2006: Predictability loss in an intermediate ENSO model due to initial error and atmospheric noise

The seasonal and interannual predictability of ENSO variability in a version of the Zebiak–Cane coupled model is examined in a perturbation experiment. Instead of assuming that the model is “perfect, ” it is assumed that a set of optimal initial conditions exists for the model. These states, obtained through a nonlinear minimization of the misfit between model trajectories and the observations, initiate model fore-casts that correlate well with the observations. Realistic estimates of the observational error magnitudes and covariance structures of sea surface temperatures, zonal wind stress, and thermocline depth are used to generate ensembles of perturbations around these optimal initial states, and the error growth is examined. The error growth in response to subseasonal stochastic wind forcing is presented for comparison. In general, from 1975 to 2002, the large-scale uncertainty in initial conditions leads to larger error growth than continuous stochastic forcing of the zonal wind stress fields. Forecast ensemble spread is shown to depend most on the calendar month at the end of the forecast rather than the initialization month, with the seasons of greatest spread corresponding to the seasons of greatest anomaly variance. It is also demon-strated that during years with negative (and rapidly decaying) Niño-3 SST anomalies (such as the time period following an El Niño event), there is a suppression of error growth. In years with large warm ENSO events, the ensemble spread is no larger than in moderately warm years. As a result, periods with high ENSO variance have greater potential prediction utility. In the realistic range of observational error, the ensemble spread has more sensitivity to the initial error in the thermocline depth than to the sea surface temperature or wind stress errors. The thermocline depth uncertainty is the principal reason why initial condition uncertainties are more important than wind noise for ensemble spread. 1