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Low-level warm clouds are a major component in multilayered cloud systems and they are generally hidden from the top-down view of satellites with passive measurements. This study conducts an investigation on oceanic warm clouds embedded in multilayered structures by using spaceborne radar data with fine vertical resolution. The occurrences of warm cloud overlapping and the geometric features of several kinds of warm cloud layers are examined. It is found that there are three main types of cloud systems that involve warm cloud layers, including warm single layer clouds, cold-warm double layer clouds, and warm-warm double layer clouds. The two types of double layer clouds account for 23% and in the double layer occurrences warm-warm double layer subsets contribute about 13%. The global distribution patterns of these three types differ from each other. Single-layer warm clouds and the lower warm clouds in the cold-warm double layer system they have nearly identical geometric parameters, while the upper and lower layer warm clouds in the warm-warm double layer system are distinct from the previous two forms of warm cloud layers. In contrast to the independence of the two cloud layers in cold-warm double layer system, the two kinds of warm cloud layers in the warm-warm double layer system may be coupled. The distance between the two layers in the warm-warm double layer system is weakly dependent on cloud thickness. Given the upper and lower cloud layer with moderate thickness of around 1 km, the cloudless gap reaches its maximum when exceeding 600 m. The cloudless gap decreases in thickness as the two cloud layers become even thinner or thicker.

Warm cumulus clouds are key components of Earth's weather and climate systems, yet mixing processes within them and their interaction with their environment remain a major source of uncertainty in climate predictions. Based on adiabatic fraction (AF), such clouds can be divided into three regions: the adiabatic core, the skin (the diluted cloud edge), and the periphery, which lies between them, in terms of AF values. Despite its large volume and mass, the periphery is still poorly understood. This study shows that the periphery is equally supplied by air coming from the core and skin. However, periphery air flows mainly toward the skin, with limited transport into the core. The skin contributes to the periphery primarily through transport associated with the toroidal vortex. Each zone also exhibits distinct microphysical properties, including differences in droplet mean radius, variance, and relative dispersion.

This study presents the first model intercomparison of aerosol‐cloud‐turbulence interactions in a controlled cloudy Rayleigh‐Bénard Convection chamber environment, utilizing the Pi Chamber at Michigan Technological University. We analyzed simulated cloud chamber‐averaged statistics of microphysics and thermodynamics in a warm‐phase, cloudy environment under steady‐state conditions at varying aerosol injection rates. Simulation results from seven distinct models (DNS, LES, and a 1D turbulence model) were compared. Our findings demonstrate that while all models qualitatively capture observed trends in droplet number concentration, mean radius, and droplet size distributions at both high and low aerosol injection rates, significant quantitative differences were observed. Notably, droplet number concentrations varied by over two orders of magnitude between models for the same injection rates, indicating sensitivities to the model treatments in droplet activation and removal and wall fluxes. Furthermore, inconsistencies in vertical relative humidity profiles and in achieving steady‐state liquid water content suggest the need for further investigation into the mechanisms driving these variations. Despite these discrepancies, the models generally reproduced consistent power‐law relationships between the microphysical variables. This model intercomparison underscores the importance of controlled cloud chamber experiments for validating and improving cloud microphysical parameterizations. Recommendations for future modeling studies are also highlighted, including constraining wall conditions and processes, investigating droplet/aerosol removal (including sidewall losses), and conducting simplified experiments to isolate specific processes contributing to model divergence and reduce model uncertainties. Plain Language Summary Understanding how tiny particles (aerosols) interact with clouds and turbulence is essential for improving weather forecasts and climate predictions, as these interactions play a crucial role in determining the properties and evolution of clouds. In this study, we compared different numerical cloud models that simulate these interactions within a controlled laboratory environment, the Pi Chamber at Michigan Technological University. We examined how these models simulated the formation and growth of cloud droplets when aerosols were injected at different rates into the chamber. Our findings show that while all models generally captured the expected trends in cloud droplet size and number concentrations, there were significant quantitative differences. These differences suggest that model results are sensitive to the different model treatments on how droplets are formed and removed, as well as how fluxes from the chamber walls are represented. Despite these differences, the models generally agreed on the overall relationships between aerosol amounts and cloud properties, matching laboratory observations. This study highlights the value of using cloud chamber experiments to test and improve these models. We suggest that to reduce model uncertainties, future research should focus on better defining the conditions at the chamber walls and investigating how particles are removed from the chamber. Key Points This study presents the first model intercomparison to study aerosol‐cloud‐turbulence interactions in a convection‐cloud chamber All models capture the observed microphysical response to varying aerosol injection rates, but large inter‐model discrepancies are present The study underscores the importance of laboratory experiments for validating and improving microphysical representation in cloud models

