NVIDIA is committed to helping address climate change. Recently our CEO announced the Earth-2 initiative, which aims to build digital twins of the Earth and a dedicated supercomputer, E-2, to power them. Two central goals of this initiative are: (i) Computational: Enable high-resolution climate predictions with credible cloud physics; and (ii) Societal: Nimbly serve interactive, useful, next-generation climate predictions via NVIDIA Omniverse. These predictions will help plan for the disastrous impacts of climate change well in advance and develop strategies to mitigate and adapt to change. Next-generation km-scale climate simulations are prohibitively expensive and produce unmanageable data volumes. Therefore, the above-mentioned goals depend on achieving orders-of-magnitude speedup and data compression via “tethering” a skillful ML surrogate to checkpoints of km-scale accelerated hybrid ML-climate models. In this context, we present results from FourCastNet, a sophisticated, transformer-based adaptive Fourier Neural Operator (FNO) deep learning model for auto-regressive forecasting. While ultimately intended for use in climate, its architecture and predictive skill limits are being refined in the context of global high-resolution weather prediction. We conclude with a roadmap of Earth-2 that encompasses weather and climate goals and outlines engineering innovations required for the breakthroughs that building digital twins of the Earth demands.