B1520
Title: Multiple testing for spatial extreme with application to climate model evaluation
Authors: Sooin Yun - CUNY Baruch College (United States) [presenting]
Abstract: Climate models use systems of partial differential equations to describe the temporal evolution of climate, oceans, atmosphere, ice, and land-use processes across a spatial domain. Scientists rely on climate models to study why the Earth's climate is changing and how it might change in the future, as well as to study the dynamics of different climate factors. An interesting question is how it should be evaluated whether a climate model simulates the Earth's real climate. Many existing methods for comparing two climate fields shed light on climate model validation. However, they are not tailored for comparing spatial extreme fields, and the learning obtained from their applications to climate model evaluation should not be directly extended to climate extremes. The large variation inherited in extreme values makes the evaluation in climate extremes more challenging than that for mean and dependency structure. A new multiple-testing approach is proposed to evaluate the extreme behaviour of climate model simulations in terms of extreme value distribution and return levels. The method can identify the regions where the simulated extremes are different from reality, and this will provide climate scientists insights on how to improve climate models.
