Using a statistical tropical cyclone genesis model for assessing differences in climate scenarios and geographic basins

Tropical cyclones (TCs) are important extreme weather phenomena that have significant negative impact on humans, infrastructure, and society. Multidecadal simulations from high resolution regional and global climate models are now being used to better understand how TC statistics change due to anthropogenic climate change. Aspects of tropical cyclones such as their genesis, evolution, intensification, and dissipation over land are important and challenging problems in climate science. Although various studies have been conducted on the climatology of tropical cyclone genesis (TCG), their analyses are often limited in that they focus on basin-specific models, discard high resolution data in favor of aggregate measures, or bias their investigation towards variables and metrics that are motivated by mathematical physics. The problems of coarse GCM resolution, together with the unsuitability of the GPI for making future TC projections, highlights the need for additional tools.

Previous work has shown that a statistical model can accurately predict TCG in the Community Atmospheric Model (CAM) Version 5.1. L1-regularized logistic regression (L1LR) was successfully applied to distinguish between TCG events and non-developing storms with high accuracy. The resulting model's active variables are generally in agreement with current leading hypotheses on favorable conditions for TCG, such as cyclonic wind velocity patterns and local pressure minima. In this study, we extend our framework by assessing TCG differences between basins. We conclude that the predictive relationships between the studied environmental variables and TCG remains statistically indistinguishable between basins. Moreover, we investigate the differences between statistical models of TCG in differing climate scenarios.