1.中国海洋大学三亚海洋研究院 海南省海洋立体观测与信息重点实验室;2.中国海洋大学 物理海洋教育部重点实验室;3.中国海洋大学 未来海洋学院;4.青岛海洋科学与技术国家实验室
国家自然科学基金项目（面上项目，重点项目，重大项目）；国家重点研发计划项目课题；海南省科技专项资助；三亚崖州湾科技城管理局重大科技项目
GUAN ShouDe
Key Laboratory of Ocean Observation and Information of Hainan Province,Sanya Oceanographic Institution,Ocean University of China；Physical Oceanography Lab/IAOS，Ocean University of China；Laboratory for Ocean and Climate Dynamics，Pilot National Laboratory for Marine Science and TechnologyZHAO Wei
Key Laboratory of Ocean Observation and Information of Hainan Province,Sanya Oceanographic Institution,Ocean University of China；Physical Oceanography Lab/IAOS，Ocean University of China；Academy of the Future Ocean，Ocean University of China；Laboratory for Ocean and Climate Dynamics，Pilot National Laboratory for Marine Science and Technology1.Key Laboratory of Ocean Observation and Information of Hainan Province,Sanya Oceanographic Institution,Ocean University of China;2.Physical Oceanography Lab/IAOS,Ocean University of China;3.Physical Oceanography Lab/IAOS，Ocean University of China;4.Academy of the Future Ocean，Ocean University of China;5.Laboratory for Ocean and Climate Dynamics，Pilot National Laboratory for Marine Science and Technology
The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)；National Key Research and Development Program of China；Special funding for science and technology in Hainan Province；Sanya Yazhou Bay Science and Technology City Administration major science and technology project
南海上层海洋温度垂向结构对海洋气候研究及海洋防灾减灾具有重要意义，然而由于现场观测数据有限，很难获取高时空分辨率的网格化数据。基于2007-2021年的Argo剖面数据、海面高度异常数据和月平均气候态数据，评估了两层动力模型和多层回归模型在南海海区反演海洋温度结构的性能。两层动力模型反演得到的26℃(D26)与20℃(D20)等温线深度的均方根误差分别13.25m和21.12m，多层回归模型的D26、D20均方根误差分别11.55m和14.32m，通过对比两种模型的结果：多层回归模型在时间与空间上反演结果性能更佳。两种模型反演的南海上层海洋热含量空间分布较为一致，均能应用于台风“威马逊”的强度评估。然而，在南海特殊的强内潮的背景下，两种模型得到的D20性能都有所降低。
The vertical structure of ocean temperature in the South China Sea is important for climate research and marine disaster prevention and mitigation. Due to the limited in-situ observation datasets, the vertical temperature structure in the South China Sea with high spatiotemporal resolution has yet to be obtained. Therefore, in order to invert the high-resolved vertical temperature structure in the South China Sea, this study evaluates the performance of the two-layer dynamic model and the multi-layer regression model in ocean temperature structure inversion based on the Argo data, the sea level anomaly (SLA) data and the World Ocean Atlas 2018 (WOA18) data from 2007 to 2021. After comparing the two models, we find the Root Mean Square Error (RMSE) of D26 and D20 of the two-layer dynamic model are 13.25m and 21.12m, while RMSE of D26 and D20 of the multi-layer regression model are 11.55m and 14.32m. Overall, the multi-layer regression model has smaller error and better performance in time and space than the two-layer model. However, further analyses indicate that the spatial distribution of tropical cyclone heat potential in the South China Sea inverted by the two models is relatively consistent, and both of them can react to the intensity assessment of Typhoon Rammasun (2014). Additionally, under the influence of the special strong internal tides in the South China Sea, the performance of D20 obtained by both models is reduced.
