Application of the El 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 Fort Collins,Colorado
2NOAA/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 (1),(2), 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 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 (SST's 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.
Employing the chosen predictors in the ENSO-CLIPER model on a 1 September 1999 initialization date yields forecasts for Sept-Oct-Nov 1999 (zero season lead) out through Jun-Jul-Aug 2001 (seven season lead). Results for just the Niño 3.4 region SST and the SOI are shown in Fig. 1. These forecasts indicate that the moderate La Niña conditions that were experienced in the summer or 1999 will become stronger, peaking in the winter of 1999-2000. These cold conditions will give way to warm conditions in the summer of 2000, with a distinct transition occurring in the spring 2000 time period. Peak warming is forecast to occur in the early part of the winter of 2000-2001. To summarize, short leads are indicating that the current La Niña conditions will intensify this winter followed by a moderate El Niño which will develop in the late spring of 2000 and will peak in the winter of 2000-2001. Short-term (lead 0 and 1) forecasts are based upon persistence of existing conditions and the trends of the SOI and Niño 3, whereas, long-term (leads 5-7) forecasts are based upon the same factors but with opposite sign. These forecasts are consistent with those made in March and June 1999.
ENSO-CLIPER predictions made over the last several seasons have verified reasonable well (Table 1, and past ELLFB issues). 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 that started in Jun-Jul-Aug 1998 was suggested to happen consistently back to early June 1997 forecasts, though its magnitude was also not correctly predicted. Lead 0 and Lead 1 forecasts continue to perform very well. In the last season or two ENSO-CLIPER has handled the development and maintenance of the La Niña conditions very well. 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 present ENSO forecast schemes (Barnston et al. 1999).
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 lead author is being supported by NOAA under contract NA67RJ0152 and is employed with the Regional Mesoscale Meteorology Team at CIRA. 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.
Barnston, A. G., M. H. Glantz, and Y. He, 1999: Predictive skill of statistical and dynamical climate models in SST forecasts during the1997-98 El Niño episode and the 1998 La Niña onset. Bull. Amer. Meteor. Soc., 80, 217-243.
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.
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 1999. Forecasts are valid for Sept-Nov. (SON) 1999, Dec-Feb. (DJF) 1999-2000, Mar-May (MAM) 2000, Jun-Aug (JJA) 2000, SON 2000, DJF 2000 - 2001 and MAM 2001, JJA 2001. 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: Recent history of ENSO-CLIPER forecasts and corresponding observations for the Niño 3.4 SST region (in degree C).
| Target Period | Forecast Made | ||||||
| 1 Mar1998 | 1June1998 | 1Sept 1998 | 1 Dec 1998 | 1 Mar1999 | 1June1999 | Observed Anomaly | |
| MAM 98 | 1.21 | -- | -- | -- | -- | -- | 1.20 |
| JJA 98 | -0.03 | -0.16 | -- | -- | -- | -- | -1.06 |
| SON 98 | -0.06 | -1.09 | -1.06 | -- | -- | -- | -1.16 |
| DJF 98 | -0.05 | -0.84 | -1.81 | -1.02 | -- | -- | -1.55 |
| MAM 99 | -0.65 | -0.32 | -1.59 | -0.28 | -0.82 | -- | -0.71 |
| JJA 99 | -0.93 | -0.13 | -0.58 | 0.02 | -0.53 | -0.36 | -0.81 |
| SON 99 | -1.54 | -0.29 | 0.13 | 0.20 | -0.80 | -0.74 | -- |
1. Study of the mean physical state of the atmosphere together with its statistical variations in both space and time as reflected in the weather behavior over a period of many years. (WMO 1992).
2. The specific study of Climate. In addition to the presentation of climatic data, it includes the analysis of the causes of differences of climate, and the application of climatic data to the solution of specific design or operational problems. (Huschke, 1959)
