Abstract

This paper focuses on the US Billion-dollar Weather/Climate Disaster report by the National Oceanic and Atmospheric Administration’s National Climatic Data Center. The current methodology for the production of this loss dataset is described, highlighting its strengths and limitations including so... Show moreThis paper focuses on the US Billion-dollar Weather/Climate Disaster report by the National Oceanic and Atmospheric Administration’s National Climatic Data Center. The current methodology for the production of this loss dataset is described, highlighting its strengths and limitations including sources of uncertainty and bias. The Insurance Services Office/Property Claims Service, the US Federal Emergency Management Agency’s National Flood Insurance Program and the US Department of Agriculture’s crop insurance program are key sources of quantified disaster loss data, among others. The methodology uses a factor approach to convert from insured losses to total direct losses, one potential limitation. An increasing trend in annual aggregate losses is shown to be primarily attributable to a statistically significant increasing trend of about 5 % per year in the frequency of billion-dollar disasters. So the question arises of how such trend estimates are affected by uncertainties and biases in the billion-dollar disaster data. The net effect of all biases appears to be an underestimation of average loss. In particular, it is shown that the factor approach can result in a considerable underestimation of average loss of roughly 10-15 %. Because this bias is systematic, any trends in losses from tropical cyclones appear to be robust to variations in insurance participation rates. Any attribution of the marked increasing trends in crop losses is complicated by a major expansion of the federally subsidized crop insurance program, as a consequence encompassing more marginal land. Recommendations concerning how the current methodology can be improved to increase the quality of the billion-dollar disaster dataset include refining the factor approach to more realistically take into account spatial and temporal variations in insurance participation rates. Show less