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A Statistical Assessment of Southern Hemisphere Tropical Cyclone Tracks in Climate Models

Ramsay, Hamish A.; Chand, Savin; Camargo, Suzana J.

Reliable projections of future changes in tropical cyclone (TC) characteristics are highly dependent on the ability of global climate models (GCMs) to simulate the observed characteristics of TCs (i.e., their frequency, genesis locations, movement, and intensity). Here, we investigate the performance of a suite of GCMs from the U.S. CLIVAR Working Group on Hurricanes in simulating observed climatological features of TCs in the Southern Hemisphere. A subset of these GCMs is also explored under three idealized warming scenarios. Two types of simulated TC tracks are evaluated on the basis of a commonly applied cluster analysis: 1) explicitly simulated tracks, and 2) downscaled tracks, derived from a statistical–dynamical technique that depends on the models’ large-scale environmental fields. Climatological TC properties such as genesis locations, annual frequency, lifetime maximum intensity (LMI), and seasonality are evaluated for both track types. Future changes to annual frequency, LMI, and the latitude of LMI are evaluated using the downscaled tracks where large sample sizes allow for statistically robust results. An ensemble approach is used to assess future changes of explicit tracks owing to their small number of realizations. We show that the downscaled tracks generally outperform the explicit tracks in relation to many of the climatological features of Southern Hemisphere TCs, despite a few notable biases. Future changes to the frequency and intensity of TCs in the downscaled simulations are found to be highly dependent on the warming scenario and model, with the most robust result being an increase in the LMI under a uniform 2°C surface warming.

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Also Published In

Journal of Climate

More About This Work

Academic Units
Lamont-Doherty Earth Observatory
Ocean and Climate Physics
Published Here
March 26, 2019


Keywords: Climatology; Hurricanes/typhoons; Climate models; Model comparison; Model evaluation/performance