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Climate change and modelling of extreme temperatures in Switzerland

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Abstract

This study models maximum temperatures in Switzerland monitored in twelve locations using the generalised extreme value (GEV) distribution. The parameters of the GEV distribution are determined within a Bayesian framework. We find that the parameters of the underlying distribution underwent a substantial change in the beginning of the 1980s. This change is characterised by an increase both in the level and the variability. We assess the likelihood of the heat wave of the summer 2003 using the fitted GEV distribution by accounting for the presence of a structural break. The estimation results do suggest that the heat wave of 2003 is not that statistically improbable if an appropriate methodology is used for dealing with nonstationarity.

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Notes

  1. http://www.meteoschweiz.admin.ch/web/en/climate/climate_today/homogeneous_data.html.

  2. The deviations are assessed as 0.2–0.3°C for provisionally inhomogenisation, reconstruction at the same place or nearby (Sion) and as −0.5° for the reconstruction in Bern, as reported by personal communication with MeteoSwiss.

  3. This approach is rather standard in extreme value statistics. More than one observation during a period can be considered by using r-largest-techniques (Coles 2001). Changepoint problems using the Poisson process in a Bayesian framework date back to Raftery and Akman (1986). Renard et al. (2006) quote Peak-over-Threshold-methods as a second standard approach alternative to the analysis of block maxima, either with a Poisson process or the Generalized Pareto-distribution. These also allow using more data information than just one extreme during one period. Other information could be considered by kriging (spatial information) or a variety of multivariate methods. Seneviratne et al. (2006) used Canonical Correlation Analysis (CCA), based on daily maximum temperature to investigate the occurrence of heat waves.

  4. An interesting extension of our work could be pursued along the lines of Perreault et al. (2000) suggesting a multivariate change-point analysis in order to establish a joint changepoint for the measurement series.

  5. The modeling of a structural change using a step-function in the temporal evolution of parameters is of course not mandatory. Smooth functions could be used instead as long as they can be implemented into the likelihood function, as well as plenty approaches for modeling one or several breakpoints exist (Carlin et al. 1992; Stephens 1994; Mohammad-Djafari and Feron 2006; Moreno et al. 2005, among others).

  6. In order to save space, we do not report the corresponding probability and quantile plots but make them available upon request.

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Acknowledgments

The earlier version of the paper has benefited from the comments made by Klaus Hasselmann, Hans von Storch, two anonymous referees as well as by participants of the Workshop on Bayesian Risk Management, Carnegie Mellon University, Pittsburgh, USA. This research has benefited from the financial support provided by the German Federal Ministry of Education and Research (BMBF) in the framework of the project “Mainstreaming of climate risks and opportunities in the financial sector” (http://www.climate-mainstreaming.net/). We also would like to thank MeteoSwiss for sharing the data with us.

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Authors and Affiliations

  1. ETH Zürich, KOF Swiss Economic Institute, Weinbergstrasse 35, 8092, Zurich, Switzerland

    Boriss Siliverstovs

  2. University of Potsdam, Potsdam, Germany

    Rainald Ötsch

  3. DIW Berlin, Berlin, Germany

    Claudia Kemfert & Hans Kremers

  4. PIK, Potsdam, Germany

    Carlo C. Jaeger & Armin Haas

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  1. Boriss Siliverstovs
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  2. Rainald Ötsch
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  3. Claudia Kemfert
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  4. Carlo C. Jaeger
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  5. Armin Haas
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  6. Hans Kremers
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Correspondence to Boriss Siliverstovs.

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Siliverstovs, B., Ötsch, R., Kemfert, C. et al. Climate change and modelling of extreme temperatures in Switzerland. Stoch Environ Res Risk Assess 24, 311–326 (2010). https://doi.org/10.1007/s00477-009-0321-3

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