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Clouds, crucial for understanding climate, begin with droplet formation from aerosols, but observations of this fleeting activation step are lacking in the atmosphere. Here we use a time-gated time-correlated single-photon counting lidar to observe cloud base structures at decimeter scales. Results show that the air–cloud interface is not a perfect boundary but rather a transition zone where the transformation of aerosol particles into cloud droplets occurs. The observed distributions of first-arriving photons within the transition zone reflect vertical development of a cloud, including droplet activation and condensational growth. Further, the highly resolved vertical profile of backscattered photons above the cloud base enables remote estimation of droplet concentration, an elusive but critical property to understanding aerosol–cloud interactions. Our results show the feasibility of remotely monitoring cloud properties at submeter scales, thus providing much-needed insights into the impacts of atmospheric pollution on clouds and aerosol-cloud interactions that influence climate.

 

Water vapor supersaturation in clouds is a random variable that drives activation and growth of cloud droplets. The Pi Convection–Cloud Chamber generates a turbulent cloud with a microphysical steady state that can be varied from clean to polluted by adjusting the aerosol injection rate. The supersaturation distribution and its moments, e.g., mean and variance, are investigated for varying cloud microphysical conditions. High-speed and collocated Eulerian measurements of temperature and water vapor concentration are combined to obtain the temporally resolved supersaturation distribution. This allows quantification of the contributions of variances and covariances between water vapor and temperature. Results are consistent with expectations for a convection chamber, with strong correlation between water vapor and temperature; departures from ideal behavior can be explained as resulting from dry regions on the warm boundary, analogous to entrainment. The saturation ratio distribution is measured under conditions that show monotonic increase of liquid water content and decrease of mean droplet diameter with increasing aerosol injection rate. The change in liquid water content is proportional to the change in water vapor concentration between no-cloud and cloudy conditions. Variability in the supersaturation remains even after cloud droplets are formed, and no significant buffering is observed. Results are interpreted in terms of a cloud microphysical Damköhler number (Da), under conditions corresponding to, i.e., the slow-microphysics regime. This implies that clouds with very clean regions, such thatis satisfied, will experience supersaturation fluctuations without them being buffered by cloud droplet growth.

The saturation ratio (humidity) in clouds controls the growth rate and formation of cloud droplets. When air in a turbulent cloud mixes, the humidity varies in space and time throughout the cloud. This is important because it means cloud droplets experience different growth histories, thereby resulting in broader size distributions. It is often assumed that growth and evaporation of cloud droplets buffers out some of the humidity variations. Measuring these variations has been difficult, especially in the field. The purpose of this study is to measure the saturation ratio distribution in clouds with a range of conditions. We measure the in-cloud saturation ratio using a convection cloud chamber with clean to polluted cloud properties. We found in clouds with low concentrations of droplets that the variations in the saturation ratio are not suppressed.

 

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.

 

Turbulent fluctuations of scalar and velocity fields are critical for cloud microphysical processes, e.g., droplet activation and size distribution evolution, and can therefore influence cloud radiative forcing and precipitation formation. Lagrangian and Eulerian water vapor, temperature, and supersaturation statistics are investigated in direct numerical simulations (DNS) of turbulent Rayleigh–Bénard convection in the Pi Convection Cloud Chamber to provide a foundation for parameterizing subgrid-scale fluctuations in atmospheric models. A subgrid model for water vapor and temperature variances and covariance and supersaturation variance is proposed, valid for both clear and cloudy conditions. Evaluation of phase change contributions through an a priori test using DNS data shows good performance of the model. Supersaturation is a nonlinear function of temperature and water vapor, and relative external fluxes of water vapor and heat (e.g., during entrainment-mixing and phase change) influence turbulent supersaturation fluctuations. Although supersaturation has autocorrelation and structure functions similar to the independent scalars (temperature and water vapor), the autocorrelation time scale of supersaturation differs. Relative scalar fluxes in DNS without cloud make supersaturation PDFs less skewed than the adiabatic case, where they are highly negatively skewed. However, droplet condensation changes the PDF shape response: it becomes positively skewed for the adiabatic case and negatively skewed when the sidewall relative fluxes are large. Condensation also increases correlations between water vapor and temperature in the presence of relative scalar fluxes but decreases correlations for the adiabatic case. These changes in correlation suppress supersaturation variability for the nonadiabatic cases and increase it for the adiabatic case. Implications of this work for subgrid microphysics modeling using a Lagrangian stochastic scheme are also discussed.

 

Abstract. Heterogeneous ice nucleation is thought to be the primary pathway for the formation of ice in mixed-phase clouds, with the number of active ice-nucleating particles (INPs) increasing rapidly with decreasing temperature. Here, molecular-dynamics simulations of heterogeneous ice nucleation demonstrate that the ice nucleation rate is also sensitive to pressure and that negative pressure within supercooled water shifts freezing temperatures to higher temperatures. Negative pressure, or tension, occurs naturally in water capillary bridges and pores and can also result from water agitation. Capillary bridge simulations presented in this study confirm that negative Laplace pressure within the water increases heterogeneous-freezing temperatures. The increase in freezing temperatures with negative pressure is approximately linear within the atmospherically relevant range of 1 to −1000 atm. An equation describing the slope depends on the latent heat of freezing and the molar volume difference between liquid water and ice. Results indicate that negative pressures of −500 atm, which correspond to nanometer-scale water surface curvatures, lead to a roughly 4 K increase in heterogeneous-freezing temperatures. In mixed-phase clouds, this would result in an increase of approximately 1 order of magnitude in active INP concentrations. The findings presented here indicate that any process leading to negative pressure in supercooled water may play a role in ice formation, consistent with experimental evidence of enhanced ice nucleation due to surface geometry or mechanical agitation of water droplets. This points towards the potential for dynamic processes such as contact nucleation and droplet collision or breakup to increase ice nucleation rates through pressure perturbations.

 

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. 

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.

 

The effect of aerosols on the properties of clouds is a large source of uncertainty in predictions of weather and climate. These aerosol‐cloud interactions depend critically on the ability of aerosol particles to form cloud droplets. A challenge in modeling aerosol‐cloud interactions is the representation of interactions between turbulence and cloud microphysics. Turbulent mixing leads to small‐scale fluctuations in water vapor and temperature that are unresolved in large‐scale atmospheric models. To quantify the impact of turbulent fluctuations on cloud condensation nuclei (CCN) activation, we used a high‐resolution Large Eddy Simulation of a convective cloud chamber to drive particle‐based cloud microphysics simulations. We show small‐scale fluctuations strongly impact CCN activity. Once activated, the relatively long timescales of evaporation compared to fluctuations causes droplets to persist in subsaturated regions, which further increases droplet concentrations.

 

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 ofH = 1, 2, 4, and (for dry conditions only) 8 m are performed. The key findings are: Velocity fluctuations scale asH^1/3, consistent with the Deardorff expression for convective velocity, and implying that the turbulence correlation time scales asH^2/3. Temperature and other scalar fluctuations scale asH^−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., asH²for 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.