NIVA用多模态大模型学习海洋与大气耦合规律，提升长期气候预测能力。
NIVA: A Multimodal Foundation Model for Actionable Earth System Intelligence
- 构建跨海洋与大气的多模态基础模型，统一学习地球系统特征
- 在大规模模拟数据上训练，准确预测主要气候指数
- 为亚季节到季节预测提供物理可解释的建模基础，适合气候研究者
近年来，基于AI的天气与气候建模在提升预报精度的同时降低了计算成本。然而，现有数据驱动方法难以有效建模耦合的地球系统动态，制约了预测时效突破两周的能力。为此，我们提出NIVA，一种多模态基础模型，旨在学习地球系统各组成部分的统一表征。尽管完整框架涵盖大气、海洋、冰层和陆地相互作用，本文聚焦于海洋与大气的双模态设定，作为可控验证案例，评估基础模型是否能学习耦合动力学。模型在大规模地球系统模拟数据上训练，成功捕捉到具有物理意义的跨模态结构，为亚季节到季节预测奠定基础。初步验证表明，NIVA能准确预测主要气候指数，再现关键气候变率模态。
原文摘要 · Abstract (English)
Recent advances in AI-driven weather and climate modeling have improved forecast skill while reducing computational cost. However, existing data-driven approaches are limited in their ability to model coupled Earth system dynamics, which is required for extending predictability beyond the ~2-week horizon. To address this, we introduce NIVA, a multimodal foundation model designed to learn unified representations across Earth system components. While the full framework targets atmosphere, ocean, ice, and land interactions, we focus here on a two-modality setting (ocean and atmosphere) as a controlled proof of concept to evaluate whether foundation models can learn coupled dynamics. Trained on large-scale Earth system simulations, NIVA learns physically meaningful cross-modal structure, providing a foundation for subseasonal-to-seasonal prediction. As initial validation, we show that NIVA captures key modes of climate variability through accurate prediction of major climate indices.
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