Detecting Abrupt Shifts and Coherent Spatial Domains in Earth System Data with TOAD

We present TOAD v1.0 (Tipping and Other Abrupt events Detector), an open-source Python framework for the systematic detection and analysis of abrupt shifts in gridded Earth system data. TOAD provides a user-oriented workflow that combines grid-level shift detection with spatio-temporal clustering to identify domains where abrupt change co-occurs. An optional ensemble consensus step then identifies spatial patterns that are robust across ensemble members, models, variables, or method configurations, and quantifies associated statistics. The framework is method-agnostic, allowing different detection and clustering algorithms to be compared within a reproducible analysis pipeline. TOAD serves as an introspection tool for exploratory analysis of abrupt change across scales and addresses key questions in tipping-point research by identifying where such changes occur and providing first-order information on when they emerge along a time or forcing trajectory. The framework supports coordinated analyses in large model ensembles and intercomparison projects, such as TIPMIP and CMIP.