Simulation of the Recent Multidecadal Increase of Atlantic Hurricane Activity Using an 18-km-Grid Regional Model

In this study, a new modeling framework for simulating Atlantic hurricane activity is introduced. The model is an 18-km-grid nonhydrostatic regional model, run over observed specified SSTs and nudged toward observed time-varying large-scale atmospheric conditions (Atlantic domain wavenumbers 0-2) derived from the National Centers for Environmental Prediction (NCEP) reanalyses. Using this perfect large-scale model approach for 27 recent August-October seasons (1980-2006), it is found that the model successfully reproduces the observed multidecadal increase in numbers of Atlantic hurricanes and several other tropical cyclone (TC) indices over this period. The correlation of simulated versus observed hurricane activity by year varies from 0.87 for basin-wide hurricane counts to 0.41 for U.S. landfalling hurricanes. For tropical storm count, accumulated cyclone energy, and TC power dissipation indices the correlation is similar to 0.75, for major hurricanes the correlation is 0.69, and for U.S. landfalling tropical storms, the correlation is 0.57. The model occasionally simulates hurricanes intensities of up to category 4 (similar to 942 mb) in terms of central pressure, although the surface winds (\u3c 47 in s-1) do not exceed category-2 intensity. On interannual time scales, the model reproduces the observed ENSO-Atlantic hurricane covariation reasonably well. Some notable aspects of the highly contrasting 2005 and 2006 seasons are well reproduced, although the simulated activity during the 2006 core season was excessive. The authors conclude that the model appears to be a useful tool for exploring mechanisms of hurricane variability in the Atlantic (e.g., shear versus potential intensity contributions). The model may be capable of making useful simulations/projections of pre-1980 or twentieth-century Atlantic hurricane activity. However, the reliability of these projections will depend on obtaining reliable large-scale atmospheric and SST conditions from sources external to the model