首个生成式AI模型实现海冰从日到十年的逼真演化预测
Generative AI models capture realistic sea-ice evolution from days to decades
- 基于20年模拟数据训练，以12小时为步长预测海冰全状态演化
- 可精准模拟30年海冰变化，包含裂隙、堆积等动态特征与长期体积趋势
- 仅用大气数据就能隐含学习冰-海相互作用，适合气候与极地研究者
海冰在稳定地球系统中起关键作用，但其动力学建模仍是重大挑战，因过程具有尺度不变性和高度各向异性。这导致物理模型计算成本高，而高效AI模型牺牲真实感。为此，本文提出GenSIM，首个可在12小时时间步上预测北极海冰全状态演化的生成式AI模型。该模型在20年海冰-海洋模拟数据上进行亚日预报训练，能实现长达30年的现实预测，准确再现海冰的裂缝、脊线等动力特征，并捕捉海冰体积的长期变化趋势。值得注意的是，尽管仅依赖大气再分析数据，GenSIM仍能隐式学习多年冰-海洋相互作用的隐藏信号。因此，生成式AI可从亚日预报外推至十年尺度模拟，同时保持物理一致性。
原文摘要 · Abstract (English)
Sea ice plays an important role in stabilising the Earth system. Yet, representing its dynamics remains a major challenge for models, as the underlying processes are scale-invariant and highly anisotropic. This poses a dilemma: physics-based models that faithfully reproduce the observed dynamics are computationally costly, while efficient AI models sacrifice realism. Here, to resolve this dilemma, we introduce GenSIM, the first generative AI model to predict the evolution of the full Arctic sea-ice state at 12-hour increments. Trained for sub-daily forecasting on 20 years of sea-ice-ocean simulation data, GenSIM makes realistic predictions for 30 years, while reproducing the dynamical properties of sea ice with its leads and ridges and capturing long-term trends in the sea-ice volume. Notably, although solely driven by atmospheric reanalysis, GenSIM implicitly learns hidden signatures of multi-year ice-ocean interaction. Therefore, generative AI can extrapolate from sub-daily forecasts to decadal simulations, while retaining physical consistency.
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