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Climate and solar signals in property damage losses from hurricanes affecting the United States

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  • Thomas Jagger
  • James Elsner
  • R. Burch

Abstract

The authors show that historical property damage losses from US hurricanes contain climate signals. The methodology is based on a statistical model that combines a specification for the number of loss events with a specification for the amount of loss per event. Separate models are developed for annual and extreme losses. A Markov chain Monte Carlo procedure is used to generate posterior samples from the models. Results indicate the chance of at least one loss event increases when the springtime north–south surface pressure gradient over the North Atlantic is weaker than normal, the Atlantic ocean is warmer than normal, El Niño is absent, and sunspots are few. However, given at least one loss event, the magnitude of the loss per annum is related only to ocean temperature. The 50-year return level for a loss event is largest under a scenario featuring a warm Atlantic Ocean, a weak North Atlantic surface pressure gradient, El Niño, and few sunspots. The work provides a framework for anticipating hurricane losses on seasonal and multi-year time scales. Copyright Springer Science+Business Media B.V. 2011

Suggested Citation

  • Thomas Jagger & James Elsner & R. Burch, 2011. "Climate and solar signals in property damage losses from hurricanes affecting the United States," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 58(1), pages 541-557, July.
  • Handle: RePEc:spr:nathaz:v:58:y:2011:i:1:p:541-557
    DOI: 10.1007/s11069-010-9685-4
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    References listed on IDEAS

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    1. Stuart G. Coles & Jonathan A. Tawn, 1996. "A Bayesian Analysis of Extreme Rainfall Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 45(4), pages 463-478, December.
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    3. Stanley Changnon & David Changnon, 2009. "Assessment of a method used to time adjust past storm losses," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 50(1), pages 5-12, July.
    4. James B. Elsner & James P. Kossin & Thomas H. Jagger, 2008. "The increasing intensity of the strongest tropical cyclones," Nature, Nature, vol. 455(7209), pages 92-95, September.
    5. D. Walshaw, 2000. "Modelling extreme wind speeds in regions prone to hurricanes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(1), pages 51-62.
    6. Mark A. Saunders & Adam S. Lea, 2005. "Seasonal prediction of hurricane activity reaching the coast of the United States," Nature, Nature, vol. 434(7036), pages 1005-1008, April.
    7. Ping‐Hung Hsieh, 2004. "A Data‐Analytic Method for Forecasting Next Record Catastrophe Loss," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 71(2), pages 309-322, June.
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    Cited by:

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    3. Adam Smith & Richard Katz, 2013. "US billion-dollar weather and climate disasters: data sources, trends, accuracy and biases," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 67(2), pages 387-410, June.

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