南海北部海面高度的季节内变异及其传播特征 *

doi:10.11978/2018086

热带海洋学报 ›› 2019, Vol. 38 ›› Issue (3): 1-12.doi: 10.11978/2018086CSTR: 32234.14.2018086

所属专题：南海专题

• 海洋水文学 • 下一篇

南海北部海面高度的季节内变异及其传播特征 ^*

王霞^1,^2,³,方文东²(),陈荣裕²

^1. 中国科学院广州地球化学研究所, 广东广州 510640
^2. 热带海洋环境国家重点实验室(中国科学院南海海洋研究所), 广东省海洋遥感重点实验室, 广东广州 510301
^3. 中国科学院大学, 北京 100049

收稿日期:2018-08-22 修回日期:2018-09-14 出版日期:2019-05-20 发布日期:2019-06-17
通讯作者: 方文东
作者简介:王霞(1984—), 女, 河南省开封市人, 博士研究生, 主要从事南海环流和中尺度涡研究。E-mail:wangxia@scsio.ac.cn
基金资助:
国家自然科学基金项目(41776014);国家自然科学基金项目(41430964);中国科学院前沿科学重点研究计划(QYZDJ-SSW-DQC034)

Intra-seasonal variability of sea level anomalies and their propagation features in the northern South China Sea from 25 years of satellite altimetry data

Xia WANG^1,^2,³,Wendong FANG²(),Rongyu CHEN²

^1. Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510301, China
^2. State Key Laboratory of Tropical Oceanography (South China Sea Institute of Oceanology, Chinese Academy of Sciences), Guangdong Key Lab of Ocean Remote Sensing, Guangzhou 510301, China
^3. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2018-08-22 Revised:2018-09-14 Online:2019-05-20 Published:2019-06-17
Contact: Wendong FANG
Supported by:
National Natural Science Foundation of China(41776014);National Natural Science Foundation of China(41430964);Key Research Program of Frontier Science of Chinese Academy of Sciences(QYZDJ-SSW-DQC034)

摘要/Abstract

摘要：

利用25年(1993—2017)的卫星高度计资料, 采用复经验正交函数(complex empirical orthogonal function, CEOF)方法, 分析南海北部海区海面高度季节内变异的时空分布及传播特征。标准差分析表明, 南海北部海面高度的季节内变异(intra-seasonal variability of sea level anomalies, SLA-ISV)在沿陆坡外侧区较强, 且SLA-ISV表现出明显的季节性变化, 冬半年强于夏半年。CEOF前两个主要模态能较好地揭示研究海区SLA-ISV的时空分布及其传播特征, 并表明SLA-ISV的强度受到季节性变化和年际变化的调制。全年CEOF的第一模态揭示SLA-ISV从台湾岛西南至西沙群岛以东区域的冬半年西南向传播特征; 而全年CEOF的第二模态则表现了SLA-ISV分别在台湾岛西南和东沙群岛西南的西南向传播特征。南海北部中尺度涡季节变化统计分析表明, CEOF的分解结果与南海北部的涡旋活动一致。

关键词: 南海北部, 海面高度异常, 季节内变异, 复经验正交函数分解, 中尺度涡

Abstract:

Intra-seasonal variability (ISV) of sea level anomalies (SLA) and their propagation features in the northern South China Sea (NSCS) are investigated using 25-year (1993-2017) satellite observations. The standard deviation of intra-seasonal SLA reveals that larger ISV exists in the northeastern South China Sea along the continental shelf/slope (200~2000 m) where it extends southwestward from Taiwan to Hainan. The ISV of SLA exhibits obvious seasonality, being strong in winter and weak in summer. By using Complex Empirical Orthogonal Function (CEOF) analysis, the spatial pattern and temporal variability of the ISV, as well as their inter-annual modulation, are studied. We find that there are mainly two types of ISV pattern, showing great agreement with eddy activities in the NSCS. The ISV of SLA is also modulated by seasonal and inter-annual variation. The first mode of CEOF indicates southwestward propagation of the ISV from southwest of Taiwan to east of the Xisha Islands, being especially strong in winter. The second mode of CEOF reveals two westward ISV regions: southwest to Taiwan and south to the Dongsha Islands. The statistical analysis for the seasonal variation of mesoscale eddies indicates that the CEOF results are consistent with the distribution of mesoscale eddy activities in the NSCS.

Key words: northern South China Sea, sea level anomaly, intra-seasonal variability, Complex Empirical Orthogonal Function (CEOF) analysis, mesoscale eddy

中图分类号:

P731.21

王霞,方文东,陈荣裕. 南海北部海面高度的季节内变异及其传播特征 ^*[J]. 热带海洋学报, 2019, 38(3): 1-12.

Xia WANG,Wendong FANG,Rongyu CHEN. Intra-seasonal variability of sea level anomalies and their propagation features in the northern South China Sea from 25 years of satellite altimetry data[J]. Journal of Tropical Oceanography, 2019, 38(3): 1-12.

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CEOF 模态	全年		冬半年(10—3月)		夏半年(4—9月)
CEOF 模态	方差贡献率/%	累计/%	方差贡献率/%	累计/%	方差贡献率/%	累计/%
1	19.5	19.5	24.3	24.3	13.8	13.8
2	10.8	30.3	11.0	35.3	11.3	25.1
3	7.8	38.1	8.8	44.0	8.1	33.2

南海北部海面高度的季节内变异及其传播特征 ^*

Intra-seasonal variability of sea level anomalies and their propagation features in the northern South China Sea from 25 years of satellite altimetry data

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