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On the return period of the 2003 heat wave

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  • Arthur Charpentier

Abstract

Extremal events are difficult to model since it is difficult to characterize formally those events. The 2003 heat wave in Europe was not characterized by very high temperatures, but mainly the fact that night temperature were no cool enough for a long period of time. Hence, simulation of several models (either with heavy tailed noise or long range dependence) yield different estimations for the return period of that extremal event.
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  • Arthur Charpentier, 2011. "On the return period of the 2003 heat wave," Climatic Change, Springer, vol. 109(3), pages 245-260, December.
    • Handle: RePEc:spr:climat:v:109:y:2011:i:3:p:245-260
    DOI: 10.1007/s10584-010-9944-0
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    References listed on IDEAS

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    1. Christoph Schär & Pier Luigi Vidale & Daniel Lüthi & Christoph Frei & Christian Häberli & Mark A. Liniger & Christof Appenzeller, 2004. "The role of increasing temperature variability in European summer heatwaves," Nature, Nature, vol. 427(6972), pages 332-336, January.
    2. Rocha Souza, Leonardo & Jorge Soares, Lacir, 2007. "Electricity rationing and public response," Energy Economics, Elsevier, vol. 29(2), pages 296-311, March.
    3. Peter A. Stott & D. A. Stone & M. R. Allen, 2004. "Human contribution to the European heatwave of 2003," Nature, Nature, vol. 432(7017), pages 610-614, December.
    4. Henry L. Gray & Nien‐Fan Zhang & Wayne A. Woodward, 1989. "On Generalized Fractional Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(3), pages 233-257, May.
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    1. Peter Stott & Nikolaos Christidis & Richard Betts, 2011. "Changing return periods of weather-related impacts: the attribution challenge," Climatic Change, Springer, vol. 109(3), pages 263-268, December.
    2. Yang, Yuchen & Javanroodi, Kavan & Nik, Vahid M., 2021. "Climate change and energy performance of European residential building stocks – A comprehensive impact assessment using climate big data from the coordinated regional climate downscaling experiment," Applied Energy, Elsevier, vol. 298(C).
    3. Nik, Vahid M. & Moazami, Amin, 2021. "Using collective intelligence to enhance demand flexibility and climate resilience in urban areas," Applied Energy, Elsevier, vol. 281(C).
    4. E. M. Fischer & U. Beyerle & L. Bloin-Wibe & C. Gessner & V. Humphrey & F. Lehner & A. G. Pendergrass & S. Sippel & J. Zeder & R. Knutti, 2023. "Storylines for unprecedented heatwaves based on ensemble boosting," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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