Abstract
Climate emulators are a powerful instrument for climate modeling, especially in terms of reducing the computational load for simulating spatiotemporal processes associated with climate systems. The most important type of emulators are statistical emulators trained on the output of an ensemble of simulations from various climate models. However, such emulators oftentimes fail to capture the “physics” of a system that can be detrimental for unveiling critical processes that lead to climate tipping points. Historically, statistical mechanics emerged as a tool to resolve the constraints on physics using statistics. We discuss how climate emulators rooted in statistical mechanics and machine learning can give rise to new climate models that are more reliable and require less observational and computational resources. Our goal is to stimulate discussion on how statistical climate emulators can further be improved with the help of statistical mechanics which, in turn, may reignite the interest of statistical community in statistical mechanics of complex systems.
| Original language | English (US) |
|---|---|
| Journal | Environmental Data Science |
| Volume | 1 |
| DOIs | |
| State | Published - Nov 11 2022 |
Bibliographical note
KAUST Repository Item: Exported on 2022-11-16Acknowledgements: The authors thank Mr. Andrews T. Anum (Ph.D. candidate in Computational Science at the University of Texas in El Paso, El Paso, Texas, USA) for assistance with preparing BibTEX references. Comments, recommendations, and improvement suggestions from Editor-in-Chief Professor Monteleoni, Editor Professor Rao, and two anonymous referees are greatly appreciated. I.S. gratefully acknowledges support from the Division of Physics at the U.S. National Science Foundation (NSF) through Grant No. PHY-2102906. M.P. was partially supported by the U.S. Department of Education (Award No. P120A180101). D.L. was partially supported by KAUST baseline funding.
