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arXiv 2510.02415physics.ao-phcs.LG
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大气机器学习模型对均匀海表温度变暖的平衡响应

The Equilibrium Response of Atmospheric Machine-Learning Models to Uniform Sea Surface Temperature Warming

University of Washington（华盛顿大学）

Bosong Zhang, Timothy M. Merlis

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AI总结：

本文评估了几种先进的机器学习模型对均匀海表温度变暖的气候响应，探讨了这些模型在气候预测中的潜力与局限性。

摘要：

近年来，能够产生稳定、多年气候模拟的全球大气机器学习模型已得到发展。然而，这些机器学习模型超越训练分布进行泛化的能力仍是一个开放性问题。在本研究中，我们评估了几种最先进的机器学习模型（ACE2-ERA5、NeuralGCM和cBottle）对均匀海表温度变暖的气候响应，这是一种广泛用于评估气候变化的基准测试。我们评估了这些机器学习模型相对于基于物理的一般环流模型（NOAA的Geophysical Fluid Dynamics Laboratory AM4）在关键诊断指标上的性能，包括地表空气温度、降水量、温度和风廓线以及大气顶部辐射。尽管机器学习模型能够再现物理模型响应的关键方面，特别是降水量的响应，但某些模型在辐射响应和陆地区域变暖方面表现出显著偏离稳健的物理响应。我们的结果突显了机器学习模型在气候变化应用中的潜力和当前的局限性，并表明需要进一步改进以实现稳健的样本外泛化。

英文摘要：

Machine learning models for the global atmosphere that are capable of producing stable, multi-year simulations of Earth's climate have recently been developed. However, the ability of these ML models to generalize beyond the training distribution remains an open question. In this study, we evaluate the climate response of several state-of-the-art ML models (ACE2-ERA5, NeuralGCM, and cBottle) to a uniform sea surface temperature warming, a widely used benchmark for evaluating climate change. We assess each ML model's performance relative to a physics-based general circulation model (NOAA's Geophysical Fluid Dynamics Laboratory AM4) across key diagnostics, including surface air temperature, precipitation, temperature and wind profiles, and top-of-atmosphere radiation. While the ML models reproduce key aspects of the physical model response, particularly the response of precipitation, some exhibit notable departures from robust physical responses, including radiative responses and land region warming. Our results highlight the promise and current limitations of ML models for climate change applications and suggest that further improvements are needed for robust out-of-sample generalization.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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