Editorial Type:
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Article Type:
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Supersaturation Variability from Scalar Mixing: Evaluation of a New Subgrid-Scale Model Using Direct Numerical Simulations of Turbulent Rayleigh–Bénard Convection

Kamal Kant Chandrakar aNational Center for Atmospheric Research, Boulder, Colorado

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Hugh Morrison aNational Center for Atmospheric Research, Boulder, Colorado

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Wojciech W. Grabowski aNational Center for Atmospheric Research, Boulder, Colorado

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George H. Bryan aNational Center for Atmospheric Research, Boulder, Colorado

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Raymond A. Shaw bMichigan Technological University, Houghton, Michigan

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Online Publication:
05 Apr 2022
Print Publication:
01 Apr 2022
DOI:
https://doi.org/10.1175/JAS-D-21-0250.1
Page(s):
1191–1210
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Abstract

Supersaturation fluctuations in the atmosphere are critical for cloud processes. A nonlinear dependence on two scalars—water vapor and temperature—leads to different behavior than single scalars in turbulent convection. For modeling such multiscalar processes at subgrid scales (SGS) in large-eddy simulations (LES) or convection-permitting models, a new SGS scheme is implemented in CM1 that solves equations for SGS water vapor and temperature fluctuations and their covariance. The SGS model is evaluated using benchmark direct-numerical simulations (DNS) of turbulent Rayleigh–Bénard convection with water vapor as in the Michigan Tech Pi Cloud Chamber. This idealized setup allows thorough evaluation of the SGS model without complications from other atmospheric processes. DNS results compare favorably with measurements from the chamber. Results from LES using the new SGS model compare well with DNS, including profiles of water vapor and temperature variances, their covariance, and supersaturation variance. SGS supersaturation fluctuations scale appropriately with changes to the LES grid spacing, with the magnitude of SGS fluctuations decreasing relative to those at the resolved scale as the grid spacing is decreased. Sensitivities of covariance and supersaturation statistics to changes in water vapor flux relative to thermal flux are also investigated by modifying the sidewall conditions. Relative changes in water vapor flux substantially decrease the covariance and increase supersaturation fluctuations even away from boundaries.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Kamal Kant Chandrakar, kkchandr@ucar.edu

Abstract

Supersaturation fluctuations in the atmosphere are critical for cloud processes. A nonlinear dependence on two scalars—water vapor and temperature—leads to different behavior than single scalars in turbulent convection. For modeling such multiscalar processes at subgrid scales (SGS) in large-eddy simulations (LES) or convection-permitting models, a new SGS scheme is implemented in CM1 that solves equations for SGS water vapor and temperature fluctuations and their covariance. The SGS model is evaluated using benchmark direct-numerical simulations (DNS) of turbulent Rayleigh–Bénard convection with water vapor as in the Michigan Tech Pi Cloud Chamber. This idealized setup allows thorough evaluation of the SGS model without complications from other atmospheric processes. DNS results compare favorably with measurements from the chamber. Results from LES using the new SGS model compare well with DNS, including profiles of water vapor and temperature variances, their covariance, and supersaturation variance. SGS supersaturation fluctuations scale appropriately with changes to the LES grid spacing, with the magnitude of SGS fluctuations decreasing relative to those at the resolved scale as the grid spacing is decreased. Sensitivities of covariance and supersaturation statistics to changes in water vapor flux relative to thermal flux are also investigated by modifying the sidewall conditions. Relative changes in water vapor flux substantially decrease the covariance and increase supersaturation fluctuations even away from boundaries.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Kamal Kant Chandrakar, kkchandr@ucar.edu
Keywords: Turbulence; Cloud microphysics; Large eddy simulations; Subgrid-scale processes

1. Introduction

Supersaturation is a key variable for cloud droplet activation and growth and depends linearly on water vapor mixing ratio and nonlinearly on temperature approximately through the Clausius–Clapeyron equation. Here, supersaturation is defined as the ratio of water vapor mixing ratio to its equilibrium value over a flat surface minus one. Supersaturation fluctuations can be generated through fluctuations in water vapor and temperature fields in a turbulent environment. For example, temperature fluctuations generated from vertical velocity fluctuations in a stratified environment drive supersaturation fluctuations (Politovich and Cooper 1988; Korolev and Mazin 2003). Similarly, temperature and water vapor fluctuations during the isobaric mixing process (Korolev and Isaac 2000; Gerber 1991) can generate a fluctuating field of supersaturation. In general, multiscalar transport in turbulent flow is relevant to a variety of processes that depend on multiple scalars—chemical reactions and transport of reactive species (Gao and Wesely 1994), engineering applications and fundamental fluid problems (Warhaft 1981), wave propagation in a turbulent medium (Wyngaard et al. 1978), etc. For example, the refractive index of air depends on both water vapor and temperature, and covariance of water vapor and temperature fluctuations affect the electromagnetic and acoustic wave propagation in turbulent flows (Wyngaard et al. 1978). The focus of the current study is on the supersaturation fluctuations driven by the process of scalar mixing in turbulence.

