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seminaire

New ways for dynamical prediction of extreme heat waves: rare event simulations and machine learning with deep neural networks

Freddy Bouchet ENS Lyon

Séminaire dans le cadre de la nouvelle saison des séminaires « AI4Climate »

        En ligne et en présentiel

Date début 19/10/2021 14:00
Date fin 19/10/2021
Organisateur

Description

In the climate system, extreme events or transitions between climate attractors are of primarily importance for understanding the impact of climate change. Recent extreme heat waves with huge impacts are striking examples. However, it is very hard to study those events with conventional approaches, because of the lack of statistics, because they are too rare for historical data and because realistic models are too complex to be run long enough.

We cope with this lack of data issue using rare event simulations. Using some of the best climate models, we oversample extremely rare events and obtain several hundreds more events than with usual climate runs, at a fixed numerical cost. Coupled with deep neural networks this approach improves drastically the prediction of extreme heat waves.

This shed new light on the fluid mechanics processes which lead to extreme heat waves. We will describe quasi-stationary patterns of turbulent Rossby waves that lead to global teleconnection patterns in connection with heat waves and analyze their dynamics. We stress the relevance of these patterns for recently observed extreme heat waves and the prediction potential of our approach.
Le groupe IA et Climat (AI4climate.lip6.fr) réunit des chercheurs de l’IPSL et le LIP6 impliqués dans la recherche pluridisciplinaire concernant l’utilisation des méthodes d’intelligence Artificielle dans les sciences de l’environnement et du climat. Cette initiative est soutenue par le groupe de travail SAMA (Statistiques pour l’analyse, la modélisation et l’assimilation) de l’IPSL.

Informations supplémentaires

Lieu : Sorbonne Université, Campus Pierre & Marie Curie – SCAI seminar room, bâtiment « Esclangon , 1er étage