发表机构
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research; Forschungszentrum Jülich GmbH; Karlsruhe Institute of Technology; European Center for Medium-Range Weather Forecasts; University of Cologne; University of Bremen（阿尔弗雷德·魏格纳亥姆霍兹极地与海洋研究中心; 于利希研究中心; 卡尔斯鲁厄理工学院; 欧洲中期天气预报中心; 科隆大学; 不来梅大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
HClimRep-Ocean提出一种在非结构化网格上运行的全球海洋模拟器，利用209年AWI-CM3训练数据，在30天海流预报中超越参考模型，并在OceanBench基准上取得最低RMSE，验证了原生网格方法的竞争力。
AI 中文摘要
近年来，用于大气过程的机器学习（ML）模拟器发展迅速，彻底改变了天气预报领域。尽管目前已有早期的ML海洋预报模型，但其发展程度仍不及大气对应模型。与大气不同，海洋的大部分动能存在于中尺度涡旋中，其特征空间尺度比类似的大气特征小约一个数量级。此外，复杂的海岸线、狭窄的海峡以及冰覆盖海域使得边界表示成为大气模型所不面临的核心挑战。因此，数值海洋模拟通常采用局部加密甚至完全非结构化的网格。然而，数据驱动的海洋模型迄今为止仍基于经纬度网格构建。我们提出了HClimRep-Ocean，一种直接在FESOM2原生非结构化网格上运行的海洋模拟器。该模拟器在长达209年的AWI-CM3控制积分数据上进行训练，并在无大气强迫的条件下运行，仅在初始化时接收大气状态，从而隔离了海洋状态本身所携带的可预测性。其技能强烈依赖于具体变量：对于海流，HClimRep-Ocean在30天预报中优于所有参考模型；而对于温度和盐度，阻尼异常持续性预报仍是更准确的估计方法。这一行为在物理上可解释：海流变率主要受地转作用驱动且由内部产生，而海表温度和盐度的波动则受天气尺度大气强迫的驱动。在OceanBench基准上独立评估时，HClimRep-Ocean的一个基于再分析训练的变体在所有评估系统中实现了相对于GLORYS再分析数据的最低均方根误差（RMSE），证实了原生网格方法的竞争力。
英文摘要
Machine-learning (ML) emulators for atmospheric processes have advanced rapidly in recent years, transforming weather forecasting. Although early ML ocean forecasting models now exist, they remain less developed than their atmospheric counterparts. Unlike the atmosphere, much of the ocean's kinetic energy resides in mesoscale eddies whose characteristic spatial scales are approximately an order of magnitude smaller than those of comparable atmospheric features. Moreover, complex coastlines, narrow straits, and ice-covered seas make boundary representation a central challenge that atmospheric models do not face. Consequently, numerical ocean simulations commonly use locally refined or even completely unstructured meshes. However, their data-driven counterparts have so far been built around latitude-longitude grids. We present HClimRep-Ocean, an ocean emulator that operates directly on the native unstructured mesh of FESOM2. The emulator is trained on a 209-year AWI-CM3 control integration and is run without atmospheric forcing, receiving the atmospheric state only at initialisation time, which isolates the predictability carried by the ocean state itself. Skill is strongly field-dependent: for currents, HClimRep-Ocean outperforms every reference at 30 day forecast, whereas for temperature and salinity a damped-anomaly persistence forecast remains the more accurate estimator. This behaviour is physically interpretable: current variability is largely geostrophic and internally generated, whereas sea-surface temperature and salinity fluctuations are driven by atmospheric forcing through weather state. Evaluated independently on the OceanBench benchmark, a reanalysis-trained variant of HClimRep-Ocean achieves the lowest RMSE against GLORYS reanalysis among all assessed systems, confirming the competitiveness of the native-mesh approach.
