Abstract
An experimental ENSO prediction system is presented, based on an ocean general circulation model (GCM) coupled to a statistical atmosphere and the adjoint method of 4D variational data assimilation. The adjoint method is used to initialize the coupled model, and predictions are performed for the period 1980‐99. The coupled model is also initialized using two simpler assimilation techniques: forcing the ocean model with observed sea surface temperature and surface fluxes, and a 3D variational data assimilation (3DVAR) method, similar to that used by the National Centers for Environmental Prediction (NCEP) for operational ENSO prediction. The prediction skill of the coupled model initialized by the three assimilation methods is then analyzed and compared. The effect of the assimilation period used in the adjoint method is studied by using 3-, 6-, and 9-month assimilation periods. Finally, the possibility of assimilating only the anomalies with respect to observed climatology in order to circumvent systematic model biases is examined. It is found that the adjoint method does seem to have the potential for improving over simpler assimilation schemes. The improved skill is mainly at prediction intervals of more than 6 months, where the coupled model dynamics start to influence the model solution. At shorter prediction time intervals, the initialization using the forced ocean model or the 3DVAR may result in a better prediction skill. The assimilation of anomalies did not have a substantial effect on the prediction skill of the coupled model. This seems to indicate that in this model the climatology bias, which is compensated for by the anomaly assimilation, is less significant for the predictive skill than the bias in the model variability, which cannot be eliminated using the anomaly assimilation. Changing the optimization period from 6 to 3 to 9 months showed that the period of 6 months seems to be a near-optimal choice for this model.
Highlights
Much progress has been made during the past decade both in developing a variety of models and theories for El Nino–Southern Oscillation (ENSO) (Neelin et al 1998) and in developing data assimilation methods for the initialization of ENSO predictions (Latif et al 1998)
In this study we investigated an experimental ENSO prediction system based on an ocean general circulation model (GCM) coupled to a statistical atmosphere and the adjoint method of 4D variational data assimilation
A comparison between the skill of the coupled model when initialized with the adjoint method and the skill of the coupled model initialized using a forced ocean model and the 3D variational data assimilation (3DVAR) method shows that the adjoint method does seem to have the potential of improving simpler assimilation schemes
Summary
Much progress has been made during the past decade both in developing a variety of models and theories for El Nino–Southern Oscillation (ENSO) (Neelin et al 1998) and in developing data assimilation methods for the initialization of ENSO predictions (Latif et al 1998). The adjoint method of data assimilation has been used to initialize intermediate ENSO prediction models in the framework of ocean-only initialization (Bonekamp et al 2001; Kleeman et al 1995; Weaver et al 2002) as well as in the framework of a coupled model initialization (Lee et al 2000). In order to estimate the performance of the adjoint assimilation in initializing the coupled model for ENSO prediction, we compare it to a three-dimensional variational data assimilation (3DVAR) method and to a simple nudging technique, all applied to the same model.
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