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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 15, issue 3
Nonlin. Processes Geophys., 15, 365–378, 2008
https://doi.org/10.5194/npg-15-365-2008
© Author(s) 2008. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

Special issue: Extreme Events: Nonlinear Dynamics and Time Series Analysis

Nonlin. Processes Geophys., 15, 365–378, 2008
https://doi.org/10.5194/npg-15-365-2008
© Author(s) 2008. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  06 May 2008

06 May 2008

Weather regime dependence of extreme value statistics for summer temperature and precipitation

P. Yiou1, K. Goubanova2, Z. X. Li2, and M. Nogaj3 P. Yiou et al.
  • 1Laboratoire des Sciences du Climat et de l'Environnement/IPSL, CE Saclay l'Orme des Merisiers, 91191 Gif-sur-Yvette, France
  • 2Laboratoire de Météorologie Dynamique/IPSL, 4 Place Jussieu, 75005 Paris, France
  • 3EDF, Département Mécanique des Fluides, Energies et Environnement, 78401 Chatou, France

Abstract. Extreme Value Theory (EVT) is a useful tool to describe the statistical properties of extreme events. Its underlying assumptions include some form of temporal stationarity in the data. Previous studies have been able to treat long-term trends in datasets, to obtain the time dependence of EVT parameters in a parametric form. Since there is also a dependence of surface temperature and precipitation to weather patterns obtained from pressure data, we determine the EVT parameters of those meteorological variables over France conditional to the occurrence of North Atlantic weather patterns in the summer. We use a clustering algorithm on geopotential height data over the North Atlantic to obtain those patterns. This approach refines the straightforward application of EVT on climate data by allowing us to assess the role of atmospheric variability on temperature and precipitation extreme parameters. This study also investigates the statistical robustness of this relation. Our results show how weather regimes can modulate the different behavior of mean climate variables and their extremes. Such a modulation can be very different for the mean and extreme precipitation.

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