A Study of the Predictability of Tropical Pacific SST in a Coupled Atmosphere–Ocean Model Using Singular Vector Analysis: The Role of the Annual Cycle and the ENSO Cycle*

Abstract

The authors examine the sensitivity of the Battisti coupled atmosphere-ocean model-considered as a forecast model for the El Niño-Southern Oscillation (ENSO)-to perturbations in the sea surface temperature (SST) field applied at the beginning of a model integration. 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. Perturbation growth about the following four reference trajectories is considered: (i) the annual cycle, (ii) a freely evolving model ENSO cycle with an annual cycle in the basic state, (iii) the annual mean basic state, and (iv) a freely evolving model ENSO cycle with an annual mean basic state. Singular vectors with optimal growth over periods of 3, 6, and 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 over which perturbations are allowed to evolve. 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 spanning the entire tropical Pacific basin superimposed on a north-south dipole in the eastern tropical Pacific. A simple physical interpretation for the optimal pattern is provided. In most cases investigated, there is only one structure that exhibits growth.Maximum perturbation growth takes place for integrations that include the period June-August, and the minimum growth for integrations that 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 variation in the amplitude of the optimal perturbation growth are identified. The implications of these results for the predictability of short-term climate in the tropical Pacific are discussed.

ENSO ensemble prediction and predictability for the past 148 years from 1856--2003.

“…2.1a and Fig. 2.1b are similar to that in the SV1 and FP of the Battisti coupled atmosphere-ocean model (Chen et al 1997) and an older version of the ZC model (Xue et al 1997a). …”

“…Xue (1997a, b) constructed a forward tangent linear model for the ZC model using a Markov model and multi-variable EOF method performed on the reduced model physical space. Their SV spatial distribution was similar to that in Chen et al (1997). Fan et al (2000), using a different intermediate complexity coupled model, found that optimal error growth depends critically on the seasonal cycle and ENSO phase as well as the lead time of prediction.…”

“…The first kind of strategy was often used by SV analysis (e.g., Lorenz 1965;Chen et al 1997;Xue et al 1997a, b;Battisti 1988;Fan et al 2000;Cai et al 2003) whereas the latter was performed in the framework of the stochastic optimal theory (e.g., Kleeman and Moore 1997, Moore and Kleeman 1998Tang et al 2005). Significant progress has been made in using these optimal perturbations to study ENSO predictability as cited above.…”

“…In recent years, a number of models have been used to explore optimal perturbation growth of ENSO predictions using SV analysis. Chen et al (1997) used the Battisti (1988) version of ZebiakCane (ZC) model to calculate the SV and found that the optimal perturbation pattern consists of an east-west dipole in the entire tropical Pacific basin superimposed on a north-south dipole in the eastern tropical Pacific. Xue et al (1997a, b) constructed a tangent linear model in an EOFreduced space for the ZC model via the Markov method.…”

“…ii) The singular vectors derived from the TLM were compared with the SVs in Chen et al (1997) and Xue et al (1997a For the whole model domain, the amplitude of perturbation growth is defined as below,…”