Data assimilation by an intermediate coupled ocean-atmosphere model: Application to the 1997-1998 El Nino

Sea surface temperature, sea level, and pseudo wind stress anomaly data from late 1996 to early 1998 are assimilated into an intermediate coupled model of the Tropical Pacific. Model data consistency is examined. Impact of the assimilation on forecast is evaluated. The ocean component of the coupled model consists of a shallow water model with two baroclinic modes, an Ekman shear layer, and a mixed layer temperature equation. The atmospheric model is a statistical one (based on dominant covariance of historical surface temperature and pseudo wind stress anomaly data). The adjoint method is used to fit the coupled model to 6 months of data by optimally adjusting the initial state and model parameters. A forecast is performed using the end state of an assimilation experiment as initial conditions and using parameters estimated during the assimilation period. Thus the model state during the assimilation and that during the forecast belong to the same model trajectory in different periods. Such an initialization procedure is useful in avoiding initial shock during forecast due to inconsistency of an initial state with the coupled model physics. As a result of optimal adjustments of initial state and parameters, the model is able to reproduce observed interannual variability of sea surface temperature and sea level reasonably well. The averaged residual model data misfits over various 6 month periods are 0.5 degreesC and 5 cm, respectively. The model has a limited skill in reproducing much of the off-equatorial wind anomalies. The residual model data misfit in pseudo wind stress anomaly is larger than 10 m(2) s(-2). Forecasts initialized from the assimilation product are overall more realistic than those simply initialized from wind-forced ocean states. Consistent improvement due to optimal initialization is found for sea surface temperature and sea level anomalies in the central-eastern Pacific and zonal pseudo wind stress anomaly in the central Pacific, both in terms of root-mean-squared deviation from and correlation with the data. The adjustments of parameters in addition to initial state in a coupled context is found to be important to improving the model data consistency during the assimilation and the forecast. In particular, the estimated drag and damping coefficients properly regulate the relative strength of forcing and damping of the ocean state so as to fit the three types of observations during the assimilation (initialization) period, which facilitates the development of a large-amplitude warming event during the forecast. The study demonstrates the utility of oceanic and atmospheric data to estimate initial state and model parameters in a coupled context, which is useful to the evaluation, improvement, and initialization of El Nino-Southern Oscillation forecast models.