Dr. Francesco Ragone

Laboratoire de Physique of ENS-Lyon

Studying rare extreme events with complex numerical climate models is computationallychallenging, since very long simulations are needed to sample a number of events that issufficient to provide a reliable statistics. I will discuss how the problem of sampling extremes inclimate models can be tackled using rare event algorithms. Rare event algorithms are numericaltools developed in the past decades in mathematics and statistical physics, dedicated to thereduction of the computational effort required to sample rare events in dynamical systems.Typically they are designed as genetic algorithms, in which a set of killing and cloning rules areapplied to an ensemble simulation in order to focus the computational effort on the trajectoriesleading to the events of interest. I will present a rare event algorithm developed in the context oflarge deviation theory, and I will show how it can be used to sample very efficiently time persistentevents like extreme European heat waves in simulations with a climate model. This allows to characterise the statistics of heat waves with extremely large return times, with computational costsorders of magnitude smaller than with direct sampling. The algorithm samples a large number oftrajectories leading to very rare events, which can be used to study their characteristic dynamics,allowing for example to highlight peculiar teleconnection patterns for the most extreme heat waves.This method allows also to observe ultra rare events that would have never been observed in adirect simulation for the same computational cost. I will then discuss how these techniques can beapplied to study other processes with complex climate models.