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Mixed-phase clouds affect precipitation and radiation differently from liquid and ice clouds, posing greater challenges to their representation in numerical simulations. Recent laboratory experiments using the Pi Cloud Chamber explored cloud glaciation conditions based on increased injection of ice-nucleating particles. In this study, we use two approaches to reproduce the results of the laboratory experiments: a bulk scalar mixing model and large-eddy simulation (LES) with bin microphysics. The first approach assumes a well-mixed domain to provide an efficient assessment of the mean cloud properties for a wide range of conditions. The second approach resolves the energy-carrying turbulence, the particle size distribution, and their spatial distribution to provide more details. These modeling approaches enable a separate and detailed examination of liquid and ice properties, which is challenging in the laboratory. Both approaches demonstrate that, with an increased ice number concentration, the flow and microphysical properties exhibit the same changes in trends. Additionally, both approaches show that the ice integral radius reaches the theoretical glaciation threshold when the cloud is subsaturated with respect to liquid water. The main difference between the results of the two approaches is that the bulk model allows for the complete glaciation of the cloud. However, LES reveals that, in a dynamic system, the cloud is not completely glaciated as liquid water droplets are continuously produced near the warm lower boundary and subsequently mixed into the chamber interior. These results highlight the importance of the ice mass fraction in distinguishing the mixed-phase clouds and ice clouds.

Drizzle initiation through the collision and coalescence of cloud droplets plays a crucial role in warm cloud precipitation. Recent theoretical studies suggest that the influence of collisional growth on the droplet size distribution can be quantified by a non‐dimensional drizzle number (Dz). Here, large‐eddy simulations with Lagrangian microphysics are employed to evaluate the theory by simulating a tall convection‐cloud chamber under various conditions. Results show that the smaller the Dz, the larger the impact of collisions on the right tail of the droplet size distribution, consistent with the theory. The simulations confirm that the collision rate can be estimated from the droplet size distribution interacting only with cloud droplets of the same size at the mode radius. This suggests that the idealized theory can be a useful tool to design a cloud chamber for drizzle investigation, as well as to represent drizzle formation in models of real atmospheric clouds.

Recent in situ observations show that haze particles exist in a convection cloud chamber. The microphysics schemes previously used for large‐eddy simulations of the cloud chamber could not fully resolve haze particles and the associated processes, including their activation and deactivation. Specifically, cloud droplet activation was modeled based on Twomey‐type parameterizations, wherein cloud droplets were formed when a critical supersaturation for the available cloud condensation nuclei (CCN) was exceeded and haze particles were not explicitly resolved. Here, we develop and adapt haze‐capable bin and Lagrangian microphysics schemes to properly resolve the activation and deactivation processes. Results are compared with the Twomey‐type CCN‐based bin microphysics scheme in which haze particles are not fully resolved. We find that results from the haze‐capable bin microphysics scheme agree well with those from the Lagrangian microphysics scheme. However, both schemes significantly differ from those from a CCN‐based bin microphysics scheme unless CCN recycling is considered. Haze particles from the recycling of deactivated cloud droplets can strongly enhance cloud droplet number concentration due to a positive feedback in haze‐cloud interactions in the cloud chamber. Haze particle size distributions are more realistic when considering solute and curvature effects that enable representing the complete physics of the activation process. Our study suggests that haze particles and their interactions with cloud droplets may have a strong impact on cloud properties when supersaturation fluctuations are comparable to mean supersaturation, as is the case in the cloud chamber and likely is the case in the atmosphere, especially in polluted conditions. Plain Language Summary In atmospheric models, cloud droplet formation is usually simulated to occur when a submicron dry aerosol particle encounters supersaturated conditions. In reality, dry aerosol particles composed of water‐soluble compounds form aqueous haze particles first before they activate to cloud droplets. However, haze particles and the associated interactions with cloud droplets are usually not fully resolved in atmospheric models. In this study, we develop two types of microphysics schemes to explore haze‐cloud interactions in a convection cloud chamber. Our results show that recycling of deactivated cloud droplets through either dry aerosol or haze particles can significantly enhance the cloud droplet number concentration in the cloud chamber. Our study indicates that it is important to properly resolve haze particles and haze‐cloud interactions for cloud chamber simulations, which is likely also true for atmospheric cloud simulations, especially under polluted conditions. Key Points Bin and Lagrangian microphysics schemes with various levels of complexity are used to handle aerosol‐cloud interactions in a cloud chamber Simulations using haze‐capable bin and Lagrangian schemes capture the observed haze mode in the chamber Activation and deactivation rates are overestimated when using a CCN‐based bin scheme compared with haze‐capable schemes

