EGU2020-19803, updated on 10 Jan 2024
EGU General Assembly 2020
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Grand Multi-Model Seasonal Forecasts in the SECLI-FIRM project

Andrea Alessandri1, Franco Catalano2, Matteo De Felice2, Kristian Nielsen3, Alberto Troccoli4, Marco Formenton5, and Gaia Piccioni5
Andrea Alessandri et al.
  • 1Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands (
  • 2Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Italy
  • 3Underwriters Laboratory, Barcelona, Spain
  • 4University of East Anglia, Norwich, UK
  • 5Ente Nazionale per l'Energia Elettrica (ENEL), Italy

A key objective of the Added Value of Seasonal Climate Forecasts for Integrated Risk Management Decisions (SECLI-FIRM, project is the optimisation of the performance of seasonal climate forecasts provided by many producing centers, in a Grand Multi-Model approach, for predictands relevant for the specific case studies considered in SECLI-FIRM.

The Grand Multi-Model Ensemble (MME) consists of the five Seasonal Prediction Systems (SPSs) provided by the European Copernicus C3S and a selection of other five SPSs independently developed by centres outside Europe, four by the North American (NMME) plus the SPS by the Japan Meteorological Agency (JMA).

All the possible multi-model combinations have been evaluated showing that, in general, only a limited number of SPSs is required to obtain the maximum attainable performance. Although the selection of models that perform better is usually different depending on the region/phenomenon under consideration, it is shown that the performance of the Grand-MME seasonal predictions is enhanced with the increase of the independence of the contributing SPSs, i.e. by mixing European SPSs with those from NMME-JMA.

Starting from the definition of the Brier score a novel metric has been developed, named the Brier score covariance (BScov), which estimates the relative independence of the prediction systems. BScov is used to quantify independence among the SPSs and, together with probabilistic skill metrics, used to develop a strategy for the identification of the combinations that optimize the probabilistic performance of seasonal predictions for the study cases.

How to cite: Alessandri, A., Catalano, F., De Felice, M., Nielsen, K., Troccoli, A., Formenton, M., and Piccioni, G.: Grand Multi-Model Seasonal Forecasts in the SECLI-FIRM project, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19803,, 2020.


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