issue in climate science, are particularly useful or novel, provide data at different higher resolutions than before etc.
Paragraph 4 (required)
This paragraph is one sentence only:
 “This data was produced on behalf of the Copernicus Climate Change Service by [insert name of institution(s) here].”
Or:
“This dataset is brokered by Copernicus Climate Change Service on behalf of [insert name of institution(s) here].”

Main variable - spatial coverage - temporal coverage - data source

This dataset provides global estimates of Burned Area (BA) obtained from the MODIS (Aqua and Terra) and Sentinel-3 OLCI satellite sensors. It is the primary product of the Fire Essential Climate Variable (ECV), as defined by the Global Climate Observing System (GCOS) programme. As fire both influences and is influenced by climate, this dataset is useful for studying climate change. Crucially, it provides information on burned biomass, which can be translated into greenhouse gas (GHG) emissions and other contaminants. Additionally, these BA products support studies of land cover change, as well as fire management and fire risk analyses.
This dataset is comprised of two BA products, one on a regular latitude-longitude grid, and the other as a pixel-level product. The gridded product represents the total burned surface area per grid cell, while the pixel-level product specifies the date on which the fire was first detected. Importantly, the gridded product also contains information on the type of land cover burned, using data from the Copernicus Climate Change Service (C3S) Land Cover dataset which is also available on the Copernicus Climate Data Store (CDS).
A companion dataset, also available through the CDS, provides data for the other two Fire ECV products: Active Fires and Fire Radiative Power. Alongside this, there are numerous complementary datasets available on the CDS, including surface albedo and aerosol properties.
Data v1.1 were produced on behalf of the C3S. The other versions, v5.0cds and v5.1.1 (the latter offering a longer time series and more mature algorithm) were developed as part of the European Space Agency Fire Climate Change Initiative Project (ESA Fire CCI). Two other deprecated versions remain available to download for transparency and traceability reasons but should not be used; please see the ‘Known Issues’ table in the Documentation tab for further information.

Fire burned area from 2001 to present 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 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 test intersect constraints false

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 dataset includes both the PECDv4.1 and PECDv4.2 versions. The PECDv4.1 version only includes data from three climate models and one emission scenario. In contrast, the PECDv4.2 version includes data from six climate models and four emission scenarios, which improves the representation of uncertainty. Using a larger set of models is essential to adequately capture the uncertainty inherent in climate projections. The latter version also includes new bias adjustments for wind speed, enhanced energy modeling, extended temporal coverage, and new spatial aggregation zones. More information can be found under the "Documentation" tab.

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

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.

The Comprehensive Upper-air Observation Network (CUON) dataset is designed 
to contain all available observations from balloons ascents through the atmosphere. 
The main geophysical variables are temperature, humidity, and wind speed and direction,
as a function of pressure. The observation platforms include pilot balloons, radiosondes, and ozonesondes.
The main observation data sources are:
- the NOAA Integrated Global Radiosonde Archive (IGRA version 2),
- the NCAR Upper-Air Database (UADB), and
- the observation feedback of the ERA5 global reanalysis, which assimilated observations acquired or rescued by the EU FP7 ERA-CLIM and ERA-CLIM2 projects.
Additional sources of observations collected by balloon-borne sensors are described in the Product User Guide (PUG), available from the documentation tab. They include, for example, the World Ozone and Ultraviolet Radiation Data Centre WOUDC and the NCEP/NCAR Arctic Marine Rawinsonde Archive.
The documentation presents more details about this dataset, and how it is assembled.
Key processing steps are as follows. 
The data sources are first subject to quality controls. 
This step helps to inform the choice to be made when duplicates are found. 
The resulting merged dataset is homogenized in order to reduce systematic errors or biases. 
The homogenization method is mainly based on analysing background departure time series from reanalyses, 
as documented in peer-reviewed literature (see documentation tab).
Accounting for these biases helps to reduce the overall observation uncertainty. 
Estimates of observation random uncertainty are produced when background and analysis departure information 
from a prior reanalysis are available.
For balloon ascents featuring wind observations, the balloon track is reconstructed, with
observation coordinates (latitude, longitude) that slowly vary as the balloon ascends and 
drifts away from its launch position.
The times of launches are also refined as far as possible from the nominal times of reporting, 
using available metadata.
Users can download the data, as well as the suggested homogenization bias adjustments 
and estimated uncertainties associated with the main geophysical variables. 
The present dataset aims to support climate applications, including global and regional reanalyses. 
Users should be aware that uncertainties are generally larger for data in the 20th century (especially first half) than in the 21st century. In addition, post-2010 balloon ascents can contain sometimes up to more than one thousand levels, when initial soundings may only feature only a few levels.
The dataset can be downloaded as NetCDF files (CDM-Obs-Core, see documentation) or as comma-separated values (CSV) files.