Collisional growth of cloud droplets is an essential yet uncertain process for drizzle and precipitation formation. To improve the quantitative understanding of this key component of cloud‐aerosol‐turbulence interactions, observational studies of collision‐coalescence in a controlled laboratory environment are needed. In an existing convection‐cloud chamber (the Pi Chamber), collisional growth is limited by low liquid water content and short droplet residence times. In this work, we use numerical simulations to explore various configurations of a convection‐cloud chamber that may intensify collision‐coalescence. We employ a large‐eddy simulation (LES) model with a size‐resolved (bin) cloud microphysics scheme to explore how cloud properties and the intensity of collision‐coalescence are affected by the chamber size and aspect ratio, surface roughness, side‐wall wetness, side‐wall temperature arrangement, and aerosol injection rate. Simulations without condensation and evaporation within the domain are first performed to explore the turbulence dynamics and wall fluxes. The LES wall fluxes are used to modify the Scalar Flux‐budget Model, which is then applied to demonstrate the need for non‐uniform side‐wall temperature (two side walls as warm as the bottom and the two others as cold as the top) to maintain high supersaturation in a tall chamber. The results of LES with full cloud microphysics reveal that collision‐coalescence is greatly enhanced by employing a taller chamber with saturated side walls, non‐uniform side‐wall temperature, and rough surfaces. For the conditions explored, although lowering the aerosol injection rate broadens the droplet size distribution, favoring collision‐coalescence, the reduced droplet number concentration decreases the frequency of collisions. Plain Language Summary A convection‐cloud chamber is useful in understanding how turbulence affects the interaction between aerosols and cloud droplets. The current convection‐cloud chamber (the Pi Chamber) is likely too small to explore how turbulence affects the collision‐coalescence among cloud droplets. To see whether collisional growth may be observable in a larger cloud chamber, we use numerical simulations to model the cloud droplet size distributions under several different configurations of the cloud chamber. The results suggest that the likelihood of detectable collisional growth increases significantly in a tall chamber with two warm and two cold saturated side walls and rough wall surfaces. Key Points Collision‐coalescence effects on a steady‐state droplet size distribution are stronger in a taller chamber Wet side walls are essential for maintaining cloud liquid water in a chamber with a low width‐to‐height aspect ratio Rougher surfaces increase surface heat and moisture fluxes, leading to larger liquid water content that promotes collision‐coalescence

The convection–cloud chamber enables measurement of aerosol and cloud microphysics, as well as their interactions, within a turbulent environment under steady‐state conditions. Increasing the size of a convection–cloud chamber, while holding the imposed temperature difference constant, leads to increased Rayleigh, Reynolds and Nusselt numbers. Large–eddy simulation coupled with a bin microphysics model allows the influence of increased velocity, time, and spatial scales on cloud microphysical properties to be explored. Simulations of a convection–cloud chamber, with fixed aspect ratio and increasing heights of H = 1, 2, 4, and (for dry conditions only) 8 m are performed. The key findings are: Velocity fluctuations scale as H1/3, consistent with the Deardorff expression for convective velocity, and implying that the turbulence correlation time scales as H2/3. Temperature and other scalar fluctuations scale as H−3/7. Droplet size distributions from chambers of different sizes can be matched by adjusting the total aerosol injection rate as the horizontal cross‐sectional area (i.e., as H2 for constant aspect ratio). Injection of aerosols at a point versus distributed throughout the volume makes no difference for polluted conditions, but can lead to cloud droplet size distribution broadening in clean conditions. Cloud droplet growth by collision and coalescence leads to a broader right tail of the distribution compared to condensation growth alone, and this tail increases in magnitude and extent monotonically as the increase of chamber height. These results also have implications for scaling within turbulent, cloudy mixed‐layers in the atmosphere, such as fog layers. Plain Language Summary In a convection–cloud chamber, turbulent convection is generated by heating the bottom surface and cooling the top surface. Water‐supersaturated conditions are achieved by maintaining the bottom and top surfaces wet. When aerosols are injected into the resulting turbulent, supersaturated flow, cloud droplets are formed and grow by vapor condensation, and possibly by collision and coalescence. The relative roles of condensation due to mean properties and fluctuating properties, as well as the role of collisional growth, depend on the time and velocity scales of the turbulence, all of which depend on the height of the chamber. In this work, turbulence and cloud properties in a convection–cloud chamber are simulated for several chamber heights. It is found that time and velocity scales increase with chamber height; cloud droplet size distributions can be approximately matched by appropriately increasing the aerosol injection rate; and the relevance of collisional growth increases with chamber height. These findings will help guide future computational and laboratory implementations of cloud formation in thermal, moist convection. Key Points Increasing the height of a convection‐cloud chamber leads to an increase in characteristic velocity and time scales Cloud droplet size distributions can be approximately matched by increasing the total aerosol injection rate as the square of the height Concentration of large cloud droplets due to collision and coalescence increases monotonically with increasing height

