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.

地球科学基础模型通过整合大量跨学科数据模拟和理解地球系统动态，以数据为中心的人工智能范例从结构化和非结构化数据中发现见解，灵活的任务规格、多样的输入和输出以及多模态的知识表示使得全面分析成为可能，但在验证和验证、规模、可解释性、知识表示和社会偏见等方面仍存在挑战，提高模型一体化、分辨率、准确性和公平性的跨学科团队合作是关键，尽管当前存在限制，地球科学基础模型通过探索场景和量化不确定性向气候变化、自然灾害和可持续性等紧迫问题提供了关键见解，其不断向集成化的数据驱动建模演化具有改变范式的潜力。