Comprehensive upper-air observation network from 1903 to present

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

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


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

Please be aware that the generation of this dataset is using an alternative source for the [ERA5 Land data](https://doi.org/10.24381/cds.e2161bac) 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-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 gridded sea ice thickness data for the Arctic, derived from satellite observations. Sea ice plays a vital role in the climate system, influencing air-sea interactions, the Earth’s energy balance, and marine ecosystems. It is recognised by the Global Climate Observing System as an Essential Climate Variable. Sea ice thickness is a key characteristic used alongside other parameters such as concentration, edge, and type, all available in the Climate Data Store.
Sea ice thickness is estimated from satellite radar altimetry, which measures the sea ice freeboard—the height difference between the ice surface and open water in leads (areas of open water within the sea ice). Since most sea ice floats below the surface, total thickness is roughly 10 times the freeboard. Snow cover, however, complicates this relationship, requiring additional data on snow depth and density for accurate estimates.
This dataset provides both a Level-3 Collated (L3C) product (with spatial gaps) based on altimetry measurements from Envisat and CryoSat-2, and a Level-4 (L4) product (gap-filled) based on a combination of altimetry measurements from CryoSat-2 and L-band radiometry measurements from Soil Moisture and Ocean Salinity (SMOS) mission. More details about each product are provided below. The L4 product offers improved accuracy for thin ice, higher temporal resolution, and complete spatial coverage. However, its shorter time span makes it less suitable for assessing long-term climate trends compared to L3C. Further details about each product are provided below. Both products are limited to the Arctic and the winter months (October–April), as melt conditions in other months introduce biases. There is currently no sea ice thickness data of sufficient quality available for the Southern Hemisphere. The Level-3 algorithm was developed through the ESA Climate Change Initiative on Sea Ice.

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

Agrometeorological indicators test intersect constraints true

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

Agrometeorological indicators from 1979 to present derived from reanalysis

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

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

The dataset provides global annual glacier mass changes distributed on a global regular grid at 0.5° resolution (latitude, longitude) based on the Fluctuations of Glaciers (FoG) database of the World Glacier Monitoring Service (WGMS). Glaciers play a fundamental role in the Earth’s water cycles. They are one of the most important freshwater resources for societies and ecosystems and the recent increase in ice melt contributes directly to the rise of ocean levels. Due to this they have been declared as an Essential Climate Variable (ECV) by GCOS, the Global Climate Observing System. Within the Copernicus Services, the global gridded annual glacier mass change dataset provides information on changing glacier resources by combining glacier change observations from the Fluctuations of Glaciers (FoG) database that is brokered from World Glacier Monitoring Service (WGMS).
Inspired by previous methodological frameworks, a new approach was developed to combine the glacier mass balance and elevation change observations, providing a new and unique product of annual glacier mass change and related uncertainties for every hydrological year since 1975/76 distributed on a 0.5° global regular grid. The present dataset bridges the gap regarding the spatio-temporal coverage of glacier change observations, providing for the first time in the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) an annually resolved glacier mass change product using the glacier elevation change sample as calibration. This goal has become feasible at the global scale thanks to a new globally near-complete (96% of the world glaciers) dataset of glacier elevation change observations ingested by the FoG database.
To develop the distributed glacier change product, the use of glacier outlines from the C3S Glacier Area product version 2 are used. A glacier is considered to belong to a grid-point when its geometric centroid lies within the grid point. The centroid is obtained from the glacier outlines from the C3S Glacier Area product version 2. The glacier changes in Gt correspond to the total mass of water lost/gained over the glacier surface during a given year. Note that to propagate to mm/cm/m of water column on the grid cell, the grid cell area needs to be considered. Note that hydrological year vary on the Southern Hemisphere (October to September next year) and Northern Hemispheres (April to March next year). The annual distributed glacier change dataset cannot resolve for this seasonal difference and is important for the user to account for them when using the datasets. This issue can only be resolved with a monthly distributed glacier change product.
This dataset has been produced by researchers at the WGMS on behalf of Copernicus Climate Change Service.