A large convection cloud chamber has been proposed for exploring aerosol–cloud–drizzle interactions under well‐controlled turbulent conditions. Recent theoretical and numerical studies suggest that a convection cloud chamber with two heated and two cooled sidewalls can significantly enhance the liquid water content and thus benefit drizzle initiation. However, a chamber with such a sidewall configuration develops stable stratification and extremely weak turbulence therein. In this study, we conduct large‐eddy simulations of a tall convection chamber with five different sidewall configurations consisting of alternating warm and cold patches. For each configuration, the total surface area of warm patches equals that of cold patches, resulting in the same expected cloud‐free supersaturation based on a flux budget model. Results show that changing the sidewall configuration, while keeping all other factors constant, can substantially enhance turbulent mixing and improve the uniformity of thermodynamic and cloud microphysical properties in the bulk region of the chamber. In addition, turbulence strength is positively correlated with liquid water content and negatively correlated with cloud droplet number concentration, consistent with theoretical predictions. Our results highlight the advantage of building a large cloud chamber using modular patches with individually controllable temperature and humidity to achieve well‐mixed conditions. Plain Language Summary Theoretical and numerical studies have suggested that cloud droplets in a tall convection cloud chamber with a height of about 10 m can collide with each other to form drizzle drops naturally—a key, but still not fully understood, process for warm rain precipitation. Due to the large height‐to‐width ratio, a previous study shows that a tall convection chamber with two hot sidewalls and another two cold sidewalls is needed to achieve high supersaturation and liquid water content that can benefit drizzle initiation. However, such a sidewall configuration would lead to weak turbulence and stable stratification in the bulk region of the chamber. This study shows that using tiled sidewalls consisting of alternating hot and cold patches can enhance turbulent mixing and the uniformity of cloud microphysical properties, while keeping other factors constant. In addition, increasing turbulent strength increases liquid water content but decreases cloud droplet number concentration, and their scaling relationships from the simulation are consistent with theoretical predictions. Key Points We conduct large‐eddy simulations of a tall convection chamber with tiled sidewalls configured with alternating warm and cold patches Changing tiled sidewall patterns alone can enhance turbulent mixing and microphysical uniformity in a convection cloud chamber Enhanced mixing correlates positively with liquid water content but negatively with number concentration, matching theoretical predictions

