Studying low-likelihood high-impact extreme weather and climate events in a warming world requires massive ensembles to capture long tails of multi-variate distributions. In combination, it is simply impossible to generate massive ensembles, of say 1000 members, using traditional numerical simulations of climate models at high resolution.

We describe how to bring the power of machine learning (ML) to replace traditional numerical simulations for short week-long hindcasts of massive ensembles, where ML has proven to be successful in terms of accuracy and fidelity, at five orders-of-magnitude lower computational cost than numerical methods. Because the ensembles are reproducible to machine precision, ML also provides a data compression mechanism to avoid storing the data produced from massive ensembles.

The machine learning algorithm FourCastNet is based on {Fourier Neural Operators (FNO)} and {Transformers}, proven to be efficient and powerful in modeling a wide range of chaotic dynamical systems, including turbulent flows and atmospheric dynamics. FourCastNet has already been proven to be highly scalable on NVIDIA-GPU HPC systems.

Until today, generating 1,000- or 10,000-member ensembles of hindcasts was simply impossible because of prohibitive compute and data storage costs. For the first time, we can now generate such massive ensembles using ML at five orders-of-magnitude faster completion than traditional numerical simulations.