AI 中文总结
该研究针对地球科学回归任务，将XAI方法应用于Lorenz-63系统模拟器，考察其适用性、局限性及应用情况，提出研究缺口与实践建议，为XAI在地球科学回归中的有效应用提供支撑
AI 中文摘要
随着人工智能（AI）系统从研究原型向地球系统科学与预报领域的业务工具过渡，建立对其预测结果的信任变得愈发重要。尽管模型的输入与输出是可观测的，但现代AI模型的内部决策过程仍十分复杂且难以解释，因此被贴上“黑箱”的标签。可解释人工智能（XAI）提供了洞察这些过程的技术。然而，大多数XAI方法是为分类任务开发的，这引发了它们是否适用于地球科学应用中占主导地位的回归问题的疑问。我们从这一视角对XAI方法进行综述，将其组织为结构化框架，并考察它们的理论基础与实际表现。为了让讨论更具根基，我们将选取的方法应用于Lorenz 1963系统的机器学习模拟器，这是一种典型的混沌模型，提供了一种易于处理且具有物理意义的场景，可用于揭示通用XAI在回归场景中的局限性与失效模式。随后，我们调研了这些及相关方法在各类地球系统科学中的应用情况。我们进一步将XAI置于模型开发生命周期中，将方法选择与业务地球系统科学中不同利益相关群体的需求相联系。最后，我们指出现有方法的缺口，并概述前瞻性研究议程，为XAI在地球科学建模与预报的回归应用中负责任、有效的使用提供实践建议。
英文摘要
As artificial intelligence (AI) systems transition from research prototypes to operational tools in Earth system science and forecasting, establishing trust in their predictions becomes increasingly important. Although model inputs and outputs are observable, the internal decision-making of modern AI models remains complex and hard to interpret, earning them the label ``black boxes.'' Explainable artificial intelligence (XAI) offers techniques to provide insight into these processes. However, most XAI methods were developed for classification tasks, raising questions about their suitability for the regression problems that dominate geoscientific applications. We review XAI approaches through this lens, organising them into a structured framework and examining both their theoretical foundations and practical behaviour. To ground this discussion, we apply a selection of methods to a machine learning emulator of the Lorenz 1963 system, an archetypal chaotic model that provides a tractable, physically meaningful setting for exposing the limitations and failure modes of general-purpose XAI in regression contexts. We then survey how these and related methods have been applied across a variety of Earth system sciences. We further situate XAI within the model development lifecycle, linking methodological choices to the needs of different stakeholder groups across operational Earth system science. We close by identifying gaps in existing methodologies and outlining a forward-looking research agenda, with practical recommendations for the responsible, effective use of XAI in regression applications of geoscientific modelling and forecasting.
Comments66 pages, 14+8 figures in main+appendix. This Work has been submitted to Artificial Intelligence for the Earth Systems. Copyright in this Work may be transferred without further notice
