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                <authority type="jury">Juliette Blanchet [Président]</authority>
                <authority type="jury">Pedram Hassanzadeh [Rapporteur]</authority>
                <authority type="jury">Ronan Fablet [Rapporteur]</authority>
                <authority type="jury">Claire Monteleoni</authority>
                <authority type="jury">Éric Simonnet</authority>
                <authority type="jury">Corentin Herbert</authority>
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                <term xml:lang="en">Climate</term>
                <term xml:lang="en">Heatwaves</term>
                <term xml:lang="en">Machine Learning</term>
                <term xml:lang="en">Explainable artificial intelligence</term>
                <term xml:lang="en">Rare events algorithms</term>
                <term xml:lang="en">Extreme events</term>
                <term xml:lang="fr">Climat</term>
                <term xml:lang="fr">Canicules</term>
                <term xml:lang="fr">Apprentissage automatique</term>
                <term xml:lang="fr">Intelligence artificielle interprétable</term>
                <term xml:lang="fr">Algorithmes d’événements rares</term>
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              <p>Extreme events in weather and climate are among the most detrimental effects of the Climate Crisis. Extreme heatwaves, for instance, have been responsible for significant excess mortality. Moreover, as the climate warms, there is a risk, still unsatisfactorily quantified, that extreme events could make us cross Tipping Points in the Earth System, leading to abrupt changes in the current climate.It is thus of paramount importance to improve our understanding of such extreme events and our ability to forecast them. However, by their nature, extreme events are rare, so there are very few instances in observational data and simulating them with state-of-the-art climate models can be very expensive. To counter this lack of data issue, Rare Event Algorithms can be applied to significantly improve the efficiency in simulating extreme events. Such algorithms need an estimate of the probability of occurrence of the event conditioned on the state of the system, and this is exactly what the prediction task provides.This thesis develops in two main directions. The first is to use Machine Learning (ML) to estimate from long climate model simulations the probabilities of extreme heatwaves over France. In particular, through a hierarchy of increasingly complex ML models, the tradeoffs between amount of data, performance and interpretability of the predictions are investigated. The second is to apply a Rare Event Algorithm to the study of the abrupt collapse of the Atlantic Meridional Overturning Circulation (AMOC). Finally these two pieces are put together to investigate how coupling Machine Learning and Rare Event Algorithms may improve our ability to sample and predict rare events.</p>
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              <p>Les phénomènes météorologiques et climatiques extrêmes sont parmi les effets les plus néfastes de la crise climatique, causant des surmortalités importantes, comme lors des vagues de chaleur extrême. Le réchauffement climatique augmente également le risque, encore mal quantifié, de franchir des points de basculement entraînant des changements brusques du climat.Il est donc crucial d'améliorer notre compréhension et notre capacité de prévision de ces événements extrêmes. Cependant, en raison de leur rareté, les observations sont limitées et les simulations climatiques de pointe sont coûteuses. Pour pallier ce manque de données, des Algorithmes d'Événements Rares peuvent améliorer l'efficacité des simulations. Ces algorithmes nécessitent une estimation de la probabilité d'occurrence de l'événement en fonction de l'état du système, qui est exactement ce que la prédiction fournit. Cette thèse explore deux directions principales. La première utilise l’apprentissage automatique pour estimer, à partir de données issues de simulations climatiques, les probabilités de vagues de chaleur extrême en France. En examinant une hiérarchie de modèles ML de complexité croissante, elle étudie les compromis entre la quantité de données, la performance et l'interprétabilité des prédictions. La deuxième direction applique un algorithme d’événements rares à l'étude de l'effondrement brutal de la circulation méridienne de retournement de l'Atlantique (AMOC). Enfin, la thèse combine ces deux approches pour étudier comment le couplage de l’apprentissage automatique et des algorithmes des événements rares peut améliorer notre capacité à échantillonner et à prédire les événements extrêmes.</p>
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