Application of uncertain hurricane climate change projections to catastrophe risk models

There is great interest in trying to understand how to take climate model projections of possible changes in hurricane behaviour due to climate change and apply them to hurricane risk models. In Knutson et al. (Bull Am Meteorol Soc 101:E303-E322, 2020), projections from many climate models were combined to form distributions of possible changes in hurricane frequency and intensity. It has been shown that propagating the uncertainty represented by these distributions is necessary to estimate the impact on risk correctly. Building on these results, we now consider how distributions of changes in hurricane frequency and intensity can be applied to hurricane risk models that are formulated in the standard event loss table and year loss table formats. Because of the uncertainty, this requires the use of novel simulation and weighting techniques that extend standard methods for adjusting risk models. We demonstrate that these novel techniques work in a simple hurricane risk model. We also present new analytical solutions that show how means and variances of risk change due to the application of uncertain adjustments. Finally, we use emulators to explore how the output from just a single evaluation of a hurricane risk model can be used to derive sensitivity estimates that would otherwise require a large number of evaluations of the model. The methods we present could readily be applied to full complexity hurricane risk models and will hopefully contribute to efforts to quantify the possible effects of climate change on present and future hurricane risk.

Automated summary

This study aims to apply climate model projections of hurricane behavior changes due to climate change to hurricane risk models and explore how to deal with the uncertainty. By using novel simulation and weighting techniques, we demonstrated the feasibility of these methods in a simple hurricane risk model and provided new analytical solutions to evaluate the changes in risk. Additionally, we used emulators to derive sensitivity estimates, reducing the workload of evaluating risk models.