A Study on the Variability of Asian Summer Monsoon and Taiwan Climate

• Main Authors: Shu-Ling Kuo, 郭漱泠
• Other Authors: 呂芳川
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/41794910572363069482

博士 === 國防大學中正理工學院 === 國防科學研究所 === 94 === The purpose in this study is to diagnosis the characteristics of summer (June-July-August) climate variability in Taiwan under the influence of Western North Pacific summer monsoon (WNPSM) and to investigate the role of SST on WNPSM interannual variation. In Taiwan, summer rainfall and temperature anomalies tend to be negative correlated：anomalous cold summer is often wet, and vice versa. The major regulatory mechanism influences the summer temperature and seasonal rainfall in Taiwan is anomalous vertical motion over an east-west elongated low-level circulation anomaly across Taiwan, a feature associated with the WNPSM variability. Regarding the rainfall induced by tropical cyclone (TC) passages, it is also influenced by the low-level circulation anomaly via modulating TC genesis location and movement track. It is shown that there are two mechanisms to maintain WNPSM anomaly. Firstly, the large-scale west-east SST anomaly contrast in tropics modulates the Walker circulation and enhance anticyclonic anomaly by Rossby wave response. In the second place, the modulated local Hadley cell is needed for mass balance. The diagnosis on heat flux and water shows that the anomaly of summer SST around South China Sea / Western North Pacific closely relate to the atmospheric conditions, which is mainly driven by the gradient of SST over Indian Ocean and Western North Pacific before summer. The performance of CWB GFS hindcast simulation, which is driven by SST in one-way mode, supports the conclusion that the SST around South China Sea/Western North Pacific can’t be simply regarded as boundary forcing. In some years, SST is more or less modified by atmospheric circulation. This study also suggests that the weight of initial conditions in dynamical seasonal prediction is dependent on seasonality and geographical location.