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

TL;DR

This paper introduces a large deviation algorithm tailored for climate models to efficiently simulate and analyze rare extreme heat waves, revealing their global teleconnection patterns and enabling better climate change impact assessments.

Contribution

A novel large deviation algorithm specifically designed for extreme heat wave simulation in climate models, significantly improving sampling efficiency over traditional methods.

Findings

Over two orders of magnitude improvement in sampling efficiency.

European heat waves linked to North American and Asian teleconnection patterns.

Enables quantitative assessment of climate change impacts on extreme events.

Abstract

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 in order 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 approaches. This 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 a full range of studies, so far impossible, aimed at assessing quantitatively climate change impacts.