Abstract
The prediction of winter in the United States from Pacific sea surface temperatures was examined using a jackknifed regression scheme and a measure of intraseasonal atmospheric circulation variability. Employing a jackknifed regression methodology when deriving objective prediction equations allowed forecast to be better quantified than in past studies by greatly increasing the effective independent sample size. The procedures were repeated on three datasets: 1) all winters in the period 1950–79 (30 winters), 2) the 15 winters having the highest Variability Index (VI), and 3) the 15 winters having the lowest VI. The Variability Index was constructed to measure the intraseasonal variability of five-day period mean 700 mb heights for a portion of the Northern Hemisphere. Verification results showed that statistically significant skill was achieved in the complete sample (overall mean percent correct of 39 and 59 for three- and two-category forecasts respectively), but improved somewhat for the low VI sample. In that case, corresponding scores were (34 and 64 percent correct. In contrast, the high VI sample scores were lower (34 and 58 percent correct) than for the complete sample, indicating that skill is likely dependent on the degree of interaseasonal circulation variability.
