Beyond the Unseen: Assessing AI Climate Emulators’ Capacity to Simulate Very Rare Events

The risk of extreme weather under climate change is of paramount importance, but remains one of the most difficult problems to study using conventional physics-based global climate models (GCMs). This is due to the high uncertainty in estimates of extreme weather return times owing to the computational cost of evolving these models for long enough to observe very rare events. AI models trained on historical reanalysis to emulate the dynamics of the global atmosphere have demonstrated both high forecast accuracy and greatly reduced computational cost. Some of these AI emulators can generate stable, decades-long trajectories, which, in conjunction with their affordability, have the potential to greatly reduce extreme weather uncertainties. However, it is impossible to validate if AI emulations can accurately estimate the risk of extreme weather events with return times longer than the historical record. In a first-of-its-kind experiment to assess this capability, we simulate 100,000 years of a stationary climate using PlaSim, a coarse resolution GCM. We then train a selection of stable AI emulators using only 100 years of data, and compare the emulated and true return times of extreme heat waves over Western Europe and the Pacific Northwest. We finally assess how the addition of a land moisture component to these AI emulators improves the accuracy of return time estimates.