Statistical mechanics in climate emulation: Challenges and perspectives
KAUST DepartmentVisual Computing Center (VCC)
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/685755
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AbstractClimate 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.
CitationSudakow, I., Pokojovy, M., & Lyakhov, D. (2022). Statistical mechanics in climate emulation: Challenges and perspectives. Environmental Data Science, 1. https://doi.org/10.1017/eds.2022.15
SponsorsThe 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.
PublisherCambridge University Press (CUP)
JournalEnvironmental Data Science
Except where otherwise noted, this item's license is described as Archived with thanks to Environmental Data Science under a Creative Commons license, details at: http://creativecommons.org/licenses/by/4.0