A0303
Title: Spatiotemporal modeling for record-breaking temperature events
Authors: Ana C Cebrian - University of Zaragoza (Spain) [presenting]
Jorge Castillo-Mateo - University of Zaragoza (Spain)
Alan Gelfand - Duke University (United States)
Jesus Asin - University of Zaragoza (Spain)
Zeus Gracia - University of Zaragoza (Spain)
Abstract: The occurrence of record-breaking temperature events is one of the evidence of climate change. In this context, an approach is presented to investigate the occurrence of record-breaking temperatures across years for any given day within the year within a space-time framework. Formal statistical analysis of record-breaking events has primarily been developed within the probability community, using results from the stationary record-breaking setting. However, that framework is not sufficient for analyzing actual record-breaking data, which requires rich modeling of the indicator events defining record-breaking series. A dataset consisting of over sixty years of daily maximum temperatures across Peninsular Spain is used. A novel and thorough exploratory data analysis leads to the proposal of hierarchical conditional models for the indicator events. The final model includes an explicit trend, necessary autoregression terms, spatial behavior captured by the distance to the coast, useful interactions, helpful spatial random effects, and very strong daily random effects. The fitted model shows that global warming trends have increased the number of records expected in the past decade almost two-fold, but it also estimates highly differentiated climate warming rates in space and by season.
