Impact of Data Assimilation on Ocean Initialization and El Niño Prediction

Abstract

In this study, the authors compare skills of forecasts of tropical Pacific sea surface temperatures from the National Centers for Environmental Prediction (NCEP) coupled general circulation model that were initiated using different sets of ocean initial conditions. These were produced with and without assimilation of observed subsurface upper-ocean temperature data from expendable bathythermographs (XBTs) and from the Tropical Ocean Global Atmosphere?Tropical Atmosphere Ocean (TOGA?TAO) buoys. These experiments show that assimilation of observed subsurface temperature data in the determining of the initial conditions, especially for summer and fall starts, results in significantly improved forecasts 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 subsurface temperature data collected by the TOGA?TAO buoys. This is a consequence of the current predominance of TAO data in the tropical Pacific in recent years. Results suggest that in the presence of erroneous wind forcing and inadequate physical parameterizations in the ocean model ocean data assimilation can improve ocean initialization and thus can improve the skill of the forecasts. However, the need for assimilation can create imbalances between the mean states of the oceanic initial conditions and the coupled model. These imbalances and errors in the coupled model can be significant limiting factors to forecast skill, especially for forecasts initiated in the northern winter. These limiting factors cannot be avoided by using data assimilation and must be corrected by improving the models and the forcing fields.