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Non-stationary extreme value analysis in a changing climate

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  • Linyin Cheng
  • Amir AghaKouchak
  • Eric Gilleland
  • Richard Katz

Abstract

This paper introduces a framework for estimating stationary and non-stationary return levels, return periods, and risks of climatic extremes using Bayesian inference. This framework is implemented in the Non-stationary Extreme Value Analysis (NEVA) software package, explicitly designed to facilitate analysis of extremes in the geosciences. In a Bayesian approach, NEVA estimates the extreme value parameters with a Differential Evolution Markov Chain (DE-MC) approach for global optimization over the parameter space. NEVA includes posterior probability intervals (uncertainty bounds) of estimated return levels through Bayesian inference, with its inherent advantages in uncertainty quantification. The software presents the results of non-stationary extreme value analysis using various exceedance probability methods. We evaluate both stationary and non-stationary components of the package for a case study consisting of annual temperature maxima for a gridded global temperature dataset. The results show that NEVA can reliably describe extremes and their return levels. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Linyin Cheng & Amir AghaKouchak & Eric Gilleland & Richard Katz, 2014. "Non-stationary extreme value analysis in a changing climate," Climatic Change, Springer, vol. 127(2), pages 353-369, November.
    • Handle: RePEc:spr:climat:v:127:y:2014:i:2:p:353-369
    DOI: 10.1007/s10584-014-1254-5
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    1. Amir AghaKouchak & Nasrin Nasrollahi, 2010. "Semi-parametric and Parametric Inference of Extreme Value Models for Rainfall Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(6), pages 1229-1249, April.
    2. Cooley, Daniel & Nychka, Douglas & Naveau, Philippe, 2007. "Bayesian Spatial Modeling of Extreme Precipitation Return Levels," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 824-840, September.
    3. Noah Diffenbaugh & Filippo Giorgi, 2012. "Climate change hotspots in the CMIP5 global climate model ensemble," Climatic Change, Springer, vol. 114(3), pages 813-822, October.
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    16. Jittima Singvejsakul & Chukiat Chaiboonsri & Songsak Sriboonchitta, 2021. "The Optimization of Bayesian Extreme Value: Empirical Evidence for the Agricultural Commodities in the US," Economies, MDPI, vol. 9(1), pages 1-10, March.
    17. Chi Zhang & Xuezhi Gu & Lei Ye & Qian Xin & Xiaoyang Li & Hairong Zhang, 2023. "Climate Informed Non-stationary Modeling of Extreme Precipitation in China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3319-3341, July.
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