Multiple Testing for Spatial Extremes with Application to Climate Model Evaluation

Music City Center 

Climate models are the primary tools for scientists to study climate change. Evaluating whether a climate model simulates the actual climate becomes critical in improving climate models. While most climate model evaluations focused on the mean and dependency of climate process, we focus on marginal extreme behavior, including return levels that often have devastating impacts on our ecosystems and societies. In particular, we aim to identify where the two climate extreme fields exhibit different marginal behavior, by simultaneously evaluating the differences over all spatial locations through multiple testing techniques. However, the large variation inherited in extreme model fitting makes this evaluation more challenging than that for mean and dependency structure. We propose a new multiple testing procedure, bivariate conditional local FDR (BiCLfdr), to efficiently detect signals from highly variable but spatially correlated hypotheses. Our method takes advantage of both the smoothness of large scale spatial variability and the local spatial correlation to enhance the power of comparing the marginal extreme distribution of two spatial extremes. We apply our method to identify where a regional climate model fails to represent the actual extreme behavior of winter precipitation. Our results provide climate scientists with more insights on improving climate models.

Multiple Testing

Spatial Extremes

Climate Models

Model Evaluation