An ensemble system of regional reanalyses will be developed and run for the climatological time scale. The
information content of the datasets and their uncertainties will be assessed in several ways and statistics
analysed.
The...
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Descripción del proyecto
An ensemble system of regional reanalyses will be developed and run for the climatological time scale. The
information content of the datasets and their uncertainties will be assessed in several ways and statistics
analysed.
The project will provide long-term datasets of Essential Climate Variables (ECVs) on the European regional
scale in order to support adaptation action and policy development. The datasets will contribute to Climate
services for Copernicus, climate monitoring and research.
Regional reanalyses will be made in Ensemble mode and there will also be individual reanalyses with different
models and methods. Observational data rescue (digitizing data in archives not currently available for use) will
continue from EURO4M and enhanced gridded datasets will be developed and used for validation. The
ensembles together with gridded datasets will form the basis for estimation of uncertainties of the constituent
members. Large datasets will be built up and open and user-friendly access methods will be established together
with the work in other topics of the SPACE call.
This project builds on the experiences and developments in EURO4M but now the reanalyses will cover
climate analysis time scales (30 to 50 years) and pioneer ensemble techniques for regional scale and highresolution
reanalysis. It will build on and extend the global reanalyses (topic -01 ERA-20C, ERA-CLIM2 and ERA-Interim).
Further downscaled high-resolution reanalyses will also be performed where more near surface ECV
observations can be utilized. Indicator information (including uncertainties) will be applied to the reanalyses and
there will be extensive validation against remote sensing and hydrological data. They will add to the validation
effort, respond to user requirements, and demonstrate how the reanalysis information is best utilized for
understanding past climates and climate change.