Measurement of GCM Skill in Predicting Variables Relevant for Hydroclimatological Assessments

Abstract

Simulations from general circulation models are now being used for a variety of studies and purposes. With up to 23 different GCMs now available, it is desirable to determine whether a specific variable from a particular model is representative of the ensemble mean, which is often assumed to indicate the likely state of that variable in the future. The answers are important for decision makers and researchers using selective model outputs for follow-on studies such as statistical downscaling, which currently assume all model outputs are simulated with equal reliability. A skill score, termed the variable convergence score (VCS), has been derived that can be used to rank variables based on the coefficient of variation of the ensemble. The key benefit is the development of a simple methodology that allows for a quantitative assessment between different hydroclimatic variables. The VCS methodology has been applied to the outputs of nine GCMs for eight different variables and two emission scenarios to provide a relative ranking of the variables averaged across Australia and over different climatic regions of the country. The methodology, however, would be applicable for any region or any variable of interest from GCMs. It was found that the surface variables with the highest scores are pressure, temperature, and humidity. Regionally in Australia, models again show the best agreement in the surface pressure projections. The tropical and southwestern temperate zones show the overall highest variable convergence when all variables are considered. The desert zone shows relatively low model agreement, particularly in the projections of precipitation and specific humidity.