Relative roles of Ekman transport and Ekman pumping in driving summer upwelling in the South China Sea （SCS） are examined using QuikSCAT scatterometer wind data. The major upwelling regions in the SCS are the coastal regions east and south- east of Vietnam （UESEV）, east and southeast of Hainan Island （UESEH）, and southeast of Guangdong province （USEG）. It is shown that the Ekman transport due to alongshore winds and Ekman pumping due to offshore wind stress curl play different roles in the three upwelling systems. In UESEV, Ekman pumping and Ekman transport are equally important in generating upwelling. The Ek- man transport increases linearly from 0.49 Sv in May to 1.23 Sv in August, while the Ekman pumping increases from 0.36 to 1.22 Sv during the same period. In UESEH, the mean estimates of Ekman transport and Ekman pumping are 0.14 and 0.07 Sv, respectively, indicating that 33% of the total wind-driven upwelling is due to Ekman pumping. In USEC~ the mean Ekman transport is 0.041 Sv with the peak occurring in July, while Ekman pumping is much smaller （0.003 on average）, indicating that the upwelling in this area is primarily driven by Ekman transport. In the summers of 2003 and 2007 following E1 Nifio-Southern Oscillation （ENSO） events, both Ekman transport and Ekman pumping decrease in UESEV due to the abnormally weak southwest monsoon. During the same events, however, Ekman transport is slightly enhanced and Ekman pumping is weakened in UESEH and USEG.
The computational cost required by the Ensemble Kalman Filter （EnKF） is much larger than that of some simpler assimilation schemes, such as Optimal Interpolation （OI） or three-dimension variational as- similation （3DVAR）. Ensemble optimal interpolation （EnOI）, a crudely simplified implementation of EnKF, is sometimes used as a substitute in some oceanic applications and requires much less computational time than EnKF. In this paper, to compromise between computational cost and dynamic covariance, we use the idea of ＂dressing＂ a small size dynamical ensemble with a larger number of static ensembles in order to form an approximate dynamic covariance. The term ＂dressing＂ means that a dynamical ensemble seed from model runs is perturbed by adding the anomalies of some static ensembles. This dressing EnKF （DrEnKF for short） scheme is tested in assimilation of real altimetry data in the Pacific using the HYbrid Coordinate Ocean Model （HYCOM） over a four-year period. Ten dynamical ensemble seeds are each dressed by 10 static ensemble members selected from a 100-member static ensemble. Results are compared to two EnKF assimilation runs that use 10 and 100 dynamical ensemble members. Both temperature and salinity fields from the DrEnKF and the EnKF are compared to observations from Argo floats and an OI SST dataset. The results show that the DrEnKF and the 100-member EnKF yield similar root mean square errors （RMSE） at every model level. Error covariance matrices from the DrEnKF and the 100-member EnKF are also compared and show good agreement.
A algal bloom process had been simulated via field mesocosm experiment, and the change of phytoplankton assemblage of different sizes in different growing phases had been studied. Nutrients addition could promote the growth of phytoplankton In the mesocosm of Prorocentrum donghaiense （M1） and the mesocosm of natural waters （M2）, and the peaks of chlorophyll a were 112.79 mg/m and 235.60 mg/m, respectively. The restraining effect of nano-phytoplankton on pico-phytoplankton growth was stronger in M2 than in M1. When nutrients were abundant, the relative growth rate of diatom was higher than that of P. donghaiense, and they reached the peak quickly and then came to die out very fast. The decreasing of Si promoted diatom bloom to die out.