In situ aircraft measurements of aerosols and clouds were performed over the eastern Loess Plateau in Shanxi Province, China. The microphysical properties of both aerosols and warm clouds, including aerosol number concentration (N a ), particle effective diameter (ED), number concentration of cloud droplets (N c ), cloud droplet diameter (D c ), and liquid water content (LWC) of clouds, determined through six flight observations performed in May 2013 were obtained and analysed. The mean magnitude of measured N a over the six flights was 10 3  cm −3 , and accumulation mode particles dominated the majority. Most EDs of aerosol particles were less than 1 μm. Large amounts of fine aerosol particles were constrained to the lower layer, with ED smaller than 0.5 μm, and N a decreased with height. The base heights of warm clouds ranged from 1000 to 2800 m. The maximum and average values of the measured N c ranged from 147 to 311 cm −3 and 51 to 157 cm −3 , respectively. The maximum and average D c ranged from 13.5 to 28.9 and 5.8 to 13.1 μm, respectively. The average LWC of warm clouds was 0.05 g·m −3 . N a was negatively correlated with N c either in the vertical or horizontal direction. N c was higher with a smaller size of cloud droplets under high aerosol loading conditions. A small number of cloud droplets with larger size were formed under low aerosol loading conditions. At a certain range of LWC, N c and D c showed a negative correlation. The increase in LWC was related to an increase in the size of cloud droplets rather than the number of cloud droplets. The cloud droplet size distribution showed that small droplets dominated the total cloud droplet concentration. A bimodal lognormal distribution function can be efficiently used to describe the average spectrum of warm cloud droplets.

Cloud susceptibility to aerosol (CSA) refers to their sensitivity to increases in condensation nuclei (CN) concentration. Yet, CSA in mixed-phase clouds remains largely unexplored. This study investigates CSA in marine boundary layer clouds during cold-air outbreaks, where ice particles are often present, using the WRF model coupled with the NTU microphysical scheme. In warm clouds, the CSA of cloud albedo (precipitation) remains positive (negative); however, this sensitivity weakens as CN concentration increases. In mixed-phase clouds, CSA signal remains positive in sign but becomes weaker (relative to the warm clouds) and non-monotonic. Reflecting the interplay of multiple microphysical processes under varying aerosol conditions, cold precipitation increases with increasing aerosol loadings and peaks at intermediate CN concentrations, shaping the CSA of cloud albedo into an inversed-N pattern. Aside from microphysical responses, cloud fraction—a macro-physical feature—also shows susceptibility to aerosol effects. As CN increases, cloud fraction tends to decrease, which can mitigate up to one-third of the cloud albedo CSA in warm clouds and three-fourths in mixed-phase clouds.

Increased aerosol concentrations can brighten low‐level clouds and extend their lifetimes, but aerosol–cloud interactions (ACI) remain highly uncertain and difficult to quantify. We show that part of this uncertainty is caused by topographical influences on clouds, that is, those arising from land–water contrasts. This is demonstrated using satellite retrievals in regions with extensive river networks, such as the Amazon Basin. 15 years of MODerate resolution Imaging Spectroradiometer (MODIS) satellite data show cloud formation over the Amazon River basin is suppressed by 26% with warm low clouds above the river exhibiting a 22% smaller droplet effective radius and 18% higher droplet concentration (NdN_(d) ) compared to adjacent land clouds. Thus, clouds above the river may appear polluted but are actually influenced by river‐breeze circulations driven by the thermal contrast between the river and the surrounding land. These responses are robust in both wet and dry seasons, and tests using an improved MODIS retrieval product show cloud differences are unlikely due to retrieval artifacts. In situ measurements from the Green Ocean Amazon Experiment (GoAmazon) confirm that NdN_(d)is elevated above rivers and are also higher when carbon monoxide concentrations are elevated near the large city of Manaus. Lagrangian airmass tracking over Manaus shows that regional‐scale river‐breeze circulations impact NdN_(d)as much as the urban aerosol plume, complicating ACI attribution and highlighting the need to isolate land‐surface effects to assess ACI in continental regions. Plain Language Summary Aerosols, tiny particles suspended in the air, play an important role in cloud formation by acting as seeds for cloud droplets to grow, thus affecting cloud brightness. Cloud brightness and size regulate how clouds cool the atmosphere by reflecting sunlight, but isolating how aerosols influence clouds over land is challenging because surface properties also affect cloud properties. Using 15 years of satellite data from the Amazon river basin, it was found that river‐breeze circulations, caused by temperature differences between cooler river surfaces and warmer surrounding land, significantly affect cloud properties. Clouds over rivers have smaller droplet sizes and more numerous droplets compared to clouds over nearby land. These clouds can look “polluted” by high aerosol concentrations even when their characteristics are shaped by natural processes rather than human activity. Data from the GoAmazon experiment supports these findings, and tests with improved satellite retrieval products confirm that these cloud differences are not due to satellite data errors. This research emphasizes the importance of separating the natural effects of rivers on cloud properties from aerosol impacts to accurately attribute human‐related influences, such as those from cities and fires, and improve our understanding of how aerosols affect clouds and the energy budget. Key Points Daytime low‐cloud fraction is strongly suppressed over Amazon and other major rivers compared to adjacent land Urban aerosols from Manaus significantly increase cloud droplet concentrations, locally, and up to 200 km from the city River‐breeze circulations influence low‐cloud properties as much as the aerosol sources, supporting greater drop concentrations over rivers

