Geographic tipping points in ecosystems, climate subsystems, or ice sheets pose severe challenges for localized early warning. Classical spatial indicators such as Moran's I summarize global spatial structure, but they struggle with three issues: spatial dilution, Euclidean assumptions, and correlated noise. This paper introduces SpatioTemporal Causal Network Diagnostics (ST-CND), a framework that addresses these three issues by representing the geographic field as a time-evolving directed causal network. The core workflow is as follows: (1) infer which spatial nodes help predict other nodes via transfer entropy, replacing fixed Euclidean neighborhoods with data-driven information-flow topology; (2) estimate local recovery rates within each candidate subnetwork via dynamic mode decomposition; and (3) identify the most vulnerable subnetwork by combining three signals, namely high internal fluctuation, high internal synchronization, and low external coupling, thereby suppressing false alarms from spatially correlated noise. Validated on synthetic bifurcations and two observational sea-surface temperature benchmarks, namely Indo-Pacific SST and North Atlantic AMOC, ST-CND delivers localized and interpretable warnings. On the AMOC task, it achieves an AUROC of 0.783 and a critical-subnetwork IoU of 0.378, outperforming recurrence-network and lambda-AR1 baselines. The framework provides an interpretable and scalable pipeline for spatial early warning in Earth system science.

翻译：生态系统、气候子系统或冰盖中的地理临界点对局部早期预警构成了严峻挑战。经典的空间指标（如莫兰指数）能概括全局空间结构，但面临三个问题：空间稀释、欧几里得假设和相关噪声。本文提出了时空因果网络诊断框架（ST-CND），通过将地理场表示为随时间演化的有向因果网络来解决上述三个问题。其核心流程如下：（1）通过传递熵推断哪些空间节点有助于预测其他节点，用数据驱动的信息流拓扑替代固定的欧几里得邻域；（2）利用动态模态分解估计每个候选子网络内的局部恢复率；（3）结合高内部波动、高内部同步和低外部耦合三个信号识别最脆弱的子网络，从而抑制空间相关噪声引发的误报。经过合成分岔实验以及两个观测性海表温度基准（即印太海表温度和北大西洋经向翻转环流）的验证，ST-CND提供了局部化且可解释的预警。在AMOC任务中，其AUROC达到0.783，关键子网络IoU为0.378，优于递归网络和lambda-AR1基线方法。该框架为地球系统科学中的空间早期预警提供了可解释且可扩展的解决方案。