Advances in Emulating Earth System Models

Primary convener for session "Advances in Emulating Earth System Models"

Running Earth system models (ESMs) requires a heavy computational cost. As a result, ESMs are often only run for a few emission scenarios with a limited number of realizations per scenario. Emulators or reduced-complexity models are used to interpolate in between the simulated scenarios or reduce internal variability uncertainty. But, existing emulators, such as linear pattern scaling, can break down for emulating irreversible changes, aerosol forcings, and compound and rarest extreme events. Further, emulating high spatiotemporal outputs that capture the internal variability of the Earth system remains challenging. With autoregressive machine learning emulators on the rise, we also welcome submissions from the reduced complexity modeling communities to encourage an interdisciplinary exchange including, but not only:-Benchmarking/validation protocols

-Applications of emulators -Theoretical insights for interpretability, consistency, etc. -Reduced-complexity models -Probabilistic emulation techniques, such as Weather generators, diffusion, GPs, EVT -(Hybrid) deep learning approaches, including operator, autoregressive, foundation models-Atmosphere-ocean coupled emulators