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 Nio 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 Nio, and few sunspots. The work provides a framework for anticipating hurricane losses on seasonal and multi-year time scales.