Distribution Characteristics Of The Sea Surface Salinity Of The South China Sea

Sea surface salinity (SSS) is one of the key variables to describe the basic stateof the ocean, and the study of its distribution and change law contributes tounderstand the ocean circulation, the ocean carbon cycle, the global water cycle, theocean-air interaction, and their impact on the global climate. With the growingimportance of SSS, and the gradual improvement of its measurement methods, theSSS research at home and abroad not only refers to its spatial and temporaldistribution characteristic, its influence factors, and its effects on the climatecharacteristics, like the global water cycle, the ocean circulation and so on, but alsothe SSS satellite remote sensing data inversion and its accuracy calibration. On thebasis of summarizing the SSS research progress at home and abroad, combined withthe South China Sea special geographical location and its climate characteristics, theSouth China Sea`s SSS is considered as a significant factor on the ocean circulationand the ocean-air interaction generated in the South China Sea, while owing to thespatial and temporal data missing of SSS in the South China Sea, the SSS researchrecently lay emphasis on the calibration and validation work for satellite remotesensing data. Therefore, to analyze the distribution characteristics of the South ChinaSea is conducive to understand it`s influence on the South China Sea circulation, it`swater cycle, as well as the climate. Meanwhile, it`s available to provide data andobservation results for the prospective improvement of satellite sea surface salinityretrieval accuracy. On account of this, by using the monthly averaged SSS dataderived from the Simple Ocean Data Assimilation (SODA) from January1980toDecember2011and the daily averaged SSS data derived from the high-resolutionHYCOM/NCODA data from1January2011to31December2011, this paper focusedon the sea surface salinity of the South China Sea SSS distribution and varianceanalysis. The primary study contents as follow：(1)Using the least square method linear fitting to analyze the SODA monthly averaged SSS data for observing the SSS abnormal change trend. The resultsindicate that the South China Sea SSS has a declining trend overall.(2) Using the Empirical Orthogonal Function (EOF) analysis method toundertake Spatial and temporal decomposition on the SODA monthly averaged SSSdata. The first EOF mode reveals the South China Sea SSS do have a decliningtrend and accord with the result in the first part. Besides, the second and third EOFmodes show that in different sea area of South China Sea, the variability of SSSA isdifferent. In the south area and north area, the SSSA changes largely, and has anegative correlation, as well as the area adjacent to other oceans and the continentalshelves. While, the SSSA changes little near to the central of the South China Sea.(3)Mainly discussed the difference between HYCOM/NCODA daily averagedSSS data and SODA monthly averaged SSS data in the South China Sea in2011allover the year, and analyzing their distribution, after doing a projection onHYCOM/NCODA for obtaining the same monthly grid data with SODA monthlyaveraged SSS data. It turns out that the mean SSS of the monthly averaged SSS in2011have a trend that rising first then declining and rising again. At the same time, bythe means of comparing HYCOM/NCODA SSS deviation to SODA SSS deviation, itcomes out that both of them heaving and dipping as their own mean SSS, the changeof the former is more regular in time, while the latter changes largely in different area.After subtracting SODA SSS from HYCOM/NCODA SSS, the result shows thatdifference represents different changes in different areas in the South China Sea,basically related to the SSS seasonal variation. Using least square method linearfitting to analyze HYCOM/NCODA SSS data and SODA SSS data and computingtheir RMSE, it shows that they have a positive correlation, and though theircorrelation is not significant their presentation on the south China sea SSS distributionis almost the same. Besides, this paper carried on the preliminary analysis on a1°ｘ1°box sample to examine if the mean SSS can represent the overall SSS in the box,and the result showed that the SSS distribution in the sample is uniform except inseveral grid points. But it indicates that it`s significant for the the calibration andvalidation effort for satellite remote sensing data.