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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.

 

Activation is the first step in aerosol‐cloud interactions, which have been identified as one of the principal uncertainties in Earth's climate system. Aerosol particles become cloud droplets, or activate, when the ambient saturation ratio exceeds a threshold, which depends on the particle's size and hygroscopicity. In the traditional formulation of the process, only average, uniform saturation ratios are considered. However, turbulent environments like clouds intrinsically have fluctuations around mean values in the scalar fields of temperature and water vapor concentration, which determine the saturation ratio. Through laboratory measurements, we show that these fluctuations are an important parameter that needs to be addressed to fully describe activation. Our results show, even for single‐sized, chemically homogeneous aerosols, that fluctuations blur the correspondence between activation and a particle's size and chemical composition, that turbulence can increase the fraction of aerosol particles which activate, and that the activated fraction decreases monotonically as the concentration of aerosol increases. Taken together, our data demonstrate that fluctuations can have effects equivalent to the aerosol limited and updraft limited regimes, known from adiabatic parcel theory.

 

Aerosol particles, cloud droplets, and ice crystals, coupled through the supersaturation field, play an important role in the buoyancy and life cycle of convective clouds. This letter reports laboratory observations of copious cloud droplets and ice crystals formed in the wake of a warm, falling water drop, which is a laboratory surrogate for a relatively warm hydrometeor in atmospheric clouds, such as a graupel particle in the wet growth regime. Aerosols were activated in the regions of very high supersaturation due to mixing in the wake. A mechanism is explored for attaining very high supersaturations capable of activating significant fractions of the interstitial aerosols within the lifetime of a convective cloud. The latent heat released from the activation of interstitial aerosols and subsequent growth may provide an additional source of buoyancy for cloud invigoration and may lead to larger concentrations of ice crystals.

 

Data collected with a holographic instrument [Holographic Detector for Clouds (HOLODEC)] on board the High-Performance Instrumented Airborne Platform for Environmental Research Gulfstream-V (HIAPER GV) aircraft from marine stratocumulus clouds during the Cloud System Evolution in the Trades (CSET) field project are examined for spatial uniformity. During one flight leg at 1190 m altitude, 1816 consecutive holograms were taken, which were approximately 40 m apart with individual hologram dimensions of 1.16 cm × 0.68 cm × 12.0 cm and with droplet concentrations of up to 500 cm−3. Unlike earlier studies, minimally intrusive data processing (e.g., bypassing calculation of number concentrations, binning, and parametric fitting) is used to test for spatial uniformity of clouds on intra- and interhologram spatial scales (a few centimeters and 40 m, respectively). As a means to test this, measured droplet count fluctuations are normalized with the expected standard deviation from theoretical Poisson distributions, which signifies randomness. Despite the absence of trends in the mean concentration, it is found that the null hypothesis of spatial uniformity on both spatial scales can be rejected with compelling statistical confidence. Monte Carlo simulations suggest that weak clustering explains this signature. These findings also hold for size-resolved analysis but with less certainty. Clustering of droplets caused by, for example, entrainment and turbulence, is size dependent and is likely to influence key processes such as droplet growth and thus cloud lifetime.

 

Droplet growth due to stochastic condensation has been considered as one of the mechanisms to cause broadening of cloud droplet size distributions and jump the bottleneck between droplet growth due to diffusion and collision‐coalescence. Digital in‐line holography is used to measure variations in droplet number concentration and droplet size in marine boundary layer clouds. Distributions of phase relaxation times are quite broad for some clouds. Turbulence correlation times are estimated, and the comparison of these with phase relaxation times suggests that clouds exist in both fast and slow microphysical regimes. Presumed signatures of stochastic condensation, such as increasing relative size dispersion and increasing droplet size with decreasing number density, are observed.

 

Soot particles form during combustion of carbonaceous materials and impact climate and air quality. When freshly emitted, they are typically fractal-like aggregates. After atmospheric aging, they can act as cloud condensation nuclei, and water condensation or evaporation restructure them to more compact aggregates, affecting their optical, aerodynamic, and surface properties. Here we survey the morphology of ambient soot particles from various locations and different environmental and aging conditions. We used electron microscopy and show extensive soot compaction after cloud processing. We further performed laboratory experiments to simulate atmospheric cloud processing under controlled conditions. We find that soot particles sampled after evaporating the cloud droplets, are significantly more compact than freshly emitted and interstitial soot, confirming that cloud processing, not just exposure to high humidity, compacts soot. Our findings have implications for how the radiative, surface, and aerodynamic properties, and the fate of soot particles are represented in numerical models.

