融合物理规律的神经网络模型，提升北极海冰浓度预测精度。
Physics-Knowledge-Guided Hybrid Neural Learning for Arctic Sea Ice Concentration Evolution and Short-Range Prediction
- 基于海冰连续性方程构建可微分网络结构，分解冰移动、冻结融化与局部误差。
- 在再分析数据驱动下保持冰缘稳定，预报条件下仍具短期预测能力。
- 适合气候建模与极地气象研究者，尤其关注物理机制与数据驱动结合者。
准确模拟海冰浓度（SIC）演变对极地气候评估和短期海冰预测至关重要。数值方法依赖复杂参数化且计算量大，数据驱动方法则常缺乏物理约束。本文提出物理信息混合冰模型（PIHIM），一种用于每日SIC演变的可微分数据驱动混合模型，其网络结构依据海冰连续性方程中的物理依赖关系设计，显式建模动力输运、热力驱动的面积变化及未解析的局部过程。PIHIM 在保持深度学习表达能力的同时，提供冰位移、冻融面积变化与局部误差闭合的过程解耦形式。评估设置包括：再分析强迫模拟（检验再分析强迫下的演变稳定性）与预报强迫预测（评估预报条件下的短期性能），分别以再分析和观测SIC为验证参考。结果表明，再分析强迫下模型显著提升冰缘保持能力并控制误差增长；在预报强迫下仍具备可测量的短期预测技能。代码将在论文接收后公开。
原文摘要 · Abstract (English)
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.
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