首个全球海洋概率预报机器学习系统，5天内精准预测海温与洋流。
Data-driven ensemble prediction of the global ocean
- 用物理结构化扰动和大气编码模块构建机器学习集成预报框架。
- 在1°网格上实现365天的海表温度、盐度等多变量5日预报，性能优于传统方法。
- 比传统系统快数个数量级，适合气候风险评估与实时应用。
数据驱动模型已推动确定性海洋预报发展，但将机器学习拓展至全球概率海洋预报仍是开放挑战。本文提出首个基于机器学习的全球海洋集成预报系统FuXi-ONS，可在全球1°网格上提供长达365天的5日预报，涵盖海表温度、海表高度、次表层温度、盐度及海洋流速。该系统不依赖计算成本高昂的数值模型重复积分，而是学习物理结构化扰动，并引入大气编码模块以稳定长周期预报。在GLORYS12再分析数据上的评估显示，相比确定性基线与噪声扰动基线，FuXi-ONS在集合均值精度和概率预报质量上均有提升；其在海表温度和尼诺3.4变异性的季节预报中表现媲美现有参考系统，且运行速度比传统集成系统快数个数量级。这些结果展示了机器学习在推进海洋科学核心问题上的潜力，为高效概率海洋预报与气候风险评估提供了可行路径。
原文摘要 · Abstract (English)
Data-driven models have advanced deterministic ocean forecasting, but extending machine learning to probabilistic global ocean prediction remains an open challenge. Here we introduce FuXi-ONS, the first machine-learning ensemble forecasting system for the global ocean, providing 5-day forecasts on a global 1° grid up to 365 days for sea-surface temperature, sea-surface height, subsurface temperature, salinity and ocean currents. Rather than relying on repeated integration of computationally expensive numerical models, FuXi-ONS learns physically structured perturbations and incorporates an atmospheric encoding module to stabilize long-range forecasts. Evaluated against GLORYS12 reanalysis, FuXi-ONS improves both ensemble-mean skill and probabilistic forecast quality relative to deterministic and noise-perturbed baselines, and shows competitive performance against established seasonal forecast references for SST and Niño3.4 variability, while running orders of magnitude faster than conventional ensemble systems. These results provide a strong example of machine learning advancing a core problem in ocean science, and establish a practical path toward efficient probabilistic ocean forecasting and climate risk assessment.
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