Reply on RC1

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> There has been a growing concern that most climate models predict too frequent precipitation, likely due to lack of reliable sub-grid variability and vertical variations of microphysical processes in low-level warm clouds. In this study, the warm cloud physics parameterizations in the singe-column configurations of NCAR Community Atmospheric Model version 6 and 5 (SCAM6 and SCAM5, respectively) are evaluated using ground-based and airborne observations from the DOE ARM Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign near the Azores islands during 2017&ndash;2018. Eight-month SCM simulations show that both SCAM6 and SCAM5 can generally reproduce marine boundary-layer cloud structure, major macrophysical properties, and their transition. The improvement of warm cloud properties from CAM5 to CAM6 physics can be found compared to the observations. Meanwhile, both physical schemes underestimate cloud liquid water content, cloud droplet size, and rain liquid water content, but overestimate surface rainfall. Modeled cloud condensation nuclei (CCN) concentrations are comparable with aircraft observed ones in the summer but overestimated by a factor of two in winter, largely due to the biases in the long-range transport of anthropogenic aerosols like sulfate. We also test the newly recalibrated autoconversion and accretion parameterizations that account for vertical variations of droplet size. Compared to the observations, more significant improvement is found in SCAM5 than in SCAM6. This result is likely explained by the introduction of sub-grid variations of cloud properties in CAM6 cloud microphysics, which further suppresses the scheme sensitivity to individual warm rain microphysical parameters. The predicted cloud susceptibilities to CCN perturbations in CAM6 are within a reasonable range, indicating significant progress since CAM5 which produces too strong aerosol indirect effect. The present study emphasizes the importance of understanding biases in cloud physics parameterizations by combining SCM with in situ observations.