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.

翻译：海冰在稳定地球系统中扮演着重要角色。然而，由于其底层过程具有尺度不变性和高度各向异性，准确表征其动力学行为仍是模型构建面临的主要挑战。这导致了一个两难困境：能够忠实再现观测动力学的物理模型计算成本高昂，而高效的人工智能模型则牺牲了真实性。为解决这一困境，我们提出了GenSIM——首个能够以12小时为增量预测整个北极海冰状态演化的生成式人工智能模型。该模型基于20年的海冰-海洋模拟数据进行亚日尺度预报训练，能够生成长达30年的真实预测，同时通过其冰间水道与冰脊结构再现海冰的动力学特性，并捕捉海冰体积的长期变化趋势。值得注意的是，尽管仅由大气再分析数据驱动，GenSIM仍能隐式学习多年冰-海洋相互作用中的隐藏特征。因此，生成式人工智能能够从亚日尺度预报外推至年代际模拟，同时保持物理一致性。