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Abstract. 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.

 

The simulations of clouds and surface radiation from 10 CMIP6 models and their CMIP5 predecessors are compared to the ARM ground-based observations over different climate regions. Compared to the ARM radar-lidar derived total cloud fractions (CF_T) and cloud fraction vertical distributions over the six selected sites, both CMIP5 and CMIP6 significantly underestimated CF_Tand low-level CF over the Northern Hemispheric midlatitude sites (SGPC1 and ENAC1), although the biases are generally smaller in CMIP6. Over the tropical oceanic site (TWPC2), 5 out of 10 CMIP6 models better simulated low-level CF than their CMIP5 predecessors. CMIP6 simulations generally agreed well with the ARM observations in CF_Tand cloud fraction vertical distributions over the tropical continental (MAOM1) and coastal (TWPC3) sites but missed the transitions between dry and wet seasons, similar to CMIP5 simulations. The improvements in downwelling shortwave fluxes (SW_dn) at the surface from the majority of CMIP6 compared to CMIP5 primarily resulted from the improved cloud fraction simulations, especially over the SGPC1, ENAC1, and TWPC3 sites. By contrast, both CMIP5 and CMIP6 models exhibited diverse performances of clouds and shortwave radiation over the Arctic site (NSAC1), where CMIP6 models produced more clouds than CMIP5 models, especially for the low-level clouds. The comparisons between observations and CMIP5 and CMIP6 simulations provide valuable quantitative assessments of the accuracy of mean states and variabilities in the model simulations and shed light on general directions to improve climate models in different regions.

 

This study compares macrophysical and microphysical properties of single‐layered, liquid‐dominant MBL clouds from the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) (above 60°S) and the ARM East North Atlantic (ENA) site during the Aerosol and Cloud Experiments in Eastern North Atlantic (ACE‐ENA) field campaign. A total of 1,136 (16.5% of clouds) and 6,034 5‐min cloud samples are selected from MARCUS and ARM ENA in this study. MARCUS clouds have higher cloud‐top heights, thicker cloud layers, larger liquid water path, and colder cloud temperatures than ENA. Thinner, warmer MBL clouds at ENA can contain higher layer‐mean liquid water content due to higher cloud and ocean surface temperatures along with greater precipitable water vapor (PWV). MARCUS has a higher drizzle frequency rate (71.8%) than ENA (45.1%). Retrieved cloud and drizzle microphysical properties from each field campaign show key differences. MARCUS clouds feature smaller cloud droplets, whereas ENA clouds have larger cloud droplets, especially at the upper region of the cloud. From cloud top to cloud base, drizzle drop sizes increase while number concentrations decrease. Drizzle drop radius and number concentration decrease from cloud base to drizzle base due to net evaporation, and MARCUS' lower specific humidity leads to a higher drizzle base than ENA. The broader surface pressure and lower tropospheric stability (LTS) distributions during MARCUS have demonstrated that there are different synoptic patterns for selected cases during MARCUS with less PWV, while ENA is dominated by high pressure systems with nearly doubled PWV.

 

Abstract. Over the eastern North Atlantic (ENA) ocean, a total of 20 non-precipitating single-layer marine boundary layer (MBL) stratus and stratocumuluscloud cases are selected to investigate the impacts of the environmental variables on the aerosol–cloud interaction (ACIr) using theground-based measurements from the Department of Energy Atmospheric Radiation Measurement (ARM) facility at the ENA site during 2016–2018. TheACIr represents the relative change in cloud droplet effective radius re with respect to the relative change in cloudcondensation nuclei (CCN) number concentration at 0.2 % supersaturation (NCCN,0.2 %) in the stratified water vaporenvironment. The ACIr values vary from −0.01 to 0.22 with increasing sub-cloud boundary layer precipitable water vapor (PWVBL)conditions, indicating that re is more sensitive to the CCN loading under sufficient water vapor supply, owing to the combined effectof enhanced condensational growth and coalescence processes associated with higher Nc and PWVBL. The principal componentanalysis shows that the most pronounced pattern during the selected cases is the co-variations in the MBL conditions characterized by the verticalcomponent of turbulence kinetic energy (TKEw), the decoupling index (Di), and PWVBL. The environmental effects onACIr emerge after the data are stratified into different TKEw regimes. The ACIr values, under both lowerand higher PWVBL conditions, more than double from the low-TKEw to high-TKEw regime. This can be explained bythe fact that stronger boundary layer turbulence maintains a well-mixed MBL, strengthening the connection between cloud microphysical properties andthe below-cloud CCN and moisture sources. With sufficient water vapor and low CCN loading, the active coalescence process broadens the cloud dropletsize spectra and consequently results in an enlargement of re. The enhanced activation of CCN and the cloud droplet condensationalgrowth induced by the higher below-cloud CCN loading can effectively decrease re, which jointly presents as the increasedACIr. This study examines the importance of environmental effects on the ACIr assessments and provides observational constraintsto future model evaluations of aerosol–cloud interactions. 