Glacier mass change gridded data from 1976 to present derived from the Fluctuations of Glaciers Database

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

This dataset provides daily gridded fields of sea ice drift vectors for both hemispheres derived from satellite passive microwave brightness temperatures and atmospheric reanalysis data. Sea ice is an important component of our climate system and a sensitive indicator of climate change. Its presence or retreat significantly affects ocean-atmosphere interactions, the Earth’s energy budget as well as marine ecosystems. It is classified as an Essential Climate Variable by the Global Climate Observing System.
Sea ice drift refers to the movement of ice, represented by vectors showing how far and in which direction the ice travels over a set time period (here 24 hours). It is one of the parameters commonly used to characterise sea ice. This dataset is of relatively coarse resolution and based on data from several satellite sensors:

the Special Sensor Microwave/Imager (SSM/I; 1991–2006)
the Special Sensor Microwave Imager/Sounder (SSMIS; 2005 onward)
the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sensor (2002–2011) and its successor, AMSR2 (2012–2017)

Wind data from the ERA5 atmospheric reanalysis is also used during the summer melt season. The underlying algorithm uses satellite data from the near-90GHz channel on SSM/I and SSMIS, and from the 37GHz channel on AMSR-E and AMSR2. The satellite images are processed to enhance key features, making it easier to track the movement of sea ice. Data from different satellites are combined to create a single product. During summer and when satellite data is unavailable, missing data is filled using a free-drift model based on ERA5 wind data and trained on AMSR-E and AMSR2 drift data. The product is gap-filled with spatial interpolation where needed.
This dataset is brokered on behalf of the Copernicus Climate Change Service by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF).

Sea ice drift daily gridded data derived from satellite observations

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 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 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

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

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.

Ice sheet velocity for Antarctica and Greenland derived from satellite observations

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

Upper Tropospheric Humidity (UTH) is of key importance to the Earth’s greenhouse effect and understanding of climate change. It is considered an Essential Climate Variable  (ECV), because it controls key atmospheric processes, including those involved in water vapour and cloud feedbacks, that can amplify the climate system’s response to increases in other greenhouse gases.
The Upper Tropospheric Humidity is defined as the integrated amount of Water Vapour in the atmospheric layer between ~500 hPa and ~100 hPa. 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 spatially and -temporally averaged data record. The data are available as twice daily (one for the ascending and the other for the descending passes) averages on a regular latitude/longitude grid. Additionally, the daily mean UTH is provided as a weighted average of ascending and descending orbits for all the grid points with valid ascending and descending observations.
The 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. Besides, UTH, Total Column Water Vapour and Tropospheric Humidity Profiles are part of the Water Vapour ECV data from satellites in the CDS, see related datasets.

Upper tropospheric humidity gridded data derived from 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

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

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

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

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

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).

Monthly and 6-hourly total column water vapour over ocean from 1988 to 2020 derived from satellite 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 provides a consistent view of the water and energy cycles at surface level during several decades.
It contains a detailed record from 1950 onwards, with a temporal resolution of 1 hour. The native spatial resolution of the ERA5-Land reanalysis dataset is 9km on a reduced Gaussian grid (TCo1279). The data in the CDS has been regridded to a regular lat-lon grid of 0.1x0.1 degrees.
The data presented here is a post-processed subset of the full ERA5-Land dataset. Monthly-mean averages have been pre-calculated to facilitate many applications requiring easy and fast access to the data, when sub-monthly fields are not required.

ERA5-Land monthly averaged data from 1950 to present

This 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

Monthly averages of  Arctic regional reanalysis data from 1991 to present

This dataset provides climate and energy variables data at global scale from 1950 to present. The climate variables are provided both in NetCDF and in CSV formats, while energy variables are provided only in CSV format.
This dataset contributes to the C3S/CDS mission by expanding to a global coverage the analogous datasets that were already available at European level.
This dataset has been developed by taking ERA5 reanalysis climate data as input. The data has undergone many different processes, such as temporal and spatial interpolation. The energy variables have been computed starting from the climate variables, with a different model for each energy indicator.
This dataset was produced on behalf of the Copernicus Climate Change Service.

SIS C3S-426-Lot1: Climate and energy global daily and subdai-ly data on single levels from 1950 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

Daily mean Arctic regional reanalysis data 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.

