Computation of extremes values of time averaged observables in climate models with large deviation techniques

Abstract

One of the goals of climate science is to characterize the statistics of extreme and potentially dangerous events in the present and future climate. Extreme events like heat waves, droughts, or floods due to persisting rains are characterized by large anomalies of the time average of an observable over a long time. The framework of Donsker-Varadhan large deviation theory could therefore be useful for their analysis. In this paper we discuss how concepts and numerical algorithms developed in relation with large deviation theory can be applied to study extreme, rare fluctuations of time averages of surface temperatures at regional scale with comprehensive numerical climate models. We study the convergence of large deviation functions for the time averaged European surface temperature obtained with direct numerical simulation of the climate model Plasim, and discuss their climate implications. We show how using a rare event algorithm can improve the efficiency of the computation of the large deviation rate functions. We discuss the relevance of the large deviation asymptotics for applications, and we show how rare event algorithms can be used also to improve the statistics of events on time scales shorter than the one needed for reaching the large deviation asymptotics.

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• Collapse of transitional wall turbulence captured using a rare events algorithm
• Robust intra-model teleconnection patterns for extreme heatwaves
• Using rare event algorithms to understand the statistics and dynamics of extreme heatwave seasons in South Asia
• Introduction to the Special Issue on the Statistical Mechanics of Climate
• Non-Markovian feedback control and acausality: an experimental study