GFDL - Geophysical Fluid Dynamics Laboratory

Publication 8601

Harnack, R., Harnack, J. and J. Lanzante, 1986: Seasonal temperature
predictions using a jackknife approach with an intraseasonal variability
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
a 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.