Quantifying tropical cyclone intensity change induced by sea surface temperature. (27th December 2021)

• Record Type: Journal Article
• Authors: Sun, Jia; Wang, Guihua; Jin, Shanshan; Ju, Xia; Xiong, Xuejun
• Is Part Of: International journal of climatology. Volume 42:Number 9(2022)
• Page Start: 4716
• Page End: 4727
• Publication Date: 2021-12-27
• Subjects: quantifiable solution -- sea surface temperature -- tropical cyclone intensity; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.7499
• Languages: English
• ISSNs: 0899-8418

Abstract

Abstract: The relationship between sea surface temperature (SST) and tropical cyclone (TC) intensity change exhibits a strong dependence on the current TC intensity. Using western North Pacific TC observations from 1982 to 2018, a threshold SST (TSST) is identified as the SST required to maintain TC intensity. TSST increases with TC intensity, with TCs intensifying and weakening when SST is higher and lower than TSST, respectively. Across the dataset, mean TC intensity change is proportional to the difference between SST and TSST. This study also formulates an equation to quantify TC intensity change using SST and current TC intensity, which replicates 99.46% of the mean observed TC intensity changes. This equation could serve as an alternative to the linear regression‐based relationship between SST and TC intensity change that is widely used in statistical‐dynamical intensity models, thereby improving intensity forecasts. Abstract : The change in tropical cyclone intensity caused by the sea surface temperature showed great dependence on the current tropical cyclone intensity which is shown in the figure. A nonlinear regression equation is proposed to quantify changes in tropical cyclone intensity using both sea surface temperature and current tropical cyclone intensity based on the climatology data. The equation provides an alternative to the linear regression scheme in tropical cyclone forecast models and thus helps to improve tropical cyclone intensity forecast skill.