Studies of Extreme Weather using Machine Learning and Climate Emulators

Studying low-likelihood high-impact climate events in a warming world requires massive ensembles of hindcasts to capture their statistics. It is currently not feasible to generate these ensembles using traditional weather or climate models, especially at sufficiently high spatial resolution.

We describe how to bring the power of machine learning (ML) to generate climate hindcasts at four to five orders-of-magnitude lower computational cost than conventional numerical methods. We show how to evaluate ML climate emulators using the same rigorous metrics developed for operational numerical weather prediction. We conclude by discussing the prospects for studying the causes and statistics of low-likelihood high-impact extremes using huge ensembles generated using these ML emulators.