We examine the sensitivity of the Battisti (1988) coupled atmosphere-ocean model, considered as a forecast model for the El Nino - Southern Oscillation (ENSO), to perturbations in the sea surface temperature (SST) field applied at the time of model integrations. The spatial structures of the fastest growing SST perturbations are determined by singular vector analysis of an approximation to the propagator for the linearized system. Three idealized scenarios are analyzed: perturbation growth about the annual cycle, perturbation growth about a freely evolving model ENSO cycle, and perturbation growth about a freely evolving model ENSO cycle with an annual mean basic state reference trajectories. Singular vectors with optimal growth over periods of 3, 6, 9 months are computed.
The magnitude of maximum perturbation growth is highly dependent on both the phase of the seasonal cycle and the phase of the ENSO cycle at which the perturbation is applied and on the duration that perturbations are allowed to evolved. However, the spatial structure of the optimal perturbation is remarkably insensitive to these factors. The structure of the optimal perturbation consists of an east-west dipole in the entire tropical Pacific basin superimposed on a north-south dipole in the eastern tropical Pacific: a simple physical explanation for the optimal pattern is provided. In most cases investigated, there is only one vector structure which exhibits growth.
The maximum perturbation growth takes place for integrations which include the period June - August, and the minimum growth for integrations which include the period January - April. Maxima in potential growth also occur for forecasts of ENSO onset and decay, while minima occur for forecasts initialized during the beginning of a warm event, after the transition from a warm to a cold event, and continuing through the cold event. The physical processes responsible for the large variability in the amplitude of the optimal perturbation growth are identified. We discuss the implications of our results for the predictability of short-term climate in the tropical Pacific.