In the Michigan Tech Pi Convection Cloud Chamber (Chang et al. 2016; Chandrakar et al. 2016), supersaturation fluctuations are generated via scalar mixing in Rayleigh–Bénard convection. The moist Rayleigh number, Ra=gΔTH3/(ToνDt)+gϵΔqυH3/(νDt), signifies the magnitude of the buoyant forcing in Rayleigh–Bénard convection with water vapor (Niedermeier et al. 2018; Chandrakar et al. 2020a) and is one of the main parameters of the system. Here, g is the gravitational acceleration, To is the mean temperature, ΔT and Δqυ are the difference in temperature and water vapor between the top and bottom boundaries, the parameter ϵ is the ratio of molecular mass of dry air and water vapor minus one, H is the height of the chamber, ν is kinematic viscosity, and Dt is the thermal diffusivity. Ra controls the surface fluxes and turbulence statistics in the chamber. The Pi Cloud Chamber provides a controlled condition for studying cloud–turbulence interactions and is well suited for developing and testing subgrid-scale (SGS) models. MacMillan et al. (2022) performed the first DNS of the Pi Chamber with droplets. However, the Rayleigh number for these simulations was three orders of magnitude smaller than the real Pi Chamber, and periodic sidewall boundaries were used. Thus, the turbulence and moisture statistics were idealized, and different from those in the Pi Chamber. In the current study, we perform DNS of convection in the Pi Chamber at the actual Rayleigh number of 1.5 × 109, and we include realistic sidewall boundary conditions. Detailed data from these DNS complement measurements in the chamber. These simulations provide insights into the role of scalar mixing and relative scalar fluxes in generating supersaturation fluctuations in a simplified setting. They also serve as a benchmark for testing a new SGS supersaturation model introduced herein based on prognosing SGS scalar variances and covariance. In the current simulations, cloud droplets are not included to avoid complications in evaluating the SGS model and to understand the role of relative scalar fluxes better in a simplified setting. A study including effects of cloud droplet growth is currently underway, and will be reported later.

In actual cloud turbulence, there is an enormous range of scales from the O(1 mm) Kolmogorov microscale to O(1–10)-km integral scales. Interactions of cloud and aerosol particles with thermodynamic and dynamic fields occur even below the Kolmogorov microscale. Thus, an accurate representation of clouds in atmospheric models across scales poses a significant challenge, and small-scale cloud processes are a significant source of uncertainty in weather prediction and climate models (Morrison et al. 2020). The turbulent flow field affects clouds at macroscales, such as entrainment of noncloudy air and large-scale organization (Mellado 2017; De Rooy et al. 2013; Feingold et al. 2010; Pauluis and Schumacher 2011). Microscale interactions of turbulence and clouds affect droplet activation (Chandrakar et al. 2017; Abade et al. 2018; Prabhakaran et al. 2020), condensation growth (Cooper 1989; Vaillancourt et al. 2002; Lanotte et al. 2009; Paoli and Shariff 2009; Field et al. 2014; Sardina et al. 2015; Chandrakar et al. 2016, 2018, 2020b; Grabowski and Abade 2017; Desai et al. 2018; Saito et al. 2019; Hoffmann et al. 2019; Thomas et al. 2020; Chandrakar et al. 2021), and collisional growth of droplets (Shaw 2003; Grabowski and Wang 2013; Devenish et al. 2012). How can we account for the impact of SGS supersaturation fluctuations on droplet activation and growth in models? A few different approaches based on Gaussian stochastic models have gained recent popularity (e.g., Paoli and Shariff 2009; Sardina et al. 2015; Chandrakar et al. 2016; Grabowski and Abade 2017; Hoffmann et al. 2019). Most of these SGS schemes are suitable for parcel models or Lagrangian microphysics schemes in a three-dimensional dynamical model. Some of these schemes require estimating the strength of the supersaturation fluctuation forcing. A few of them (e.g., Sardina et al. 2015; Grabowski and Abade 2017) represent supersaturation fluctuations driven by SGS vertical velocity fluctuations and neglect contributions from the scalar mixing process. Hoffmann et al. (2019) and Hoffmann and Feingold (2019) account for SGS entrainment mixing using the one-dimensional linear-eddy model (Kerstein 1988) but without separately treating water vapor and temperature fluctuations. Thus, the contribution of SGS water vapor and temperature covariance to supersaturation fluctuations was not accounted for explicitly. The current paper introduces a new SGS model of supersaturation fluctuations that separately treats water vapor and temperature fluctuations and accounts for SGS supersaturation driven by scalar mixing process in addition to SGS vertical velocity fluctuations.

