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

contributed by Christopher W. Landsea¹ and John A. Knaff²

¹NOAA/AOML/Hurricane Research Division, Miami, Florida

²Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

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 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). Note that the forecasts are now based upon a 1961-1990 long-term mean. The program to run ENSO-CLIPER is also available at:

http://www.aoml.noaa.gov/hrd/Landsea/ensocliper/readme.html

Employing the chosen predictors in the ENSO-CLIPER model on a 1 March 2001 initialization date yields forecasts for March to May 2001 (0 season lead) out through December 2002 to February 2003 (7 season lead). Results for just the Niño 3.4 region SST and the SOI are shown in Fig. 1. These forecasts indicate that the current La Niña will weaken and that near neutral conditions may prevail for the next several seasons. The 0 lead forecast of Niño 3.4 is primarily based upon persistence of existing conditions and secondarily upon the trend of Niño 4. Leads 1through 3 are based upon trends of both Niño 4 and Niño 1+2. At the lead 4, the prediction is based upon persistence of Niño 3 oppositely signed and the trend of Niño 3.4. The long-term (leads 5-7) forecasts rely upon persistence of Niño 3 oppositely signed and the initial conditions and trend of the SOI.

ENSO-CLIPER predictions made over the last several seasons have verified reasonably well (Table 1, and past ELLFB issues). The third peak of the current La Niña in the winter of 2000-01 was first suggested to occur with the 1 Sep. 2000 ENSO-CLIPER forecast (a 1 season lead). Moderate El Niño conditions were incorrectly predicted for the winter of 2000-01 at the long lead times (4 and 5 season leads), though the performance of ENSO-CLIPER beyond a one year lead time is quite limited.

Over the last few years the performance of this model has been very competitive with both statistical and numerical ENSO forecast models.

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, 2000a: How much skill was there in forecasting the very strong 1997-98 El Niño? Bull. Amer. Meteor. Soc. 81, 2107-2119.

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.

Figure 1: Forecast of Niño 3.4 and SOI using data available through 1 March 200. Forecasts are valid for Mar.-May (MAM) 2001, Jun.-Aug. (JJA) 2001, Sep.-Nov. (SON) 2001, Dec.-Feb. (DJF) 2001-02, MAM 2002, JJA 2002, SON 2002, and DJF 2002-3. Actual numerical forecast values for these times are shown on each plot along with estimated RMSE bars. These anomalies are based upon a 1961-1990 mean.

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