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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.

 

A state‐of‐the‐art Lagrangian microphysics scheme is used in a large‐eddy simulation to investigate the stratocumulus transition from closed to open cell structure. Processes controlling precipitation development, which is a key to the transition, are analyzed by leveraging unique benefits of Lagrangian microphysics, particularly the ability to track computational drops in the flow. Sufficient time is needed for coalescence growth of cloud drops to drizzle within the updraft‐downdraft cycle of large eddies. This favors broad drop size distributions (DSDs) and drizzle growth in downdrafts, where drops are typically much older than in updrafts. During the closed cell stage, mean cloud drop radius is too small, and the DSDs are too narrow, so that the timescale for coalescence is much longer than the large eddy turnover time and drizzle growth is limited. The closed‐to‐open cell transition occurs when these timescales become comparable and the precipitation flux increases sharply.

 

Moist Rayleigh–Bénard convection with water saturated boundaries is explored using a One-Dimensional Turbulence model. The system involves both temperature $T$ and water vapour pressure $e_{v}$ as driving scalars. The emphasis of the work is on a supersaturation $s$ , a nonlinear combination of $T$ and $e_{v}$ that is crucial to cloud formation. Its mean as well as fluctuation statistics determine cloud droplet growth and therefore precipitation formation and cloud optical properties. To explore the role of relative scalar diffusivities for temperature ( $D_{t}$ ) and water vapour ( $D_{v}$ ), three different regimes are considered: $D_{v}>D_{t}$ , $D_{v}\approx D_{t}$ and $D_{v} more » « less

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This study examines two factors impacting initiation of moist deep convection: free-tropospheric environmental relative humidity (ϕ_E) and horizontal scale of subcloud ascent (R_sub), the latter exerting a dominant control on cumulus cloud width. A simple theoretical model is used to formulate a “scale selection” hypothesis: that a minimumR_subis required for moist convection to go deep, and that this minimum scale decreases with increasingϕ_E. Specifically, the ratio ofto saturation deficit (1 −ϕ_E) must exceed a certain threshold value that depends on cloud-layer environmental lapse rate. Idealized, large-eddy simulations of moist convection forced by horizontally varying surface fluxes show strong sensitivity of maximum cumulus height to bothϕ_EandR_subconsistent with the hypothesis. IncreasingR_subby only 300–400 m can lead to a large increase (>5 km) in cloud height. A passive tracer analysis shows that the bulk fractional entrainment rate decreases rapidly withR_subbut depends little onϕ_E. However, buoyancy dilution increases as eitherR_suborϕ_Edecreases; buoyancy above the level of free convection is rapidly depleted in dry environments whenR_subis small. While deep convective initiation occurs with an increase in relative humidity of the near environment from moistening by earlier convection, the importance of this moisture preconditioning is inconclusive as it is accompanied by an increase inR_sub. Overall, it is concluded that small changes toR_subdriven by external forcing or by convection itself could be a dominant regulator of deep convective initiation.

 

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.

 

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.