Supersaturation variance depends on water vapor and temperature variances and their covariance (Kulmala et al. 1997). In turbulent convection, scalar variance is directly related to scalar fluxes, e.g., surface fluxes or turbulent fluxes at different levels of turbulent Rayleigh–Bénard and atmospheric boundary layer convection, entrainment flux in cloud parcels. The covariance of different scalars (in this case, water vapor and temperature) depends on their relative fluxes. In the atmospheric boundary layer or clouds, entrainment of air from the free troposphere, condensation/evaporation, and surface inhomogeneities introduce relative fluxes of water vapor and heat. This can make the normalized covariance close to +1 near the surface but completely anticorrelated (−1) near the top of the boundary layer (Wyngaard et al. 1978; Wyngaard and LeMone 1980; Williams et al. 1997). The top-down mixing of entrained air in the boundary layer is thought of as a significant source of decrease in the correlation of the two scalars in a well-mixed layer (Wyngaard et al. 1978; Wyngaard and LeMone 1980; Williams et al. 1997; Katul et al. 2008). Via high-resolution measurements, Siebert and Shaw (2017) reported normalized covariance between water vapor and temperature significantly less than one in shallow cumulus clouds. Similarly, the presence of sidewalls in the Pi Cloud Chamber introduces relative fluxes of heat and moisture (Thomas et al. 2019). To understand how the relative fluxes of heat and water vapor affect their covariance in turbulent convection, we compare DNS of the Rayleigh–Bénard convection with adiabatic sidewalls to DNS with different sidewall conditions that drive different degrees of relative fluxes of both scalars.

This study introduces a new SGS model for supersaturation fluctuations that accounts for the generation of supersaturation fluctuations from scalar mixing apart from the vertical velocity fluctuations. For that purpose, the model explicitly predicts water vapor and temperature variances and their covariance in an LES model. The approach is similar to that of Deardorff (1974a). A DNS of Rayleigh–Bénard convection with realistic sidewall boundaries (motivated by Michigan Tech Pi Cloud Chamber) is used as a benchmark to test the SGS scheme in LES at varying grid resolutions. We also investigate the roles of scalar mixing and relative fluxes of water vapor and sensible heat on supersaturation fluctuations using DNS with different sidewall conditions. The current study builds on past studies of Rayleigh–Bénard convection in the Pi Chamber (e.g., Chandrakar et al. 2020a; Thomas et al. 2021; MacMillan et al. 2022) using more detailed and realistic simulations (e.g., similar Rayleigh number as the real Pi Chamber and with realistic sidewall boundaries).

We focus on the following science questions:

  • How well does the new SGS supersaturation model (based on scalar variance and covariance prognosis) perform compared to a benchmark DNS of turbulent Rayleigh–Bénard convection?

  • How do the sidewall fluxes of heat and moisture affect turbulence statistics for Rayleigh–Bénard convection?

  • How do the relative fluxes of heat and moisture during turbulent mixing influence supersaturation fluctuations?

2. Subgrid-scale model of scalar fluctuations

a. Subgrid supersaturation variance

As discussed in the introduction, some previous approaches to represent SGS supersaturation fluctuations (e.g., Field et al. 2014; Sardina et al. 2015; Grabowski and Abade 2017; Chandrakar et al. 2021) only considered fluctuations driven from vertical velocity and therefore had to assume zero external water vapor and heat fluxes in the cloud system. However, supersaturation is a function of temperature and water vapor, and in a turbulent environment both fluctuate if there is a flux convergence or divergence of either locally. Other approaches account for SGS scalar mixing in supersaturation fluctuations (e.g., Hoffmann et al. 2019) but do not treat water vapor and temperature fluctuations separately. Our new SGS modeling approach includes supersaturation fluctuations generated from fluctuations in temperature and water vapor during turbulent mixing apart from supersaturation fluctuations driven by vertical velocity. Thus, this model can also predict SGS supersaturation fluctuations driven by turbulent entrainment mixing in clouds. Moreover, a separate treatment of water vapor and temperature fluctuations is a more rigorous way to determine the SGS supersaturation fluctuations since the relative fluxes of water vapor and temperature affect the supersaturation statistics significantly (as shown in section 4a).

