A non-parametric entropy-based approach to detect changes in climate extremes

Philippe Naveau, Armelle Guillou and Théo Rietsch

Journal of the Royal Statistical Society Series B, 2014, vol. 76, issue 5, 861-884

Abstract: type="main" xml:id="rssb12058-abs-0001">

The paper focuses primarily on temperature extremes measured at 24 European stations with at least 90 years of data. Here, the term extremes refers to rare excesses of daily maxima and minima. As mean temperatures in this region have been warming over the last century, it is automatic that this positive shift can be detected also in extremes. After removing this warming trend, we focus on the question of determining whether other changes are still detectable in such extreme events. As we do not want to hypothesize any parametric form of such possible changes, we propose a new non-parametric estimator based on the Kullback–Leibler divergence tailored for extreme events. The properties of our estimator are studied theoretically and tested with a simulation study. Our approach is also applied to seasonal extremes of daily maxima and minima for our 24 selected stations.

Date: 2014
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