NIVA: A Multimodal Foundation Model for Actionable Earth System Intelligence

• URL: http://arxiv.org/abs/2606.28546v1
• Date: Fri, 26 Jun 2026 19:01:41 GMT
• Title: NIVA: A Multimodal Foundation Model for Actionable Earth System Intelligence
• Abstract summary: We introduce NIVA, a foundation model designed to learn unified representations across Earth system components.<n>Trained on large-scale Earth system simulations, NIVA learns physically meaningful cross-modal structure.<n>As initial validation, we show that NIVA captures key modes of climate variability through accurate prediction of major climate indices.
• Score: 1.2641198540127232
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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