Skillful seasonal forecasting of tropical cyclone (TC; wind speed ≥17.5 m s-1) activity is challenging, even more so when the focus is on major hurricanes (wind speed ≥49.4 m s-1), the most intense hurricanes (category 4 and 5; wind speed ≥58.1 m s-1), and landfalling TCs. This study shows that a 25-km-resolution global climate model [High-Resolution Forecast-Oriented Low Ocean Resolution (FLOR) model (HiFLOR)] developed at the Geophysical Fluid Dynamics Laboratory (GFDL) has improved skill in predicting the frequencies of major hurricanes and category 4 and 5 hurricanes in the North Atlantic as well as landfalling TCs over the United States and Caribbean islands a few months in advance, relative to its 50-km-resolution predecessor climate model (FLOR). HiFLOR also shows significant skill in predicting category 4 and 5 hurricanes in the western North Pacific and eastern North Pacific, while both models show comparable skills in predicting basin-total and landfalling TC frequency in the basins. The improved skillful forecasts of basin-total TCs, major hurricanes, and category 4 and 5 hurricane activity in the North Atlantic by HiFLOR are obtained mainly by improved representation of the TCs and their response to climate from the increased horizontal resolution rather than by improvements in large-scale parameters.
All Science Journal Classification (ASJC) codes
- Atmospheric Science
- Coupled models
- Seasonal forecasting
- Tropical cyclones