 

The Pi Cloud Chamber offers a unique opportunity to study aerosol‐cloud microphysics interactions in a steady‐state, turbulent environment. In this work, an atmospheric large‐eddy simulation (LES) model with spectral bin microphysics is scaled down to simulate these interactions, allowing comparison with experimental results. A simple scalar flux budget model is developed and used to explore the effect of sidewalls on the bulk mixing temperature, water vapor mixing ratio, and supersaturation. The scaled simulation and the simple scalar flux budget model produce comparable bulk mixing scalar values. The LES dynamics results are compared with particle image velocimetry measurements of turbulent kinetic energy, energy dissipation rates, and large‐scale oscillation frequencies from the cloud chamber. These simulated results match quantitatively to experimental results. Finally, with the bin microphysics included the LES is able to simulate steady‐state cloud conditions and broadening of the cloud droplet size distributions with decreasing droplet number concentration, as observed in the experiments. The results further suggest that collision‐coalescence does not contribute significantly to this broadening. This opens a path for further detailed intercomparison of laboratory and simulation results for model validation and exploration of specific physical processes.

 

Precipitation efficiency and optical properties of clouds, both central to determining Earth's weather and climate, depend on the size distribution of cloud particles. In this work theoretical expressions for cloud droplet size distribution shape are evaluated using measurements from controlled experiments in a convective‐cloud chamber. The experiments are a unique opportunity to constrain theory because they are in steady‐state and because the initial and boundary conditions are well characterized compared to typical atmospheric measurements. Three theoretical distributions obtained from a Langevin drift‐diffusion approach to cloud formation via stochastic condensation are tested: (a) stochastic condensation with a constant removal time‐scale; (b) stochastic condensation with a size‐dependent removal time‐scale; (c) droplet growth in a fixed supersaturation condition and with size‐dependent removal. In addition, a similar Weibull distribution that can be obtained from the drift‐diffusion approach, as well as from mechanism‐independent probabilistic arguments (e.g., maximum entropy), is tested as a fourth hypothesis. Statistical techniques such as theχ²test, sum of squared errors of prediction, and residual analysis are employed to judge relative success or failure of the theoretical distributions to describe the experimental data. An extensive set of cloud droplet size distributions are measured under different aerosol injection rates. Five different aerosol injection rates are run both for size‐selected aerosol particles, and six aerosol injection rates are run for broad‐distribution, polydisperse aerosol particles. In relative comparison, the most favourable comparison to the measurements is the expression for stochastic condensation with size‐dependent droplet removal rate. However, even this optimal distribution breaks down for broad aerosol size distributions, primarily due to deviations from the measured large‐droplet tail. A possible explanation for the deviation is the Ostwald ripening effect coupled with deactivation/activation in polluted cloud conditions.

 

Abstract The role played by fluctuations of supersaturation in the growth of cloud droplets is examined in this study. The stochastic condensation framework and the three regimes of activation of cloud droplets— namely, mean dominant, fluctuation influenced, and fluctuation dominant—are used for analyzing the data from high-resolution large-eddy simulations of the Pi convection-cloud chamber. Based on a detailed budget analysis the significance of all the terms in the evolution of the droplet size distribution equation is evaluated in all three regimes. The analysis indicates that the mean-growth rate is a dominant process in shaping the droplet size distribution in all three regimes. Turbulence introduces two sources of stochasticity, turbulent transport and particle lifetime, and supersaturation fluctuations. The transport of cloud droplets plays an important role in all three regimes, whereas the direct effect of supersaturation fluctuations is primarily related to the activation and growth of the small droplets in the fluctuation-influenced and fluctuation-dominant regimes. We compare our results against the previous studies (experimental and theory) of the Pi chamber, and discuss the limitations of the existing models based on the stochastic condensation framework. Furthermore, we extend the discussion of our results to atmospheric clouds, and in particular focus on recent adiabatic turbulent cloud parcel simulations based on the stochastic condensation framework, and emphasize the importance of entrainment/mixing and turbulent transport in shaping the droplet size distribution.