The supersaturation variance can be expressed as a function of the variances and covariances of temperature, water vapor mixing ratio, and pressure fluctuations using Taylor series expansion, and neglecting higher-order terms (Kulmala et al. 1997; Siebert and Shaw 2017; Chandrakar et al. 2020a). In the current case, we derived the supersaturation variance as a function of variances and covariances of potential temperature (θ), water vapor mixing ratio (qυ), and pressure (p) fluctuations to be consistent with the governing equations of CM1 (Bryan and Fritsch 2002):
σs2≈S2(θ¯,qυ¯,p¯)[β2 (θ2¯−θ¯2)θ¯2+(qυ2¯−qυ¯2)qυ¯2+(βRCp−1)2(p2¯−p¯2)p¯2−2β (θqυ¯−θ¯qυ¯)qυ¯θ¯],
where the overline1 denotes spatial filter for different variables, β=L/(RυT¯), T¯ is the gridscale temperature, Rυ and R are the gas constants for water vapor and air, S=qυ¯/qυs(θ¯), and qυ is the saturation vapor mixing ratio. In this equation, the higher-order fluctuation terms and cross covariance of other scalars with pressure fluctuations are neglected. Nonhydrostatic pressure fluctuations are expected to negligibly influence the moist thermodynamics in a typical cloud-scale LES or cloud resolving model configuration (Kurowski et al. 2014). Thus, pressure fluctuations are assumed hydrostatic and can be modeled in terms of vertical velocity fluctuations: (p2¯−p¯2)≈ρ¯2g2(w2¯−w¯2)τt2. Here, τt is the eddy turnover time or the turbulent mixing time scale. Therefore, the variance of supersaturation fluctuations can be expressed as
σs2≈S2(θ¯,qυ¯,P¯)[β2 (θ2¯−θ¯2)θ¯2+(qυ2¯−qυ¯2)qυ¯2+(βgCpT¯−gRT¯)2(w2¯−w¯2)τt2−2β (θqυ¯−θ¯qυ¯)qυ¯θ¯],
where w is vertical velocity. In the simulations presented here, the pressure fluctuation term (or the vertical velocity fluctuation term) can be neglected because of the small vertical extent of the domain (1 m). However, in atmospheric simulations it is an important component of the supersaturation fluctuations. To obtain the SGS supersaturation variance at each grid box using Eq. (2), we need to get the SGS variance of θ, qυ, and w, and the covariance between θ and qυ at each time step. The following subsection discusses new prognostic scalar variance and covariance equations implemented in CM1 to obtain the first, second, and fourth terms in Eq. (2). The third (updraft) term can be estimated from the existing SGS prognostic TKE scheme based on Deardorff (1980) in CM1 by assuming w2¯−w¯2≈(2/3)e (where e is subgrid TKE).

b. Subgrid-scale model for water vapor and potential temperature variance and covariance

