发表机构
University of Salento; CMCC Foundation - Euro-Mediterranean Center on Climate Change; Columbia University; CUNY New York City College of Technology（萨伦托大学; CMCC基金会——地中海气候变化中心; 哥伦比亚大学; 纽约市立大学纽约城市技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Neptune是一个端到端数据驱动的全球海洋次季节预测模型，结合CNN和SFNO，在60天内以高分辨率模拟海洋状态，并稳定再现其时空演变。
AI 中文摘要
次季节到季节（S2S）预报具有重要的社会意义，支持从水资源和农业管理到灾害风险降低、能源规划和保险等领域的决策制定。要在这些时间尺度上实现可靠的预测，需要表征海洋及其动力学，但传统的基于物理的海洋环流模型（OGCMs）由于代码复杂性，计算成本高昂且难以开发和改进。在本工作中，我们提出了Neptune，一个端到端的数据驱动框架，用于全球海洋和海冰组件的模拟，专为S2S时间尺度（最长60天）设计。Neptune结合了卷积神经网络（CNNs）和球面傅里叶神经算子（SFNOs），以有效捕捉局部特征和全局跨尺度相互作用，从而获得海洋状态的连贯表征。在给定的每日大气场驱动下，Neptune模拟海洋状态变量，从温度和盐度到纬向和经向海流，从海面高度到海冰厚度和密集度，以每日输出形式提供海洋表面和整个水柱的状态。具体而言，我们提出了Neptune的两个变体，Neptune-1和Neptune-025，分别能够以1°和0.25°的分辨率模拟海洋状态。通过一系列指标评估，包括统计指标（RMSE、CRPS和ACC）、物理一致性（海洋热含量、涡动能和冰Brier分数）以及气候指数（ENSO和Z20指标、IOD），Neptune成功再现了长达60天的海洋场时空演变，并在长时间尺度上保持稳定。Neptune提供了令人信服的证据，表明端到端的数据驱动海洋模拟器可以成为下一代S2S预报系统的强大组成部分，以高时空分辨率模拟海洋状态。
英文摘要
Subseasonal-to-seasonal (S2S) forecasting is societally critical, supporting decision-making in sectors ranging from water and agricultural management to disaster risk reduction, energy planning, and insurance. Achieving reliable predictions at these timescales requires representing the ocean and its dynamics, but traditional physics-based Ocean General Circulation Models (OGCMs), are computationally expensive and difficult to develop and improve because of the code complexity. In this work, we propose Neptune, an end-to-end data-driven framework for global ocean and sea-ice components emulation tailored for S2S timescales, up to 60 days. Neptune combines Convolutional Neural Networks (CNNs) and Spherical Fourier Neural Operators (SFNOs) to effectively capture local features and global cross-scale interactions, thereby obtaining a coherent representation of the ocean state. Forced by prescribed daily atmospheric fields, Neptune emulates ocean state variables, from temperature and salinity, to zonal and meridional currents, from sea surface height to sea ice thickness and concentration, with daily outputs at the ocean surface and through the water column. Specifically, we propose two variants of Neptune, Neptune-1 and Neptune-025, capable of emulating the ocean state at 1° and 0.25° resolution, respectively. Evaluated against a suite of metrics, including statistics (RMSE, CRPS and ACC), physical coherency (Ocean Heat Content, Eddy Kinetic Energy and Ice Brier Score) and climate indices (ENSO and Z20 metric, IOD), Neptune successfully reproduces the spatio-temporal evolution of the oceanic fields up to 60 days, and is stable over long timescales. Neptune provides compelling evidence that end-to-end data-driven ocean emulators can become a powerful component of next-generation S2S forecasting systems, emulating ocean state at high spatio-temporal resolution.
