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.

翻译：地球科学基础模型通过整合大规模跨学科数据，以模拟和理解地球系统动力学，代表了地球科学领域的革命性方法。作为一种以数据为中心的人工智能范式，它们从PB级的结构化和非结构化数据中挖掘洞见。灵活的任务规范、多样的输入输出以及多模态知识表征，能够实现对单一数据源无法完成的综合分析。关键在于，地球科学模型的可扩展性与泛化能力使其能够应对与地球系统相互作用相关的预测、模拟和决策挑战。领域专家与计算机科学家的协作推动了这些宝贵工具的创新，为理解地球的过去、现在和未来提供了可能。然而，在验证与确认、规模尺度、可解释性、知识表征以及社会偏差方面仍存在挑战。未来，通过跨学科团队协作提升模型集成度、分辨率、准确性与公平性将是关键。尽管当前存在局限，地球科学基础模型通过其探索情景和量化不确定性的能力，在气候变化、自然灾害和可持续发展等紧迫问题上展现出了提供关键洞见的潜力。其向集成化、数据驱动建模的持续演进，有望为地球科学带来范式转变。