Thanks to Rich Pawlowicz for suggesting the use of the bootstrap method and for helpful discussions. Benyang Tang carried out the EEOF analysis of the SLP and SST data. William Hsieh and Adam Monahan reviewed a preliminary version of this paper and contributed many useful comments. This work is supported by grants to William Hsieh from Environment Canada and from the Natural Sciences and Engineering Research Council of Canada.
Allen, D., 1974: The relationship between variable selection and data augmentation and a method for prediction. Technometrics,16, 125–127.
Barnston, A. G., and H. M. van den Dool, 1993: A degeneracy in cross-validation skill in regression-based forecasts. J. Climate,6, 963–977.
——, and Coauthors, 1994: Long-lead seasonal forecasting—Where do we stand? Bull. Amer. Meteor. Soc.,75, 2097–2114.
——, M. H. Glantz, and Y. He, 1999: Predictive skill of statistical and dynamical climate models in SST forecasts during the 1997–98 El Niño episode and the 1998 La Niña onset. Bull. Amer. Meteor. Soc.,80, 217–243.
Beran, R. J., 1997: Diagnosing bootstrap success. Ann. Inst. Stat. Math.,49, 1–24.
Bishop, C. M., 1995: Neural Networks for Pattern Recognition. Clarendon Press, 482 pp.
Cichocki, A., and R. Unbehauen, 1993: Neural Networks for Optimization and Signal Processing. John Wiley and Sons, 526 pp.
Davison, A. C., and D. V. Hinkley, 1997: Bootstrap Methods and their Application. Cambridge University Press, 582 pp.
Efron, B., 1992: Jackknife-after-bootstrap standard errors and influence functions. J. Roy. Stat. Soc.,B54, 83–127.
——, and R. J. Tibshirani, 1993: An Introduction to the Bootstrap. Chapman & Hall, 436 pp.
Enfield, D. B., 1989: El Niño, past and present. Rev. Geophys.,27, 159–187.
Golub, G. H., M. Heath, and G. Wahba, 1979: Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics,21, 215–223.
Goswami, P., and Srividya, 1996: A novel neural network design for long range prediction of rainfall pattern. Curr. Sci.,70, 447–457.
Haber, E., and D. W. Oldenburg, 2000: A GCV based method for nonlinear ill-posed problems. Comput. Geosci.,4, 41–63.
Hastenrath, S., L. Greischar, and J. van Heerden, 1995: Prediction of the summer rainfall over South Africa. J. Climate,8, 1511–1518.
Keppenne, C. L., and M. Ghil, 1992: Adaptive filtering and prediction of the Southern Oscillation Index. J. Geophys. Res.,97, 20 449–20 454.
Knaff, J. A., and C. W. Landsea, 1997: An El Niño–Southern Oscillation climatology and persistence (CLIPER) forecasting scheme. Wea. Forecasting,12, 633–652.
Michaelsen, J., 1987: Cross-validation in statistical climate forecast models. J. Climate Appl. Meteor.,26, 1589–1600.
Penland, C., 1989: Random forcing and forecasting using Principal Oscillation Pattern analysis. Mon. Wea. Rev.,117, 2165–2185.
——, and T. Magorian, 1993: Prediction of Niño 3 sea surface temperatures using linear inverse modeling. J. Climate,6, 1067–1076.
——, L. Matrosova, K. Weickmann, and C. Smith, 1999: Forecast of Tropical SSTs using linear inverse modeling (LIM). Exp. Long-Lead Forecast Bull.,8, 38–41.
Reynolds, R. W., and T. M. Smith, 1994: Improved global sea surface temperature analysis using optimum interpolation. J. Climate,7, 929–948.
Ropelewski, C. F., and M. S. Halpert, 1987: Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation. Mon. Wea. Rev.,115, 1606–1626.
Saunders, A., M. Ghil, and J. D. Neelin, 1999: Forecasts of Niño 3 SST anomalies and SOI based on singular spectrum analysis combined with the maximum entropy method. Exp. Long-Lead Forecast Bull.,8, 39–41.
Smith, T. M., R. W. Reynolds, R. E. Livezey, and D. C. Stokes, 1996:Reconstruction of historical sea surface temperatures using orthogonal functions. J. Climate,9, 1403–1420.
Syu, H., and J. D. Neelin, 1999: Prediction of NINO3 SST anomaly in a hybrid coupled model with a piggy-back data assimilation initialization. Exp. Long-Lead Forecast Bull.,8, 10–12.
Tang, B., W. W. Hsieh, A. H. Monahan, and F. T. Tangang, 2000: Skill comparisons between neural networks and canonical correlation analysis in predicting the equatorial Pacific sea surface temperatures. J. Climate,13, 287–293.
Tangang, F. T., W. W. Hsieh, and B. Tang, 1998a: Forecasting the regional sea surface temperature of the tropical Pacific by neural network models, with wind stress and sea level pressure as predictors. J. Geophys. Res.,103, 7511–7522.
——, B. Tang, A. H. Monahan, and W. W. Hsieh, 1998b: Forecasting ENSO events: A neural network–extended EOF approach. J. Climate,11, 29–41.
Trenberth, K. E., 1997: The definition of El Niño. Bull. Amer. Meteor. Soc.,78, 2771–2777.
Wahba, G., 1990: Spline Models for Observational Data. SIAM, 169 pp.
Ward, M. N., and C. K. Folland, 1991: Prediction of seasonal rainfall in the north Nordeste of Brazil using eigenvectors of sea-surface temperature. Int. J. Climatol.,11, 711–743.
Weare, B. C., and J. S. Nasstrom, 1982: Examples of extended empirical orthogonal function analysis. Mon. Wea. Rev.,110, 481–485.
Woodruff, S. D., R. J. Slutz, R. L. Jenne, and P. M. Steurer, 1987: A comprehensive ocean–atmosphere data set. Bull. Amer. Meteor. Soc.,68, 1239–1250.
Yuval, 2000: Neural network training for prediction of climatological time series, regularized by minimization of the generalized cross-validation function. Mon. Wea. Rev.,128, 1456–1473.
Zebiak, S. E., M. A. Cane, and D. Chen, 1999: Forecast of Tropical Pacific SST using a simple ocean–atmosphere dynamical model. Exp. Long-Lead Forecast Bull.,8, 1–5.