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Detecting Extreme Temperature Events Using Gaussian Mixture Models
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  • Aytaç PAÇAL,
  • Birgit Hassler,
  • Katja Weigel,
  • Mehmet Levent Kurnaz,
  • Michael F Wehner,
  • Veronika Eyring
Deutsches Zentrum für Luft- und Raumfahrt

Corresponding Author:aytac.pacal@dlr.de

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Birgit Hassler
Deutsches Zentrum fur Luft- und Raumfahrt (DLR), Institut fur Physik der Atmosphare
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Katja Weigel
University of Bremen
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Mehmet Levent Kurnaz
Bogazici University
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Michael F Wehner
Lawrence Berkeley National Laboratory (DOE)
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Veronika Eyring
Deutsches Zentrum für Luft- und Raumfahrt
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Extreme temperature events have traditionally been detected assuming a unimodal distribution of temperature data. We found that surface temperature data can be described more accurately with a multimodal rather than a unimodal distribution. Here, we applied Gaussian Mixture Models (GMM) to daily near-surface maximum air temperature data from the historical and future Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations for 46 land regions defined by the Intergovernmental Panel on Climate Change (IPCC). Using the multimodal distribution, we found that temperature extremes, defined based on daily data in the warmest mode of the GMM distributions, are getting more frequent in all regions. Globally, a 10-year extreme temperature event relative to 1980-2010 conditions will occur 15 times more frequently in the future under 3.0oC of Global Warming Levels (GWL). The frequency increase can be even higher in tropical regions, such that 10-year extreme temperature events will occur almost twice a week. Additionally, we analysed the change in future temperature distributions under different GWL and found that the hot temperatures are increasing faster than cold temperatures in low latitudes, while the cold temperatures are increasing faster than the hot temperatures in high latitudes. The smallest changes in temperature distribution can be found in tropical regions, where the annual temperature range is small. Our method captures the differences in geographical regions and shows that the frequency of extreme events will be even higher than reported in previous studies.
21 Apr 2023Submitted to ESS Open Archive
29 Apr 2023Published in ESS Open Archive
13 Sep 2023Published in Journal of Geophysical Research: Atmospheres. https://doi.org/10.1029/2023JD038906