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More than four years of ground-based measurements taken at the ARM Eastern North Atlantic (ENA) site between July 2015 and September 2019 have been collected and processed in this study. Monthly and hourly means of clear-sky, all-sky, total cloud fraction (CFT), and single-layered low (CFL) and high (CFH) clouds, the impacts of all scene types on the surface radiation budget (SRB), and their cloud radiative effects (CREs) have been examined. The annual averages of CFT, CFL, and CFH are 0.785, 0.342, and 0.123, respectively. The annual averages of the SW (LW) CREs for all sky, total, low, and high clouds are −56.7 (37.7), −76.6 (48.5), −73.7 (51.4), and −26.8 (13.9) W m−2, respectively, resulting in the NET CREs of −19.0, −28.0, −22.2, and −12.9 W m−2. Comparing the cloud properties and CREs at both ARM ENA and Southern Great Plains (SGP) sites, we found that the clear-sky downwelling SW and LW fluxes at the two sites are similar to each other due to their similar atmospheric background. Compared to SGP, the lower all-sky SW and higher LW fluxes at ENA are caused by its higher CFT and all-sky precipitable water vapor (PWV). With different low cloud microphysical properties and cloud condensation nuclei at the two sites, much higher cloud optical depth at SGP plays an important role in determining its lower SW flux, while Tb and PWV are important for downwelling LW flux at the surface. A sensitivity study has shown that the all-sky SW CREs at SGP are more sensitive to CFT (−1.07 W m−2 %−1) than at ENA (−0.689 W m−2 %−1), with the same conclusion for all-sky LW CREs (0.735 W m−2 %−1 at SGP vs 0.318 W m−2 %−1 at ENA). The results over the two sites shed new light on the impacts of clouds on the midlatitude surface radiation budgets, over both ocean and land.

This study investigates the microphysical properties of the Monsoon frontal cloud (MFC), which were inhomogeneous and exhibited scale differences based on observations and simulations. This research aims to better understand MFC microphysical processes, which are crucial for comprehending the precipitation mechanism during the summer of the middle and lower reaches of the Yangtze river. The results show that the Ka-band Precipitation Radar (KPR) measured reflectivity factor (dBZKPR) agrees well with the observed reflectivity (dBZobs), with a correlation coefficient of 0.78 and a mean bias of 2.45 dBZ along the aircraft flight track, indicating the reliability of KPR for detecting and retrieving cloud microphysical properties. Based on this, four approaches were conducted to create the relationships between radar reflectivity (Z) and microphysical parameters such as liquid water content (LWC) and effective diameter (De) which were then validated. Differential precipitation and non-precipitation clouds proved effective in predicting LWC using dBZKPR, while De required the further division of LWC into seven bins at 100.5 g m-3 intervals. The empirical formulas used in this study produced results closest to observation data compared to previous studies. Detailed criteria for using these formulas were quantified, including differentiating grounded echoes and the size of the retrieved LWC values. This study sheds light on the microphysical properties of the MFC and provides insights into the precipitation mechanism of the middle and lower reaches of the Yangtze river during the summer.

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 2017–2018. 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.

This study uses machine learning methods, specifically the random forest (RF), on a radar-based mesoscale convective system (MCS) tracking dataset to classify the five types of linear MCS morphology in the contiguous United States during the period 2004-2016. The algorithm is trained using radar- and satellite-derived spatial and morphological parameters, and reanalysis environmental information from 5-yr manually identified nonlinear and five linear MCS modes. The algorithm is then used to automate the classification of linear MCSs over 8 years with high accuracy, providing a systematic, long-term climatology of linear MCSs. Results reveal that nearly 40% of MCSs are classified as linear MCSs, in which half of the linear events belong to the type of system having a leading convective line. The occurrence of linear MCSs shows large annual and seasonal variations. On average, 113 linear MCSs occur annually during the warm season (through March to October), with most of these events clustered from May through August in the central eastern Great Plains. MCS characteristics, including duration, propagation speed, orientation, and system cloud size, have large variability among the different linear modes. The systems having a trailing convective line and the systems having a back-building area of convection typically move more slowly and have higher precipitation rate, and thus have higher potential in producing extreme rainfall and flash flooding. Analysis of the environmental conditions associated with linear MCSs show that the storm-relative flow is of most importance in determining the organization mode of linear MCSs.

