Neural-dynamical hybrid coupled model forecasts of the tropical Pacific sea surface temperatures

Youmin Tang and William Hsieh

The forecast model is the neural-dynamical hybrid coupled model used in our previous forecasts (Tang and Hsieh 2001a), but the NCEP sea level height anomaly (SLHA) data set is used (instead of the upper ocean heat content data) to initialize the forecast, simply because the NCEP SLHA is more quickly updated. The assimilation of the NCEP SLHA yields as great an improvement in the forecast correlation skills as the assimilation of heat content anomalies (Tang and Hsieh 2001b). Fig. 1 shows the correlation skills of the predicted SST anomalies (SSTA) in the NINO3 region in the equatorial eastern Pacific during 1990-1999 using our model with SLHA assimilation. The predictions were made at three months intervals (starting on 1 January, 1 April, 1 July and 1 October) and continued until a lead time of 15 months.

Fig.1 Correlation skills of the predicted NINO3 SSTA

Fig. 2 shows the prediction of the NINO3 SSTA starting from 1995 to 2001 at lead times of 6 months and 12 months.

Fig.2 Observed and predicted NINO3 SSTA at lead times of 6 and 12 months

Fig 3 shows our latest forecasts (initialized using data till the end of October, 2001), indicating that the moderate cool anomalies present during spring, 2002, will further intensify towards La Nina conditions by summer, 2002, and continuing onto fall and winter.

Fig.3 Predicted SSTA of the tropical Pacific

Contour intervals are 0.5 degree Celsius. Positive anomalies above 1 degree are shaded in red, and negative anomalies below -1 degree are in blue. The zero contour is in purple.


  • Tang, Y. and W.W. Hsieh, 2001a: Neural-dynamical hybrid coupled model forecasts of the tropical Pacific sea surface temperatures. Experimental Long-Lead Forecast Bulletin, March and June 2001.

  • Tang, Y. and W. W. Hsieh, 2001b: Impact of data assimilation on ENSO simulations and predictions in a hybrid coupled model framework. J. of Climate (submitted). [ papers.html]

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