Experimental Forecast with the latest Version of the LDEO Model

contributed by Dake Chen, Stephen E. Zebiak, and Mark A. Cane

Lamont-Doherty Earth Observatory of Columbia University, Palisades, New York

For more than a decade, the LDEO model (Cane et al., 1986; Zebiak and Cane, 1987) has played an important role in our understanding and prediction of ENSO. However, the predictive skill of the original Lamont model (LDEO1) is severely limited by its unbalanced initialization scheme, its sole dependence on wind data and its large systematic biases. In the last few years, we have made considerable improvements in model initialization, data assimilation, and bias correction, resulting in LDEO2, LDEO3 and LDEO4 (Chen et al., 1995, 1998, 1999, 2000). Most recently, we have further improved the model by introducing a statistical correction term in the model SST equation. It is now more straightforward to assimilate data for model initialization because of much reduced model-data incompatibility. The new version of the model not only performs better in retrospective forecasting, but also exhibits a more realistic internal variability. Here we present the current forecasts of the latest version of the LDEO model (LDEO5). Figure 1 shows model predicted SST and wind stress anomalies in the tropical Pacific for the next three seasons. These are ensemble averages of the forecasts started from Jun-Jul-Aug conditions, with observed monthly SST, wind and sea level data assimilated. Three sets of forecasts were made in the middle of each month with three different kinds of wind stress data (QuikScat, NCEP and FSU) for initialization. CAC SST and TOPEX sea level data were used all three cases. Thus the ensembles shown in Figure 1 are based on nine individual forecasts. A closer look at the forecast integrations for NINO3 is provided in Figure 2, which shows individual 9 month forecasts beginning from 1-month-apart initial conditions from Aug 2001 to Aug 2003. Note that the FSU winds are from the new objective analysis. The LDEO model is predicting near normal conditions for the next few seasons.

References:

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