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Consolidated Forecasts of Tropical Pacific SST in Niño 3.4
Using Two Dynamical Models and Two Statistical Models
contributed by David Unger, Anthony Barnston, Huug van den Dool and Vern Kousky
Climate Prediction Center, NOAA, Camp Springs, Maryland
In this Bulletin we find a fairly large number of forecasts for the east-central tropical Pacific SST
for the coming year. Our objective here is to synthesize information from some of the predictive
sources into a single objective estimate of the likely evolution of the SST's in the tropical Pacific.
One approach to the problem is to combine, or consolidate, the forecasts of several models into a
single forecast. This could be done on the basis of the past behavior of each contributing model,
as well as the overlap of information among the models. There are several methods by which this
can be done. A common method, and the one used here, is linear multiple regression. In effect, a
statistical scheme is used to combine outputs of entire models whose natures themselves may be
statistical, dynamical, or a mixture of the two. In this case we use four input models. Two are
dynamical: the Lamont-Doherty (LD) Earth Observatory's simple coupled model, (the improved
LDEO2; Chen et al. 1995; Cane and Zebiak 1986), and the recently implemented CMP12 NCEP
coupled model (Ji et al. 1994). The other two models are statistical: the NCEP constructed
analogue (CA) model (Van den Dool 1994; Van den Dool and Barnston 1995), and the NCEP
canonical correlation analysis (CCA) model (Barnston 1994). The individual forecasts of each
model are shown elsewhere in this Bulletin issue.
To derive the multiple regression equations for each target season for each lead time, histories of
the forecasts of each model were obtained. The CCA and CA models have histories covering
1956-1996. The Lamont coupled model has a 1972-96 history, and the NCEP coupled model
1981-96. To circumvent the problem of the differing units and climatologies used, all forecasts
were converted to actual C forecasts. The observations were expressed likewise. The regressions
are based on forecasts for the Niño 3.4 region (5N-5S, 120-170W), except for the LD model,
from which we receive forecasts for the Niño 3 region. The Niño 3 forecast histories from the LD
model were used as a predictor for Niño 3.4 in the equation development. The regression
coefficients compensate for the slight differences between Niño 3 and Niño 3.4 to obtain the least
squares fit for Niño 3.4. We expect to begin receiving gridded forecast fields from Lamont
shortly, and will then be able to use Lamont's Niño 3.4 forecasts directly.
The desired lead times of the consolidated forecasts range from 0.5 months to 12.5 months by 1
month increments, where lead time is defined as the time skipped between the time of the forecast
and the beginning of the forecasted (target) period. For example, the forecasts shown here, which
are issued in the middle of June 1997, have target periods including Jul-Aug-Sep 1997,
Aug-Sep-Oct 1997,..., Jul-Aug-Sep 1998. Three of the four individual models have forecast
histories whose leads range to 12.5 months or greater, while one (the NCEP coupled model) has a
maximum lead of only 8.5 months. Consolidated forecasts for lead times higher than 8.5 months,
therefore, are based only on the other three models. A temporal smoother has been applied to the
forecasts to avoid any discontinuity in the forecast time series, however, a noticeable increase in
the error estimates occurs when the NCEP model drops from the consolidation.
Because the NCEP coupled model forecast only has a 1981-96 history, the training period for the
regression is limited to that period and thus results in greater uncertainty in the coefficients than
would be the case if a longer history could be used. When that model is not included in the
consolidation process for the longer lead times, the 1972-96 period is used to derive the
regression equations, making for a more favorable training sample. Data from three lead times
were pooled together to help equation stability and help smooth forecasts from projection to
projection. Predictor and predictand data from the season preceding and following the target
season were combined to form the regression equation. The first (last) target season shares the
equation with the adjacent season.
The consolidated forecast for Niño 3.4 made in mid-June 1997 is shown in Fig. 1. Forecasts are
expressed as standardized anomalies relative to the 1961-1990 climatology. The box and whisker
intervals for the forecasts indicate the one and two error standard deviations, based on estimated
skill following shrinkage of the dependent sample skill results in accordance with the sample size
and number of predictors. The compo-nent forecasts are displayed on the same chart for
comparison. Note that the LD anomalies are for Niño 3. The observed SST anomaly for the
most recent 3-mo period is also shown.
Three of the four models show temperatures substantially warmer than normal through mid-1998,
while one model (LD) indicates colder than normal temperatures. The consolidation forecast
clearly discounts the LD prediction, with the LD solution well below the -2 standard error
"whisker" on all but the last two projections. The consolidation forecast indicates highest
anomalies will occur late in the year and will peak at 1.54 standard deviations above normal,
which is about 1.4 C higher than normal.
Temperatures are forecast to moderate early in 1998, although temperatures remain more than
one standard deviation above normal through MJJ 1998. The increase in standardized anomalies
from FMA to AMJ 1998 is due in part to the decreasing standard deviation of the Niño 3.4 SST
in that time of the year. The actual anomaly is around .65 C and increases only slightly in that
time period. The forecast certainty is substantially lower past AMJ 1998; however, CCA and CA
begin to moderate in the later projections, so a return to more normal conditions appears likely for
mid-1998.
Acknowledgments: We are grateful to Stephen Zebiak and Mark Cane of Lamont Doherty Earth
Observatory, and Ming Ji and Ants Leetmaa from the National Centers for Environmental
Prediction, for providing the forecast histories from their respective dynamical models, as well
as their current real-time forecasts.
Barnston, A.G., 1994: Linear statistical short-term climate predictive skill in the Northern
Hemisphere. J. Climate, 5, 1514-1564.
Cane, M., S.E. Zebiak and S.C. Dolan, 1986: Experimental forecasts of El Niño. Nature, 321,
827-832.
Chen, D., S.E. Zebiak, A.J. Busalacchi and M.A. Cane, 1995: An improved procedure for El Niño
forecasting: Implications for predictability. Science, 269, 1699-1702.
Ji, M., A. Kumar and A. Leetmaa, 1994: An experimental coupled forecast system at the
National Meteorological Center: Some early results. Tellus, 46A, 398-418.
van den Dool, H.M., 1994: Searching for analogues, how long must we wait? Tellus, 46A,
314-324.
van den Dool, H.M. and A.G. Barnston, 1995: Forecasts of global sea surface temperature out to
a year using the constructed analogue method. Proceedings of the 19th Annual Climate
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Fig. 1. Consolidated forecast (thick line) for the standardized anomaly of the SST in the Niño
3.4 region (5N-5S, 120-170W) for the next 13-running 3-month periods. Month labels on the
abscissa denote the middle months of the 3-month predictand period. Box and whiskers for each
point indicate the one and two error standard deviation intervals. The latest observation
(Mar-Apr-May, 1997) is also shown by the filled ellipse. The prediction from each component
model is shown for comparison.