Here, we present two models that can help identify these types of potential risks when combined. A TC hazard model ingests data from a subset of CMIP6 models and uses a statistical-dynamical model to generate an ensemble of synthetic storm tracks that capture the statistical distribution of TCs for a given time period and warming scenario. Wind and precipitation fields are then added to each track to capture approximate impacts over time.
Event distributions are extracted for each location within the asset portfolio to plug into a probabilistic TC loss model. Building characteristics are identified and used to help extract wind-driven damage curves for each structure and are combined with the hazard data to help simulate the financial impacts.
Probabilistic TC losses are important for companies with a physical asset portfolio. Beyond understanding their exposure to TC risk, a business can make actionable decisions in prioritizing buildings that should be refitted and determining the optimal regions for expansion which would mitigate risk. Efforts to quantify, and understand, the possible impacts of different climate change scenarios provide an impetus for businesses to promote actions towards mitigation.
1NOAA National Centers for Environmental Information (NCEI) U.S. Billion-Dollar Weather and Climate Disasters (2023). https://www.ncei.noaa.gov/access/billions/, DOI: 10.25921/stkw-7w73

