AI 中文总结
研究人员提出深度学习地球系统模型DLESyM-Ocean，采用patch能量评分损失训练，可模拟当前全球海冰与上层海洋，经多案例验证具稳定性与技巧性，计算效率高，有望用于次季节到季节预报。
AI 中文摘要
尽管人工智能在大气与气象预报领域已展现出显著潜力，但利用人工智能准确模拟地球系统其他组成部分仍是活跃的前沿研究方向。本文提出DLESyM-Ocean，这是一种模拟当前全球海冰与上层海洋状况的深度学习地球系统模型。与通过扩散目标或连续等级概率评分等损失函数优化的传统概率模型不同，DLESyM-Ocean采用patch能量评分损失进行训练。在大气强迫驱动下，DLESyM-Ocean生成校准良好、空间连贯且具有技巧性的海冰与上层海洋集合，相对于再分析产品偏差极小。DLESyM-Ocean在自回归运行多年模拟时保持稳定，生成的气候态与变率相对于再分析产品偏差极小。本文评估了多个案例研究，包括近期的一次海冰极端事件、一次严重的海洋热浪、2023年厄尔尼诺转变以及2023年全球平均气温峰值。在所有这些案例研究中，DLESyM-Ocean在常见大气强迫下生成了真实的表层与次表层轨迹及充足的集合多样性，表明其已学习到自回归海洋动力学。当与大气等其他地球系统组成部分耦合时，DLESyM-Ocean的计算效率使其成为次季节到季节预报的有前景工具。
英文摘要
While AI has shown remarkable promise in atmospheric and meteorological forecasting, accurately simulating other components of the Earth system with AI remains an active frontier. We present DLESyM-Ocean, a Deep Learning Earth System Model that simulates global present-day sea ice and upper ocean conditions. Unlike conventional probabilistic models optimized via diffusion objectives or losses such as continuous-ranked probability score, DLESyM-Ocean is trained using a patch energy score loss. When driven by atmospheric forcing, DLESyM-Ocean produces a well-calibrated, spatially coherent, and skillful ensemble of sea ice and upper ocean conditions with minimal bias relative to reanalysis products. DLESyM-Ocean is stable when autoregressively run for multi-year simulations and produces a climatology and variability with minimal bias compared with reanalysis. We evaluate case studies including a recent sea ice extreme, a severe marine heatwave, the 2023 El Niño transition, and the 2023 spike in global mean temperature. In all of these case studies, DLESyM-Ocean produces realistic surface and subsurface trajectories and ample ensemble diversity in response to common atmospheric forcing, suggestive of learned autoregressive ocean dynamics. When coupled with other Earth system components, such as the atmosphere, the computational efficiency of DLESyM-Ocean makes it a promising tool for subseasonal to seasonal forecasting.
Comments35 pages, 21 figures
