Two new species (Nitzschia sinensisLiu, sp.nov.and Podosira granulataLiu, sp.nov.) and one new variety (Xanthiopyxis microspinosa var.ellipticusLiu, var.nov.) collected from the urface sediments off the southern Huanghai Sea and the East China Sea are described and a list of diatoms from the surface sediments in the survey area and some new records in China are attached.
Combined refraction and diffraction models in the form of linear parabolic approximation are derived through smallparameter method.More strictly theoretical basis and more accuracy in the models than Lozano's (1980) are obtained.Some theoretical defects in Liu's model (1985) with consideration of current are not only found but also eliminated.More strict and accurate models are, therefore, presented in this paper. The calculation results and analysis in applying the models to actual wave field with consideration of bottom friction will be given in the following paper.
The one-dimensional Kraus-Tumer mixed layer model improved by Liu is developed to consider the effect of salinity and the equations of temperature and salinity under the mixed layer. On this basis, the processes of growth and death of surface layer temperature inversion is numerically simulated under different environmental parameters. At the same time, the physical mechanism is preliminarily discussed combining the observations at the station of TOGA-COARE 0°N, 156°E. The results indicate that temperature inversion sensitively depends on the mixed layer depth, sea surface wind speed and solar shortwave radiation, etc., and appropriately meteorological and hydrological conditions often lead to the similarly periodical occurrence of this inversion phenomenon.
Based on the data and method offered by Liu et al. (2009), the direct wind and Stokes drift-induced energy inputs into the Ekman layer within the Antarctic Circumpolar Current (ACC) area are reestimated since the results of the former have been proved to be underestimated. And the result shows that the total rate of energy input into the Ekman-Stokes layer within the ACC area is 852.41 GW, including 649.75 GW of direct wind energy input (76%) and 202.66 GW of Stoke drift-induced energy input (24%). Total increased energy input, due to wave-induced Coriolis-Stokes forcing added to the classical Ekman model, is 52.05 GW, accounting for 6.5% of the wind energy input into the classical Ekman layer. The long-term variability of direct wind and Stokes drift-induced energy inputs into the Ekman layer within the ACC is also investigated, and the result shows that the Stokes drift hinders the decadal increasing trend of direct wind energy input. Meanwhile, there is a period of 4-5 a in the energy spectrums, as same as the Antarctic circumpolar wave.
The simulated ENSO and Indian Ocean dipole (IOD) mode events from three coupled GCMs with the same oceanic component model,CPM0,CPM1 and FGCM0,are compared.The only difference between the CPM0 and the CPM1 comes from the coupling scheme at the air-sea interface,e.g.,flux anomaly coupling scheme for the former and direct coupling scheme for the latter.The FGCM0 is also a directly coupled GCM,but its atmospheric component model is the NCAR CCM3 rather than the NCC T63AGCM as in the other two coupled GCMs CPM0 and CPM1.All three coupled models show El Niño-like interannual variability in the tropic Pacific,but the FGCM0 shows a bit stronger amplitude of El Niño events and both the CPM0 and the CPM1 show much weaker amplitude than the observed one.In the meanwhile,the quasi-biennial variability dominates in the FGCM0 simulations,and 4 a and longer periods are significant in both the CPM0 and CPM1 models.As the El Niño events simulated by the three coupled GCMs,the simulated Indian Ocean dipole mode events are stronger from the coupled model FGCM0 and weaker from both the CPM0 and CPM1 models than those from observation.
The Coupling of three model components, WRF/PCE (polar climate extension version of weather research and forecasting model (WRF)), ROMS (regional ocean modeling system), and CICE (community ice code), has been implemented, and the regional atmosphere-ocean-sea ice coupled model named WRF/PCEROMS-CICE has been validated against ERA-interim reanalysis data sets for 1989. To better understand the reasons that generate model biases, the WRF/PCE-ROMS-CICE results were compared with those of its components, the WRF/PCE and the ROMS-CICE. There are cold biases in surface air temperature (SAT) over the Arctic Ocean, which contribute to the sea ice concentration (SIC) and sea surface temperature (SST) biases in the results of the WRF/PCE-ROMS-CICE. The cold SAT biases also appear in results of the atmospheric component with a mild temperature in winter and similar temperature in summer. Compared to results from the WRF/PCE, due to influences of different distributions of the SIC and the SST and inclusion of interactions of air-sea-sea ice in the WRF/PCE-ROMS-CICE, the simulated SAT has new features. These influences also lead to apparent differences at higher levels of the atmosphere, which can be thought as responses to biases in the SST and sea ice extent. There are similar atmospheric responses in feature of distribution to sea ice biases at 700 and 500 hPa, and the strength of responses weakens when the pressure decreases in January. The atmospheric responses in July reach up to 200 hPa. There are surplus sea ice extents in the Greenland Sea, the Barents Sea, the Davis Strait and the Chukchi Sea in winter and in the Beaufort Sea, the Chukchi Sea, the East Siberian Sea and the Laptev Sea in summer in the ROMS-CICE. These differences in the SIC distribution can all be explained by those in the SST distributions. These features in the simulated SST and SIC from ROMS-CICE also appear in the WRF/PCE-ROMS-CICE. It is shown that the performance of the WRF/PCE-ROMS-CICE is determined to a large extent by its components, the WRF/PCE and the ROMS-CICE.