| 利用海洋温度剖面与海表盐度反演盐度剖面方法研究 |
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| 引用本文： | 何子康,王喜冬,陈志强,范开桂.利用海洋温度剖面与海表盐度反演盐度剖面方法研究[J].热带海洋学报,2021,40(6):41-51. |
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| 作者姓名： | 何子康 王喜冬 陈志强 范开桂 |
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| 作者单位： | 1.河海大学自然资源部海洋灾害预报技术重点实验室, 江苏 南京2100982.河海大学海洋学院, 江苏 南京 2100983.南方海洋科学与工程广东省实验室(珠海), 广东 珠海 519000 |
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| 基金项目： | 国家自然科学基金(4177060056) |
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| 摘 要： | 为解决海洋中大量观测数据只含有温度剖面而缺乏盐度观测的问题, 基于历史观测的温盐剖面资料, 考虑到盐度卫星数据的发展, 采用回归分析方法, 在孟加拉湾建立了盐度与温度、经纬度、表层盐度的关系, 并对不同反演方法的反演结果进行检验评估。结果发现, 在不引入海表盐度(sea surface salinity, SSS)时, 最佳反演模型是温度、温度的二次项与经纬度确定的回归模型, 而SSS的引入则可以进一步优化反演结果。将反演结果与观测结果进行对比, 显示用反演的盐度剖面计算的比容海面高度误差超过2cm, 而引入SSS后的误差低于1.5cm。SSS的引入能够较为真实地反映海洋盐度场的垂直结构和内部变化特征, 既能够捕捉到对上混合层有重要影响的SSS信号, 又能够反映盐度在跃层上的季节内变化以及盐度障碍层的季节变化。水团分析显示, 与气候态相比, 盐度反演结果可以更好地表征海洋上层水团的变化特征。
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| 关 键 词： | 海表盐度 盐度剖面反演 比容海面高度 海水水团 孟加拉湾 |
| 收稿时间： | 2020-12-01 |
| 修稿时间： | 2021-03-05 |
Reconstructing salinity profile using temperature profile and sea surface salinity |
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| HE Zikang,WANG Xidong,CHEN Zhiqiang,FAN Kaigui.Reconstructing salinity profile using temperature profile and sea surface salinity[J].Journal of Tropical Oceanography,2021,40(6):41-51. |
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| Authors: | HE Zikang WANG Xidong CHEN Zhiqiang FAN Kaigui |
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| Institution: | 1. Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing 210098, China2. College of Oceanography, Hohai University, Nanjing 210098, China3. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China |
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| Abstract: | A large number of marine observations contain only temperature profiles, but not salinity profiles that are important for understanding ocean dynamics. To construct salinity profiles, we use regression analysis methods to establish relationship of ocean salinity with historical ocean temperature, longitude, latitude, and satellite-based sea surface salinity (SSS) in the Bay of Bengal. The results of different inversion methods are then tested and evaluated. We find that without introducing SSS, the best reconstruct model is using temperature, namely, using the secondary items of temperature with longitude and latitude to determine the regression model. However, the introduction of SSS can further improve the inversion results. By comparing the reconstructions with the observations, we show that the steric height error calculated by the salinity profile inversion is more than 2.0 cm, while the error calculated after introducing SSS is less than 1.5 cm. The introduction of SSS can truly reflect the vertical structure and internal variation characteristics of ocean salinity profile. It can not only capture the SSS signal that has an important influence on the upper mixing layer, but also reflect the seasonal change of salinity on the thermocline and the seasonal change of the barrier layer. The inversion results are compared with the climatology, and the observed water mass is analyzed, showing that compared with the climatology, the inversion can better represent the variation characteristics of the surface water mass. However, below the mixing layer, there is no significant difference between the inversion and climatology. |
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| Keywords: | sea surface salinity salinity profile reconstruction temperature profile steric height water mass Bay of Bengal |
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