A measure of the average distance between climate model predictions of multiple fields and observations has been developed that is based on the use of empirical orthogonal functions (EOFs). The application of EOFs provides a means to use information about spatial correlations in natural variability to provide a more balanced view of the significance of changes in model predictions across multiple fields, seasons, and regions. A comparison is made between the EOF-based measure and measures that are normalized by grid point variance and spatial variance for changes in the National Center for Atmospheric Research Community Climate Model, Version 3.10 (CCM3.10), parameter controlling initial cloud downdraft mass flux (ALFA), an important parameter within the Zhang and McFarlane <1995> convection scheme. All measures present consistent views that increasing ALFA from its default value creates significant improvements in precipitation, shortwave radiation reaching the surface, and surface latent heat fluxes at the expense of degrading predictions of total cloud cover, near-surface air temperature, net shortwave radiation at the top of the atmosphere, and relative humidity. However, the relative importance of each of these changes, and therefore the average view of the change in model performance, is significantly impacted by the details of how each measure of model performance handles regions with little or no internal variability. In general, the EOF-based measure emphasizes regions where modeled-observational differences are large, excluding those regions where internal variability is small.