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 Niño 3.4 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 to 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.

ENSO-CLIPER captures the climatological aspects (Huschke 1959, WMO 1992) of the whole ENSO complex capturing both mean conditions and propagation of those features in time. In essence, this model given initial conditions of ENSO (SSTs in Niño regions 1&2, 3, 3.4, and 4, and the SOI) and the recent past valid at a particular time will fit, using regression techniques, the best evolution from those initial conditions. The method has been frozen following its development (42 years), and yields the mean climatological evolutions for that period (1951-1992 to 1953-1994 depending on lead-time). This procedure is analogous to statistical tropical cyclone track forecasting using a CLIPER approach (see Neumann 1972).

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 estimated 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). The program to run ENSO-CLIPER is also available upon request.

ENSO-CLIPER predictions made over the last several seasons have verified reasonable well (Table 1, and past ELLFB issues). The second peak of the current La Niña in winter of 1999-2000 was first suggested to occur with the 1 March 1999 ENSO-CLIPER forecast (a 3 season lead), with slightly warm conditions incorrectly predicted at longer lead times. The 0 and 1 season lead forecasts in particular have performed very well, being within 0.6 C of the actual anomaly all the way back to those issued in late 1998 with the exception of the DJF 1999-2000 verification. Over the last couple of years the performance of this model has been very competitive with both statistical and numerical ENSO forecast models. This fact suggests that there is little skill associated with the present ENSO forecast schemes (Landsea and Knaff 2000).

Acknowledgments: The authors wish to thank William Gray, Tony Barnston, John Sheaffer, Dave Enfield Dennis Mayer, Barb Brumit, Amie Hedstrom, Bill Thorson and Rick Taft for all their help and comments concerning this work. The second author is being supported by NOAA under contract NA67RJ0152 and is employed with the Regional Mesoscale Meteorology Team at CIRA.

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.

Huschke, R. E., 1959: Glossary of Meteorology. Second Printing, American Meteorological Society, 45 Beacon St., Boston, MA, 02108, 638 pp.

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

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

Landsea, C. W., and J. A. Knaff, 2000: How much skill was there in forecasting the very strong 1997-98 El Niño? Bull. Amer. Meteor. Soc. (in press, September issue).

Neumann, C. J., 1972: An alternative to the HURRAN tropical cyclone model system. NOAA Tech Memo. NWS SR-62. 22 pp.

Shapiro, 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.

WMO, 1992: International Meteorological Vocabulary. Second Edition, Secretariat of the World Meteorological Organization, Geneva, Switzerland, 784pp.

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