Abstract
Seasonal precipitation is specified for the United States by matching various area-averaged precipitation statistics as predictands with three different predictors in turn: 700 mb heights, North Pacific SST and North Atlantic SST. Predictors are in the form of empirical orthogonal function (EOF) amplitude time series. The predictands used in trials include total precipitation and precipitation frequencies derived using three different critical values: 2.5, 12.7 and 25.4 mm. Screening multiple linear regression is used to relate predictands to predictors for samples ranging from 24 to 35 years in length; initially trials are compared in terms of area-averaged true skill and percent area of local significance. In order to assess specification skill on an independent sample, additional tests are made using a jackknife regression approach.
Results suggest that skillful seasonal precipitation prediction will continue to be very difficult using predictors and methods presently in common use based on the use of specification equations on an independent sample. Generally, area-averaged explained variances are less than 10% and the area of significant local skill is less than 50%. Based on the low level of specification skill, predictive skill for precipitation using specification equations with imprecisely known specifier fields (like 700 mb heights) as input would be effectively zero.
Other conclusions are:
700 mb heights specify seasonal precipitation about equally well in winter, spring and summer, but worse in fall.
Among the three predictor types employed, 700 mb heights are best for all seasons but fall, when Pacific SST does best. Specification using Atlantic SST is poor in all instances and inferior to the use of the other predictor fields.
Overall among the four precipitation statistics used as predictands, the frequency statistics have a slightly better relationship with 700 mb heights or Pacific SST than do precipitation totals.
