Insights of warm-cloud biases in Community Atmospheric Model 5 and 6 from the single-column modeling framework and Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) observations

There has been a growing concern that most climate models predict precipitation that is too frequent, likely due to lack of reliable subgrid variabilityand vertical variations in microphysical processes in low-level warm clouds.In this study, the warm-cloud physics parameterizations in the singe-columnconfigurations of NCAR Community Atmospheric Model version 6 and 5 (SCAM6and SCAM5, respectively) are evaluated using ground-based and airborneobservations from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the EasternNorth Atlantic (ACE-ENA) field campaign near the Azores islands during2017-2018. The 8-month single-column model (SCM) simulations show that both SCAM6 and SCAM5 cangenerally reproduce marine boundary layer cloud structure, majormacrophysical properties, and their transition. The improvement in warm-cloud properties from the Community Atmospheric Model 5 and 6 (CAM5 to CAM6) physics can be found through comparison with the observations. Meanwhile, both physical schemes underestimate cloud liquidwater content, cloud droplet size, and rain liquid water content butoverestimate surface rainfall. Modeled cloud condensation nuclei (CCN)concentrations are comparable with aircraft-observed ones in the summer but areoverestimated by a factor of 2 in winter, largely due to the biases in thelong-range transport of anthropogenic aerosols like sulfate. We also testthe newly recalibrated autoconversion and accretion parameterizations thataccount for vertical variations in droplet size. Compared to theobservations, more significant improvement is found in SCAM5 than in SCAM6.This result is likely explained by the introduction of subgrid variationsin cloud properties in CAM6 cloud microphysics, which further suppresses thescheme's sensitivity to individual warm-rain microphysical parameters. Thepredicted cloud susceptibilities to CCN perturbations in CAM6 are within areasonable range, indicating significant progress since CAM5 which produces anaerosol indirect effect that is too strong. The present study emphasizes theimportance of understanding biases in cloud physics parameterizations bycombining SCM with in situ observations.