The budget equations for scalar variances and covariances can be derived from the conservation equation of the individual scalars, and they are presented in different turbulent flow textbooks (e.g., Wyngaard 2010). Similarly, the budget equations for SGS potential temperature and water vapor mixing ratio variance can be obtained as (Wyngaard 2010; Deardorff 1974b,a; Jiménez et al. 2001; Zilitinkevich et al. 2007; Shi et al. 2019)
D(θ2¯−θ¯2)Dt=−1ρ¯ ∂∂xi[ρ¯(uiθ2¯−ui¯θ2¯)]+2θ¯ρ¯ ∂∂xi[ρ¯(uiθ¯−ui¯θ¯)]+∂∂xi[Dt ∂(θ2¯−θ¯2)∂xi]−(2Dt ∂θ∂xi∂θ∂xi¯−2Dt ∂θ¯∂xi ∂θ¯∂xi)+LCp(q˙lθ/Π¯−q˙¯lθ¯/Π¯−q˙¯lθ/Π¯−q˙lθ¯/Π¯−q˙l/Π¯θ¯),
D(qυ2¯−qυ¯2)Dt=−1ρ¯ ∂∂xi[ρ¯(uiqυ2¯−ui¯qυ2¯)]+2qυ¯ρ¯ ∂∂xi[ρ¯(uiqυ¯−ui¯qυ¯)]+∂∂xi[Dυ ∂(qυ2¯−qυ¯2)∂xi]−(2Dυ ∂qυ∂xi∂qυ∂xi¯−2Dυ ∂qυ¯∂xi ∂qυ¯∂xi)−(q˙lqυ¯−q˙l¯qυ¯),
where D/Dt represent the material derivative, the dot represents the rate of change with time, ui is a flow velocity component, ρ is the air density, Dt and Dυ are molecular diffusivities of heat and moisture, L is the latent heat of condensation, Cp is the specific heat capacity at constant pressure, ql is the liquid water mixing ratio, and Π is the Exner function. The first term on the right-hand side is turbulent transport. The second is the turbulent production, the third is the molecular diffusion of variance, the fourth is the dissipation, and the last is the diabatic production because of condensation and evaporation. With the use of the eddy diffusivity model (Kθ and Kqυ: eddy diffusivities of potential temperature and water vapor) for the turbulent transport and neglecting the diabatic term (for the moist case without condensation or evaporation), the above equations can be simplified to (Jiménez et al. 2001; Zilitinkevich et al. 2007; Shi et al. 2019)
D(θ2¯−θ¯2)Dt=1ρ¯ ∂∂xi[ρ¯Kθ ∂(θ2¯−θ¯2)∂xi]+2Kθ ∂θ¯∂xi ∂θ¯∂xi+∂∂xi[Dt ∂(θ2¯−θ¯2)∂xi]−(2Dt ∂θ∂xi∂θ∂xi¯−2Dt ∂θ¯∂xi ∂θ¯∂xi),
D(qυ2¯−qυ¯2)Dt=1ρ¯ ∂∂xi[ρ¯Kqυ ∂(qυ2¯−qυ¯2)∂xi]+2Kqυ ∂qυ¯∂xi ∂qυ¯∂xi+∂∂xi[Dυ ∂(qυ2¯−qυ¯2)∂xi]−(2Dυ ∂qυ∂xi∂qυ∂xi¯−2Dυ ∂qυ¯∂xi ∂qυ¯∂xi).
The molecular diffusion terms, i.e., the third terms on the right side of the above equations, are neglected since their contribution is expected to be much smaller than turbulent mixing at high Reynolds number. The fourth terms representing molecular destruction are leading order and thus cannot be neglected.
In the past, scalar covariance has been explored in the atmospheric boundary layer considering its importance in wave propagation and chemistry (Wyngaard et al. 1978; Katul et al. 2009). This paper investigates the covariance budget of the water vapor and temperature fields to determine its contribution to supersaturation fluctuations. Similar to the above scalar variance equations, the budget equation for the covariance between qυ and θ fluctuations (neglecting higher-order diffusion term apart from the dissipation) is (Tennekes and Lumley 2018; Wyngaard et al. 1978; Deardorff 1974a; Katul et al. 2008)
D(qυθ¯−qυ¯θ¯)Dt=−∂qυ¯∂xi(uiθ¯−ui¯θ¯)−∂θ¯∂xi(uiqυ¯−ui ¯qυ¯)−1ρ¯ ∂∂xi[ρ¯(uiθqυ¯−ui¯θ¯qυ¯−ui¯θqυ¯−θ¯uiqυ¯−qυ¯uiθ¯)]−2(Dυ+Dt)(∂qυ∂xi∂θ∂xi¯−∂qυ¯∂xi ∂θ¯∂xi)+LCp(q˙lqυ/Π¯−q˙¯lqυ¯/Π¯−q˙¯lqυ/Π¯−q˙lqυ¯/Π¯−q˙l/Π¯qυ¯)−(q˙lθ¯−q˙l¯θ¯).
Equation (7) is further simplified with the use of the eddy diffusivity model (Ks: eddy diffusivity of a scalar) and neglecting higher-order diabatic contributions consistent with the variance equations:
D(qυθ¯−qυ¯θ¯)Dt=2Ks ∂θ¯∂xi ∂qυ¯∂xi+1ρ¯ ∂∂xi[ρ¯Ks ∂(qυθ¯−qυ¯θ¯)∂xi]−2(Dυ+Dt)(∂qυ∂xi∂θ∂xi¯−∂qυ¯∂xi ∂θ¯∂xi).

The dissipation terms in the above equations for variances and covariance are modeled in terms of SGS TKE and TKE dissipation rate by assuming proportionality between the turbulent mixing time scale for momentum and scalars (Deardorff 1974b; Jiménez et al. 2001; Shi et al. 2019). Therefore, the dissipation rate for a scalar (ϕ) is ϵ(ϕ2¯−ϕ¯2)/(Ce). Here, ϵ is the turbulent kinetic energy dissipation rate. The proportionality constant, C, is set to 0.55 in the current simulations. e and ϵ are determined from the prognostic TKE scheme based on Deardorff (1980) in the standard CM1. The eddy diffusivities for water vapor and temperature (Kqυ=Kθ=Ks=chℓe1/2) are assumed to be the same in CM1 and determined based on the Deardorff (1980) scheme. ℓ=[(2/3)( e/Nm2)]1/2 is the subgrid-scale mixing length and depends on e and the Brunt–Väisälä frequency: Nm (the maximum value of ℓ is equal to the model grid size). The coefficient ch depends on the mixing length and grid size [see Shi et al. (2018) for further details]. The departure of the turbulent Lewis number (ratio of turbulent heat and water vapor diffusivities) from unity can affect the resolved- and SGS-scale water vapor and temperature covariance (and individual scalar variances as well) because of the relative scalar mixing. Thus, this could affect supersaturation fluctuations in the simulated turbulent convections similar to the effect of the physical diffusivity difference between water vapor and temperature (e.g., Chandrakar et al. 2020a). The performance of the SGS model could be further improved (tuned) with more accurate values of the turbulent diffusivities. The advection of the above variances and covariance is performed using the same advection scheme as other scalars in CM1 (see Sec. 3 for details). Similarly, the turbulence transport tendencies are calculated using the same algorithm as used for other scalars in CM1. The tendencies related to the resolved scalar gradients are calculated from the gridscale values of qυ and θ at each time step obtained from solving their conservation equations in CM1 [see Bryan and Fritsch (2002) for details about the governing equations].

