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

contributed by John A. Knaff and Christopher W. Landsea

Cooperative Institute for Research in the Atmosphere, Colorado State University Fort Collins, Colorado and NOAA/AOML/Hurricane Research Division, Miami, Florida

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 Nino 1+2, Nino 3, Nino 4 and Nino 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, WMO1992) 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 Nino 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.

Employing the chosen predictors in the ENSO-CLIPER model on a 1 September 2001 initialization date yields forecasts for Sep.-Oct.-Nov 2001 (0 season lead) out through Jun.-Jul.-Aug. 2003 (7 season lead). Results for the SSTs in Nino 3.4 and Nino 3 as well as the SOI are shown in Fig 1.1, Fig 1.2, and Fig 1.3. These forecasts indicate that near neutral conditions continue through winter of 2001-2002, with a continued slightly warmer than normal conditions in Nino 4 this winter Also forecast is a cooling in Nino 3 forecast to occur next Spring. All forecasts suggest long-lived neutral condition through to winter with the warmest water near the dateline. All regions show a persistence of near neutral conditions throughout most of the forecast period. The short leads of Nino 3.4 are based primarily upon persistence of trends in Nino 3.4, and trends in the SOI conditions, while forecasts beyond a year lead are inversely related to trends in the Central Pacific SSTs and SOI. ENSO-CLIPER predictions made over the last several seasons have verified reasonably well, for lead times less than a year (Table 1, and past ELLFB issues). The longer leads are strongly biennial and thus have poorly forecast the persistent cool event in 2000.

The 0 and 1 season lead forecasts in particular have performed very well, being within 0.6 Cof 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 forecast 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 Nino-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 Nino? _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 captions:

Figure 1.1, 1.2, and 1.3: Forecast of Nino 3.4, Nino 3 and SOI using data available through 1 September 2001. Forecasts are valid for Sep.-Nov. (SON) 2001, Dec.-Feb. (DJF) 2001-02, Mar.-May (MAM) 2002, Jun.-Aug (JJA) 2002, SON 2002, DJF 2002-03, MAM 2003, and JJA 2003. Forecast for these times are shown on each plot along with estimated RMSE bars. These anomalies are based upon a 1960-90 mean.

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