Using the methods previously described in issues of the Experimental Long-Lead Forecast Bulletin, in Penland and Magorian (1993), and in Penland and Matrosova (1997), the pattern of IndoPacific sea-surface temperature anomalies (SSTAs; Fig. 1), as well as SSTA in the Niño 3 region (6^oN-6^oS, 90 -150 ^oW; Fig. 2a), the SSTA in the Niño 4 region (6^oN-6^oS, 150^oW-160^oE; Fig. 2b), the tropical North Atlantic (Figs. 3 and 4), and the Caribbean (Figs. 3 and 5) are predicted. A prediction at lead time τ is made by applying a statistically-obtained Green function G(τ) to an observed initial condition consisting of SSTAs in an appropriate domain. Although the parameters of the model are obtained statistically, the dynamical assumption of stable linearity implicit in the method (an assumption that in the case of tropical SSTs is largely corroborated by data) requires a fixed-point attractor in phase space. The technique, therefore, cannot be considered a purely statistical prediction method (Penland 1989; Penland and Sardeshmukh 1995). Data have been provided by NCEP, courtesy of R.W. Reynolds. Two sets of predictor/predictands are used, one for the IndoPacific and one for the tropical Atlantic. In both cases, three-month running means of the temperature anomalies are used, the seasonal cycle has been removed, and the data have been projected onto the 20 leading empirical orthogonal functions (EOFs).

The prediction of IndoPacific SSTAs uses tropical SSTAs in the region 30^oN-30^oS, 30^oE-70^oW as predic-tors. The COADS 1950-79 climatological annual cycle has been removed, and the leading 20 EOFs explain about 70% of the variance. The Niño 3 region has an RMS temperature anomaly of about 0.7^oC; the inverse modeling prediction method has an RMS error of about 0.5^oC at a lead time of nine months and approaches the RMS value at lead times of 18 months to two years. The predicted IndoPacific SSTA patterns based on the JFM 1998 initial condition for the following AMJ, JAS, OND, and JFM are shown in Fig. 1 (Contour interval = 0.2C). Fig. 2a shows the predictions (light solid lines) of the Niño 3 anomaly for initial conditions SON, OND, NDJ, DJF, and JFM 1997-98. Light dotted lines indicate the standard deviation expected error for the prediction assuming a perfect model based on SON 1997. Fig. 2b is the same, but for the Niño 4 region. Verfications including the truncation (heavy dotted line) and omitting the truncation error (heavy solid line) are also shown. Fig. 2a shows predictions of a premature decay of the Niño 3 anomaly, which is typical for predictions initialized during the warmest phase of the warm event. The early evolution of the Niño 4 forecast (Fig. 2b), however, agree with the observations. As more of the SST anomaly variance is explained by the retained 20EOFs than has in previous forecasts, we expect these predictions of SST anomaly in both regions to be improved over our previous forecast.

The prediction of tropical Atlantic SSTAs is confined to the north tropical Atlantic (NTA) and Caribbean (Car) sectors (Fig. 3) since persistence is a remarkably good predictor of SSTA in the equatorial and south tropical Atlantic (Penland and Matrosova 1998). The added predictability in the northern tropical Atlantic is primarily due to the effect of El Niño, so SSTAs in the global tropical strip (30^oN-30^oS) are used as predictors. Forecast skill is indicated in the March 1997 issue of this Bulletin. The COADS annual cycle has been removed from three-month running means of SSTs and anomalies were projected onto the leading 20 EOFs containing about 67% of the variance. The predictions are characterized by an upturn of SSTA in those regions (Figs. 4 and 5), possibly due to the influence of the current warm event in the Pacific.

Penland, C., 1989: Random forcing and forecast-ing using Principal Oscillation Pattern Analysis. Mon. Wea. Rev., 117, 2165-2185.

Penland, C. and T. Magorian, 1993: Prediction of Niño 3 sea-surface temperatures using linear inverse- modeling. J. Climate, 6, 1067-1076.

Penland, C. And P.D. Sardeshmukh, 1995: The optimal growth of tropical sea surface temperature anomalies. J. Climate, 8, 1999-2024.

Penland, C. and L. Matrosova, 1998: Prediction of tropical Atlantic sea surface temperatures using Linear Inverse Modeling. J. Climate, 11, 483-496.

Fig. 1: Forecasts of IndoPacific SST anomalies projected onto 20 leading EOFs, based on JFM 1998 initial conditions. Anomalies were calculated relative to the 1950-1979 COADS climatology. SST data were provided by NCEP, courtesy of R.W. Reynolds, and summarized onto COADS-compatible monthly statistics at CDC^. The contour interval is 0.2C. Positive anomalies are represented by heavy solid lines, negative anomalies by dashed lines.

Fig. 2: a) Predictions (light solid lines) of Niño 3 anomaly for initial conditions SON, OND, NDJ, DJF, and JFM 1997-98. Light dotted lines indicate the 1 standard deviation expected error for the prediction assuming a perfect model based on SON 1997. Verifications including the truncation error (heavy dotted line) and omitting the truncation error (heavy solid line) are also shown. b) As in a), but for the Niño 4 region.

Fig. 3: Map showing the North Tropical Atlantic (NTA) and Caribbean (CAR) regions, within which the average SSTA is predicted.

Fig. 4: Time series of linear inverse modeling (LIM) predictions (light solid line) of NTA SSTA for lead times of 3, 6, 9 and 12 months. Also shown are the verification series (heavy solid line) and the one standard deviation confidence interval appropriate to the LIM forecast (dotted lines). 

Fig. 5: As in Fig. 4, but for CAR SSTA.