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

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.

Estimating impacts of North Atlantic tropical cyclones using an index of damage potential

“…Yet, normalization introduces unverified relationships between damage and the normalizing factor/s (Czajkowski and Done 2014), and climate change may have influenced TC activity over recent decades (Holland and Bruyère 2014). Moreover, Jagger et al (2011) find climate signals in normalized total economic losses indicating a role for climate variability, and therefore potentially climate change, in compounding the effect of increasing exposure and vulnerability.…”

The suitability of disaster loss databases to measure loss and damage from climate change

“…Escalating losses from floods, tropical cyclones, droughts, and so forth, were generally attributed to the changes in the social system meaning the continued placement of people and assets in high-risk zones (IPCC, 2012). In recent years, however, both the scientific as well as professional community have raised concerns in regard to the role and degree of climate change in this context (Jagger et al, 2011;Munich Re, 2009). This is not surprising given that climate change-induced shifts in rainfall and temperature patterns along with sealevel rise are forecast to worsen the impacts of climate-sensitive hazards such as tropical cyclones or droughts resulting in more extreme weather events in areas accustomed and unaccustomed to them (Nicholls et al, 1999;Emanuel, 2005;IPCC, 2012).…”

U.S. Hurricanes and Economic Damage: Extreme Value Perspective

“…Katz (2002) employed a compound Poisson process for the total economic damage associated with hurricanes and included the state of the El Niño-Southern Oscillation (ENSO) as a covariate. Jagger et al (2008Jagger et al ( , 2010 used hierarchical Bayesian models to generate forecasts of annual insured losses, employing preseason index values of the North Atlantic Oscillation, Atlantic Ocean surface temperature, and ENSO as predictors. Whereas forecasting the distribution of total losses based on climatic precursors is of interest, the sensitivity of annual damage to individual extreme events implies that estimating the risk of extreme losses is more crucial for financial planning (Jaffee et al 2008).…”

“…Whereas forecasting the distribution of total losses based on climatic precursors is of interest, the sensitivity of annual damage to individual extreme events implies that estimating the risk of extreme losses is more crucial for financial planning (Jaffee et al 2008). To that end, Jagger et al (2008Jagger et al ( , 2010 demonstrated that the family of generalized Pareto distributions (GPDs) is appropriate for modeling extreme events involving large economic losses such as hurricane landfalls.…”