Rare event simulations, emulators, machine learning, and Bayesian GEV estimation, for predicting extreme heat waves and extremes of renewable electricity production

In the climate system, extreme events and tipping points (transitions between climate attractors) are of primary importance for understanding the impacts of climate change and for designing effective adaptation and mitigation strategies. Recent extreme heat waves with severe societal consequences, as well as prolonged periods of very low renewable energy production in electricity systems, are striking examples. A key challenge in studying such phenomena is the lack of available data: these events are inherently rare, and realistic climate models are computationally expensive and highly complex. This data scarcity severely limits the applicability of traditional approaches, whether based on modelling, physics, or statistical analysis.

In this talk, I will present new algorithms and theoretical approaches based on rare-event simulations, climate-model emulators, machine-learning methods for stochastic processes, and up to date blend of data and model use to estimate generalized extreme value (GEV) distribution. These methods are specifically designed to predict the probability that an extremely rare event will occur, to produce huge catalogues of dynamical trajectories leading to the event, and to use the best available historical and model data. The rare event simulation/emulator approach combines, on the one hand, state-of-the-art AI-based emulators that reproduce the full atmospheric dynamics of climate models, and, on the other hand, rare-event simulation techniques that reduce by several orders of magnitude the computational cost of sampling extremely rare events. In parallel the Bayesian GEV approach mix information from historical observation and CMIP model output to produce the best possible estimate of extreme event probabilities.

To illustrate the performance of these tools, I will present results on midlatitude extreme heat waves and on extremes of renewable energy production, with a particular focus on their implications for the resilience of electricity systems.