Wednesday, 12 January 2005
Southern Hemisphere teleconnection indices associated with SACZ in model simulations
The CPTEC/COLA Atmospheric Global Circulation Model (AGCM) has been used to do seasonal prediction at CPTEC since 1995. Results of simulation and prediction show that the model is able to represent well the anomalous precipitation over Northeast and Southern Brazil in ENSO years, when the SST forcing is large. The southeast region of Brazil has low predictability, in part because it is a transition region between the tropical regime to the north and the extratropical regime to the south. It is noticed that the dispersion among members is very large in this region, which is affected by frontal systems that can have different behavior in different integrations. This region is also affected by the South Atlantic Convergence Zone (SACZ) that occurs mainly in the austral summer. The integrated precipitation in the monthly and seasonal scale associated with this system is not well predicted by the model. Climatological analyses show that the model represents a NW-SE band of precipitation associated with the SACZ, but the tropical sector is subestimated and the southern sector is overestimated. However, in the intraseasonal time scale, the model shows features similar to the observations, as MJO and PSA patterns, that affect South America and the SACZ. In this study, centers of action of the main teleconnection patterns, obtained in model results, are analyzed and indices are generated, using results of a climate simulation, to be related to convection in the SACZ area. Several centers are investigated to form teleconnection indices in the atmosphere: areas of Southeast Pacific, centers of stationary waves, Indonesia area, SPCZ area and over South America. The main results show that the indices represent quite well connections of the centers of action with SACZ convection in the model results. Comparisons with observations show that corrections have to be made in order to use the indices as a potential tool to improve the seasonal prediction.