采用POMgcs(Princeton Ocean Model with generalized coordinate system)和MITgcm(MIT General Circulation Model)两个海洋数值模式,研究了M-Y2.0、基于固壁近似假定的M-Y2.5、基于波浪破碎作用的M-Y2.5和KPP 4种垂向混合参数化方案对模拟黄海夏季上层温度结构的影响。结果表明,M-Y2.0和基于固壁近似假定的M-Y2.5方案低估了黄海上层的湍动能,模拟的黄海夏季温度上混合层的效果与实测相比均偏浅,不能够很好地重构黄海夏季温度的垂直结构。而基于波浪破碎作用的M-Y2.5和KPP方案均可以增加海洋上层湍动能的输入量,模拟的黄海夏季温度上混合层的效果与实测较为一致。故推测黄海夏季的上层结构是受波浪混合和流场剪切等物理机制共同调节的,若通过合理的垂向混合参数化方案将这些物理机制的作用加以体现,将会较真实地模拟和重构出黄海夏季海温上层结构。
To further explore enthalpy-based sea-ice assimilation, a one-dimensional （1D） enthalpy sea-ice model is implemented into a simple pycnocline prediction model. The 1D enthalpy sea-ice model includes the physical processes such as brine expulsion, flushing, and salt diffusion. After being coupled with the atmosphere and ocean components, the enthalpy sea-ice model can be integrated stably and serves as an important modulator of model variability. Results from a twin experiment show that the sea-ice data assimilation in the enthalpy space can produce smaller root-mean-square errors of model variables than the traditional scheme that assimilates the observations of ice concentration, especially for slow-varying states. This study provides some insights into the improvement of sea-ice data assimilation in a coupled general circulation model.
The first version of a global ocean reanalysis over multiple decades （1979-2008） has been completed by the National Marine Data and Information Service within the China Ocean Reanalysis （CORA） project. The global ocean model employed is based upon the ocean general circulation model of the Massachusetts Institute of Technology. A sequential data assimilation scheme within the framework of 3D variational （3DVar） analysis, called multi-grid 3DVar, is implemented in 3D space for retrieving multiple-scale observational information. Assimilated oceanic observations include sea level anomalies （SLAs） from multi-altimeters, sea surface temperatures （SSTs） from remote sensing satellites, and in-situ temperature/salinity profiles. Evaluation showed that compared to the model simulation, the annual mean heat content of the global reanalysis is significantly approaching that of World Ocean Atlas 2009 （WOA09） data. The quality of the global temperature climatology was found to be comparable with the product of Simple Ocean Data Assimilation （SODA）, and the major ENSO events were reconstructed. The global and Atlantic meridional overturning circulations showed some similarity as SODA, although significant differences were found to exist. The analysis of temperature and salinity in the current version has relatively larger errors at high latitudes and improvements are ongoing in an updated version. CORA was found to provide a simulation of the subsurface current in the equatorial Pacific with a correlation coefficient beyond about 0.6 compared with the Tropical Atmosphere Ocean （TAO） mooring data. The mean difference of SLAs between altimetry data and CORA was less than 0.1 m in most years.
A new regional ocean reanalysis over multiple decades （1958 2008） for the coastal waters of China and adjacent seas has been completed by the National Marine Data and Information Service （NMD[S） under the CORA （China Ocean ReAnalysis） project. Evaluations were performed on three aspects： （1） the improvement of general reanalysis quality; （2） eddy structures; and （3） decadal variability of sea surface height anomalies （SSHAs）. Results showed that the quality of the new reanalysis has been enhanced beyond ~40% （39% for temperature, 44% for salinity） in terms of the reduction of root mean squared errors （RMSEs） for which the reanalysis values were compared to observed values in the observational space. Compared to the trial version released to public in 2009, the new reanalysis is able to reproduce more detailed eddy structures as seen in satellite and in situ observations. EOF analysis of the reanalysis SSHAs showed that the new reanalysis reconstructs the leading modes of SSHAs much better than the old version. These evaluations suggest that the new CORA regional reanalysis represents a much more useful dataset for the community of the coastal waters of China and adjacent seas.
