Harnack, R., Harnack, J. and J. Lanzante, 1986: Seasonal temperature predictions using a jackknife approach with an intraseasonal variability index. Mon. Wea. Rev., 114, 1950-1954.
The prediction of seasonal temperatures in the United States from Pacific sea surface temperatures was examined using a jackknifed regression scheme and a measure of intraseasonal atmospheric circulation variability. Predictions were made using both a one month and a one season lead. Employing jackknifed regression methodology when deriving objective prediction equations allowed forecast skill to be better quantified than in past studies, by greatly increasing the effective independent sample size. The procedures were repeated on three data sets for each season: 1) all years in the period 1950-79 (29 or 30 years); 2) high intraseasonal variability index (VI) years; and 3) low intraseasonal VI years. The VI was constructed to measure the intraseasonal variability of 5-day period mean 700 mb heights for a portion of the Northern Hemisphere. The following results were obtained from the study: 1) for winter and summer, significant models were found, though skill is modest (less than 60% correct for two-class forecasts), but the relationship between intraseasonal variability and skill is not consistent; 2) generally no significant skill was found for spring or fall models; 3) the use of the jackknife procedure for increasing the number of independent tests available for short data sets appears to be a real asset, which may allow more accurate assessment of true skill.