Computation of extreme heat waves in climate models using a large deviation algorithm

Abstract

<p id="d10096986e202">We propose an algorithm to sample rare events in climate models with a computational cost from 100 to 1,000 times less than direct sampling of the model. Applied to the study of extreme heat waves, we estimate the probability of events that cannot be studied otherwise because they are too rare, and we get a huge ensemble of realizations of an extreme event. Using these results, we describe the teleconnection pattern for the extreme European heat waves. This method should change the paradigm for the study of extreme events in climate models: It will allow us to study extremes with higher-complexity models, to make intermodel comparison easier, and to study the dynamics of extreme events with unprecedented statistics. </p><p class="first" id="d10096986e205">Studying extreme events and how they evolve in a changing climate is one of the most important current scientific challenges. Starting from complex climate models, a key difficulty is to be able to run long enough simulations to observe those extremely rare events. In physics, chemistry, and biology, rare event algorithms have recently been developed to compute probabilities of events that cannot be observed in direct numerical simulations. Here we propose such an algorithm, specifically designed for extreme heat or cold waves, based on statistical physics. This approach gives an improvement of more than two orders of magnitude in the sampling efficiency. We describe the dynamics of events that would not be observed otherwise. We show that European extreme heat waves are related to a global teleconnection pattern involving North America and Asia. This tool opens up a wide range of possible studies to quantitatively assess the impact of climate change. </p>