Datasets

500 hPa Geopotential - Anomaly Correlation Coefficient - SEAS5

This chart shows the spatial variation in the Anomaly Correlation Coefficient (ACC) for the ...

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500 hPa Geopotential - Reliability Diagram - SEAS5

The reliability diagram shows the reliability of the ECMWF seasonal forecast system (SEAS5) with ...

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500 hPa Geopotential - ROC Diagram - SEAS5

This chart shows the Relative Operating Characteristics (ROC) diagram for the three-month ...

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500 hPa Geopotential - ROC Skill Scores - SEAS5

This chart shows the spatial variation in the Relative Operating Characteristics (ROC) skill ...

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500 hPa Geopotential Anomaly - SEAS5

The ECMWF seasonal forecasts (SEAS5) are produced every month with a 51-member ensemble at a ...

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500 hPa geopotential height and 850 hPa temperature

The 850 hPa level is usually just above the boundary layer and at this level the day-night variation in temperature is generally negligible...

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500 hPa height: Weekly mean anomalies

This chart shows 7-day mean anomalies of 500hPa geopotential height from the ECMWF Sub-seasonal ...

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500-1000 hPa thickness and mean sea level pressure

500-1000 hPa thickness is a measure of the mean temperature of a column of the atmosphere between these pressure levels and can be used to distinguish between warm and cold air masses and...

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Lead time of anomaly correlation coefficient (ACC) reaching multiple thresholds ( High resolution (HRES) 500 hPa height forecasts)

This diagram gives a measure of the effectiveness of the model in forecasting 500 hPa heights at ...

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Scores of forecasts of upper-air parameters by AIFS - experimental machine learning model

These plots compare recent IFS and experimental AIFS verification scores for 500 hPa ...

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Scores of forecasts of upper-air parameters by experimental machine learning models

These plots compare recent IFS and experimental AIFS verification scores for 500 hPa ...

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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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Experimental: Aurora ML model: 500 hPa geopotential height and 850 hPa temperature

Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.

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Experimental: Aurora ML model: Temperature and geopotential at various pressure levels

Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.

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Experimental: Aurora ML model: Wind and geopotential heights at various pressure levels

Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.

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Experimental: FourCastNet ML model: 500 hPa geopotential height and 850 hPa temperature

FourCastNet v2-small:a deep learning-based system developed by NVIDIA in collaboration with researchers at several US universities.It is initialised with ECMWF analysis. FourCastNet operates at 0.25° resolution.

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Experimental: FourCastNet ML model: Temperature and geopotential at various pressure levels

FourCastNet v2-small:a deep learning-based system developed by NVIDIA in collaboration with researchers at several US universities.It is initialised with ECMWF analysis. FourCastNet operates at 0.25° resolution.

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Experimental: FourCastNet ML model: Wind and geopotential heights at various pressure levels

FourCastNet v2-small:a deep learning-based system developed by NVIDIA in collaboration with researchers at several US universities.It is initialised with ECMWF analysis. FourCastNet operates at 0.25° resolution.

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