When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence System

Geoscience foundation models represent a revolutionary approach in the field of Earth sciences by integrating massive cross-disciplinary data to simulate and understand the Earth systems dynamics. As a data-centric artificial intelligence (AI) paradigm, they uncover insights from petabytes of structured and unstructured data. Flexible task specification, diverse inputs and outputs and multi-modal knowledge representation enable comprehensive analysis infeasible with individual data sources. Critically, the scalability and generalizability of geoscience models allow for tackling diverse prediction, simulation, and decision challenges related to Earth systems interactions. Collaboration between domain experts and computer scientists leads to innovations in these invaluable tools for understanding the past, present, and future of our planet. However, challenges remain in validation and verification, scale, interpretability, knowledge representation, and social bias. Going forward, enhancing model integration, resolution, accuracy, and equity through cross-disciplinary teamwork is key. Despite current limitations, geoscience foundation models show promise for providing critical insights into pressing issues including climate change, natural hazards, and sustainability through their ability to probe scenarios and quantify uncertainties. Their continued evolution toward integrated, data-driven modeling holds paradigm-shifting potential for Earth science.