Monthly and daily Arctic regional reanalysis data on pressure levels

Arctic regional reanalysis on pressure levels from 1991 to present

Monthly and daily Arctic regional reanalysis data on single levels

Arctic regional reanalysis on single levels from 1991 to present

MEAN Arctic regional reanalysis on single levels from 1991 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

Requests for non-consecutive date ranges combined with non-UTC time-zones may contain undersampled data points for the dates before and after the gap in the date range. See known-issues table on documentation tab for more details.

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-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

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

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 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 rainfall erosivity (R factor), associated indicators, and the potential for soil loss induced by water erosion for Italy. The dataset is derived from integrating rainfall data included in Climate Data Store with non-climate data to assess soil susceptibility to water erosion according to Revised Universal Soil Loss Equation (RUSLE) approach. The gridded dataset can support the decision-making process of many stakeholders for strategical planning purposes across different sectors addressed by the Copernicus Climate Change Service. The dataset provides:


Key information on water erosion dynamics in terms of R factor and potential soil loss for the historical period. The assessment of R factor uses as input gridded observations (E-OBS) and reanalysis data (ERA5, ERA5-Land) for precipitation included in Climate Data Store. The soil loss assessment is obtained by further operating the R factor derived from ERA5-Land with non-climate gridded data representing soil susceptibility to water erosion in accordance to the RUSLE formulation. These are provided at ≈500m horizontal resolution in this dataset.


Key information on water erosion dynamics in terms of R factor and potential soil loss for future periods. The assessment is again based on the RUSLE approach and uses as input precipitation data included in the EURO-CORDEX ensemble climate projections (0.11°) under several Representative Concentration Pathways (RCPs). In this case, bias-corrected monthly precipitation data are used and the R factor is provided both at native horizontal resolution and regridded to ≈500m to be operated with other RUSLE factors to derive potential soil loss for the future. Additional R factor proxy variables, based on daily precipitation data, are provided at native horizontal resolution and as anomalies with respect to the historical period, with no bias-correction.


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

Soil erosion indicators for Italy from 1981 to 2080

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

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 indicators on European windstorms derived from ERA5 reanalysis. The dataset permits an assessment of the nature and temporal evolution of windstorms associated with Extra-Tropical Cyclones (ETCs). The information produced is mainly targeted at insurance sector stakeholders. 
The dataset, developed within the Enhances Windstorm Service (EWS) provides detection and tracking of pan-European potentially harmful windstorms, associated with ETCs. This represents a continuation and enhancement of the current C3S Windstorm Service. The dataset provides a temporal and seasonal extension of the current Windstorm Service, characterising events over the whole ERA5 reanalysis period (1940-present) and the whole year, with periodic updates in a quasi-operational fashion. 
EWS is constituted of two main datasets:


Windstorm tracks; EWS introduces a second tracking algorithm, TempestExtremes (TE, Ullrich et al., 2021), completing the one already used in the current service (TRACK, Hodges 1995, 1999, Hoskins and Hodges 2002).  Windstorm tracks are identified in ERA5 reanalysis data from 1940 to present with period updates linked to ERA5 new release.  The automated cyclone tracking algorithms use mean sea level pressure and 850hPa relative vorticity (in TE and TRACK respectively) to identify and track the cyclones.


Windstorm-associated footprints, mapping the maximum value of a 10m-height wind gust during a specific event. This latter is the maximum 10m wind gust at each grid point in the domain over a 72-hour time window (Roberts et al., 2014). The footprints are statistically downscaled from the ERA5 31km grid to a 1km grid using multiple linear regression to include orography-driven and wind shear effects on footprint wind gusts.


Summary indicators, annual-based statistics derived on decontaminated footprints considering both the tracking algorithms. Three wind gust thresholds (15, 20, and 25 m/s) are considered.


Datasets will be updated through an automatised procedure running codes underlying data production over the periodic ERA5 dataset new releases.
This dataset was produced on behalf of the Copernicus Climate Change Service

Storm tracks and footprints derived from ERA5 over Europe between 1940 to present

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 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 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 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

