Statistical Procedures for Making Inferences about Precipitation Changes Simulated by an Atmospheric General Circulation Model

Abstract

A statistical methodology is presented for making inferences about changes in mean daily precipitation from the results of general circulation model (GCM) climate experiments. A specialized approach is required because precipitation is inherently a discontinuous process. The proposed procedure is based upon a probabilistic model that simultaneously represents both occurrence and intensity components of the precipitation process, with the occurrence process allowed to be correlated in time and the intensifies allowed to have a non-Gaussian distribution. In addition to establishing whether the difference between experiment and control daily means is statistically significant, the procedure provides confidence intervals for the ratio of experiment to control median daily precipitation intensities and for the difference between experiment and control probabilities of daily precipitation occurrence. The technique is applied to the comparison of winter and summer precipitation data generated in a control integration of the Oregon State University atmospheric GCM.