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南海海平面长期变化及其趋势研究
引用本文:徐莹,林明森,郑全安,宋清涛,叶小敏. 南海海平面长期变化及其趋势研究[J]. 海洋学报(英文版), 2016, 35(9): 22-33. DOI: 10.1007/s13131-016-0788-3
作者姓名:徐莹  林明森  郑全安  宋清涛  叶小敏
作者单位:中国海洋大学信息科学与工程学院, 山东 青岛 266100;国家卫星海洋应用中心, 北京 100081;国家海洋局空间海洋遥感与应用研究重点实验室, 北京 100081,国家卫星海洋应用中心, 北京 100081;国家海洋局空间海洋遥感与应用研究重点实验室, 北京 100081,马里兰大学大气海洋系, 美国 马里兰州学院公园市 20742,国家卫星海洋应用中心, 北京 100081;国家海洋局空间海洋遥感与应用研究重点实验室, 北京 100081,中国海洋大学信息科学与工程学院, 山东 青岛 266100;国家卫星海洋应用中心, 北京 100081;国家海洋局空间海洋遥感与应用研究重点实验室, 北京 100081
摘    要:
On the basis of the satellite maps of sea level anomaly(MSLA) data and in situ tidal gauge sea level data,correlation analysis and empirical mode decomposition(EMD) are employed to investigate the applicability of MSLA data,sea level correlation,long-term sea level variability(SLV) trend,sea level rise(SLR) rate and its geographic distribution in the South China Sea(SCS).The findings show that for Dongfang Station,Haikou Station,Shanwei Station and Zhapo Station,the minimum correlation coefficient between the closest MSLA grid point and tidal station is 0.61.This suggests that the satellite altimeter MSLA data are effective to observe the coastal SLV in the SCS.On the monthly scale,coastal SLV in the western and northern part of SCS are highly associated with coastal currents.On the seasonal scale,SLV of the coastal area in the western part of the SCS is still strongly influenced by the coastal current system in summer and winter.The Pacific change can affect the SCS mainly in winter rather than summer and the affected area mostly concentrated in the northeastern and eastern parts of the SCS.Overall,the average SLR in the SCS is 90.8 mm with a rising rate of(5.0±0.4) mm/a during1993–2010.The SLR rate from the southern Luzon Strait through the Huangyan Seamount area to the Xisha Islands area is higher than that of other areas of the SCS.

关 键 词:南海  海平面变化  趋势  相关分析  经验模态分解
收稿时间:2015-10-15
修稿时间:2015-12-07

A study of sea level variability and its long-term trend in the South China Sea
XU Ying,LIN Mingsen,ZHENG Quan''an,SONG Qingtao and YE Xiaomin. A study of sea level variability and its long-term trend in the South China Sea[J]. Acta Oceanologica Sinica, 2016, 35(9): 22-33. DOI: 10.1007/s13131-016-0788-3
Authors:XU Ying  LIN Mingsen  ZHENG Quan''an  SONG Qingtao  YE Xiaomin
Affiliation:College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China;National Satellite Ocean Application Service, State Oceanic Administration, Beijing 100081, China;Key Laboratory of Space Ocean Remote Sensing and Application, State Oceanic Administration, Beijing 100081, China,National Satellite Ocean Application Service, State Oceanic Administration, Beijing 100081, China;Key Laboratory of Space Ocean Remote Sensing and Application, State Oceanic Administration, Beijing 100081, China,Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742, USA,National Satellite Ocean Application Service, State Oceanic Administration, Beijing 100081, China;Key Laboratory of Space Ocean Remote Sensing and Application, State Oceanic Administration, Beijing 100081, China and College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China;National Satellite Ocean Application Service, State Oceanic Administration, Beijing 100081, China;Key Laboratory of Space Ocean Remote Sensing and Application, State Oceanic Administration, Beijing 100081, China
Abstract:
On the basis of the satellite maps of sea level anomaly (MSLA) data and in situ tidal gauge sea level data, correlation analysis and empirical mode decomposition (EMD) are employed to investigate the applicability of MSLA data, sea level correlation, long-term sea level variability (SLV) trend, sea level rise (SLR) rate and its geographic distribution in the South China Sea (SCS). The findings show that for Dongfang Station, Haikou Station, Shanwei Station and Zhapo Station, the minimum correlation coefficient between the closest MSLA grid point and tidal station is 0.61. This suggests that the satellite altimeter MSLA data are effective to observe the coastal SLV in the SCS. On the monthly scale, coastal SLV in the western and northern part of SCS are highly associated with coastal currents. On the seasonal scale, SLV of the coastal area in the western part of the SCS is still strongly influenced by the coastal current system in summer and winter. The Pacific change can affect the SCS mainly in winter rather than summer and the affected area mostly concentrated in the northeastern and eastern parts of the SCS. Overall, the average SLR in the SCS is 90.8 mm with a rising rate of (5.0±0.4) mm/a during 1993-2010. The SLR rate from the southern Luzon Strait through the Huangyan Seamount area to the Xisha Islands area is higher than that of other areas of the SCS.
Keywords:South China Sea  sea level variability  correlation analysis  empirical mode decomposition
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