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| European Climate Prediction system | |
|---|---|
| Name | European Climate Prediction system |
| Abbreviation | ECPS |
| Founded | 2010s |
| Type | Research infrastructure |
| Headquarters | Bologna |
European Climate Prediction system The European Climate Prediction system is a continental-scale initiative integrating Copernicus Programme, European Centre for Medium-Range Weather Forecasts, ECMWF Member States, Horizon 2020 research and operational resources to deliver seasonal to decadal Intergovernmental Panel on Climate Change-aligned United Nations Framework Convention on Climate Change-relevant forecasts. It links national agencies such as Météo-France, Deutscher Wetterdienst, Met Office (United Kingdom), Istituto Superiore per la Protezione e la Ricerca Ambientale with research institutions like Max Planck Institute for Meteorology, KNMI, Barcelona Supercomputing Center, and international efforts including World Meteorological Organization, European Space Agency, National Aeronautics and Space Administration, and National Oceanic and Atmospheric Administration.
The system provides integrated seasonal and decadal predictions across Europe, connecting European Commission priorities, United Nations Educational, Scientific and Cultural Organization programs, European Research Council-funded projects, and regional providers such as Arctic Council participants. It supports stakeholders across European Parliament, European Investment Bank, World Health Organization Regional Office for Europe, and Food and Agriculture Organization by combining outputs from ECMWF, Copernicus Climate Change Service, EUMETSAT, and national services like Instituto Português do Mar e da Atmosfera.
Development traces through successive programs: early prototypes at ECMWF and Met Office (United Kingdom) in the 1990s, consolidation under Copernicus Programme and Horizon 2020 projects like EUCLEIA and SPECS, and integration with Copernicus Climate Change Service infrastructures. Key milestones involved partnerships with European Space Agency, funding rounds from European Commission Directorate-General for Research and Innovation, and scientific coordination with Intergovernmental Panel on Climate Change, World Climate Research Programme, Climate Change Committee (UK), and academic centers including University of Cambridge, ETH Zurich, Sorbonne University, and University of Oxford.
The architecture combines high-performance computing clusters at Barcelona Supercomputing Center, ECMWF Headquarters, and national centers like Météo-France Centre de Calcul with data repositories at Copernicus Climate Data Store. Core components include dynamical models from ECMWF Integrated Forecasting System, ocean models used by NEMO (ocean model), sea-ice models developed with European Polar Board partners, land-surface schemes from Joint Research Centre (European Commission), and ensemble systems inspired by ENSEMBLES project. Supporting elements link to observation services such as EUMETSAT satellites, climate reanalysis products like ERA5, and archetypal workflows from World Data Center for Climate.
Observational inputs draw from satellite platforms operated by EUMETSAT, Copernicus Sentinel programme, European Space Agency, and in situ networks maintained by Global Climate Observing System, Integrated Carbon Observation System, EMEP, European Marine Observation and Data Network, national meteorological services including Deutscher Wetterdienst and Météo-France, as well as research arrays from CLIVAR, Argo (oceanography), ICOS, and polar campaigns by British Antarctic Survey. Reanalysis and assimilated datasets such as ERA5 and MERRA are core inputs, complemented by paleoclimate proxies from European Pollen Database and PAGES studies.
Prediction methods combine ensemble initialization techniques from ECMWF and Met Office (United Kingdom), coupled ocean–atmosphere models like NEMO (ocean model) and CICE (sea ice model), data assimilation approaches pioneered by Four-dimensional variational data assimilation and Ensemble Kalman filter communities, and statistical post-processing inspired by Copernicus Climate Change Service guidance. Methods incorporate parameterizations developed by Max Planck Institute for Meteorology, bias-correction strategies used by EURO-CORDEX experiments, and multi-model combination protocols from CMIP6 coordination. Machine learning efforts collaborate with groups at ETH Zurich, EPFL, and Imperial College London.
Services target sectors represented by European Commission Directorate-General for Climate Action, European Committee for Standardization, World Health Organization Regional Office for Europe, European Environment Agency, agriculture stakeholders like Food and Agriculture Organization, energy firms and grid operators, and insurance companies including European Insurance and Occupational Pensions Authority. Products include seasonal temperature and precipitation outlooks used by European Network of Transmission System Operators for Electricity, marine forecasts for Baltic Marine Environment Protection Commission (HELCOM), and drought services coordinated with European Drought Observatory.
Governance is multi-layered with oversight from European Commission, programmatic alignment through Copernicus Programme, scientific steering from World Meteorological Organization panels and Intergovernmental Panel on Climate Change authors, and operational partnerships among ECMWF, national meteorological services like AEMET and SMHI, research councils including German Research Foundation and Agence Nationale de la Recherche, and funding from Horizon 2020, Horizon Europe, and national ministries. Collaboration spans universities such as University of Leeds, ETH Zurich, University of Copenhagen, research institutes like SMHI, and international agencies including United Nations Development Programme.
Evaluation leverages verification frameworks used by ECMWF and Copernicus Climate Change Service, benchmarking against CMIP6 and reanalysis like ERA5, and skill assessments endorsed by World Meteorological Organization and European Centre for Medium-Range Weather Forecasts. Challenges include computational demands at centers like Barcelona Supercomputing Center, data heterogeneity across EUMETSAT and national networks, attribution issues addressed by Intergovernmental Panel on Climate Change guidance, and integration of paleoclimate constraints from PAGES. Emerging priorities involve improving decadal predictability highlighted by World Climate Research Programme and operationalizing machine-learning methods validated by European Research Council grants.
Category:Climate models