In this work we study the global solvability of moisture dynamics with phase changes for warm clouds. We thereby in comparison to previous studies (Hittmeir et al. in Nonlinearity 30:3676–3718, 2017) take into account the different gas constants for dry air and water vapor as well as the different heat capacities for dry air, water vapor and liquid water, which leads to a much stronger coupling of the moisture balances and the thermodynamic equation. This refined thermodynamic setting has been demonstrated to be essential, e.g. in the case of deep convective cloud columns in Hittmeir and Klein (Theoret Comput Fluid Dyn 32(2):137–164, 2017). The more complicated structure requires careful derivations of sufficient a priori estimates for proving global existence and uniqueness of solutions.

The millimeter-wave cloud radar, ceilometer, and disdrometer have been widely used to observe clouds and precipitation. However, there are some drawbacks when those three instruments are solely employed due to their own limitations, such as the fact that radars usually suffer from signal attenuation and ceilometers/disdrometers cannot provide measurements of the hydrometeors of aloft clouds and precipitation. Thus, in this paper, we developed an integrated technology by combining and utilizing the advantages of three instruments together to investigate the vertical structure and diurnal variation of warm clouds and precipitation, and the raindrop size distribution. Specifically, the technology consists of appropriate data processing, quality control, and retrieval methods. It was implemented to study the warm clouds and precipitation in South China during the pre-flood season of 2016. The results showed that the hydrometeors of warm clouds and precipitation were mainly distributed below 2.5 km and most of the rainfall events were very light with a rain rate less than 1 mm h−1, however, the stronger precipitation primarily contributed the accumulated rain amount. Furthermore, a rising trend of cloud base height from 1000 to 1900 BJT was found. The cloud top height and cloud thickness gradually increased from 1200 BJT to reach a maximum at 1600 BJT (Beijing Standard Time, UTC+8), and then decreased until 2000 BJT. Also, three periods of the apparent rainfall on the ground of the day, namely, 0400–0700 BJT, 1400–1800 BJT, and 2300–2400 BJT were observed. During three periods, the raindrops had wider size spectra, higher number concentrations, larger rain rates, and higher water contents than at other times. The hydrometeor type, size, and concentration were gradually changed in the vertical orientation. The raindrop size distributions of warm precipitation in the air and on the ground were different, which can be expressed by γ distributions N(D) = 1.49 × 104D−0.9484exp(−6.79D) in the air and N(D) = 1.875 × 103D0.862exp(−2.444D) on the ground, where D and N(D) denote the diameter and number concentration of the raindrops, respectively.

Characterization of dynamics inside clouds remains a challenging task for weather forecasting and climate modeling as cloud properties depend on interdependent natural processes at micro- and macro-scales. Turbulence plays an important role in particle dynamics inside clouds; however, turbulence mechanisms are not yet fully understood partly due to the difficulty of measuring clouds at the smallest scales. To address these knowledge gaps, an experimental method for measuring the influence of small-scale turbulence in cloud formation in situ and producing an in-field cloud Lagrangian dataset is being developed by means of innovative ultralight radioprobes. This paper presents the electronic system design along with the obtained results from laboratory and field experiments regarding these compact (diameter ≈30 cm), lightweight (≈20 g), and expendable devices designed to passively float and track small-scale turbulence fluctuations inside warm clouds. The fully customized mini-radioprobe board (5 cm × 5 cm) embeds sensors to measure local fluctuations and transmit data to the ground in near real time. The tests confirm that the newly developed probes perform well, providing accurate information about atmospheric turbulence as referenced in space. The integration of multiple radioprobes allows for a systematic and accurate monitoring of atmospheric turbulence and its impact on cloud formation.

Base on January and July 4-times daily ECMWF Interim data from 2009 to 2018 over the Northeast Sphere (0–180E,0–90N), the condensed moisture profile of experiential methods and that of ECMWF analysis are compared. The result shows that, the meridional-height distribution of mean cloud condensed moisture has a maximum slab spreading near ground in the Arctic region in July, and the maximum takes a circular shape at 700 hPa above 30N latitude in January. The distribution feature unlike the universal profile, it distributes in a single or double peak function manner, instead of a constant value. The quick decreasing level height and thickness varies with latitude, especially in January. The second experiential profile concerning warm cloud assumes air parcel lifting adiabatically, the liquid water path (LWP) is compared for general information. The result shows that the experiential LWP is much larger than that of the reanalysis by 1 to 2 order, decreasing with latitudes. The possible reason of LWP difference is from the critic water content value of cloud boundary identification. If the value is small, the thickness of warm cloud will be large, temperature and pressure at the cloud base are both large too, results in a larger LWP. These results will enrich the knowledge of the condensed moisture characteristics of ECMWF reanalysis and the experiential moisture profile methods.