A single-column model is used to evaluate the performance of two types of autoconversion parameterizations. The model results are compared to data collected at the Atmospheric Radiation Measurement Program's Southern U.S. Great Plains site. The model is run over a period covering 2 years (2000--2001), and the results are analyzed for time periods varying from hourly to seasonal. During a relatively short 27-hour period during March 2000 characterized primarily by shallow frontal clouds, modeled values of cloud liquid water were better simulated using a Manton-Cotton-type autoconversion parameterization. However, over longer timescales representing a multitude of different cloud types and meteorological conditions, a Sundqvist-type parameterization produced better results. Analysis of the model results indicates that the Manton-Cotton-type parameterization does better during periods when shallow clouds are present without any overlying clouds, while the Sundqvist-type parameterization is preferred during periods when high and low clouds coexist. A possible explanation is that precipitation from high clouds may not be represented well by the SCM, thus affecting the precipitation formation rates in any lower clouds. Sensitivity tests using the Manton-Cotton parameterization indicate that the autoconversion rate is sensitive to the specification of the cloud droplet number concentration (Nc). The single-column model, as well as many general circulation models, specify Nc as a constant value. However, limited in situ measurements suggest that Nc varies significantly in time. The mean modeled top-of-atmosphere cloud radiative forcing during the 2-year period 2000--2001 differed by 3 W m-2 as the cloud droplet concentration was varied between minimum and maximum values suggested by the in situ measurements. These results imply that model-produced hydrological cycle and cloud-radiation interactions could be better modeled using an accurate time-dependent measure of the cloud droplet concentration.