南海上层海洋温度垂向结构的反演

Inversion of ocean temperature structure in the South China Sea

摘要:

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。

Abstract:

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.