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全球海温异常年代际分量的方差贡献及其与中国气候异常的相关
引用本文:周晓霞,王盘兴,祝昌汉,覃军.全球海温异常年代际分量的方差贡献及其与中国气候异常的相关[J].应用气象学报,2005,16(1):96-104.
作者姓名:周晓霞  王盘兴  祝昌汉  覃军
作者单位:1.南京气象学院大气科学系, 南京 210044
基金项目:国家自然科学基金重点课题“江淮梅雨的年际和年代际变化规律及其机制研究”(课题号 40 2 3 3 0 3 7),科技部社会公益研究专项资金“珠江流域广西境内流域面雨量预报与汛期洪涝监测业务体系研究”(课题号 3 70 2 0 )共同资助
摘    要:将全球海洋海表温度异常 (SSTA) 分解为年代际、年际变化两部分, 用方差分析法给出年代际变化分量显著的洋区及其季节变化, 用奇异值分解 (SVD) 方法分析了它与中国冬温 (冬季平均气温)、夏雨 (夏季总降水量) 年代际异常分量的同期和时滞相关联系的时空结构。结果表明, 无论冬、夏, SSTA的方差构成中, 年代际变化分量方差均较年际变化分量显著; SSTA年代际分量正异常时, 中国 (以北方和东部为主) 易出现一致的冬温正异常, 长江流域、华南夏雨易出现正异常, 而华北夏雨易出现负异常; 反之亦然。

关 键 词:全球SSTA    中国冬温/夏雨异常    年代际变化    同期和时滞相关
收稿时间:2003-09-02

Variance of Interdecadal Component of Global Sea Surface Temperature Anomaly and the Correlations to the Climate of China
Zhou Xiaoxia,Wang Panxing,Zhu Changhan,Qin Jun.Variance of Interdecadal Component of Global Sea Surface Temperature Anomaly and the Correlations to the Climate of China[J].Quarterly Journal of Applied Meteorology,2005,16(1):96-104.
Authors:Zhou Xiaoxia  Wang Panxing  Zhu Changhan  Qin Jun
Institution:1.Department of Atmospheric Sciences, Nanjing Institute of Meteorology, Nanjing 2100442.National Climate Center, Beijing 100081
Abstract:The interdecadal and interannual components of global sea surface temperature anomaly (SSTA) were obtained by periodical analysis, and their significant variations over different areas of global sea and seasonal change were displayed by variance analysis. Then using singular value decomposition (SVD), the simultaneous and time-lag correlations of interdecadal sea surface anomaly and winter temperature/summer rainfall of China were investigated. Results show that the interdecadal SSTA is more evident than the interannual one both in winter and summer, and positive SSTA is usually assotiated with positive winter temperature anomaly in North China and positive rainfall anomaly in the Changjiang valley and South China, but negative rainfall anomaly in North China, and vice versa.
Keywords:Global sea surface temperature anomaly  Anomaly of winter temperature/summer rainfall of China  Interdecadal variation  Simultaneous and time-lag correlations
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