To investigate the cloud phase and macrophysical properties over the Southern Ocean (SO), the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF2) was installed on the Australian icebreaker research vessel (R/V) Aurora Australis during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign (41 to 69∘ S, 60 to 160∘ E) from October 2017 to March 2018. To examine cloud properties over the midlatitude and polar regions, the study domain is separated into the northern (NSO) and southern (SSO) parts of the SO, with a demarcation line of 60∘ S. The total cloud fractions (CFs) were 77.9 %, 67.6 %, and 90.3 % for the entire domain, NSO and SSO, respectively, indicating that higher CFs were observed in the polar region. Low-level clouds and deep convective clouds are the two most common cloud types over the SO. A new method was developed to classify liquid, mixed-phase, and ice clouds in single-layered, low-level clouds (LOW), where mixed-phase clouds dominate with an occurrence frequency (Freq) of 54.5 %, while the Freqs of the liquid and ice clouds were 10.1 % (most drizzling) and 17.4 % (least drizzling). The meridional distributions of low-level cloud boundaries are nearly independent of latitude, whereas the cloud temperatures increased by ∼8 K, and atmospheric precipitable water vapor increased from ∼5 mm at 69∘ S to ∼18 mm at 43∘ S. The mean cloud liquid water paths over NSO were much larger than those over SSO. Most liquid clouds occurred over NSO, with very few over SSO, whereas more mixed-phase clouds occurred over SSO than over NSO. There were no significant differences for the ice cloud Freq between NSO and SSO. The ice particle sizes are comparable to cloud droplets and drizzle drops and well mixed in the cloud layer. These results will be valuable for advancing our understanding of the meridional and vertical distributions of clouds and can be used to improve model simulations over the SO.

The mei-yu season, typically occurring from mid-June to mid-July, on average, contributes to 32% of the annual precipitation over the Yangtze–Huai River Valley (YHRV) and represents one of the three heavy-rainfall periods in China. Here, we briefly review the large-scale background, synoptic pattern, moisture transport, and cloud and precipitation characteristics of the mei-yu frontal systems in the context of the ongoing Integrative Monsoon Frontal Rainfall Experiment (IMFRE) field campaign. Phase one of the campaign, IMFRE-I, was conducted from 10 June to 10 July 2018 in the middle reaches of the YHRV. Led by the Wuhan Institute of Heavy Rain (IHR) with primary support from the National Natural Science Foundation of China, IMFRE-I maximizes the use of our observational capacity enabled by a suite of ground-based and remote sensing instruments, most notably the IHR Mesoscale Heavy Rainfall Observing System (MHROS), including different wavelengths of radars, microwave radiometers, and disdrometers. The KA350 (Shanxi King-Air) aircraft participating in the campaign is equipped with Ka-band cloud radar and different probes. The comprehensive datasets from both the MHROS and aircraft instruments are combined with available satellite observations and model simulations to answer the three scientific questions of IMFRE-I. Some highlights from a previously published special issue are included in this review, and we also briefly introduce the IMFRE-II field campaign, conducted during June–July 2020, where the focus was on the spatiotemporal evolutions of the mei-yu frontal systems over the middle and lower reaches of the YHRV.

