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The effect of a short observational record on the statistics of temperature extremes
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  • Joel Zeder,
  • Sebastian Sippel,
  • Olivier Colin Pasche,
  • Sebastian Engelke,
  • Erich Markus Fischer
Joel Zeder
ETH Zurich

Corresponding Author:[email protected]

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Sebastian Sippel
University of Leipzig, ETH Zurich
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Olivier Colin Pasche
Research Center for Statistics, University of Geneva
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Sebastian Engelke
Research Center for Statistics, University of Geneva
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Erich Markus Fischer
ETH Zurich
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Abstract

In June 2021, the Pacific Northwest experienced a heatwave that broke all previous records. Estimated return levels based on observations up to the year before the event suggested that reaching such high temperatures is not possible in today's climate. We here assess the suitability of the prevalent statistical approach by analyzing extreme temperature events in climate model large ensemble and synthetic extreme value data. We demonstrate that the method is subject to biases, as high return levels are generally underestimated and, correspondingly, the return period of low-likelihood heatwave events is overestimated, if the underlying extreme value distribution is derived from a short historical record. These biases have even increased in recent decades due to the emergence of a pronounced climate change signal. Furthermore, if the analysis is triggered by an extreme event, the implicit selection bias affects the likelihood assessment depending on whether the event is included in the modeling.
14 Apr 2023Submitted to ESS Open Archive
16 Apr 2023Published in ESS Open Archive