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 dataset provides global estimates of precipitation based on satellite observations. Precipitation is the main component of water transport from the atmosphere to the Earth’s surface within the hydrological cycle. It varies strongly, depending on geographical location, season, synopsis, and other meteorological factors. The supply with freshwater through precipitation is vital for many subsystems of the climate and the environment, but there are also hazards related to extensive precipitation or the lack of precipitation. The present dataset allows the investigation and quantification of these aspects of precipitation, possibly in conjunction with other datasets covering components of the hydrological cycle.
The data represent the current state-of-the-art for satellite-based precipitation climate data record production in Europe, which is in line with the “Systematic observation requirements for satellite-based products for climate” as defined by GCOS (Global Climate Observing System). Spaceborne passive microwave (MW) imagers and sounders, available on different Low Earth Orbit (LEO) platforms, provide the most effective measurements for the remote sensing of precipitation because of the sensitivity of the MW upwelling radiation to the cloud microphysical properties, especially the emission and scattering of precipitation-size hydrometeors (solid and liquid). However, they are available at low spatial and temporal resolution, due to the limited number of overpasses per day (depending on latitude and number of platforms) at each location. A further ECV Precipitation product only based on MW observations, COBRA, is also available in the CDS. On the other hand, infrared (IR) sensors onboard geostationary (GEO) platforms, provide only information on the upper-level cloud structure, but at much higher temporal and spatial resolution, for example improving the representative sampling of intermittent precipitation. Since precipitation is not directly observed in the infrared, these measurements are often merged with microwave-based precipitation estimates.
This precipitation data record and its processing chain are called Global Interpolated RAinFall Estimate (GIRAFE). GIRAFE provides a global 1° gridded daily accumulated precipitation amount together with uncertainty estimates coming from the sampling, and a global 1° gridded monthly mean of daily accumulation. In the above sense, GIRAFE optimizes the sampling of precipitation by merging observations by LEO MW imagers and sounders (Level-2 data) with GEO-Ring IR brightness temperatures (Level-1 data). The daily accumulated precipitation is also aggregated to monthly mean precipitation.
This dataset has been produced by the EUMETSAT Satellite Application Facility on Climate Monitoring.

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

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

TEST ENTRY FOR THE ARCO ZARR ADAPTOR

This is a test dataset for data hosted by EUM, available via EUMDAC. The Earth’s Radiation Budget (ERB)

EUMDAC erb

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

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

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 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 (C3S) (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. 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 (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.

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

This is a test dataset for data hosted by EUM, available via EUMDAC. ECV Cloud Properties CLARA-A3.

EUMDAC clouds

Only dataset specific abstract in JSON
The Advanced Microwave Sounding Unit-A (AMSU-A) is a 15-channel microwave radiometer that is used for measuring global atmospheric temperature profiles and will provide information on atmospheric water in all of its phases (with the exception of small ice particles, which are transparent at microwave frequencies). AMSU-A will provide information even in cloudy conditions. AMSU-A measures Earth radiance at frequencies (in GHz) as listed under the instrument channel information.\r\n\r\n\r\nMetop-A data are available in the Data Centre up to 15/11/2021.

EUMDAC amsu-a 1b

This is a test dataset for data hosted by EUM, available via EUMDAC. This dataset provides the Surface Radiation Budget (SRB) ECV.

EUMDAC srb

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

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 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

E-OBS is a daily gridded land-only observational dataset over Europe. The blended time series from the station network of the European Climate Assessment & Dataset (ECA&D) project form the basis for the E-OBS gridded dataset. All station data are sourced directly from the European National Meteorological and Hydrological Services (NMHSs) or other data holding institutions. For a considerable number of countries the number of stations used is the complete national network and therefore much more dense than the station network that is routinely shared among NMHSs (which is the basis of other gridded datasets). The density of stations gradually increases through collaborations with NMHSs within European research contracts. 
Initially, in 2008, this gridded dataset was developed to provide validation for the suite of Europe-wide climate model simulations produced as part of the European Union ENSEMBLES project. While E-OBS remains an important dataset for model validation, it is also used more generally for monitoring the climate across Europe, particularly with regard to the assessment of the magnitude and frequency of daily extremes.
The position of E-OBS is unique in Europe because of the relatively high spatial horizontal grid spacing, the daily resolution of the dataset, the provision of multiple variables and the length of the dataset. Finally, the station data on which E-OBS is based are available through the ECA&D webpages (where the owner of the data has given permission to do so). In these respects it contrasts with other datasets.
The dataset is daily, meaning the observations cover 24 hours per time step. The exact 24-hour period can be different per region. The reason for this is that some data providers measure between midnight to midnight while others might measure from morning to morning. Since E-OBS is an observational dataset, no attempts have been made to adjust time series for this 24-hour offset. It is made sure, where known, that the largest part of the measured 24-hour period corresponds to the day attached to the time step in E-OBS (and ECA&D).

