Seasonal Forecasts of Tropical Cyclones Using GFDL SPEAR and HiFLOR-S

Abstract The seasonal prediction skill of tropical cyclone (TC) activity is evaluated using the Seamless System for Prediction and Earth System Research (SPEAR), a modeling system developed at the Geophysical Fluid Dynamics Laboratory (GFDL) for experimental real-time seasonal forecasts. Compared with previous GFDL seasonal prediction models, SPEAR demonstrates improved skill in predicting TC activity for the western North Pacific, while exhibiting comparable or slightly degraded skill for the eastern North Pacific and North Atlantic. These changes in prediction skill do not always align with changes in prediction skill in large-scale variables, particularly over the North Atlantic. This study highlights that changes in the model’s response of TCs to large-scale variables, as well as the changes in the amplitude of interannual variations in TC genesis frequency, are crucial for the changes in TC prediction skill. Using the predicted sea surface temperatures from SPEAR as lower boundary conditions, the High-Resolution Forecast-Oriented Low Ocean Resolution (HiFLOR-S) model was employed to predict intense TCs, demonstrating skillful predictions of major hurricanes that are comparable to the previous HiFLOR coupled model predictions. Significance Statement This study reveals the prediction skill in the seasonal forecasting of tropical cyclones using a new experimental real-time seasonal prediction system developed at the Geophysical Fluid Dynamics Laboratory. The new system demonstrates skillful prediction of tropical cyclones in the western North Pacific, eastern North Pacific, and North Atlantic a few months before the hurricane season, with notable differences in the skill compared to the previous prediction system. The findings suggest that higher prediction skill in large-scale variables, such as vertical wind shear and sea surface temperatures, does not necessarily lead to higher prediction skill for tropical cyclones. This underscores that even when a model accurately predicts large-scale variables, its predictions of tropical cyclones could still be inaccurate. Our findings emphasize the need to refine the model’s response of tropical cyclones to specific large-scale environments, rather than focusing only on improving large-scale environment predictions, to enhance the accuracy of dynamical seasonal predictions for tropical cyclones.