Climate and solar signals in property damage losses from hurricanes affecting the United States
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.
KeywordsHurricanes Property damage Loss model Environment Risk compound Poisson MCMC
We thank Gary Kerney of the Property Claims Service for providing the damage loss data. This research is supported by Florida State University’s Catastrophic Storm Risk Management Center, the Risk Prediction Initiative of the Bermuda Institute for Ocean Studies (RPI-08-02-002), and by the US National Science Foundation (ATM-0738172). The views expressed within are those of the authors and do not reflect those of the funding agency.
- Coles S (2001) An introduction to statistical modeling of extreme values. Springer, LondonGoogle Scholar
- Coles SG, Tawn JA (1996) A Bayesian analysis of extreme rainfall data. J Royal Stat Soc Ser C (Appl Stat) 45(4):463–478, http://www.jstor.org/stable/2986068
- Elsner JB, Jagger TH (2008) United States and Caribbean tropical cyclone activity related to the solar cycle. Geophys Res Lett 35(18). doi: 10.1029/2008GL034431
- Elsner JB, Jagger TH, Hodges RE (2010) Daily tropical cyclone intensity response to solar ultraviolet radiation. Geophys Res Lett 37. doi: 10.1029/2010GL043091
- Gilks WR, Richardson S, Spiegelhalter DJ (1998) Markov chain Monte Carlo in practice. Chapman & Hall, Boca Raton, Fla., 98033429 edited by W.R. Gilks, S. Richardson, and D.J. Spiegelhalter. ill. ; 25 cm. Previously published: London : Chapman & Hall, 1996. Includes bibliographical references and indexGoogle Scholar
- Jagger TH, Elsner JB, Saunders MA (2008) Forecasting US insured hurricane losses. In: Murnane RJ, Diaz HF (eds) Climate extremes and society, chap 10. Cambridge University Press, CambridgeGoogle Scholar
- Jagger TH, Elsner JB, Burch KR (2010) Environmental signals in property damage losses from hurricanes. In: Elsner JB, Hodges RE, Malmstadt JC, Scheitlin KN (eds) Hurricanes and climate change, vol. 2, chap 6. Springer, New YorkGoogle Scholar
- Leckebusch GC, Ulbrich U, Froehlich L, Pinto JG (2007) Property loss potentials for European midlatitude storms in a changing climate. Geophys Res Lett 34(5). doi: 10.1029/2006GL027663
- Murnane RJ, Barton C, Collins E, Donnelly J, Elsner J, Emanuel K, Ginis I, Howard S, Landsea C, Liu K, Malmquist D, McKay M, Michaels A, Nelson N, O’Brien J, Scott D, Webb T III (2000) Model estimates hurricane wind speed probabilities. EOS Trans 81:433–433. doi: 10.1029/00EO00319 CrossRefGoogle Scholar
- Spiegelhalter DJ, Best NG, Gilks WR, Inskip H (1996) Hepatitis B: a case study in MCMC methods. In: Gilks WR, Richardson S, Spiegelhalter DJ (eds) Markov Chain Monte Carlo in practice, chap 2.. Chapman & Hall/CRC, London, pp 21–43Google Scholar