3. Model description and simulation setup

The CM1 dynamical model (Bryan and Fritsch 2002) is used in DNS and LES configurations for the simulations discussed in this article. The compressible Boussinesq equation set (Boussinesq approximation for buoyancy in the momentum equation with a prognostic pressure equation) is solved using a three-step Runge–Kutta time integration with a fifth-order advection scheme. Scalars are advected using a weighted essentially nonoscillatory (WENO) scheme. The compressible solver employed here uses Klemp–Wilhelmson time-split steps for acoustic terms. The above compressible Boussinesq solver was successfully used in the past for an idealized simulation of a channel flow with shear [see Bryan and Rotunno (2014) for details]. Here, a compressible solver is used instead of an incompressible one because it has been optimized for distributed-memory supercomputers, which allows us to use large domains with O(1 × 109) grid points. The differences between the compressible and incompressible solver are expected to be negligible for the current case. A prognostic SGS turbulent kinetic energy scheme (Deardorff 1980) is used for the SGS mixing in the LES configuration. The DNS configuration of the model is similar to Moeng and Rotunno (1990) with explicit diffusion and diffusivity terms based on physical Prandtl (Pr ≡ ν/Dt = 0.72) and Schmidt (Sc ≡ ν/Dυ = 0.62) numbers. The diffusion terms are discretized using a second-order finite difference method. Although a grid independence test is not performed for the current simulations, the grid spacing for the DNS is determined from the criteria for resolving the smallest eddies and boundary layer following Grötzbach (1983). This grid resolution in the current DNS gives a surface heat flux consistent with recent simulations (e.g., Stevens et al. 2014) and follows the accepted Nusselt–Rayleigh relation for our Prandtl number. More recent investigations of resolution requirements for Rayleigh–Bénard convection (Shishkina et al. 2010) confirm that the earlier findings of Grötzbach (1983) are sufficient for bulk properties, but they suggest that capturing the details of plume dynamics in the boundary layers requires a factor-of-3 higher local resolution. For the work presented here, our interest is primarily in bulk convection properties; further exploration of the boundary layer structure remains an interesting problem for additional research. An adaptive time step based on the Courant number (maximum value of 0.8) is used for the numerical integration.

The simulations presented here are motivated by the Pi Cloud Chamber that generates supersaturation variability through turbulent Rayleigh–Bénard convection (Chang et al. 2016; Chandrakar et al. 2016; Niedermeier et al. 2018). The present simulations are similar to the model setup of Thomas et al. (2019). The vertical dimension of the domain is the same (1 m), but the aspect ratio (width to height ratio) is Γ = 1 compared to Γ = 2 for the Pi Chamber. The aspect ratio of the chamber primarily influences the large-scale circulation, and influences the turbulence statistics slightly. However, the main parameter governing Rayleigh–Bénard convection, the Rayleigh number (1.52 × 109), is the same in our DNS as the Pi Chamber. The top and bottom boundaries of the chamber are maintained at water saturation at different temperatures to create a steady moist environment. The temperature and water vapor differences between the top and bottom boundaries drive turbulent Rayleigh–Bénard convection with humidity fluctuations. Ideally, the isobaric mixing of air from the upper and lower boundaries should create a supersaturated condition inside the chamber, even for a minimal temperature gradient. However, the presence of sidewalls depletes water vapor substantially through condensation and allows domain mean supersaturation to be maintained only for certain conditions (Thomas et al. 2019). The sidewalls are generally held at the average temperature and are not saturated with water since condensation forms droplets on the sidewalls (not a uniform film of water) if they do not consist of porous material.

