J. Climate, 27, 9171-9196.

Testing the performance of tropical cyclone genesis indices in future climates using the HIRAM model

Suzana J. Camargo
Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY.

Michael K. Tippett
Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY, and Center of Excellence for Climate Change Research, King Abdulaziz University, Jeddah, Saudi Arabia

Adam H. Sobel
Department of Applied Physics and Applied Mathematics, Department of Earth and Environmental Sciences, and Lamont-Doherty Earth Observatory, Columbia University, New York, NY

Gabriel A. Vecchi and Ming Zhao
NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ


Tropical cyclone genesis indices (TCGIs) are functions of the large-scale environment which are designed to be proxies for the probability of tropical cyclone (TC) genesis. While the performance of TCGIs in the current climate can be assessed by comparison to observations of TC formation, their ability to represent future TC activity based on projections of the large-scale environment cannot. Here we examine the performance of TCGIs in high-resolution climate model simulations of current and projected climates, with a particular interest in determining whether the index, when derived from the climatological seasonal cycle and spatial distribution of both TC genesis frequency and large-scale fields from present climate, but then computed from large-scale fields taken from simulations forced with SST patterns derived from coupled simulations of future, warmer, climate scenarios can capture the global mean decreases in TC frequency found in those future scenarios. This decrease is captured only when the humidity predictor is column saturation deficit (the difference between actual and saturation water vapor) rather than relative humidity (the ratio of these quantities). Using saturation deficit with relative SST as the other thermodynamic predictor over-predicts the TC frequency decrease, but using potential intensity as the thermodynamic predictor gives a good prediction of the decrease’s magnitude. These positive results appear to depend on the spatial and seasonal patterns in the imposed SST changes; none of the indices captures correctly the frequency decrease in simulations in which the only climate forcings are spatially uniform, whether a globally uniform increase in SST of 2K, or a doubling of CO2 with no change in SST.