The surface radiative imbalance has large impacts on the long-term trends and year-to-year variability of Arctic sea ice. Clouds are believed to be a key factor in regulating this radiative imbalance, whose underlying processes and mechanisms, however, are not well understood. Compared with observations, the Community Earth System Model version 1 (CESM1) is known to underestimate Arctic cloud liquid water. Here, the following hypothesis is proposed and tested: this underestimation is caused by an overactive Wegener–Bergeron–Findeisen (WBF) process in model as too many supercooled liquid droplets are scavenged by ice crystals via deposition. In this study, the efficiency of the WBF process in CESM1 was reduced to investigate the Arctic climate response, and differentiate the responses induced by atmosphere–ocean–sea ice coupling and global warming. By weakening the WBF process, CESM1 simulated liquid cloud fractions increased, especially in winter and spring. The cloud response resulted in increased downwelling longwave flux and decreased shortwave flux at the surface. Arctic clouds and radiation in simulations with reduced WBF efficiency show a better agreement with satellite retrievals. In addition, both coupling and global warming amplify the cloud response to a less efficient WBF process, due to increased relative humidity and enhanced evaporation, respectively. As a response, the sea ice tends to melt over the North Atlantic Ocean, most likely caused by a positive feedback process between clouds, radiation and sea ice during non-summer months. These results improve our understanding of large-scale effects of the WBF process and the role of cloud liquid water in the Arctic climate system.

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 rc were the largest (smallest) for rain (nondrizzling) clouds, and the nighttime values were greater than the daytime values. Drizzle number concentration Nd and liquid water content LWCd were three orders and one order lower, respectively, than their cloud counterparts. The rc and LWCc increased from the cloud base to zi ≈ 0.75 by condensational growth, while drizzle median radii rd 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.

In this study, the mesoscale convective systems (MCSs) are tracked using high-resolution radar and satellite observations over the U.S. Great Plains during April–August from 2010 to 2012. The spatiotemporal variability of MCS precipitation is then characterized using the Stage IV product. We found that the spatial variability and nocturnal peaks of MCS precipitation are primarily driven by the MCS occurrence rather than the precipitation intensity. The tracked MCSs are further classified into convective core (CC), stratiform rain (SR), and anvil clouds regions. The spatial variability and diurnal cycle of precipitation in the SR regions of MCSs are not as significant as those of MCS precipitation. In the SR regions, the high-resolution, long-term ice cloud microphysical properties [ice water content (IWC) and ice water paths (IWPs)] are provided. The IWCs generally decrease with height. Spatially, the IWC, IWP, and precipitation are all higher over the southern Great Plains than over the northern Great Plains. Seasonally, those ice and precipitation properties are all higher in summer than in spring. Comparing the peak timings of MCS precipitation and IWPs from the diurnal cycles and their composite evolutions, it is found that when using the peak timing of IWPSR as a reference, the heaviest precipitation in the MCS convective core occurs earlier, while the strongest SR precipitation occurs later. The shift of peak timings could be explained by the stratiform precipitation formation process. The IWP and precipitation relationships are different at MCS genesis, mature, and decay stages. The relationships and the transition processes from ice particles to precipitation also depend on the low-level humidity.

Mesoscale convective systems (MCSs) play an important role in water and energy cycles as they produce heavy rainfall and modify the radiative profile in the tropics and midlatitudes. An accurate representation of MCSs’ rainfall is therefore crucial in understanding their impact on the climate system. The V06B Integrated Multisatellite Retrievals from Global Precipitation Measurement (IMERG) half-hourly precipitation final product is a useful tool to study the precipitation characteristics of MCSs because of its global coverage and fine spatiotemporal resolutions. However, errors and uncertainties in IMERG should be quantified before applying it to hydrology and climate applications. This study evaluates IMERG performance on capturing and detecting MCSs’ precipitation in the central and eastern United States during a 3-yr study period against the radar-based Stage IV product. The tracked MCSs are divided into four seasons and are analyzed separately for both datasets. IMERG shows a wet bias in total precipitation but a dry bias in hourly mean precipitation during all seasons due to the false classification of nonprecipitating pixels as precipitating. These false alarm events are possibly caused by evaporation under the cloud base or the misrepresentation of MCS cold anvil regions as precipitating clouds by the algorithm. IMERG agrees reasonably well with Stage IV in terms of the seasonal spatial distribution and diurnal cycle of MCSs precipitation. A relative humidity (RH)-based correction has been applied to the IMERG precipitation product, which helps reduce the number of false alarm pixels and improves the overall performance of IMERG with respect to Stage IV.