Abstract In this study, more than 4 years of ground-based observations and retrievals were collected and analyzed to investigate the seasonal and diurnal variations of single-layered MBL (with three subsets: nondrizzling, virga, and rain) cloud and drizzle properties, as well as their vertical and horizontal variations. The annual mean drizzle frequency was ~55%, with ~70% in winter and ~45% in summer. The cloud-top (cloud-base) height for rain clouds was the highest (lowest), resulting in the deepest cloud layer, i.e., 0.8 km, which is 4 (2) times that of nondrizzling (virga) clouds. The retrieved cloud-droplet effective radii r c were the largest (smallest) for rain (nondrizzling) clouds, and the nighttime values were greater than the daytime values. Drizzle number concentration N d and liquid water content LWC d were three orders and one order lower, respectively, than their cloud counterparts. The r c and LWC c increased from the cloud base to z i ≈ 0.75 by condensational growth, while drizzle median radii r d increased from the cloud top downward the cloud base by collision–coalescence. The adiabaticity values monotonically increased from the cloud top to the cloud base with maxima of ~0.7 (0.3) for nondrizzling (rain) clouds. The drizzling process decreases the adiabaticity by 0.25 to 0.4, and the cloud-top entrainment mixing impacts as deep as upper 40% of the cloud layers. Cloud and drizzle homogeneities decreased with increased horizontal sampling lengths. Cloud homogeneity increases with increasing cloud fraction. These results can serve as baselines for studying MBL cloud-to-rain conversion and growth processes over the Azores. 

The rapid Arctic sea ice retreat in the early 21^stcentury is believed to be driven by several dynamic and thermodynamic feedbacks, such as ice-albedo feedback and water vapor feedback. However, the role of clouds in these feedbacks remains unclear since the causality between clouds and these processes is complex. Here, we use NASA CERES satellite products and NCAR CESM model simulations to suggest that summertime low clouds have played an important role in driving sea ice melt by amplifying the adiabatic warming induced by a stronger anticyclonic circulation aloft. The upper-level high pressure regulates low clouds through stronger downward motion and increasing lower troposphere relative humidity. The increased low clouds favor more sea ice melt via emitting stronger longwave radiation. Then decreased surface albedo triggers a positive ice-albedo feedback, which further enhances sea ice melt. Considering the importance of summertime low clouds, accurate simulation of this process is a prerequisite for climate models to produce reliable future projections of Arctic sea ice.

 

Abstract. The aerosol indirect effect on cloud microphysical and radiative propertiesis one of the largest uncertainties in climate simulations. In order toinvestigate the aerosol–cloud interactions, a total of 16 low-level stratuscloud cases under daytime coupled boundary-layer conditions are selectedover the southern Great Plains (SGP) region of the United States. Thephysicochemical properties of aerosols and their impacts on cloudmicrophysical properties are examined using data collected from theDepartment of Energy Atmospheric Radiation Measurement (ARM) facility at the SGP site. The aerosol–cloud interaction index (ACIr) is used to quantify the aerosol impacts with respect to cloud-droplet effective radius. The mean value of ACIr calculated from all selected samples is0.145±0.05 and ranges from 0.09 to 0.24 at a range of cloudliquid water paths (LWPs; LWP=20–300 g m−2). The magnitude of ACIr decreases with an increasing LWP, which suggests a diminished cloud microphysical response to aerosol loading, presumably due to enhanced condensational growth processes and enlarged particle sizes. The impact of aerosols with different light-absorbing abilities on the sensitivity of cloud microphysical responses is also investigated. In the presence of weak light-absorbing aerosols, the low-level clouds feature a higher number concentration of cloud condensation nuclei (NCCN) and smaller effective radii (re), while the opposite is true for strong light-absorbing aerosols. Furthermore, the mean activation ratio of aerosols to CCN (NCCN∕Na) for weakly (strongly) absorbing aerosols is 0.54 (0.45), owing to the aerosol microphysical effects, particularly the different aerosol compositions inferred by their absorptive properties. In terms of the sensitivity of cloud-droplet number concentration (Nd) to NCCN, the fraction of CCN that converted to cloud droplets (Nd∕NCCN) for the weakly (strongly) absorptive regime is 0.69 (0.54). The measured ACIr values in the weakly absorptive regime arerelatively higher, indicating that clouds have greater microphysicalresponses to aerosols, owing to the favorable thermodynamic condition. Thereduced ACIr values in the strongly absorptive regime are due to the cloud-layer heating effect induced by strong light-absorbing aerosols. Consequently, we expect larger shortwave radiative cooling effects from clouds in the weakly absorptive regime than those in the strongly absorptive regime. 

Abstract. Vertical profiles of aerosols are inadequately observed and poorlyrepresented in climate models, contributing to the current large uncertaintyassociated with aerosol–cloud interactions. The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Aerosol and CloudExperiments in the Eastern North Atlantic (ACE-ENA) aircraft field campaignnear the Azores islands provided ample observations of verticaldistributions of aerosol and cloud properties. Here we utilize the in situaircraft measurements from the ACE-ENA and ground-based remote-sensing dataalong with an aerosol-aware Weather Research and Forecast (WRF) model tocharacterize the aerosols due to long-range transport over a remote regionand to assess their possible influence on marine-boundary-layer (MBL)clouds. The vertical profiles of aerosol and cloud properties measured viaaircraft during the ACE-ENA campaign provide detailed information revealingthe physical contact between transported aerosols and MBL clouds. TheEuropean Centre for Medium-Range Weather Forecasts Copernicus Atmosphere Monitoring Service (ECMWF-CAMS) aerosol reanalysis data can reproduce the key features of aerosolvertical profiles in the remote region. The cloud-resolving WRF sensitivityexperiments with distinctive aerosol profiles suggest that the transportedaerosols and MBL cloud interactions (ACIs) require not only aerosol plumes to get close to the marine-boundary-layer top but also large cloud topheight variations. Based on those criteria, the observations show that theoccurrence of ACIs involving the transport of aerosol over the eastern NorthAtlantic (ENA) is about 62 % in summer. For the case with noticeable long-range-transport aerosol effects on MBL clouds, the susceptibilities of dropleteffective radius and liquid water content are −0.11 and +0.14,respectively. When varying by a similar magnitude, aerosols originatingfrom the boundary layer exert larger microphysical influence on MBL cloudsthan those entrained from the free troposphere.