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 10,000
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 (FCN) is based on Fourier
Neural Operators and Transformers, proven to be efficient and
powerful in modeling a wide range of chaotic dynamical systems,
including turbulent flows and atmospheric dynamics. FCN has
already been proven to be highly scalable on GPU-based HPC
systems.

We discuss our evaluation of FCN using statistical
metrics for extremes adopted from operational NWP centers to show
that FCN is as accurate as NWP as an emulator of these
phenomena. We also show how to construct huge ensembles through a
combination of perturbed-parameter techniques and a variant of
bred vectors to generate a large suite of initial conditions that
maximize growth rates of ensemble spread. We demonstrate that
these ensembles exhibit a ratio of ensemble spread relative to
RMSE that is nearly identical to one, a key metric of successful
near-term NWP systems. We conclude by applying FCN to generate a
huge (7500-member) ensemble of 15-day hindcasts for June-August, 2023
to study the range of plausible heat waves that could have occurred in
the hottest summer in the last 2000 years.