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dc.contributor.authorZhong, Peng
dc.contributor.authorHuser, Raphaël
dc.contributor.authorOpitz, Thomas
dc.date.accessioned2020-06-17T12:28:43Z
dc.date.available2020-06-17T12:28:43Z
dc.date.issued2020-06-02
dc.identifier.urihttp://hdl.handle.net/10754/663646
dc.description.abstractThe modeling of spatio-temporal trends in temperature extremes can help better understand the structure and frequency of heatwaves in a changing climate. Here, we study annual temperature maxima over Southern Europe using a century-spanning dataset observed at 44 monitoring stations. Extending the spectral representation of max-stable processes, our modeling framework relies on a novel construction of max-infinitely divisible processes, which include covariates to capture spatio-temporal non-stationarities. Our new model keeps a popular max-stable process on the boundary of the parameter space, while flexibly capturing weakening extremal dependence at increasing quantile levels and asymptotic independence. This is achieved by linking the overall magnitude of a spatial event to its spatial correlation range, in such a way that more extreme events become less spatially dependent, thus more localized. Our model reveals salient features of the spatio-temporal variability of European temperature extremes, and it clearly outperforms natural alternative models. Results show that the spatial extent of heatwaves is smaller for more severe events at higher altitudes, and that recent heatwaves are moderately wider. Our probabilistic assessment of the 2019 annual maxima confirms the severity of the 2019 heatwaves both spatially and at individual sites, especially when compared to climatic conditions prevailing in 1950-1975.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2006.01569
dc.rightsArchived with thanks to arXiv
dc.titleAssessing Non-Stationary Heatwave Hazard with Magnitude-Dependent Spatial Extremal Dependence
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics
dc.contributor.departmentStatistics Program
dc.eprint.versionPre-print
dc.contributor.institutionBioSP, INRAE, Avignon, 84914, France.
dc.identifier.arxivid2006.01569
kaust.personZhong, Peng
kaust.personHuser, Raphaël
refterms.dateFOA2020-06-17T12:29:48Z


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