Coupling AI Emulators and Rare Event Algorithms to Sample Extreme Heatwaves

Heatwaves are among the most impactful extreme weather events, posing significant risks to human health, ecosystems, and energy systems. Understanding the return times of these events and assessing how climate change alters their frequency and intensity are critical for effective adaptation strategies. However, the rarity of record-breaking heatwaves in observational datasets makes this task highly challenging. Climate models, while capable of simulating such rare events, require prohibitively long simulations to generate robust statistics for events with return times on the order of centuries.

Our study addresses these challenges by leveraging a dual approach combining rare event simulation algorithms and AI-driven climate model emulators. Rare event algorithms, such as genetic algorithms, efficiently target the extreme trajectories leading to heatwaves while avoiding typical weather conditions, allowing for a more focused exploration of the event space. Although effective for long-duration events, these approaches are less suited to capturing shorter-term phenomena, necessitating novel methodologies for finer temporal scales.

In parallel, we leverage the advancements of deep learning in climate science by training neural networks-based climate model emulators based on Vision Transformers. These emulators drastically reduce computational costs and generate realistic climate simulations, including heatwave dynamics. Here, we explore coupling emulators with a new rare event algorithm specifically designed to sample short and extreme heatwaves. We demonstrate the efficiency of this method by calculating return times for unprecedented heatwave events.

In this work, we use data from PlaSim, a cheap-to-run climate model of intermediate complexity, which enables the verification of return periods spanning up to thousands of years. The next steps involve utilizing more state-of-the-art climate models at finer spatial resolutions and evaluating how the statistics of heatwaves may evolve under various climate change scenarios.