Abstract
While many studies of the effects of global warming on hurricanes predict an increase in various metrics of Atlantic basin-wide activity, it is less clear that this signal will emerge from background noise in measures of hurricane damage, which depend largely on rare, high-intensity landfalling events and are thus highly volatile compared to basin-wide storm metrics. Using a recently developed hurricane synthesizer driven by large-scale meteorological variables derived from global climate models, 1000 artificial 100-yr time series of Atlantic hurricanes that make landfall along the U.S. Gulf and East Coasts are generated for four climate models and for current climate conditions as well as for the warmer climate of 100 yr hence under the Intergovernmental Panel on Climate Change (IPCC) emissions scenario A1b. These synthetic hurricanes damage a portfolio of insured property according to an aggregate wind-damage function; damage from flooding is not considered here. Assuming that the hurricane climate changes linearly with time, a 1000-member ensemble of time series of property damage was created. Three of the four climate models used produce increasing damage with time, with the global warming signal emerging on time scales of 40, 113, and 170 yr, respectively. It is pointed out, however, that probabilities of damage increase significantly well before such emergence time scales and it is shown that probability density distributions of aggregate damage become appreciably separated from those of the control climate on time scales as short as 25 yr. For the fourth climate model, damages decrease with time, but the signal is weak.
