John A. Knaff and
Christopher W. Landsea
Department of Atmospheric Science
Colorado State University
Fort Collins, Colorado 80523
NOAA Climate and Global Change Fellow
NOAA/Hurricane Research Division
Christopher W. Landsea
AMS Copyright Notice
© Copyright 1997 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be "fair use" under Section 107 or that satisfies the conditions specified in Section 108 of the U.S. Copyright Law (17 USC, as revised by P.L. 94-553) does not require the Society's permission. Republication, systematic reproduction, posting in electronic form on servers, or other uses of this material, except as exempted by the above statements, requires written permission or license from the AMS. Additional details are provided in the AMS Copyright Policies, available from the AMS at 617-227-2425 or firstname.lastname@example.org. Permission to place a copy of this work on this server has been provided by the AMS. The AMS does not guarantee that the copy provided here is an accurate copy of the published work.
A statistical prediction method is developed for the El Niño-Southern Oscillation (ENSO) phenomena which is based entirely on the optimal combination of persistence, month-to-month trend of initial conditions and climatology. The selection of predictors is by design intended to avoid any pretense of predictive ability based on "model physics" and the like, but rather is to specify the optimal "no-skill" forecast as a baseline comparison for more sophisticated forecast methods. Multiple least squares regression using the method of leaps and bounds 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 (18 - 20 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 21 months) lead times. For example, expected maximum forecast ability for the Niño 3.4° SST region, depending on the initial date, reaches 92, 85, 64, 41, 36, 24, 24 and 28 percent of variance for leads of zero to seven seasons. Comparable maxima of persistence only forecasts explain 92, 77, 50, 17, 6, 14, 21 and 17 percent, respectively. More sophisticated statistical and dynamical forecasting models are encouraged to utilize this ENSO-CLIPER model in place of persistence when assessing whether they have achieved forecasting skill; to this end, real-time results for this model are made available via a Web site.