Marine boundary layer (MBL) clouds play a critical role in the Earth’s energy balance. Their microphysical and radiative properties are highly impacted by ambient aerosols. However, the representation of MBL clouds and associated aerosol-cloud interactions (ACI) in Earth System Models (ESM) remains largely uncertain. In this study, we aim to evaluate the representation of MBL clouds and the related ACI processes in the U. S. DOE's E3SM model in the single-column model (SCM) framework at 1°×1° spatial resolution, against field measurements collected during the NASA ACTIVATE campaign along with high-resolution process-model (WRF) simulations. A post-frontal cloud case associated with cold air outbreak (CAO) is selected for this study. Results show that E3SM-SCM, driven by forcings and boundary conditions from reanalysis (ERA5), well reproduces the CAO cloud properties as the high-resolution WRF simulations do. When a stronger surface forcing combined with a weaker subsidence forcing taken from regional WRF simulation, which reproduces the CAO clouds reasonably well, is used, E3SM-SCM produces much thicker clouds. This indicates that a proper match of large-scale dynamics and sub-grid scale parameterizations is needed to obtain optimal performance. In the E3SM-SCM sensitivity tests with perturbations in aerosol number, size distribution, chemical composition, and vertical variation within their observed range of values, we find that the main sensitivity of the simulated clouds is to the cloud-base aerosol number concentration. Higher aerosol number concentration leads to more numerous but smaller cloud droplets, resulting in stronger shortwave cloud forcing. This clear cloud albedo or Twomey effect is consistent with ESM results shown in previous studies. The cloud liquid water shows a strong linear response to aerosol number with weakly positive slope associated with precipitation suppression, but does not reveal the nonlinear LWP adjustment approximated from observations, LES, and ESMs in previous studies. Our findings also indicate that the SCM framework is a key tool to bridge the gap between ESM evaluation and process-level understanding obtained from observational analysis and high-resolution modeling of ACI in MBL clouds.