Physics-Based Time-dependent Risk Assessment of Destructive Tropical Cyclones in a Warming Climate

Tropical cyclones (TCs) represent a significant natural hazard, responsible for the highest insured losses globally, exceeding $480 billion in the U.S. over the last decade. However, uncertainties surround the future frequency and intensity of severe TCs, especially at local scales, within the context of ongoing climate warming. These uncertainties complicate comprehensive risk assessments and the development of effective mitigation and adaptation strategies. In this study, we present a physics-based risk assessment methodology designed to quantify the time-evolving risk associated with severe TCs over the forthcoming decades, with a focus on localized scales within the U.S. Using a statistical-deterministic TC model and employing atmospheric and oceanic data from multiple CMIP6 climate models with the specific SSP3-7.0 scenario, we generate a large ensemble of synthetic TCs annually in the Atlantic basin from 1950 to 2100. For each geographical location, we then develop a temporally dynamic distribution to quantitatively assess the evolving risk of extreme upper-tail maximum wind speeds resulting from severe TCs in the future warming decades. Employing Bayesian Markov Chain Monte Carlo (MCMC) techniques, we derive a large number of realizations of statistical moments annually to quantify uncertainties in estimating the time-evolving nonstationary risk associated with severe TCs. This study advances the physics-based risk assessment of future TC strong winds and uncertainty over warming decades at specific locations. Utilizing this methodology, we can identify vulnerability hotspots and predict the timing of potential severe TC impacts. Furthermore, this approach is versatile, allowing for the quantification of time-evolving risk associated with various TC-induced hazards, including but not limited to storm surge, heavy rainfall, and flooding.