To investigate the cloud properties over the Southern Ocean (SO), the MARCUS field campaign (41 to 69º S; 60 to 160º E) was conducted from October 2017 to March 2018, using ship-based measurements. To examine cloud properties over the mid-latitude and Polar regions, the study domain is separated into northern (NSO) and southern (SSO) parts of the SO with a demarcation line of 60° S. The total cloud fractions (CFs) were 77.9 %, 67.6 %, and 90.3 % for the entire domain, NSO and SSO, respectively, indicating that higher CFs were observed in the Polar region. Low-level clouds, deep cumulus, and shallow cumulus clouds are the three most common cloud types over the SO. For single-layered low-level clouds, mixed-phase clouds dominate with an occurrence frequency (Freq) of 54.5 %, while the Freq of the liquid and ice clouds were 10.1 % (most drizzling) and 17.4 % (least drizzling). The meridional distributions of low-level cloud boundaries are nearly independent of latitude, whereas the cloud temperatures increased ~ 8 K and atmospheric precipitable water vapor increased from ~5 mm at 69º S to ~18 mm at 43º S. The mean cloud liquid water paths over NSO were much larger than those over SSO. Most liquid clouds occurred over NSO with very few over SSO, whereas more mixed-phase clouds occurred over SSO than over NSO. There were no significant differences for ice cloud Freq between NSO and SSO. These results will be valuable for advancing our understanding of the meridional and vertical distributions of clouds and can be used to improve model simulations over the SO.

Over the eastern north Atlantic (ENA) ocean, a total of 20 non-precipitating single-layer marine boundary layer (MBL) stratus and stratocumulus cloud cases are selected in order to investigate the impacts of the environmental variables on the aerosol-cloud interaction (ACIr) using the ground-based measurements from the Department of Energy Atmospheric Radiation Measurement (ARM) facility at the ENA site during the period 2016–2018. The ACIr represents the relative change of cloud-droplet effective radius re with respect to the relative change of cloud condensation nuclei (CCN) number concentration at 0.2 % supersaturation (NCCN,0.2 %) in the water vapor stratified environment. The ACIr values vary from −0.004 to 0.207 with increasing precipitable water vapor (PWV) conditions, indicating that re is more sensitive to the CCN loading under sufficient water vapor supply, owing to the combined effect of enhanced condensational growth and coalescence processes associated with higher NC and PWV. The environmental effects on ACIr are examined by stratifying the data into different lower tropospheric stability (LTS) and vertical component of turbulence kinetic energy (TKEw) regimes. The higher LTS normally associates with a more adiabatic cloud layer and a lower boundary layer and thus results in higher CCN to cloud droplet conversion and ACIr. The ACIr values under a range of PWV double from low TKEw to high TKEw regime, indicating a strong impact of turbulence on the ACIr. The stronger boundary layer turbulence represented by higher TKEw strengthens the connection and interaction between cloud microphysical properties and the underneath CCN and moisture sources. With sufficient water vapor and low CCN loading, the active coalescence process broadens the cloud droplet size distribution spectra, and consequently results in an enlargement of re. The enhanced NC conversion and condensational growth induced by more intrusions of CCN effectively decrease re, which jointly presents as the increased ACIr. The TKEw median value of 0.08 m2 s−2 suggests a feasible way in distinguishing the turbulence-enhanced aerosol-cloud interaction in non-precipitating MBL clouds.

