AI模型实现全球60天多圈层气候预测，精度超欧洲气象中心。
A machine learning model for skillful climate system prediction
- 基于机器学习构建全耦合大气-海洋-陆地-海冰气候模型
- 在29个变量上优于ECMWF模型，尤其提升降水和海洋预测能力
- 可捕捉季节内变异，适合极端天气预警与农业决策
气候系统模型（CSMs）通过整合大气、海洋、陆地和冰冻圈之间的跨圈层相互作用，已成为理解气候动力学与提升预报能力的关键工具。近年来，人工智能驱动的气象建模在单圈层及部分耦合系统中取得显著进展。然而，实现涵盖大气-海洋-陆地-海冰完整耦合的全链路AI气候模型仍面临挑战。本文提出FengShun-CSM，一个基于AI的气候系统模型，可提供全球范围内60天的每日预报，覆盖29个关键变量，涉及大气、海洋、陆地与冰冻圈领域。该模型在多数变量上的预测性能显著优于欧洲中期天气预报中心（ECMWF）的次季节到季节（S2S）模型，尤其在降水、地表和海洋分量预测方面表现突出。这一优势主要归因于其对季节内变率模态（尤其是哈德利-朱利安振荡，MJO）的更好刻画。值得注意的是，FengShun-CSM在预测次季节极端事件方面展现出巨大潜力，有望推动气象灾害减灾、海洋生态系统保护与农业生产力提升。此外，该研究验证了利用机器学习技术开发全链路AI气候系统的可行性，为下一代地球系统建模树立了变革性范式。
原文摘要 · Abstract (English)
Climate system models (CSMs), through integrating cross-sphere interactions among the atmosphere, ocean, land, and cryosphere, have emerged as pivotal tools for deciphering climate dynamics and improving forecasting capabilities. Recent breakthroughs in artificial intelligence (AI)-driven meteorological modeling have demonstrated remarkable success in single-sphere systems and partially spheres coupled systems. However, the development of a fully coupled AI-based climate system model encompassing atmosphere-ocean-land-sea ice interactions has remained an unresolved challenge. This paper introduces FengShun-CSM, an AI-based CSM model that provides 60-day global daily forecasts for 29 critical variables across atmospheric, oceanic, terrestrial, and cryospheric domains. The model significantly outperforms the European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal-to-seasonal (S2S) model in predicting most variables, particularly precipitation, land surface, and oceanic components. This enhanced capability is primarily attributed to its improved representation of intra-seasonal variability modes, most notably the Madden-Julian Oscillation (MJO). Remarkably, FengShun-CSM exhibits substantial potential in predicting subseasonal extreme events. Such breakthroughs will advance its applications in meteorological disaster mitigation, marine ecosystem conservation, and agricultural productivity enhancement. Furthermore, it validates the feasibility of developing AI-powered CSMs through machine learning technologies, establishing a transformative paradigm for next-generation Earth system modeling.
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