Datasets
Displaying 1 - 25 of 87
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 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.
Interval/period: Tue, 01/01/1924 - Sat, 05/09/2026
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,
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,
Interval/period: Sun, 01/01/2006 - Sat, 05/09/2026
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.
Interval/period: Sun, 10/01/1978 - Mon, 09/29/2025
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.
Interval/period: Tue, 01/01/1991 - Thu, 12/31/2020
This dataset provides daily gridded data of sea ice edge and sea ice type derived from brightness temperatures measured by satellite passive microwave radiometers. Sea ice is an important component of our climate system and a sensitive indicator of climate change. Its presence or its retreat has a strong impact on air-sea interactions, the Earth’s energy budget as well as marine ecosystems. It is recognized by the Global Climate Observing System as an Essential Climate Variable. Sea ice edge and type are some of the parameters used to characterise sea ice.
Interval/period: Wed, 10/25/1978 - Wed, 09/24/2025
This dataset provides daily and monthly sea ice surface temperature (IST) fields over the polar oceans from 1982 to the present. The data are derived from thermal infrared measurements from a single satellite sensor type: the Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational (MetOp) satellites.
Interval/period: Mon, 01/01/1979 - Tue, 09/30/2025
This dataset provides gridded sea ice thickness data for the Arctic, derived from satellite observations. Sea ice plays a vital role in the climate system, influencing air-sea interactions, the Earth’s energy balance, and marine ecosystems. It is recognised by the Global Climate Observing System (GCOS) as an Essential Climate Variable (ECV). Sea ice thickness is a key characteristic used alongside other parameters such as concentration, edge, and type, all available in the Climate Data Store (CDS).
Interval/period: Tue, 10/01/2002 - Sat, 04/12/2025
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.
Interval/period: Fri, 01/01/1993 - Sun, 12/31/2023
This dataset provides global daily sea surface temperature (SST) data from the Group for High Resolution Sea Surface Temperature (GHRSST) multi-product ensemble (GMPE) produced by the European Space Agency SST Climate Change Initiative (ESA SST CCI). The GMPE system was designed to allow users to compare the outputs from different SST analysis systems and understand their similarities and differences. Although originally intended for comparison of near real time data, it has also been used to compare long historical datasets.
Interval/period: Tue, 09/01/1981 - Sat, 12/31/2016
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.
Interval/period: Mon, 01/01/1979 - Wed, 04/01/2026
This dataset contains 4 Essential Climate Variables (ECV) for the 18 bias adjusted Global Climate Models (GCM) from CMIP5: daily precipitation rate, and daily mean, maximum and minimum temperatures.
The data are bias adjusted using the Distribution Based Scaling (DBS) method versus the global reference dataset HydroGFD2.0, both bias adjustment method and global reference dataset developed by the Swedish Meteorological and Hydrological Institute (SMHI).
The DBS method is a parametric quantile-mapping variant.
The data are bias adjusted using the Distribution Based Scaling (DBS) method versus the global reference dataset HydroGFD2.0, both bias adjustment method and global reference dataset developed by the Swedish Meteorological and Hydrological Institute (SMHI).
The DBS method is a parametric quantile-mapping variant.
Interval/period: Thu, 10/12/2000 - Thu, 10/18/2018
The C3S Arctic Regional Reanalysis second generation (CARRA2) dataset contains daily and monthly meteorological variables at 2.5 km resolution. These variables are specified at single levels (including surface) and also at soil, height, pressure and model levels. These daily and monthly data are pre-calculated and have the following types depending on the variables: daily and monthly averages, extremes and totals.
Interval/period: Wed, 01/01/1986 - Sun, 12/31/2023
The C3S Arctic Regional Reanalysis second generation (CARRA2) dataset contains 3-hourly analyses at 2.5 km resolution. These variables are specified at single levels (including surface) and also at soil, height, pressure and model levels. Additionally, hourly forecasts are available between the analysis times and particularly forecasts up to 18 hours initialised from the analyses at 00 and 12 UTC.
Interval/period: Wed, 01/01/1986 - Sun, 12/31/2023
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.
Interval/period: Mon, 01/01/1979 - Mon, 09/30/2024
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.
Interval/period: Sat, 01/01/2000 - Sun, 12/31/2017
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.
Interval/period: Thu, 02/01/1940 - Fri, 01/23/2026
This dataset provides climatological indicators on European winter windstorms and their economic impact derived from ERA5 reanalysis. Also provided are risk indicators from a synthetically derived set of physically realistic windstorm events based on modelled climatic conditions. The primary users include the insurance sector, reinsurers and insurance industry service providers in response to their requirements for a catalogue of historic windstorm events within Europe.
