Single column models (SCMs) are useful tools for the evaluation of parameterizations of radiative and moist processes used in general circulation models (GCMs). SCM applications have usually been limited to regions where high-quality observations are available to derive the necessary boundary condition or forcing data. Recently, researchers have developed techniques for deriving SCM forcing data from other data sets, such as NWP (numerical weather prediction) analyses. The uncertainties inherent in these forcing data products have an unknown and possibly significant effect on SCM runs. This paper shows how an ensemble SCM (ESCM) approach can be used to minimize the uncertainty in SCM simulations resulting from uncertainties in the forcing data. Some innovative evaluation techniques have been applied to ESCM runs at the tropical western Pacific Atmospheric Radiation Measurement (ARM) program sites at Manus Island and Nauru. These techniques, making use of traditional ensemble verification methods and objectively determined cloud regimes, are shown to be able to highlight parameterization deficiencies and provide a useful tool for testing new or improved model parameterizations.