Application of the Niño-Southern Oscillation CLImatology and PERsistence (CLIPER) Forecasting Scheme

contributed by John A. Knaff¹ and Christopher W. Landsea²

¹Cooperative Institute for Research in the Atmosphere, Colorado State University, Ft. Collins, CO ²NOAA/AOML/Hurricane Research Division, Miami, FL

To provide a baseline of skill in seasonal ENSO forecasting, a multiple regression has been used to take best advantage of CLImatology, PERsistence and trend of initial conditions - the ENSO CLIPER (Knaff and Landsea, 1997). This replaces simple persistence as a skill threshold. "Skill" is then redefined as the ability to outforecast the ENSO-ClIPER - a more difficult task.

This statistical prediction method is based entirely on the optimal combination of persistence, month-to-month trend of initial conditions and climatology. Multiple least squares regression is employed to test a total of fourteen possible predictors for the selection of the best predictors, based upon 1950-1994 developmental data. A range of zero to four predictors were chosen in developing twelve separate regression models, developed separately for each initial calendar month. The predictands to be forecast include the Southern Oscillation (pressure) Index (SOI) and the Niño 1+2, Niño 3, Niño 4 and Niño3.4 SST indices for the equatorial eastern and central Pacific at lead times ranging from zero seasons (0-2 months) through seven seasons (21-23 months). Though hindcast ability is strongly seasonally dependent, substantial improvement is achieved over simple persistence wherein largest gains occur for two to seven season (6-23 months) lead times. The ENSO-CLIPER model, thus, not only offers a baseline "no-skill" forecast of ENSO variability, but a practical forecast based upon the CLIPER premise.

The regression design called leaps and bounds (IMSL, 1987) is used to develop optimal models (the best subsets of a prescribed number of predictors). Predictors include 1, 3 or 5 month averages of initial predictor anomalies as well as their recent trends. Predictors are the predictands themselves at earlier times. Some limits on predictor selection were imposed to reduce overfitting (Aczel, 1989). Skills are degraded from dependent sample results to reflect estimate independent forecast skill following Davis (1979) and Shapiro (1984). Final skill estimates reflect levels comparable to those of more sophisticated statistical and dynamical models. More details about the ENSO-CLIPER model, including its skill and its predictor selection rules, are given in the June 1997 issue of this Bulletin (p. 55).

Employing the chosen predictors in the ENSO-CLIPER model on a 1 Septemper 1998 initialization data yields forecasts for Sep-Oct-Non 1998 (zero season lead) out through June-Jul-Aug 2000 (seven season lead). Results for the Niño 3.4 region SST and the SOI are shown in Fig. 1. These forecasts indicate that a moderate to strong La Niña may peak over the next several months (-1.79C Niño 3.4 in Sep-Oct-Nov 1998 and +2/36SOI in Dec-Jan-Feb 1998-99). For even longer lead times, near average to slightly warm ENSO conditions are forecasted from Jun-Jul-Aug 1999 through Jun-Jul-Aug 2000. For the short leads, where a moderate to strong La Niña is forecasted, there are about equal contributions from the initial (Jun-Jul-Aug 1998) conditions as well as the trend in the anomalies. The near average to slightly warm values predicted at the long leads are due to a weakly negative weighting of current La Niña conditions.

ENSO-CLIPER predictions made over the last several seasons have verified reasonably well (Table 1). While the strong El Niño event during Sep-Oct-Nov 1997 and Dec-Jan-Feb 1997-98 was predicted several months in advance, the magnitude was underestimated except at the zero season lead. Additionally, the cooling begun in Jun-Jul-Aug 1998 was suggested consistently since early June 1997 forecasts, though its magnitude was also not correctly predicted.

Acknowledgments: The authors wish to thank William Gray, Tony Barnston, John Sheaffer, Dave Enfield, Dennis Mayer, Barb Brumit, Amie Hedstrom, Bill Thorson and Rick and Rick Taft for all their help and comments concerning this work. The lead author is bing supported by NOAA under contract NA37RJO202 (William Gram, PI) with supplemental support given by NSF under contracts ATM-9517563 (William Gray, PI). The second author was funded through the 1995-96 NOAA Postdoctoral Program in Climate and Global Change.

References:

Aczel, A. D., 1989: Complete Business Statistics., Richard D. Irwin, Inc., 1056 pp.

Davis, R. E., 1979: A search for short range climate productivity. Dyn. Atmos. Oceans,3, 485-497.

IMSL, 1987: FORTRAN subroutines for statistical analysis. International Mathematical & Statistical FORTRAN Library, 1232 pp.

Knaff, J. A. and C. W. Landsea, 1997: A El Niño-Southern Oscillation CLImatology and PERsistence (CLIPER) Forecasting Scheme. Wea. Forecasting, 12, 633-652.

Shaprio, L. J., 1984: Sampling errors in statistical models of tropical cyclone motion: A comparison of predictor screening and EOF techniques. Mon. Wea. Rev., 112, 1378-1388.

Figure 1: Forecast of Niño 3.4 and SOI using data available through 1 September 1998. Forecasts are valid for Sept-Nov (SON)1998, Dec-Feb. (DJF) 1998-99, Mar-May (MAM)1999, Jun-Aug (JJA)1999, SON 1999, DJF 1999-2000, MAM 2000 and JJA 2000. Actual numerical forecast values for these times are shown on each plot along with estimated RMSE bars. These anomalies are based upon a 1950-1979 mean.

Table 1: Resent history of ENSO-Clipper forecasts and corresponding observations for the Niño 3.4 SST region (in degree C).