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Name: Raphaël Huser

Talk Title: Modeling and Estimation of Extreme Red Sea Surface Temperature Hotspots

Abstract: Modeling, estimation and prediction of spatial extremes is key for risk assessment in a wide range of geo-environmental, geo-physical, and climate science applications. In this work, we propose a flexible approach for modeling and estimating extreme sea surface temperature (SST) hotspots, i.e., high threshold exceedance regions, for the whole Red Sea, a vital region of high biodiversity. Our proposed model is a semiparametric Bayesian spatial mixed-effects linear model with a flexible mean structure to capture spatially-varying trend and seasonality, while the residual spatial variability is modeled through a Dirichlet process mixture of low-rank spatial Student-t processes to efficiently handle high dimensional data with strong tail dependence. With our model, the bulk of the SST residuals influence tail inference and hotspot estimation only moderately, while our approach can automatically identify spatial extreme events without any arbitrary threshold selection. Posterior inference can be drawn efficiently through Gibbs sampling. Moreover, we show how hotspots can be estimated from the fitted model, and how to make high-resolution projections until the year 2100, based on the Representative Concentration Pathways 4.5 and 8.5. Our results show that the estimated 95% credible region for joint high threshold exceedances include large areas covering major endangered coral reefs in the southern Red Sea. This is joint work with Arnab Hazra (KAUST).

This talk is a contributed talk at EVA 2021.