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One of the goals of climate science is to characterize the statistics ofextreme and potentially dangerous events in the present and future climate.Extreme events like heat waves, droughts, or floods due to persisting rains arecharacterized by large anomalies of the time average of an observable over along time. The framework of Donsker-Varadhan large deviation theory couldtherefore be useful for their analysis. In this paper we discuss how conceptsand numerical algorithms developed in relation with large deviation theory canbe applied to study extreme, rare fluctuations of time averages of surfacetemperatures at regional scale with comprehensive numerical climate models. Westudy the convergence of large deviation functions for the time averagedEuropean surface temperature obtained with direct numerical simulation of theclimate model Plasim, and discuss their climate implications. We show how usinga rare event algorithm can improve the efficiency of the computation of thelarge deviation rate functions. We discuss the relevance of the large deviationasymptotics for applications, and we show how rare event algorithms can be usedalso to improve the statistics of events on time scales shorter than the oneneeded for reaching the large deviation asymptotics.

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