Spatially-Dependent Multiple Testing Under Model Misspecification, With Application to Detection of Anthropogenic Influence on Extreme Climate Events
In this paper, we develop a specialized Bayesian hierarchical statistical model to flexibly account for spatial coherence in the statistics of monthly extremes from ensembles of global climate model output. Additionally, we construct a criterion for systematically conducting extreme event attribution for a large collection of land-regions across the globe.
Our analysis is able to robustly identify land regions experiencing meaningful human influence on extreme weather while simultaneously controlling the number of erroneous conclusions. Furthermore, our approach greatly outperforms related techniques in terms of the signal-to-noise ratio. Our methodology will be operationalized in a workflow for publishing monthly results in near-real time.
The Weather Risk Attribution Forecast (WRAF) is a tool that uses an output from global climate models to make simultaneous attribution statements about whether and how greenhouse gas emissions have contributed to extreme weather across the globe. However, in conducting a large number of simultaneous hypothesis tests, the WRAF is prone to identifying false “discoveries.” A common technique for addressing this multiple testing problem is to adjust the procedure in a way that controls the proportion of true null hypotheses that are incorrectly rejected, or the false discovery rate (FDR). Unfortunately, generic FDR procedures suffer from low power when the hypotheses are dependent, and techniques designed to account for dependence are sensitive to misspecification of the underlying statistical model. In this paper, we develop a Bayesian decision-theoretic approach for dependent multiple testing and a nonparametric hierarchical statistical model that flexibly controls false discovery and is robust to model misspecification. We illustrate the robustness of our procedure to model error with a simulation study, using a framework that accounts for generic spatial dependence and allows the practitioner to flexibly specify the decision criteria. Finally, we apply our procedure to several seasonal forecasts and discuss implementation for the WRAF workflow.