Seasonal Predictability Study of Tropical Cyclones Using a High-Resolution Tropical Channel Model

The hyperactive 2017 Atlantic hurricane season reminds us of the threat tropical cyclones (TCs) posing on lives and property, and the value of improving seasonal TC forecasting. In this study, we present results of a seasonal predictability study of TCs using a 27-km horizontal resolution Tropical Channel Model (TCM) based on the Weather Research and Forecasting (WRF) model with latitudinal extent from 30°S to 50°N. We performed 10-member ensemble simulations of northern hemisphere hurricane seasons over the 1990 – 2016 period, using boundary conditions from the 6-hour National Centers for Environmental Prediction – Climate Forecast System (NCEP-CFS) datasets. Interannual variations of TCs over the western North Pacific (WNP), eastern North Pacific (ENP), and North Atlantic (NA) were well captured with correlation coefficients between simulated and observed accumulated cyclone energy (ACE) of 0.81, 0.70, and 0.60, respectively. Furthermore, the pronounced increasing trend of NA TC activity observed in recent decades was also well simulated. To investigate the impact of the lateral boundary conditions (LBCs) on the model simulation skill, sensitivity experiments were carried out by replacing the interannually varying LBCs with a perpetual 1996 LBC, which was characterized by a neutral Atlantic Multidecadal Oscillation (AMO), while keeping everything else unchanged. The preliminary results show that the interannual TC variability was generally insensitive to LBCs, suggesting that SST variability is the primary factor determining seasonal TC predictability. On the basis of these encouraging results, we made forecasts for the 2017 and 2018 hurricane seasons by downscaling the global seasonal forecast from CFS. We will showcase and discuss the TCM forecasts for the 2017 and 2018 hurricane seasons.