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

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  • Philippe Naveau
  • Armelle Guillou
  • Théo Rietsch

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.

Suggested Citation

  • Philippe Naveau & Armelle Guillou & Théo Rietsch, 2014. "A non-parametric entropy-based approach to detect changes in climate extremes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(5), pages 861-884, November.
    • Handle: RePEc:bla:jorssb:v:76:y:2014:i:5:p:861-884
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    File URL: http://hdl.handle.net/10.1111/rssb.2014.76.issue-5
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    Cited by:

    1. James W. Vaupel & Marie-Pier Bergeron-Boucher & Ilya Kashnitsky, 2021. "Outsurvival as a measure of the inequality of lifespans between two populations," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 44(35), pages 853-864.

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