# Physical and statistical challenges to model and simulate climate extremes

**Pascal Yiou**(CEA, Saclay)

December 01, 2023 — 11:00 — "new L2S location (IBM building), 5th floor room" (and Teams)

###### Abstract

Extreme Event Attribution (EEA) is the corpus of statistical tools to determine how climate change affects the probability of occurrence of extremes. When record breaking events occur, one faces the difficulty of estimating such probabilities, because of obvious sampling issues from observations. One way to overcome this difficulty is to design climate models and appropriate simulation protocols to sample such extremes. I will discuss how rare event algorithms can be applied to classes of climate models (from physical to stochastic models) to generate large ensembles of “unprecedented” extremes that are physically plausible. I will illustrate those models on concrete test cases.

###### References

[1] National Academies of Sciences Engineering and Medicine, ed., 2016: Attribution of Extreme Weather Events in the Context of Climate Change. The National Academies Press,.

[2] Ragone, F., J. Wouters, and F. Bouchet, 2018: Computation of extreme heat waves in climate models using a large deviation algorithm. Proc. Nat. Acad. Sci., 115, 24–29.

[3] Yiou, P., and A. Jézéquel, 2019: Simulation of Extreme Heatwaves with Empirical Importance Sampling. Geosci. Model Dev. Discuss., 2019, 1–26, https://doi.org/10.5194/gmd-2019-164.

###### Biography

My main research area is climate extreme and rare events (like heatwaves, cold spells, extreme precipitation or storms). I am interested in their statistical and mathematical modelling. I focus on properties of the atmospheric circulation (patterns, recurrences, persistence…). I have developed methods based on analogues of circulation, which have interesting mathematical properties when dealing with chaotic dynamical systems. One of the topic I work on is the attribution of extreme events (EEA), i.e. how the probability of an event is affected by forcings (anthropogenic or not). In order to do so, I focus on the relation between the atmospheric circulation and the observable on which the extreme is detected (e.g. cumulated precipitation, average temperature, max temperature…) and look at recurrence properties of atmospheric patterns, and how those properties can change. This can be called “conditional attribution”. One of the outcomes of this research on the properties of the atmospheric circulation is the development of stochastic weather generators that sample the invariant measure of an underlying attractor. This allows simulating large ensembles of climate sequences, with various hypotheses of external climate forcings.