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The South China Sea (SCS) and the Arabian Sea (AS) are both located roughly in the north tropical zone with a range of similar latitude (0°-24°N). Monsoon winds play similar roles in the upper oceanic circulations of the both seas. But the distinct patterns of chlorophyll a (Chl a) concentration are observed between the SCS and the AS. The Chl a concentration in the SCS is generally lower than that in the AS in summer (June-August); the summer Chl a concentration in the AS shows stronger interannual variation, compared with that in the SCS; Moderate resolution imaging spectroradiometer (MODIS)-derived data present higher atmospheric aerosol deposition and stronger wind speed in the AS. And it has also been found that good correlations exist between the index of the dust precipitation indicated by aerosol optical thickness (AOT) and the Chl a concentration, or between wind and Chl a concentration. These imply that the wind and the dust precipitation bring more nutrients into the AS from the sky, the sub-layer or coast regions, inducing higher Chl a concentration. The results indicate that the wind velocity and the dust precipitation can play important roles in the Chl a concentration for the AS and the SCS in summer. However aerosol impact is weak on the biological productivity in the west SCS and wind-induced upwelling is the main source. [ABSTRACT FROM AUTHOR]

Abstract: Four existing sea surface temperature (SST) assimilation schemes are evaluated in terms of their performances in assimilating the advanced very high resolution radiometer pathfinder best SST data in the South China Sea using the Princeton Ocean Model. Schemes 1 and 2 project SST directly to subsurface according to model-based correlations between SST and subsurface temperature. The difference between these two schemes is related to the order of vertical projection and horizontal optimal interpolation (OI). In Scheme 1, the spatially non-uniform SST observations are first projected to subsurface levels, followed by horizontal OI at each level. While in Scheme 2, the remotely sensed SSTs are first optimally interpolated to all grid points at the surface, followed by projecting gridded SSTs to subsurface levels. Scheme 3 assumes that the mixed layer is well mixed and has a uniform temperature vertically. In Scheme 4, SST is propagated to subsurface levels using a linear relationship of temperature between any two neighboring depths (Scheme 4a) or between surface and subsurface (Scheme 4b), which is derived by empirical orthogonal function (EOF) technique. To verify the results of the four schemes, the authors use the hydrographic data from two cruises during the South China Sea Monsoon Experiment in April and June 1998. It was shown that all four schemes could improve the SST field by reducing about 50% of the root mean square errors (RMSEs). All but Scheme 3 can improve model thermocline structure that is too diffused otherwise, though the RMSEs increase in the thermocline, especially for Scheme 2 when the model has opposite bias between upper layers and lower layers. Scheme 3 fails in the subsurface depth by increasing the thermocline depth, especially when there is a cold model bias. Projecting SST downward by EOF technique can deepen the depth of assimilation especially in Scheme 4a. Both Schemes 4a and b can correct the bias in the mixed layer and do not change the vertical thermal structure. [Copyright &y& Elsevier]