Lateral entrainment of dry environmental air in a rising cumulus thermal or cloud-top entrainment in boundary layer clouds brings additional flux of scalars (water vapor and heat) inside clouds. Subsequent mixing of dry environmental or free tropospheric and cloudy air can drive fluctuations in individual scalars as well as dependent variables, including supersaturation (Siebert and Shaw 2017). The effects of these fluxes do not just influence the turbulent fluctuations at the interface but also propagate deeper into the boundary layer or inside clouds (Mellado et al. 2017; Deardorff 1974b). To investigate the effects of scalar mixing on turbulence statistics, especially supersaturation, we performed DNS of turbulent Rayleigh–Bénard convection with different sidewall conditions to mimic the effects of entraining dry air in our simplified setup. Comparing simulations using nonadiabatic sidewalls with the simulation using adiabatic sidewalls allows investigating the role of heat and moisture relative fluxes on covariance and supersaturation statistics. This simple convection configuration allows investigating this problem in detail without interference from other feedbacks (such as atmospheric radiation) present in the atmospheric boundary layer and clouds. We have modified CM1 to treat sidewall conditions as described below.

A summary of the simulation cases is presented in Table 1. The top and bottom boundaries are kept saturated with water and at 282 and 294 K, respectively. Sidewalls are at the mean temperature of the top and bottom boundaries (288 K) (except for DNS-AD). For DNS-AD, a no flux boundary condition for scalars is applied at the sides. Different water vapor boundary conditions are applied at all sides in the DNS and LES to investigate the effects of sidewall fluxes on scalar fluctuations, assuming 90%, 95%, and 100% of the saturation vapor mixing ratio. For momentum, the no-slip boundary condition is applied to all boundaries for the DNS runs, and for the LES runs, the flux boundary condition is used along all surfaces. The scalar and momentum fluxes for the LES runs are modeled based on a bulk aerodynamic method assuming constant exchange coefficients for momentum and scalars (water vapor and temperature) and depend on the gridscale velocity magnitude near each surface. The simpler formulation of fluxes above is used instead of the Monin–Obukhov similarity to avoid complications at vertical boundaries. The value of these coefficients (same for all surfaces) is tuned to match the bottom surface heat flux (Nusselt number Nu) and turbulent kinetic energy from DNS-S95. LES of the DNS-S95 case are done to determine the SGS water vapor and temperature variances and their covariance and supersaturation variance using the SGS model introduced in section 2. Apart from the control case at 3.125-cm grid spacing (LES-CTL), two additional simulations at a higher (LES-HI) and a lower grid resolution (LES-LO) are performed to evaluate the scaling of SGS fluctuations with the grid spacing. The SGS model is expected to produce higher fluctuations relative to the resolved scale with an increase in the grid spacing. The turbulent statistics are calculated from the DNS and LES runs by spatiotemporal averaging of data over several hundred free-fall buoyancy time scale (τf = 1.5 s). The free-fall time scale is a characteristic time scale associated with buoyancy in turbulent convection. It is the ratio of the vertical length scale of the system and the buoyancy velocity (Uf=gαΔTυH, where α is the thermal expansion coefficient, and ΔTυ is the virtual temperature difference between the top and bottom boundaries).

Table 1.

Summary of all simulation cases presented in this article. Here, 〈η〉 is the average Kolmogorov length scale in the convection core from DNS runs, and qυs(Ts) is the saturation vapor mixing ratio at the sidewall temperature. The Rayleigh number for these cases is 1.52 × 109. Note: the grid spacing (Δ) is normalized based on the grid resolution criteria discussed in Grötzbach (1983).
Table 1.

4. DNS of turbulent Rayleigh–Bénard convection with water vapor

DNS results for Rayleigh–Bénard convection with water vapor (the Pi Chamber simulations) are presented in this section. The statistics presented here exclude grid cells near the sidewall boundaries (6 cm on each side) to limit direct wall effects. However, this does not exclude the grid cells within the large-scale circulation (LSC). A coherent flow structure, the LSC is an inherent feature of Rayleigh–Bénard convection and forms from the organization of plumes between the top and bottom boundaries (Krishnamurti and Howard 1981). Because of the LSC, scalar fluctuations and turbulent flow properties vary spatially, even away from the viscous layer near walls. Anderson et al. (2021) showed how Eulerian point measurements of water vapor and temperature fluctuations vary depending on the location relative to the LSC. The analysis presented here includes both updraft and downdraft branches of the circulation to avoid biases in statistics. Figure 1 shows a snapshot of isosurfaces of supersaturated (S = 1.001) regions for one of the cases (DNS-S95). In this figure, different scales of supersaturated volumes are evident. Although the domain average saturation ratio is less than one for this case, supersaturated regions are present in the domain. For this particular case, coherent structures of supersaturation seem to align with the LSC, which in this case features a convection roll along a diagonal plane.

Fig. 1.
Fig. 1.

Isosurfaces of supersaturated regions (S = 1.001) for DNS-S95. Here, the axis labels represent the domain size (in m).

