Marine boundary-layer (MBL) clouds play a critical role in the Earth’s energy balance. Their microphysical and radiative properties are highly influenced by ambient aerosols and meteorological conditions. Aerosol–cloud interactions have been identified as the largest source of uncertainty in estimating anthropogenic radiative forcing that is critical for Earth system models (ESMs) to reproduce the observed trends in global warming. Detailed multiscale physical and dynamical processes representing aerosol–cloud interactions are very challenging to treat accurately in ESMs at coarse resolutions, in part due to a lack of process-level understanding and sufficient statistics for the characterization of variability. The NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) field campaign produced an unprecedented dataset, based on spatially coordinated flights during 2020-2022 under a range of aerosol and meteorological conditions, for process-level and multiscale modeling intercomparison studies to better understand MBL aerosol–cloud interactions over the western North Atlantic. A hierarchy of modeling tools, including large-eddy simulation (LES), nested cloud-resolving model (CRM) and single-column model (SCM), was designed to simulate MBL clouds and understand aerosol–cloud interactions in the ACTIVATE process-study cases. This study aims to evaluate the performance of SCM against the ACTIVATE in-situ and remote-sensing data, and intercompare with LES/CRM simulations. We choose a cold air outbreak (CAO) case to investigate physical and dynamical processes that control the formation and evolution of MBL cloud systems as well as aerosol–cloud interactions at various spatiotemporal scales. Key findings on the proper combination of large-scale dynamics and model configurations required for accurate simulations of cloud structures and cloud susceptibility to aerosol perturbations will be discussed. Our findings suggest that the SCM framework is a key tool to bridge the gap between ESMs and high-resolution models (LES and CRM) as well as field observations, to advance process-level understanding of aerosol–cloud–meteorology interactions and facilitate evaluation and improvement of cloud parameterizations in ESMs.
 

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