Simulating Climate over Western North America Using Stochastic Weather Generators

Abstract

The climatologies of daily precipitation and of maximum and minimum temperatures over western North America are simulated using stochastic weather generators. Two types of generator, differentiated only by their method of modeling precipitation occurrence, are investigated. A second-order Markov model, in which the probability of the occurrence of precipitation is modeled as contingent upon its occurrence on the previous two days, is compared with a spell-length model, in which mass functions of wet- and dry-spell lengths are modeled. Both models are able to reproduce the observed annual and monthly climatology in the region to a high degree of accuracy. However, there is considerable over-dispersion in annual precipitation, resulting primarily from an underestimation in the interannual variability of precipitation intensity. The interannual variability of temperatures is similarly underestimated, and is most severe for minimum temperatures. There is a severe problem in estimating minimum temperature extremes, which can be attributed to the negatively skewed distribution of daily minimum temperatures. Non-normality in the distribution of daily temperatures is shown to be a problem in simulating extreme temperature maxima as well as of minima. It is suggested that the normal distribution used in the generation of daily temperatures in the widely used Richardson (1981) generator, and its derivations, be supplanted by a more appropriate distribution that permits skewness in either direction.