| 利用被动示踪物模拟对黑潮入侵南海的数值研究 |
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| 引用本文： | 刘统亚,许洁馨,何映晖,吕海滨,姚远,蔡树群.利用被动示踪物模拟对黑潮入侵南海的数值研究[J].海洋学报(英文版),2016,35(9):1-12. |
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| 作者姓名： | 刘统亚 许洁馨 何映晖 吕海滨 姚远 蔡树群 |
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| 作者单位： | 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, 广州, 510301;中国科学院大学, 北京, 100049,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, 广州, 510301,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, 广州, 510301,淮海工学院测绘工程学院, 连云港, 222005,国家海洋局秦皇岛海洋环境监测中心, 秦皇岛, 066002,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, 广州, 510301 |
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| 摘 要： | 由于缺少观测数据和对黑潮水准确定义，很难识别出从太平洋入侵到南海的黑潮水团。本文基于一个经过观测验证的三维模式MITgcm，利用被动示踪物标记黑潮水，研究了入侵南海的黑潮水的时空变化。研究表明，在冬季，黑潮水入侵的范围最广，几乎占据了18°N-23°N和114°E-121°E的区域；并有一个分支进入台湾海峡；黑潮入侵的范围随深度增加逐渐减小。在夏季，黑潮水被限制在118°E以东，且没有分支进入台湾海峡；入侵的范围从海面到约205米是增大的，之后随深度增加逐渐减小。通过分析从2003年到2012年黑潮入侵的年际变化，与厄尔尼诺年和正常年相比，冬季黑潮入侵后向台湾海峡的分支在拉尼娜年是最弱的，这可能与中国大陆东南方向的风应力旋度有关。通过吕宋海峡的黑潮入侵通量（KIT）是西向的，其年平均值约为-3.86×106 m3/s，大于吕宋海峡通量（LST，约-3.15×106 m3/s）。250米以上的KIT约占了全深度通量的60-80%。此外，从2003年到2012年KIT与Niño 3.4指数的相关系数到达0.41，小于LST与Niño 3.4指数的相关系数0.78。
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| 关 键 词： | 黑潮入侵 时空变化 体积通量 MITgcm 南海 |
| 收稿时间： | 2015/9/28 0:00:00 |
| 修稿时间： | 2015/11/13 0:00:00 |
Numerical simulation of sediment transport in coastal waves and wave-induced currents |
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| LIU Tongy,XU Jiexin,HE Yinghui,L&#; Haibin,YAO Yuan and CAI Shuqun.Numerical simulation of sediment transport in coastal waves and wave-induced currents[J].Acta Oceanologica Sinica,2016,35(9):1-12. |
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| Authors: | LIU Tongy XU Jiexin HE Yinghui L&#; Haibin YAO Yuan and CAI Shuqun |
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| Institution: | State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China;University of Chinese Academy of Sciences, Beijing 100049, China,State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China,State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China,School of Geodesy and Geomatics Engineering, Huaihai Institute of Technology, Lianyungang 222005, China,Qinhuangdao Marine Environmental Monitoring Central Station, North China Sea Branch of State Oceanic Administration, Qinhuangdao 066002, China and State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China |
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| Abstract: | Owing to lack of observational data and accurate definition, it is difficult to distinguish the Kuroshio intrusion water from the Pacific Ocean into the South China Sea (SCS). By using a passive tracer to identify the Kuroshio water based on an observation-validated three-dimensional numerical model MITgcm, the spatio-temporal variation of the Kuroshio intrusion water into the SCS has been investigated. Our result shows the Kuroshio intrusion is of distinct seasonal variation in both horizontal and vertical directions. In winter, the intruding Kuroshio water reaches the farthest, almost occupying the area from 18°N to 23°N and 114°E to 121°E, with a small branch flowing towards the Taiwan Strait. The intrusion region of the Kuroshio water decreases with depth gradually. However, in summer, the Kuroshio water is confined to the east of 118°E without any branch reaching the Taiwan Strait; meanwhile the intrusion region of the Kuroshio water increases from the surface to the depth about 205 m, then it decreases with depth. The estimated annual mean of Kuroshio Intrusion Transport (KIT) via the Luzon Strait is westward to the SCS in an amount of -3.86×106 m3/s, which is larger than the annual mean of Luzon Strait Transport (LST) of -3.15×106 m3/s. The KIT above 250 m accounts for 60%-80% of the LST throughout the entire water column. By analyzing interannual variation of the Kuroshio intrusion from the year 2003 to 2012, we find that the Kuroshio branch flowing into the Taiwan Strait is the weaker in winter of La Ni.a years than those in El Niño and normal years, which may be attributed to the wind stress curl off the southeast China then. Furthermore, the KIT correlates the Niño 3.4 index from 2003 to 2012 with a correlation coefficient of 0.41, which is lower than that of the LST with the Niño 3.4 index, i.e., 0.78. |
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| Keywords: | Kuroshio intrusion spatio-temporal variation volume transport numerical model South China Sea |
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