Added value of dynamical downscaling in sub-seasonal tropical cyclone forecast

Abstract

Improving the sub-seasonal forecast of tropical cyclones (TCs) remains a significant challenge for climate models. This study evaluated the characteristics of tropical cyclones (TCs) in sub-seasonal forecasts using the Global Seasonal Forecast System 6 (GloSea6), an operational seasonal-to-sub-seasonal forecasting model operated by the Korea Meteorological Administration (KMA) during June-September (JJAS) from 1993 to 2016 over the western North Pacific (WNP). GloSea6 was found to underestimate TC frequency, tracks, particularly in mid-latitudes, lifetime, and intensity across all months, with the most significant errors occurring in August. To further investigate whether these deficiencies could be mitigated, we conducted a single-case dynamical downscaling experiment for August 2016, a period characterized by particularly low TC forecast skill in GloSea6, based on a single forecast initialization date (25th July 2016) with seven ensemble members. The application of dynamical downscaling demonstrated potential added value by directly improving the simulation of TCs in terms of frequency, structure, intensity, and lifetime, although slight overestimations were observed. Furthermore, improved reproductions of the Indian monsoon and circumglobal teleconnection (CGT), both of which strongly influence the western North Pacific subtropical high (WNPSH), contributed to more accurate forecasts of WNPSH variability and, consequently, better predictions of TC activity in the mid-latitudes and East Asia. Therefore, dynamical downscaling shows promise for enhancing sub-seasonal TC forecasts through both direct improvements in TC characteristics and indirect improvements in the large-scale environment that influences TC activity (e.g., the WNPSH, Indian monsoon, and CGT), although its generality requires assessment across multiple cases and initialization dates.