The Marine ARM GPCI Investigation of Clouds (MAGIC) field campaign provided a wealth of information looking at the stratocumulus to cumulus transition (SCT) over the Eastern‐North Pacific (ENP), however, the lack of cloud in situ measurements gave limited information. Using the observations of Marine W‐band ARM cloud radar, ceilometer, and three‐channel microwave radiometer onboard the ship, we retrieve the single‐layer, low‐level cloud‐droplet effective radius and drizzle median radius (rc and rm,d), number concentration (Nc and Nd), and liquid water content (LWCc and LWCd) using the methods in Wu et al. (2020, https://doi.org/10.1029/2019JD032205). Based on the results during MAGIC, we found that both cloud base and top heights increase approximately 0.75 km from Los Angeles (LA) until cloud breakup (CB) before leveling off. Low cloud fractions (CFs) ranged from ∼85% halfway between LA and CB to ∼20% near Hawaii. Retrieved rc values decreased approximately 2 μm from peak CF to Hawaii while rm,d increased more than 20 μm over the same path. Mean values of rc, rm,d, Nc, Nd, LWCc, and LWCd during MAGIC are 12.1 μm, 55.8 μm, 97.9 cm−3, 0.09 cm−3, 0.40 g m−3, and 0.05 g m−3, respectively. Compared to the mean values over the Azores in Wu et al. (2020, https://doi.org/10.1029/2019JD032205), the mean cloud and drizzle microphysical properties during MAGIC, except LWCd which is roughly equal, are greater due to higher liquid water path and warmer sea surface temperature. This information allows for a better understanding of the SCT over the ENP and can be used to better improve model simulations.

A competitive neural network known as the self-organizing map (SOM) is used to objectively identify synoptic patterns in the North American Regional Reanalysis (NARR) for warm-season (April–September) precipitation events over the Southern and Northern Great Plains (SGP/NGP) from 2007 to 2014. Classifications for both regions demonstrate contrast in dominant synoptic patterns ranging from extratropical cyclones to subtropical ridges, all of which have preferred months of occurrence. Precipitation from deterministic Weather Research and Forecasting (WRF) Model simulations run by the National Severe Storms Laboratory (NSSL) are evaluated against National Centers for Environmental Prediction (NCEP) Stage IV observations. The SGP features larger observed precipitation amount, intensity, and coverage, as well as better model performance than the NGP. Both regions’ simulated convective rain intensity and coverage have good agreement with observations, whereas the stratiform rain (SR) is more problematic with weaker intensity and larger coverage. Further evaluation based on SOM regimes shows that WRF bias varies with the type of meteorological forcing, which can be traced to differences in the diurnal cycle and properties of stratiform and convective rain. The higher performance scores are generally associated with the extratropical cyclone condition than the subtropical ridge. Of the six SOM classes over both regions, the largest precipitation oversimulation is found for SR dominated classes, whereas a nocturnal negative precipitation bias exists for classes featuring upscale growth of convection.

NASA Earth and Space Science Fellowship program [80NSSC18K1339]; NASA CERES project through the University of Arizona [80NSSC19K0172]; National Center for Atmospheric Research (NCAR) - National Science Foundation (NSF) [1852977]; NASA [15-CCST15-0025]; NSF [AGS-1354402, AGS-1445956]; National Oceanic and Atmospheric Administration [NA16NWS4680013]; National Science Foundation

Climate Model Development and Validation (CMDV) program - Office of Biological and Environmental Research in the US Department of Energy Office of Science [DE-SC0017015]; NASA CERES project [NNX17AC52G]

A record of global single-layered ice cloud properties has been generated using the CloudSat and CALIPSO Ice Cloud Property Product (2C-ICE) during the period 2007–2010. These ice cloud properties are used as inputs for the NASA Langley modified Fu–Liou radiative transfer model to calculate cloud radiative heating rate profiles and are compared with the NASA CERES observed top-of-atmosphere fluxes. The radiative heating rate profiles calculated in the CloudSat/CALIPSO 2B-FLXHR-LIDAR and CCCM_CC products are also examined to assess consistency and uncertainty of their properties using independent methods. Based on the methods and definitions used herein, single-layered ice clouds have a global occurrence frequency of ~ 18%, with most of them occurring in the tropics above 12 km. Zonal mean cloud radiative heating rate profiles from the three datasets are similar in their patterns of SW warming and LW cooling with small differences in magnitude; nevertheless, all three datasets show that the strongest net heating (> + 1.0 K day−1) occurs in the tropics (latitude ~ 50°). Differences in radiative heating rates are also assessed based on composites of the 2C-ICE ice water path (IWP) and total column water vapor (TCWV) mixing ratio to facilitate model evaluation and guide ice cloud parameterization improvement. Positive net cloud radiative heating rates are maximized in the upper troposphere for large IWPs and large TCWV, with an uncertainty of 10–25% in the magnitude and vertical structure of this heating.

