Mesoscale oceanic eddies in the South China Sea from 1992 to 2012: evolution processes and statistical analysis
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摘要: 海洋中尺度涡旋的自动识别与追踪近年来成为物理海洋领域研究的热点问题之一。针对中国南海区的海洋涡旋能量和几何学特征分析已经有学者采用不同方法开展过研究, 但这些研究较少关注南海区涡旋的内部演化过程的定量分析, 因此, 本文提出了一种追踪涡旋演化的混合方法, 该方法采用涡旋中心, 各个子涡的足迹边界和涡旋的组合边界来详细刻画涡旋内部结构特征, 并基于GIS时空演化模型完整地表达海洋中尺度涡旋过程中每个时刻的状态, 同时也记录了涡旋演化过程中所发生的演进、分裂、合并、消隐与重新等复杂的变化事件, 基于该数据模型, 构建了南海区1992-2012年的海洋中尺度涡旋时空过程数据库。通过对数据库中东沙冷涡(DCEs)和南海北部三个长周期暖涡(ACEs)的分析与结果验证, 明确了这些涡旋过程的演化, 此外, 还发现了2005年到2012年的类似于DCE和ACE的涡旋过程演化。对南海区涡旋的消隐, 重现、分裂与合并的空间分布分析可以看出涡旋的这些现象出现在吕宋岛西北, 吕宋海峡西南已经沿着越南的边缘海集聚的趋势。黑潮入侵和这些区域复杂海底地形是产生这些集聚的可能的原因。Abstract: Automated identification and tracking of mesoscale ocean eddies has recently become one research hotspot in physical oceanography. Several methods have been developed and applied to survey the general kinetic and geometric characteristics of the ocean eddies in the South China Sea (SCS). However, very few studies attempt to examine eddies' internal evolution processes. In this study, we reported a hybrid method to trace eddies' propagation in the SCS based on their internal structures, which are characterized by eddy centers, footprint borders, and composite borders. Eddy identification and tracking results were represented by a GIS-based spatiotemporal model. Information on instant states, dynamic evolution processes, and events of disappearance, reappearance, split, and mergence is stored in a GIS database. Results were validated by comparing against the ten Dongsha Cyclonic Eddies (DCEs) and the three long-lived anticyclonic eddies (ACEs) in the northern SCS, which were reported in previous literature. Our study confirmed the development of these eddies. Furthermore, we found more DCE-like and ACE-like eddies in these areas from 2005 to 2012 in our database. Spatial distribution analysis of disappearing, reappearing, splitting, and merging activities shows that eddies in the SCS tend to cluster to the northwest of Luzon Island, southwest of Luzon Strait, and around the marginal sea of Vietnam. Kuroshio intrusions and the complex sea floor topography in these areas are the possible factors that lead to these spatial clusters.
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