Statistical mechanics in climate emulation: Challenges and perspectives
Statistical mechanics in climate emulation: Challenges and perspectives
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DOI：
10.1017/eds.2022.15
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
I. Sudakow;Michael Pokojovy;D. Lyakhov
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文献类型：
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作者：
I. Sudakow;Michael Pokojovy;D. Lyakhov
摘要 气候模拟器是气候建模的强大工具，特别是在减少模拟与气候系统相关的时空过程的计算负载方面。最重要的模拟器类型是根据各种气候模型的模拟集合输出进行训练的统计模拟器。然而，此类模拟器通常无法捕获系统的“物理原理”，这可能不利于揭示导致气候临界点的关键过程。从历史上看，统计力学是作为一种利用统计来解决物理学限制的工具而出现的。我们讨论植根于统计力学和机器学习的气候模拟器如何产生更可靠且需要更少观测和计算资源的新气候模型。我们的目标是激发关于如何在统计力学的帮助下进一步改进统计气候模拟器的讨论，这反过来又可能重新激发统计界对复杂系统统计力学的兴趣。
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.