Transport optimal pour quantifier l'évolution d'un attracteur climatique et corriger ses biais

Abstract : The climate system generates a strange attractor, described by a probability distribution, called the SRB measure (Sinai-Ruelle-Bowen). This measure describes the state and dynamic of the system. The goal of this thesis is first, to quantify the modification of this measure when climate changes. For this, the Wasserstein distance, stemming from the optimal transport theory, allows us determine accurately the differences between probability distributions. Used on a non-autonomous Lorenz toy model, this metric allows us to detect and quantify the alteration due to a forcing similar to anthropogenic forcing. This methodology has been applied to simulation of RCP scenarios from the IPSL model. The results are coherent with different scenarios. Second, the optimal transport gives a theoretical context for stationary bias correction: a bias correction method is equivalent to a joint probability law. A specific joint law is selected with the Wasserstein distance (Optimal Transport Correction method, OTC). This approach allows us extending bias correction methods in any dimension, correcting spatial and inter-variables dependences. An extension in the non-stationary context has been also developed (dynamical OTC method, dOTC). Those two methods have been tested in an idealized case, based on a Lorenz model, and on climate dataset (a regional climate simulation corrected with respect to the SAFRAN reanalysis).
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Submitted on : Friday, November 8, 2019 - 2:36:29 PM
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Yoann Robin. Transport optimal pour quantifier l'évolution d'un attracteur climatique et corriger ses biais. Climatologie. Sorbonne Université, 2018. Français. ⟨NNT : 2018SORUS071⟩. ⟨tel-02096277v2⟩



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