Estimating salinity to complement observed temperature: 1. Gulf of Mexico

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• 作者：W.C. Thacker
• 关键词：XBT ; CTD ; Regression ; Data assimilation ; HYCOM
• 刊名：Journal of Marine Systems
• 出版年：2007
• 期刊代码：46_09247963
• 类别：env
• 出版时间：March 2007
• 卷：65
• 期：1-4
• 页码：224-248
• 文件大小：4080 K

摘要

This paper and its companion [Thacker, W.C., Sindlinger, L., 2007-this issue. Estimating salinity to complement observed temperature: 2. Northwestern Atlantic. Journal of Marine Systems. doi:10.1016/j.jmarsys.2005.06.007.] document initial efforts in a project with the goal of developing capability for estimating salinity on a region-by-region basis for the world oceans. The primary motivation for this project is to provide information for correcting salinity, and thus density, when assimilating expendable-bathythermograph (XBT) data into numerical simulations of oceanic circulation, while a secondary motivation is to provide information for calibrating salinity from autonomous profiling floats. Empirical relationships between salinity and temperature, which can be identified from archived conductivity–temperature–depth (CTD) data, provide the basis for the salinity estimates.

The Gulf of Mexico was chosen as the first region to explore for several reasons: (1) It's geographical separation from the Caribbean Sea and the North Atlantic Ocean makes it a “small ocean” characterised by a deep central basin surrounded by a substantial continental shelf. (2) The archives contain a relatively large number of CTD data that can be used to establish empirical relationships. (3) The sharp fronts associated with the Loop Current and its rings, which separate water with different thermal and haline characteristics, pose a challenge for estimating salinity. In spite of the shelf and the fronts, the relationship between salinity and temperature was found to be sufficiently regular that a single empirical model could be used to estimate salinity on each pressure surface for the entire Gulf for all seasons. In and below the thermocline, root-mean-square estimation errors are small — less than 0.02 psu for pressures greater than 400 dbar, corresponding to potential density errors of less than 0.015 kg/m³. Errors for estimates nearer to the surface can be an order of magnitude larger.