Displaying 1 - 25 of 37

Indices (MUCAPE/Kindex/Totalx)

These charts show indices which indicate the susceptibility of the troposphere to support free convection. Typical values of the different indices depend on location and...

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Madden-Julian Oscillation (MJO) index - Sub-seasonal range forecast

This diagram shows time evolution of a Madden-Julian Oscillation (MJO) index. ...

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Sea ice concentration from 1978 to present derived from satellite observations

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.

calendar_today Interval/period: Sun, 10/01/1978 - Mon, 09/29/2025

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Sea ice drift daily gridded data derived from satellite 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.

calendar_today Interval/period: Tue, 01/01/1991 - Thu, 12/31/2020

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

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.

calendar_today Interval/period: Wed, 10/25/1978 - Wed, 09/24/2025

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Sea ice surface temperature derived from satellite observations

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.

calendar_today Interval/period: Mon, 01/01/1979 - Tue, 09/30/2025

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

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).

calendar_today Interval/period: Tue, 10/01/2002 - Sat, 04/12/2025

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

This dataset provides gridded daily and monthly mean global estimates of sea level anomaly based on satellite altimetry measurements. The rise in global mean sea level in recent decades has been one of the most important and well-known consequences of climate warming, putting a large fraction of the world population and economic infrastructure at greater risk of flooding. However, changes in the global average sea level mask regional variations that can be one order of magnitude larger.

calendar_today Interval/period: Fri, 01/01/1993 - Sun, 12/31/2023

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

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.

calendar_today Interval/period: Tue, 09/01/1981 - Sat, 12/31/2016

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Essential climate variables for assessment of climate variability from 1979 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.

calendar_today Interval/period: Mon, 01/01/1979 - Wed, 04/01/2026

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Probabilities: lightning flash density

This chart shows probability information regarding lightning flash density derived from the ...

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S2S global sub-seasonal to seasonal ensemble real-time forecasts from multiple NWP and research centres since 2015

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.

calendar_today Interval/period: Thu, 01/01/2015 - Wed, 05/06/2026

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S2S global sub-seasonal to seasonal ensemble reforecasts from multiple NWP and research centres since 2015

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.

calendar_today Interval/period: Tue, 03/01/2011 - Tue, 06/09/2026

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Probabilities: CAPE shear

This chart shows probability information regarding CAPE shear derived from the ECMWF ensemble ...

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Probabilities: MUCAPE

This chart shows probability information regarding MUCAPE derived from the ECMWF ensemble (ENS) ...

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CAMS global atmospheric composition forecasts

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.

calendar_today Interval/period: Thu, 01/01/2015 - Thu, 05/07/2026

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CAMS global atmospheric composition forecasts

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.

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CAMS global greenhouse gas reanalysis (EGG4)

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.

calendar_today Interval/period: Wed, 01/01/2003 - Thu, 12/31/2020

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CAMS global greenhouse gas reanalysis (EGG4)

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.

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CAMS global greenhouse gas reanalysis (EGG4) monthly averaged fields

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.

calendar_today Interval/period: Wed, 01/01/2003 - Thu, 12/31/2020

open_in_newview in Atmosphere Data Store

CAMS global greenhouse gas reanalysis (EGG4) monthly averaged fields

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.

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CAMS global reanalysis (EAC4)

EAC4 (ECMWF Atmospheric Composition Reanalysis 4) is the fourth generation ECMWF global reanalysis of atmospheric composition. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry.

calendar_today Interval/period: Wed, 01/01/2003 - Thu, 10/31/2024

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CAMS global reanalysis (EAC4)

EAC4 (ECMWF Atmospheric Composition Reanalysis 4) is the fourth generation ECMWF global reanalysis of atmospheric composition. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry.

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CAMS global reanalysis (EAC4) monthly averaged fields

EAC4 (ECMWF Atmospheric Composition Reanalysis 4) is the fourth generation ECMWF global reanalysis of atmospheric composition. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry.

calendar_today Interval/period: Wed, 01/01/2003 - Sun, 12/31/2023

open_in_newview in Atmosphere Data Store

CAMS global reanalysis (EAC4) monthly averaged fields

EAC4 (ECMWF Atmospheric Composition Reanalysis 4) is the fourth generation ECMWF global reanalysis of atmospheric composition. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry.

calendar_today Interval/period: N/A

open_in_newview in Destination Earth Data