Bin microphysics schemes are useful tools for cloud simulations and are often considered to provide a benchmark for model intercomparison. However, they may experience issues with numerical diffusion, which are not well quantified, and the transport of hydrometeors depends on the choice of advection scheme, which can also change cloud simulation results. Here, an atmospheric large‐eddy simulation model is adapted to simulate a statistically steady‐state cloud in a convection cloud chamber under well‐constrained conditions. Two bin microphysics schemes, a spectral bin method and the method of moments, as well as several advection methods for the transport of the microphysical variables are employed for model intercomparison. Results show that different combinations of microphysics and advection schemes can lead to considerable differences in simulated cloud properties, such as cloud droplet number concentration. We find that simulations using the advection scheme that suffers more from numerical diffusion tends to have a smaller droplet number concentration and liquid water content, while simulation with the microphysics scheme that suffers more from numerical diffusion tends to have a broader size distribution and thus larger mean droplet sizes. Sensitivities of simulations to bin resolution, spatial resolution, and temporal resolution are also tested. We find that refining the microphysical bin resolution leads to a broader cloud droplet size distribution due to the advection of hydrometeors. Our results provide insight for using different advection and microphysics schemes in cloud chamber simulations, which might also help understand the uncertainties of the schemes used in atmospheric cloud simulations. Plain Language Summary We investigate the dependence of high‐resolution cloud simulations on the algorithms used to represent the cloud microphysics and advective transport. The model setup is constrained and guided by observed steady‐state clouds in a convection cloud chamber. Two bin microphysics algorithms along with several advection methods are used to simulate the clouds with the same initial and boundary conditions. All simulations reach a steady state, and considerable intermodel variations in simulated cloud properties are found when using different advection and microphysics schemes. The case‐by‐case variations show some correlations with the estimated degree of numerical diffusion suffered by different schemes. Results from model intercomparisons help better understand uncertainties in simulations of clouds in the chamber and in the real atmosphere. Key Points Large‐eddy simulations of a convection cloud chamber are conducted using several microphysics and advection schemes Changes in advection schemes can produce similar variability in simulated cloud properties as do changes in the microphysics schemes Refining microphysical bin widths leads to a broader droplet size distribution due to the advection of hydrometeors

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

The below-cloud irradiance in The Netherlands increased by over 10 Wm−2 in the past half century. It was hypothesized that this could be due to a decrease in the aerosol serving as cloud condensation nuclei, on which the cloud droplets form, in the following way. With unchanged macrophysics, clouds with a lower number of droplets are less reflective, in other words, they transmit more solar radiation. This hypothesis cannot be substantiated with data because of a generic absence of long-term information on cloud droplet number concentrations (CDNCs) worldwide. To assess the historic trend in CDNC, we used the Boucher and Lohmann (B&L) empirical relationship between CDNC and the mass concentration of the water-attracting hygroscopic aerosol components. The B&L parameterization was tested and validated with observations from the CHIEF cloud chamber, in which the formation of marine stratocumulus, the most frequent cloud type in The Netherlands, is simulated. This study will focus on summer periods because the irradiance governs the yearly average at this latitude. The summer trend of sulfate, the most dominant hygroscopic component of observed aerosol mass concentration, was analyzed with EBAS data from 1972 onwards. The average summer CDNCs were then calculated via the B&L parameterization, showing an upper limit of 380 cm−3 in the 1970s and decreasing to around 200 cm−3 in the 2010s. The associated increase in transmission for thin marine stratocumulus without overlying clouds would be, at most, 3.5 W m−2. Unobstructed stratocumuli occur only part of the time, and the change in irradiance based on the reduction in cloud droplet number is certainly small in comparison to the empirically derived trend of 10 W m−2.

In a cloud chamber, the quantum measurement problem amounts to explaining the first droplet in a charged-particle track; subsequent droplets are explained by Mott’s 1929 wave-theoretic argument about collision-induced wavefunction collimation. I formulate a mechanism for how the first droplet in a cloud chamber track arises, making no reference to quantum measurement axioms. I look specifically at tracks of charged particles emitted in the simplest slow decays, because I can reason about rather than guess the form that wave packets take. The first visible droplet occurs when a randomly occurring, barely-subcritical vapor droplet is pushed past criticality by ionization triggered by the faint wavefunction of the emitted charged particle. This is possible because potential energy incurred when an ionized vapor molecule polarizes the other molecules in a droplet can balance the excitation energy needed for the emitted charged particle to create the ion in the first place. This degeneracy is a singular condition for Coulombic scattering, leading to infinite or near-infinite ionization cross sections, and from there to an emergent Born rule in position space, but not an operator projection as in the projection postulate. Analogous mechanisms may explain canonical quantum measurement behavior in detectors such as ionization chambers, proportional counters, photomultiplier tubes or bubble chambers. This work is important because attempts to understand canonical quantum measurement behavior and its limitations have become urgent in view of worldwide investment in quantum computing and in searches for super-rare processes (e.g., proton decay).