Reask UTC: a machine learning modeling framework to generate climate-connected tropical cyclone event sets globally

Abstract. In the early 1990s, the insurance industry pioneered the use of risk models to extrapolate tropical cyclone (TC) occurrence and severity metrics beyond historical records. These probabilistic models rely on past data and statistical modeling techniques to approximate landfall risk distributions. By design, such models are best fit to portray risk under conditions consistent with our historical experience. This poses a problem when trying to infer risk under a rapidly changing climate or in regions where we do not have a good record of historical experience. We here propose a solution to these challenges by rethinking the way TC risk models are built, putting more emphasis on the role played by climate physics in conditioning the risk distributions. The Unified Tropical Cyclone (UTC) modeling framework explicitly connects global climate data to TC activity and event behaviors, leveraging both planetary-scale signals and regional environment conditions to simulate synthetic TC events globally. In this study, we describe the UTC framework and highlight the role played by climate drivers in conditioning TC risk distributions. We then show that, when driven by climate data representative of historical conditions, the UTC is able to simulate a global view of risk consistent with historical experience. Additionally, the value of the UTC in quantifying the role of climate variability in TC risk is illustrated using the 1980–2022 period as a benchmark.