IMPACT OF DATA ASSIMILATION ON OCEAN INITIALIZATION AND EL-NINO PREDICTION

In this study, the authors compare skills of forecasts of tropical Pac ific sea surface temperatures From the National Centers for Environmen tal Prediction (NCEP) coupled general circulation model that were init iated using different sets of ocean initial conditions. These were pro duced with and without assimilation of observed subsurface upper-ocean temperature data from expendable bathythermographs (XBTs) and from th e Tropical Ocean Global Atmosphere-Tropical Atmosphere Ocean (TOGA-TAO ) buoys. These experiments show that assimilation of observed subsurfa ce temperature data in the determining of the initial conditions, espe cially far summer and fall starts, results in significantly improved f orecasts for the NCEP coupled model. The assimilation compensates For errors in the forcing fields and inadequate physical parameterizations in the ocean model. Furthermore, additional skill improvements. over that provided by XBT assimilation, result from assimilation of subsurf ace temperature data collected by the TOGA-TAO buoys. This is a conseq uence of the current predominance of TAO data in the tropical Pacific in recent years. Results suggest that in the presence of erroneous win d forcing and inadequate physical parameterizations in the ocean model ocean data assimilation can improve ocean initialization and thus cal l improve the skill of the forecasts. However, the need far assimilati on can create imbalances between the mean states of the oceanic initia l conditions and the coupled model. These imbalances and errors in til e coupled model can be significant limiting factors to forecast skill, especially for forecasts initiated in the northern winter. These limi ting factors cannot be avoided by using data assimilation and must be corrected by improving the models and the forcing fields.