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arXiv 2602.01904physics.ao-ph
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海洋在快速大气演变中的作用通过机器学习揭示

Role of the ocean for fast atmospheric evolution revealed by machine learning

University of Oxford（牛津大学）；European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）

Bobby Antonio, Kristian Strommen, Hannah M. Christensen

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AI总结：

通过机器学习揭示海洋在快速大气演变中的作用，分析GraphCast模型对海洋信息的推断能力及其对预报精度的影响。

摘要：

近年来，有许多努力致力于创建机器学习大气模拟器，旨在取代物理模型。迄今为止，这些主要集中在中短期天气预报上，其中这些'机器学习天气预测'（MLWP）模型可以超越领先的运营预报中心。然而，由于这种对短期时间尺度的关注，许多模拟器忽略了海洋的影响，并且不将任何海洋变量作为输入。我们假设这些MLWP模型已经学习了大气演变的最佳猜测，通过隐含地从大气状态推断出海洋条件，而没有访问海洋数据。将这一限制转化为优势，我们利用它来研究海洋对大气演变的作用。通过探索模型预报误差与空气-海洋界面性质之间的关系，我们推断出这些大气模拟器能够从大气数据中推断出哪些海洋信息，以及无法推断出哪些信息。这突显了海洋独立影响大气的区域和过程，特别是在快速时间尺度上。我们对GraphCast进行了这项分析，发现空气-海洋性质与海洋上的预报误差之间存在明显的关系，包括明显的季节性影响。然后我们探索了这揭示了GraphCast对海洋的内部表示。除了理解现实世界的海洋-大气相互作用外，这项分析还为提高MLWP模型的预报技能和物理真实性，以及指导未来机器学习模型如何在短期时间尺度上使用海洋信息提供了指导。

英文摘要：

There have recently been many efforts to create machine learnt atmospheric emulators designed to replace physical models. So far these have mainly focused on medium-range weather forecasting, where these `Machine Learnt Weather Prediction' (MLWP) models can outperform leading operational forecasting centres. However, because of this focus on shorter timescales, many of these emulators ignore the effects of the ocean, and take no ocean variables as inputs. We hypothesise that such MLWP models have learnt a best-guess of the evolution of the atmosphere, by implicitly inferring ocean conditions from atmospheric states, with no access to ocean data. Turning this limitation into a strength, we use it as a means to study the role of the oceans on the evolution of the atmosphere. By exploring how model forecast errors relate to properties of the air-sea interface, we infer what ocean information these atmospheric emulators are able to derive from atmospheric data alone, and what they cannot. This highlights the regions and processes through which the ocean independently influences the atmosphere on fast timescales. We perform this analysis for GraphCast, finding clear relationships between air-sea properties and the forecast errors over the ocean, including clear seasonal effects. We then explore what this reveals about GraphCast's internal representation of the ocean. In addition to understanding real-world ocean-atmosphere interactions, this analysis provides guidance for improving forecast skill and physical realism in MLWP models, and for informing how future machine learning models should use ocean information on short timescales.

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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