Editorial Type:
Article
Article Type:
Research Article

Testing the Performance of Tropical Cyclone Genesis Indices in Future Climates Using the HiRAM Model

Suzana J. Camargo Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York

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

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Adam H. Sobel Department of Earth and Environmental Sciences, and Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York

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Gabriel A. Vecchi NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

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Ming Zhao NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

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Print Publication:
15 Dec 2014
Collections:
U.S. CLIVAR - Hurricanes and Climate
DOI:
https://doi.org/10.1175/JCLI-D-13-00505.1
Page(s):
9171–9196
Restricted access

Abstract

Tropical cyclone genesis indices (TCGIs) are functions of the large-scale environment that 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 direct comparison to TC observations, their ability to represent future TC activity based on projections of the large-scale environment cannot. Here the authors examine the performance of TCGIs in high-resolution atmospheric model simulations forced with sea surface temperatures (SST) of future, warmer climate scenarios. They investigate whether the TCGIs derived for the present climate can, when computed from large-scale fields taken from future climate simulations, capture the simulated global mean decreases in TC frequency. The TCGIs differ in their choice of environmental predictors, and several choices of predictors perform well in the present climate. However, some TCGIs that perform well in the present climate do not accurately reproduce the simulated future decrease in TC frequency. This decrease is captured when the humidity predictor is the column saturation deficit rather than relative humidity. Using saturation deficit with relative SST as the other thermodynamic predictor overpredicts the TC frequency decrease, while using potential intensity in place of relative SST as the other 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 capture correctly the frequency decrease in simulations with spatially uniform climate forcings, whether a globally uniform increase in SST of 2 K, or a doubling of CO2 with no change in SST.

Corresponding author address: Suzana J. Camargo, Lamont-Doherty Earth Observatory, Columbia University, P.O. Box 1000, Palisades, NY 10960. E-mail: suzana@ldeo.columbia.edu

This article is included in the US CLIVAR Hurricanes and Climate special collection.

Abstract

Tropical cyclone genesis indices (TCGIs) are functions of the large-scale environment that 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 direct comparison to TC observations, their ability to represent future TC activity based on projections of the large-scale environment cannot. Here the authors examine the performance of TCGIs in high-resolution atmospheric model simulations forced with sea surface temperatures (SST) of future, warmer climate scenarios. They investigate whether the TCGIs derived for the present climate can, when computed from large-scale fields taken from future climate simulations, capture the simulated global mean decreases in TC frequency. The TCGIs differ in their choice of environmental predictors, and several choices of predictors perform well in the present climate. However, some TCGIs that perform well in the present climate do not accurately reproduce the simulated future decrease in TC frequency. This decrease is captured when the humidity predictor is the column saturation deficit rather than relative humidity. Using saturation deficit with relative SST as the other thermodynamic predictor overpredicts the TC frequency decrease, while using potential intensity in place of relative SST as the other 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 capture correctly the frequency decrease in simulations with spatially uniform climate forcings, whether a globally uniform increase in SST of 2 K, or a doubling of CO2 with no change in SST.

Corresponding author address: Suzana J. Camargo, Lamont-Doherty Earth Observatory, Columbia University, P.O. Box 1000, Palisades, NY 10960. E-mail: suzana@ldeo.columbia.edu

This article is included in the US CLIVAR Hurricanes and Climate special collection.

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