AI 中文总结
该研究针对识别地球极端事件原因的难题，引入TraCE-ST概率拉格朗日方法，在合成实验和现实事件中恢复因果驱动因素并估计贡献，提出因果追踪框架，为研究高影响事件提供新途径，补充现有分析方法。
AI 中文摘要
识别地球极端事件的原因具有挑战性，因为在观测世界中无法进行反事实实验，而数值实验计算成本高且存在偏差。数据驱动的因果发现提供了一条补充途径，但现有方法在欠采样、高维情况下可能失败，且可能无法恢复导致特定事件的多时间步、多变量路径。我们引入了时空因果演化追踪器（TraCE-ST），一种概率拉格朗日方法，可在多变量网格数据中生成事件条件因果轨迹。在合成实验和现实世界极端事件中，TraCE-ST能恢复已知因果驱动因素并估计其相对贡献，还能突出较少研究的驱动因素。我们提出因果追踪作为一个有效的数据驱动框架，用于合成因果证据和生成可测试假设，补充关联分析和数值建模，同时加速对高影响事件的研究。
英文摘要
Identifying the causes of Earth's extremes is challenging because counterfactual experiments are not possible in the observed world. Data-driven causal discovery complements computationally expensive and potentially biased numerical experiments, but existing methods can struggle with undersampled, high-dimensional data and fail to recover multi-timestep, multivariate pathways leading to specific events. We introduce Tracer of Causal Evolutions in Space and Time (TraCE-ST), a probabilistic Lagrangian approach that produces event-conditioned causal trajectories in multivariate gridded data. TraCE-ST recovers known causal drivers and estimates their relative contributions in synthetic experiments and real-world extremes, including the 1991 Mount Pinatubo eruption. TraCE-ST also highlights less-studied drivers, such as orography-driven vorticity for Tropical Storm Debby (2006) and anomalous ocean-surface fluxes for the 2021 Pacific Northwest heatwave. Here, we propose causal tracking as an efficient data-driven framework for synthesizing causal evidence and generating testable hypotheses, complementing association analyses and numerical modeling while accelerating the study of high-impact events.