Interval/period: Sat, 03/01/1986 - Wed, 11/30/2011
The Sub-seasonal To Seasonal dataset (S2S) consists of global ensemble real-time forecasts and reforecasts from thirteen numerical weather prediction (NWP) and research centres.
S2S project behind the dataset started in 2013 as a joint initiative of the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP).
The goal of S2S project was to improve sub-seasonal forecast skill through combining multiple forecasting systems, enable multi-model evaluations and enhance knowledge sharing between operational centres.
S2S project behind the dataset started in 2013 as a joint initiative of the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP).
The goal of S2S project was to improve sub-seasonal forecast skill through combining multiple forecasting systems, enable multi-model evaluations and enhance knowledge sharing between operational centres.
Interval/period: Thu, 01/01/2015 - Wed, 05/06/2026
The Sub-seasonal To Seasonal dataset (S2S) consists of global ensemble real-time forecasts and reforecasts from thirteen numerical weather prediction (NWP) and research centres.
S2S project behind the dataset started in 2013 as a joint initiative of the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP).
The goal of S2S project was to improve sub-seasonal forecast skill through combining multiple forecasting systems, enable multi-model evaluations and enhance knowledge sharing between operational centres.
S2S project behind the dataset started in 2013 as a joint initiative of the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP).
The goal of S2S project was to improve sub-seasonal forecast skill through combining multiple forecasting systems, enable multi-model evaluations and enhance knowledge sharing between operational centres.
Interval/period: Tue, 03/01/2011 - Tue, 06/09/2026
This dataset, also known as the Long-term Alpine Precipitation Reconstruction (LAPrec), provides gridded fields of monthly precipitation for the Alpine region (eight countries). The dataset is derived from station observations and is provided in two issues:
LAPrec1871 starts in 1871 and is based on data from 85 input series;
LAPrec1901 starts in 1901 and is based on data from 165 input series.
LAPrec1871 starts in 1871 and is based on data from 85 input series;
LAPrec1901 starts in 1901 and is based on data from 165 input series.
Interval/period: Sun, 01/01/1871 - Sat, 05/09/2026
The daily and monthly data of the C3S Arctic Regional Reanalysis (CARRA) dataset contains daily and monthly meteorological variables at 2.5 km resolution. This includes fields at the single levels (including surface) and on pressure, height, soil and model levels.
These daily and monthly data are pre-calculated and have the following types depending on the variables: daily and monthly averages, extremes and totals.
These daily and monthly data are pre-calculated and have the following types depending on the variables: daily and monthly averages, extremes and totals.
Interval/period: Sat, 09/01/1990 - Sat, 02/28/2026
CAMS produces global forecasts for atmospheric composition twice a day. The forecasts consist of more than 50 chemical species (e.g. ozone, nitrogen dioxide, carbon monoxide) and seven different types of aerosol (desert dust, sea salt, organic matter, black carbon, sulphate, nitrate and ammonium aerosol). In addition, several meteorological variables are available as well.
Interval/period: Thu, 01/01/2015 - Thu, 05/07/2026
CAMS produces global forecasts for atmospheric composition twice a day. The forecasts consist of more than 50 chemical species (e.g. ozone, nitrogen dioxide, carbon monoxide) and seven different types of aerosol (desert dust, sea salt, organic matter, black carbon, sulphate, nitrate and ammonium aerosol). In addition, several meteorological variables are available as well.
Interval/period: N/A
This dataset is part of the ECMWF Atmospheric Composition Reanalysis focusing on long-lived greenhouse gases: carbon dioxide (CO2) and methane (CH4). The emissions and natural fluxes at the surface are crucial for the evolution of the long-lived greenhouse gases in the atmosphere. In this dataset the CO2 fluxes from terrestrial vegetation are modelled in order to simulate the variability across a wide range of scales from diurnal to inter-annual.
Interval/period: Wed, 01/01/2003 - Thu, 12/31/2020
This dataset is part of the ECMWF Atmospheric Composition Reanalysis focusing on long-lived greenhouse gases: carbon dioxide (CO2) and methane (CH4). The emissions and natural fluxes at the surface are crucial for the evolution of the long-lived greenhouse gases in the atmosphere. In this dataset the CO2 fluxes from terrestrial vegetation are modelled in order to simulate the variability across a wide range of scales from diurnal to inter-annual.
Interval/period: N/A