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

This dataset provides 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 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 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, commonly known as the Randolph Glacier Inventory (RGI), provides global glacier outlines compiled from maps, aerial photographs and satellite images. The data is provided as a "snapshot" (single time-slice) constructed from images that were mostly acquired in the period 2000-2010.
The dataset represents the latest globally complete and homogenized state-of-the-art glacier outline collection. The regional datasets have been created by scientists from all over the world and were provided to the Global Land Ice Measurements from Space (GLIMS) glacier database at the National Snow and Ice Data Center (NSIDC). Among others, the dataset plays a key role in determining the contribution of glacier melt to global sea-level rise and regional hydrology, as well as modelling of past and future glacier evolution. 
The provided RGI is brokered from the NSIDC and consists of three datasets providing:

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

For easier access, the shape and csv files are arranged in 19 first order regions that cover all glacierized regions in the world. The netCDF file includes all glaciers in one file. Coordinates are in longitude and latitude with the World Geodetic System 1984 (WGS84) datum.
The RGI glacier outlines provided here are widely used by the glacier community to spatially constrain calculations of the geodetic glacier mass balance, which are also available in the Climate Data Store when forwarded to the World Glacier Monitoring Service (WGMS).

Glaciers distribution data from the Randolph Glacier Inventory for year 2000

The dataset contains air temperature, specific humidity, relative humidity and wind speed for 100 European cities for the current climate.
The data were generated using the urban climate model UrbClim, developed at VITO. This model was designed to simulate and study the urban heat island effect (UHI) and other urban climate variables at a spatial resolution of 100 metres. The unique capabilities of UrbClim allow to generate spatially explicit timeseries of hourly variables from which a variety of indicators can be retrieved in postprocessing at the scale of a city neighbourhood.
For this specific dataset, the ERA5 reanalysis large-scale weather conditions are downscaled to agglomeration-scale. UrbClim then computes the impact of urban development on the most frequent weather parameters, such as temperature and humidity. 
The 100 European cities for the urban simulations were selected based on user requirements within the health community. Furthermore, a high spatial distribution was aimed with specific focus on Eastern European countries that often lack access to relevant information.
The data was produced on behalf of the Copernicus Climate Change Service.

Climate variables for cities in Europe from 2008 to 2017

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 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

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 a set of climate suitability indicators for tourism in Europe under future climate scenarios. These indicators have been tailored for two different kinds of tourism activities; urban and beach tourism. The climatic suitability indicators consist of two indices; the Holiday Climate Index (HCI) rates the climate resources based on activities associated with urban tourism, while the Climate Index for Tourism (CIT) rates the climate resources based on activities associated with beach tourism. Tailored indicators, such as these, allow a range of climatic variables to be quantified and ranked for particular tourist destinations and activities, serving as an important decision-making tool for both tourists and the tourism industry. The use of indicators is particularly valuable given the multifaceted nature of meteorological conditions and the complex manner in which they determine tourism activities. By integrating these indicators with climate projections, this dataset aims to provide an understanding of how the influence of a changing climate will impact on tourism activities in Europe under a range of future scenarios of greenhouse gas concentrations, referred to as Representative Concentration Pathway (RCP) climate scenarios. This dataset was produced using 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).
The climate projections are derived from EURO-CORDEX ensemble model output. Also included in this dataset are historical simulations for the period 1970-2005 in order to provide a reference period for the future projections. Both these indices take into account different climate variables (daily precipitation, wind speed, total cloud cover, and effective temperature) to evaluate the climate in relation to tourism. To each of these climate variables, a score is assigned according to their weighting criteria. The scores are then aggregated, resulting in a total rating scale ranging from 0 to 100 for HCI, and from 0 to 7 for CIT (please refer to the documentation for further details). In general, the higher the value of the indicators, the more suitable for tourism activities the seasonal conditions are.
This dataset was produced on behalf of the Copernicus Climate Change Service.

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	Catalogue entry	Number of users	Downloaded data
1	reanalysis-era5-pressure-levels	842	21760 GB
2	reanalysis-era5-single-levels	1234	9161 GB
3	reanalysis-era5-land	337	6123 GB
4	reanalysis-era5-complete	102	5034 GB
5	reanalysis-era5-pressure-levels-monthly-means	53	713 GB
6	reanalysis-cerra-single-levels	18	626 GB

	Country	Number of requests	Number of users	Downloaded data
1	DE	3666	103	5705 GB
2	GB	8058	139	4141 GB
3	FR	9383	163	3721 GB
4	ES	3630	71	1446 GB
5	IT	4475	90	1094 GB
6	CH	1045	38	547 GB

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