6B.2 Atlantic basin seasonal hurricane simulations

Tuesday, 29 April 2008: 10:30 AM
Palms E (Wyndham Orlando Resort)
Tim LaRow, COAPS, Florida State University, Tallahassee, FL; and Y. K. Lim, D. W. Shin, E. Chassignet, and S. Cocke

An ensemble of seasonal Atlantic hurricane simulations are conducted using the Florida State University/Center for Ocean Atmosphere Prediction Studies (FSU/COAPS) global spectral model (Cocke and LaRow 2000) at a resolution T126L27 (a Gaussian grid spacing of 0.94˚). Four integrations comprising the ensembles were generated using the European Centre for Medium Range Weather Forecasts (ECMWF) time lagged initial atmospheric conditions centered on 1 June for the 20-years, 1986 to 2005. The sea surface temperatures (SSTs) were updated weekly using the Reynolds et al. (2002) observed data. An objective tracking algorithm obtained from the ECMWF and modified for our model's resolution was used to detect and track the storms. It was found that the model's composite storm structure and tracks lengths are realistic. In addition, the 20-year interannual variability was simulated well by the ensembles with a 0.78 ensemble mean correlation. The ensembles tend to overestimate/underestimate the numbers of storms during July/September and produced only one CAT4 level storm on the Saffir-Simpson scale. Similar problems are noted in other global model simulations. All ensembles did well in simulating the large number of storms forming in the Atlantic basin during 1995 and showed an increase in number of storms during La Niña and a decrease during El Niño events. The results are found to be sensitive to the choices of convection schemes and diffusion coefficients. The overall conclusion is that models such as the one used here are needed to better hindcast the interannual variability, however, going to even higher resolution does not guarantee better interannual variability, tracks or intensity. Improved physical parameterizations, such as using an explicit convection scheme and better representation of surface roughness at high wind speeds are likely to more accurately represent hurricane intensity.
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