Intercomparison of regional loss estimates from global synthetic tropical cyclone models

Abstract

Tropical cyclones (TCs) cause devastating damage to life and property. Historical TC data is scarce, complicating adequate TC risk assessments. Synthetic TC models are specifically designed to overcome this scarcity. While these models have been evaluated on their ability to simulate TC activity, no study to date has focused on model performance and applicability in TC risk assessments. This study performs the intercomparison of four different global-scale synthetic TC datasets in the impact space, comparing impact return period curves, probability of rare events, and hazard intensity distribution over land. We find that the model choice influences the costliest events, particularly in basins with limited TC activity. Modelled direct economic damages in the North Indian Ocean, for instance, range from 40 to 246 billion USD for the 100-yr event over the four hazard sets. We furthermore provide guidelines for the suitability of the different synthetic models for various research purposes.

A generalized framework for designing open-source natural hazard parametric insurance

“…4 illustrates that the distribution in TC intensity over land in Mozambique is similar for the historic and perturbed IBTrACS. The perturbed tracks show slightly less TC Cat.1 and more Cat.2, which suggests an improvement to the previously reported low-intensity bias (Meiler et al, 2022). The PDNA on TC Idai and Kenneth in Mozambique (UNDP, 2019) constitutes a point of reference to qualitatively ground the chosen exposure data set and modeled damages.…”

“…The method to generate the perturbed IBTrACS p can be found in the supplementary material of Gettelman et al (2018). Moreover, we here use improved perturbed tracks 6 that reduce the low intensity bias highlighted by Meiler et al (2022). The maximum 1-minute sustained wind speed is commonly used as damage proxy for TC (Eberenz et al, 2021).…”

“…Within CLIMADA, the hazard object can simply be replaced before it is passed to the subsequent methods. For example, Meiler et al (2022) compute and compare risk estimates for different TC hazard sets in CLIMADA. If a different hazard type is chosen, the vulnerability (impact function) and parametric index need to be adapted.…”

Unraveling unknowns of future tropical cyclone risks

“…In parallel, improved vulnerability representations, including changing aspects of vulnerability in the future, would advance TC risk assessment further. Nonetheless, we note that TC risk estimates also vary depending on the TC hazard model 33 . For example, future TC risk calculations based on a fully statistical TC model 32 yield comparable findings for assessing future TC risk drivers but differ in the results of the sensitivity analysis due to the differences in the underlying modelling approach 34 .…”

“…We compare the increase of future TC risk over four main global regions shown in Figure 1 a) and previously defined by Meiler et al ( 2022) 33 . The regions broadly reflect distinct TC areas with a focus on the landmasses affected by the respective TC activity in contrast to regionalizations focused on the ocean basins of TC origin.…”