The ENSO-CLIPER model offers a baseline "no-skill" forecast of ENSO variability that is more stringent than persistence based forecasts. ENSO-CLIPER is a linear multiple regression model which utilizes current ENSO values and the month-to-month time change of these current conditions (trend) plus consideration of the annual cycle to produce forecasts of ENSO-linked SST indices and SOI values. From one to four predictors are chosen out of a pool of fourteen potential predictors by a multiple regression technique using the method of leaps and bounds. If no predictors exist for a given time lead, climatological conditions are forecast. After adjustments of the hindcast ability to account for future degradation, significant improvements are shown versus simple forecasts of the persistence of current conditions. The forecast equations have been developed to be applied at the start of each calendar month. Eight forecasts are produced for each predictand, extending from a lead time of zero to seven seasons.
It is hoped that ENSO-CLIPER will either replace or at least supplement the current use of persistence as a measure of skill by which other ENSO models - both statistical and dynamical - can be judged. Using only persistence as an index of skill threshold for ENSO predictive models is overly simplistic. By optimally utilizing available climatology and persistence information as is, we are able to construct a more stringent "no-skill" test for comparison. As recognized in Barnston et al. (1994), true forecast skill cannot be judged until an adequate sample of real-time predictions have been run. To assist with such an analysis, Appendix A provides all of the independent forecasts available since 1 January 1993 and section 5 has compared these independent results versus other available schemes. Additionally, future ENSO-CLIPER will be available monthly at the Web site: http://tropical.atmos.colostate.edu/knaff. The program to run ENSO-CLIPER is also available upon request.
Much has been gained in the science of day-to-day hurricane forecasting through the use of simple models based entirely on climatology and persistence. ENSO-CLIPER-type models have proven invaluable for providing a benchmark in testing tropical cyclone track and intensity forecasting algorithms. While the tropical cyclone track model CLIPER has been improved upon by hybrid statistical-dynamical models and dynamical models, the tropical cyclone intensity model SHIFOR has yet to be surpassed by either hybrid or dynamical models. A similar scenario will likely follow for the field of ENSO forecasting. Although some current statistical and dynamical ENSO models may have difficulty showing improvements over ENSO-CLIPER, truly skillful ENSO models will undoubtedly be developed within the near future. To this end, we encourage verification of all ENSO forecasts using the ENSO-CLIPER forecasts as the "no-skill" threshold instead of persistence.
Acknowledgments
The authors wish to thank Tony Barnston, William Gray, Dennis Mayer and John Sheaffer for valuable comments of an earlier draft of this manuscript. Gerry Bell has provided many helpful discussions on the topic as well. Barb Brumit, Amie Hedstrom, Bill Thorson and Rick Taft have provided excellent technical and computing expertise. The lead author is being supported by NOAA under contract NA37RJ0202 with supplemental support given by NSF under contracts ATM-9417563. The second author was funded through the 1995-96 NOAA Postdoctoral Program in Climate and Global Change.