Predictability of late-season tropical cyclone accumulated kinetic energy around Taiwan 2 months ahead
Journal
International Journal of Climatology
Journal Volume
38
Journal Issue
4
Pages
1938-1952
Date Issued
2018
Author(s)
Abstract
Long-lead seasonal forecast of tropical cyclone (TC) activity is strongly demanded, albeit challenging, for hazard prevention and preparedness in the area prone to TCs. This article attempts to present a late-season (September–November, SON) empirical prediction model to predict the accumulative cyclone kinetic energy (ACE) around Taiwan. The predictors are the sea-surface temperatures (SSTs) and sea-level pressure (SLP) anomaly during the preceding spring and summer seasons over tropical Southeast Asia, subtropical western and central North Pacific, and subtropical North Atlantic. Three prediction models are established with the lead times of 0, 1, and 2 months. Different types of large-scale influence on the TC activity are found for the above and below-normal-ACE years, respectively. For the above-normal-ACE years, the favourable large-scale condition is warm SSTs over the west Pacific, South China Sea (SCS), and eastern Indian Ocean, and the associated anomalous cyclonic winds and low SLP over the west Pacific marginal seas. The robust precursors are the warm SSTs over mid-latitude west North Pacific and North Atlantic, and the low SLP over Atlantic during the preceding spring season. For the below-normal-ACE years, the favourable large-scale condition is cold SSTs over Indonesian seas and equatorial west Pacific, warm SST and low SLP over equatorial east Pacific, and the anomalous anticyclonic circulation over the SCS and the Philippine Sea. The robust precursors are the anomalous SST and SLP during the preceding spring season, with the opposite signs to the above-normal-ACE years. The presented models built on the precursor signals are proved able to generate skilful forecast 2 months ahead. The product ACE-SON can be used for seasonal TC activity outlook in a larger area including the coastal region of southeast China and for seasonal rainfall outlook in Taiwan.
SDGs
Type
journal article