Predictability Loss in an Intermediate ENSO Model due to Initial Error and Atmospheric Noise*

Abstract

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 forecasts 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 demonstrated 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.

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

“…4.3. This is in agreement with the result from the LDOE4 model in Karspeck et al (2006), where a sufficient spread could not be obtained until using an unrealistic strong wind forcing, with a standard deviation of 10 m/s. Fig.…”

“…Note that the ensemble construction by two or more SV patterns does not show higher resolution or reliability than that constructed from 97 the SV1 alone (not shown), thus only the SV1-based ensemble is used, so that we perturbed the initial model SST by the SVl_sst pattern. The construction of initial perturbation (Y) can be expressed by (4.4), where random numbers (X) were normalized, and a is a constant value controlling the perturbation magnitude, set to 0.25 here according to Karspeck et al (2006). (4.4) …”

“…A detailed description of the SO can be found in Moore et al (2006), Tang et al (2005), and . Specifically, at each initial time, the perturbation was held constant for a total of 30 days, as a continuous wind perturbation following Karspeck et al (2006), and then a new temporally uncorrelated perturbation was applied. The perturbations were controlled by (4.4) but using S01_wind instead of SVlsst,…”

A study of the impact of parameter optimization on ENSO predictability with an intermediate coupled model

“…4 Dependence of parameter optimization on the parameter inflation factor. Y-axis represents the time averaged ensemble mean RMSE (unit: °C) of prior SST anomaly synoptic-scale atmospheric processes, westerly wind bursts and the Madden-Julian oscillation) of the atmosphere also influences ENSO predictability (e.g., Karspeck et al 2006;Cheng et al 2010), here we mainly focus on the model error. Thus, no stochastic atmospheric forcing (wind) is imposed in ENSO prediction in this study.…”

“…Recently, CDA has been widely applied in ENSO dynamical models, including intermediate coupled models (e.g., Chen et al 1995;Lee et al 2000;Karspeck et al 2006;Karspeck and Anderson 2007) and general circulation models (e.g., Keenlyside et al 2005). In this study, we implement ensemble coupled data assimilation (ECDA, Zhang et al 2005Zhang et al , 2007 into an intermediate ENSO model (Zebiak and Cane 1987).…”