AI-boosted rare event sampling to characterize extreme weather
Abstract
Assessing the frequency and intensity of extreme weather events, and understanding how climate change affects them, is crucial for developing effective adaptation and mitigation strategies. However, observational datasets are too short and physics-based global climate models (GCMs) are too computationally expensive to obtain robust statistics for the rarest, yet most impactful, extreme events. AI-based emulators have shown promise for predictions at weather and even climate timescales, but they struggle on extreme events with few or no examples in their training dataset. Rare event sampling (RES) algorithms have previously demonstrated success for some extreme events, but their performance depends critically on a hard-to-identify "score function", which guides efficient sampling by a GCM. Here, we develop a novel algorithm, AI+RES, which uses ensemble forecasts of an AI weather emulator as the score function to guide highly efficient resampling of the GCM and generate robust (physics-based) extreme weather statistics and associated dynamics at 30-300x lower cost. We demonstrate AI+RES on mid-latitude heatwaves, a challenging test case requiring a score function with predictive skill many days in advance. AI+RES, which synergistically integrates AI, RES, and GCMs, offers a powerful, scalable tool for studying extreme events in climate science, as well as other disciplines in science and engineering where rare events and AI emulators are active areas of research.