Neural network forecasts of the tropical Pacific sea surface temperatures

Aiming Wu, William W. Hsieh and Benyang Tang

A neural network (NN) model had been used by our group to predict the SST anomalies in the Nino3.4 region in the equatorial Pacific (Tang et al, 2000). A new NN model has been built to forecast the SST anomalies over the entire tropical Pacific. Click here to see details about this model.

Using sea level pressure and sea surface temperature data up to the end of June, 2004, forecasts were made with the NN model. Ensemble-averaged forecasts for the sea surface temperature anomalies (SSTA) in the Nino3.4 region at various lead times are shown in Fig.1, showing near-normal conditions in 2004 to early 2005. The forecasted SSTA fields over the tropical Pacific are shown in Fig.2.

Figure 1. The SSTA (in degree Celsius) in the Nino3.4 area (170W-120W,5S-5N) predicted by the ensemble-averaged nonlinear model at 3, 6, 9 and 12 months of lead time (circles), with observations denoted by the solid line. Tick marks along the abscissa indicate the January of the given years. (The postscript file of Fig.1 is also available).

Figure 2. SSTA (in ºC) predicted by the ensemble-averaged nonlinear model at 3, 6, 9 and 12 months of lead time, corresponding to the four consecutive seasons starting with ASO (August - October, 2004). The zero contour is shown as a white curve. (The postscript file of Fig.2 is also available).
The values of the Nino3.4 SSTA are 0.224, 0.224, 0.189 and 0.180 ºC at 3, 6, 9 and 12 months lead time, respectively.

Tang, B., W.W. Hsieh, A.H. Monahan and F.T. Tangang, 2000. Skill comparisons between neural networks and canonical correlation analysis in predicting the equatorial Pacific sea surface temperatures. J.Climate, 13: 287-293.

Back to [UBC Climate Prediction Group Home Page]