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Improved ENSO simulation in regional coupled GCM using regressive correction method

Science china Earth sciences 2007年第8期50卷 1258-1265页

作者： fu weiwei1 & {2. {3. {4.-Zhu International Research Center (NZC), Institute of atmospheric physics, chinese academy of sciences (CAS), beijing 10002., china 2.State Key Laboratory of Numerical Modeling for atmospheric sciences and Geophysical Fluid Dynamics (LASG), Institute of At-mospheric physics, CAS, beijing 10002., china 1. nansen-Zhu International Research Center (NZC) Institute of Atmospheric Physics Chinese Academy of Sciences (CAS) Beijing 100029 China 2. State Key Laboratory of Numerical Modeling for atmospheric sciences and Geophysical Fluid Dynamics (LASG) Institute of Atmospheric Physics CAS Beijing 100029 China

A regressive correction method is presented with the primary goal of improving ENSO simulation in regional coupled GCM. It focuses on the correction of ocean-atmosphere exchanged fluxes. On the basis of numerical experiments and analysis, the method can be described as follows: first, driving the ocean model with heat and momentum flux computed from a long-term observation data set; the pro-duced SST is then applied to force the AGCM as its boundary condition; after that the AGCM’s simula-tion and the corresponding observation can be correlated by a linear regressive formula. Thus the re-gressive correction coefficients for the simulation with spatial and temporal variation could be obtained by linear fitting. Finally the coefficients are applied to redressing the variables used for the calculation of the exchanged air-sea flux in the coupled model when it starts integration. This method together with the anomaly coupling method is tested in a regional coupled model, which is composed of a global grid-point atmospheric general circulation model and a high-resolution tropical Pacific Ocean model. The comparison of the results shows that it is superior to the anomaly coupling both in reducing the coupled model ‘climate drift’ and in improving the ENSO simulation in the tropical Pacific Ocean.

关键词： anomaly coupling regressive correction method regional coupled model ENSO simulation

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A three-dimensional variational ocean data assimilation system:Scheme and preliminary results

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Science china Earth sciences 2006年第11期49卷 1212-1222页

作者： ZHU Jiang1,2. zhou guangqing1, YAN Changxiang1, {2. {4. & YOU Xiaobao1,3.1. International Center for Climate and Environment sciences, Institute of atmospheric physics, chinese academy of sciences, beijing 10002., china 2. Jiangsu Key Laboratory of Meteorological Disaster, Nanjing University of Information Science and Technology, Nanjing 2.004., china 3. beijing Institute of Applied Meteorology, beijing 10002., china 1. International Center for Climate and Environment sciences Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China 2. Jiangsu Key Laboratory of Meteorological Disaster Nanjing University of Information Science and Technology Nanjing 210044 China 3. beijing Institute of Applied Meteorology Beijing 100029 China

A new 3.VAR-based Ocean Variational Analysis System (OVALS) is developed. OVALS is capable of assimilating in situ sea water temperature and salinity observations and satellite altimetry data. As a component of OVALS, a new variational scheme is proposed to assimilate the sea surface height data. This scheme considers both the vertical correlation of background errors and the nonlinear temperature-salinity relationship which is derived from the generalization of the linear balance constraints to the nonlinear in the 3.VAR. By this scheme, the model temperature and salinity fields are directly adjusted from the altimetry data. Additionally, OVALS can assimilate the temperature and salinity profiles from the ARGO floats which have been implemented in recent years and some temperature and salinity data such as from expendable bathythermograph, moored ocean buoys, etc. A 2.-year assimilation experiment is carried out by using OVALS and the Tropical Pacific circulation model. The results show that the assimilation system may effectively improve the estimations of temperature and salinity by assimilating all kinds of observations. Moreover, the root mean square errors of temperature and salinity in the upper depth less than 4.2} m reach 0.63. and 0.3. psu.

关键词： data assimilation, 3.VAR, sea surface height, ARGO floats.

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