发表机构
Ocean University of China; University of Glasgow（中国海洋大学; 格拉斯哥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出物理信息混合冰模型（PIHIM），结合物理知识与深度学习，在北极海冰密集度演化模拟及短程预测任务中，展现出更优的冰边缘保留和误差控制能力，且代码将公开。
AI 中文摘要
准确建模海冰密集度（SIC）演化对极地气候评估和短程海冰预测至关重要。数值方法和数据驱动方法是SIC建模的两大基础，但前者常需复杂参数化和大量计算，后者则极少显式编码物理依赖关系。本研究提出物理信息混合冰模型（PIHIM），这是一种用于每日SIC演化的可微数据驱动混合冰模型，其网络结构根据海冰连续方程中编码的物理依赖关系组织，明确考虑了动力输运、热力驱动的面积增减以及未解析的局地过程。PIHIM保留了深度学习的表征能力，同时提供了冰位移、冻融面积变化和局地误差闭合的过程分解公式。采用两种评估设置：再分析强迫模拟检验再分析强迫下SIC演化的稳定性，预报强迫预测评估预报强迫条件下的短程性能，以再分析和观测SIC作为验证参考。结果表明，再分析强迫模拟中冰边缘保留能力和误差增长控制得到增强，而PIHIM在预报强迫条件下仍保持可测量的短程预测技能。我们的代码将在论文录用后公开提供。
英文摘要
Accurate modeling of sea ice concentration (SIC) evolution is essential for polar climate assessment and short?range sea ice prediction. Numerical and data-driven approaches constitute major foundations for SIC modeling, but the former often require complex parameterizations and substantial compu?tation, whereas the latter rarely encode physical dependencies explicitly. This study presents the Physics-Informed Hybrid Ice Model (PIHIM), a differentiable data-driven hybrid ice model for daily SIC evolution that organizes its network structure according to the physical dependencies encoded in the sea ice continuity equation and explicitly accounts for dynamical transport, ther?modynamically driven areal growth and loss, and unresolved local processes. PIHIM preserves the representation capacity of deep learning while providing a process-decomposed formulation of ice displacement, freeze-melt areal change, and local error closure. Two evaluation settings are adopted: reanalysis-forced simulation examines SIC evolution stability under reanalysis forcing, and forecast-forced prediction assesses short-range performance un?der forecast-forced conditions, with reanalysis and observational SIC serving as verification references. Results indicate enhanced ice-edge preservation and error-growth control in reanalysis?forced simulation, while PIHIM retains measurable short-range prediction skill under forecast-forced conditions. Our code will be made publicly available after the paper is accepted.
