Modeling Non-Stationary Temperature Maxima Based on Extremal Dependence Changing with Event Magnitude
Peng Zhong, Rapha\"el Huser, and Thomas Opitz

TL;DR
This paper introduces a novel statistical model for analyzing non-stationary temperature maxima, capturing how extremal dependence weakens with event severity and revealing insights into heatwave spatial extent and recent climate changes.
Contribution
The paper develops a new max-infinitely divisible process model that incorporates covariates to account for non-stationarities and extremal dependence weakening at higher quantiles.
Findings
Heatwave spatial extent decreases at higher altitudes for severe events.
Recent heatwaves are moderately wider than historical ones.
2019 heatwaves were more severe both spatially and locally compared to 1950-1975.
Abstract
The 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…
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Taxonomy
TopicsClimate variability and models · Ecosystem dynamics and resilience · Climate Change and Health Impacts
