Variability and predictability of North Atlantic hurricane frequency in a large ensemble of high-resolution atmospheric simulations

• Main Authors: Mei, W., Kamae, Y., Xie, S.-P., Yoshida, K.
• Other Authors: College of Arts and Sciences, Department of Marine Sciences
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Meteorological Society 2019
• Subjects: North Atlantic Ocean; Atmosphere; Climate variability; Hurricanes/typhoons; Climate models; North Atlantic
• Online Access: https://doi.org/10.17615/krjk-s706; https://cdr.lib.unc.edu/downloads/3n204744t?file=thumbnail; https://cdr.lib.unc.edu/downloads/3n204744t

Variability of North Atlantic annual hurricane frequency during 1951-2010 is studied using a 100-member ensemble of climate simulations by a 60-km atmospheric general circulation model that is forced by observed sea surface temperatures (SSTs). The ensemble mean results well capture the interannual-to-decadal variability of hurricane frequency in best track data since 1970, and suggest that the current best track data might underestimate hurricane frequency prior to 1966 when satellite measurements were unavailable. A genesis potential index (GPI) averaged over the main development region (MDR) accounts for more than 80% of the SST-forced variations in hurricane frequency, with potential intensity and vertical wind shear being the dominant factors. In line with previous studies, the difference between MDR SST and tropical mean SST is a useful predictor; a 18C increase in this SST difference produces 7.05 ± 1.39 more hurricanes. The hurricane frequency also exhibits strong internal variability that is systematically larger in the model than observations. The seasonal-mean environment is highly correlated among ensemble members and contributes to less than 10% of the ensemble spread in hurricane frequency. The strong internal variability is suggested to originate from weather to intraseasonal variability and nonlinearity. In practice, a 20-member ensemble is sufficient to capture the SST-forced variability.