A great challenge in climate modeling is how to parameterize subgrid cloud processes, such as autoconversion and accretion in warm-rain formation. In this study, we use ground-based observations and retrievals over the Azores to investigate the so-called enhancement factors, Eauto and Eaccr, which are often used in climate models to account for the influence of subgrid variance of cloud and precipitation water on the autoconversion and accretion processes. Eauto and Eaccr are computed for different equivalent model grid sizes. The calculated Eauto values increase from 1.96 (30 km) to 3.2 (180 km), and the calculated Eaccr values increase from 1.53 (30 km) to 1.76 (180 km). Comparing the prescribed enhancement factors in Morrison and Gettleman (2008, MG08) to the observed ones, we found that a higher Eauto (3.2) at small grids and lower Eaccr (1.07) are used in MG08, which might explain why most of the general circulation models (GCMs) produce too-frequent precipitation events but with too-light precipitation intensity. The ratios of the rain to cloud water mixing ratio (qr/qc) at Eaccr=1.07 and Eaccr=2.0 are 0.063 and 0.142, respectively, from observations, further suggesting that the prescribed value of Eaccr=1.07 used in MG08 is too small to simulate precipitation intensity correctly. Both Eauto and Eaccr increase when the boundary layer becomes less stable, and the values are larger in precipitating clouds (CLWP>75 gm−2) than those in non-precipitating clouds (CLWP gm−2). Therefore, the selection of Eauto and Eaccr values in GCMs should be regime- and resolution-dependent.

Low stratiform clouds have profound impacts on the hydrological cycle and the Earth’s radiation budget. However, realistic simulation of low clouds in climate models presents a major challenge. Here we employ the newly retrieved cloud and drizzle microphysical properties to improve the autoconversion and accretion parameterizations in a microphysical scheme. We find that the new autoconversion (accretion) rate contributes 14% lower (greater) to total drizzle water content than the original scheme near the cloud top. Compared to satellite results, the simulated cloud liquid water path (LWP) and shortwave cloud radiative effect using the original scheme in a climate model agree well on global average but with large regional differences. Simulations using the updated scheme show a 7.3% decrease in the light rain frequency, and a 10% increase in LWP. The updated microphysics scheme alleviates the long‐lasting problem in most climate models, that is “too frequent and too light precipitation.”

The rapid Arctic sea ice retreat in the early 21st century 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. Summertime low clouds in the Arctic play an important role in inducing sea ice melt as they amplify warming from high pressure systems aloft through radiative effects and feedbacks, according to analyses of observations and model simulations.

Marine boundary layer (MBL) clouds in subtropical regions strongly impact global energy balance, but complete understanding of the processes that control their microphysical properties remain elusive. We analyze aircraft in-situ measurements of MBL clouds for two selected cases from the ACE-ENA field campaign that contain mesoscale convective cells (MCCs) on the order of tens of kilometers embedded in the large-scale overcast cloud field. The aircraft flight tracks aligned with the MCC organization, such that vertically-stacked, horizontal flight legs alternated between sampling clouds along organized MCCs and sampling clouds between MCCs. This alignment is well-suited to study the distinctly different microphysical properties for the two cloud regimes. Clouds within organized MCCs had lower droplet concentrations, but larger droplet sizes and liquid water contents with enhanced drizzle relative to clouds between MCCs. While observed aerosol properties below these two cloud regimes are generally consistent with their corresponding cloud microphysical properties, preexisting organization of the aerosol field was probably not required in the development of the MCC organization. In contrast, the lower aerosol and CCN concentrations observed below the MCC cloud layer most likely developed from precipitation and coalescence scavenging. A cloud-resolving WRF model simulation with realistic large-scale forcing reproduces the MCC organization of the cloud field suggesting that updraft velocity is the key to explain the differences in cloud microphysics. Both observations and model simulations indicate that under moderate-heavy drizzling conditions, precipitation and coalescence scavenging dominates and drives spatial gradients of cloud droplets, aerosols and CCN concentrations rather than local sources.