Citation: Journal of the Atmospheric Sciences 79, 4; 10.1175/JAS-D-21-0250.1

Pi Chamber measurements of the temperature and water vapor standard deviation (without cloud droplets) at the same ΔT = 12 K as the current simulations reported in Anderson et al. (2021) are 0.18 K (updraft: 0.12 K; mid: 0.19 K; and downdraft: 0.22 K) and 0.07 g kg−1 (updraft: 0.05 g kg−1; mid: 0.08 g kg−1; and downdraft: 0.09 g kg−1), respectively. Additionally, the measured value of supersaturation standard deviation is 0.9% (updraft: 0.69%; mid: 0.82%; and downdraft: 1.19%). In our DNS, the magnitudes of the temperature and water vapor standard deviation away from boundaries are 0.21–0.22 K and 0.16–0.18 g kg−1. These values are in good agreement with the measurements in the Pi Chamber, considering significant spatial filtering of fluctuations in the measurements due to a large sample volume of instruments. The supersaturation fluctuations in our DNS vary with the sidewall condition. However, for a realistic sidewall condition (DNS-S90), the standard deviation of supersaturation fluctuations is 0.68% (close to measurements). Also, the turbulent kinetic energy at the midheight of the domain is approximately 4 × 10−3 m2 s−2, nearly matching the particle image velocimetry measurements at the center of the Pi Chamber (Thomas et al. 2019).

Effects of relative fluxes of heat and moisture on turbulence statistics

The sidewall boundary condition is known to influence heat transfer in Rayleigh–Bénard convection, even with the isothermal boundary condition at the mean of the top and bottom temperatures (Ahlers 2000; Stevens et al. 2014). In past DNS and experimental studies, the effects of sidewalls were explored in dry convection where the temperature difference was the only driver. Changes in the sidewall temperature not only affect the heat transfer but also affect the flow. The focus here is on the sidewall flux of water vapor. Although both temperature and water vapor differences drive buoyant convection, in our case the impact of water vapor on the buoyancy is very small (i.e., the buoyant force owing to gradients in water vapor is significantly smaller than that from temperature gradients). Thus, changes in the sidewall saturation have little impact on the flow and provide a different context for examining the effects of sidewall fluxes on scalar statistics in Rayleigh–Bénard convection compared to past studies. Figure 2 shows nondimensional heat (Nusselt number: Nu) and moisture (Sherwood number: Sh) fluxes at the top and bottom boundaries for different sidewall boundary conditions as listed in Table 1. For adiabatic sidewalls, Nu is higher than Sh. This result is also predicted by the scaling relation presented in Chandrakar et al. (2020a). As expected, the top and bottom fluxes of heat and moisture are nearly identical for DNS-AD. For other nonadiabatic sidewall conditions, the heat flux at the top boundary is slightly higher than the bottom boundary consistent with a small net heat flux from the side. However, the top and bottom Nu difference is within one standard deviation. At high Ra, as in the current case, the effect of sidewall heat transfer at the mean temperature is expected to be negligible (Stevens et al. 2014) consistent with the current case. However, the difference between the top and bottom moisture fluxes is significant and increases with a decrease in the sidewall saturation level. The presence of saturated or subsaturated sidewalls “short circuits” the moisture fluxes at the top and bottom boundaries. The deviation of sidewall water vapor concentration from the mean of the top and bottom boundaries enhances the local water vapor gradient. It drives more sidewall flux near the bottom boundary and subsequently reduces the water vapor gradient at the top boundary, a “short circuit” of water vapor flux. Thus, the amount of moisture flux at the bottom boundary increases with decreasing the sidewall saturation level, but the top flux decreases nearly identically compared to DNS-AD.

Fig. 2.
Fig. 2.

Nusselt (squares) and Sherwood (circles) numbers at the top (black) and bottom (red) boundaries for different DNS cases as listed in Table 1. The error bars show the standard deviation of the flux data (the standard errors are approximately the size of the symbols). The sign of the top fluxes is negative but they are displayed as positive nondimensional values.

Citation: Journal of the Atmospheric Sciences 79, 4; 10.1175/JAS-D-21-0250.1

Figure 3 shows the mean temperature and water vapor mixing ratio profiles for the various sidewall conditions. The shape of the scalar profiles is very similar to past studies of moist Rayleigh–Bénard convection and Rayleigh–Bénard convection generally (e.g., Chandrakar et al. 2020a; Ahlers et al. 2012, and references therein). The temperature profile is nearly identical for all cases consistent with the heat flux data shown in Fig. 2. The water vapor mixing ratio profiles have different mean values in the core region that decrease with a reduction of the sidewall saturation, similar to the LES study of Thomas et al. (2019). In the viscous layer near the boundaries, the slope of mean water vapor profiles also changes with the sidewall saturation. A steeper slope near the bottom boundary and shallower slope near the top for the lowest sidewall saturation is also consistent with the flux data presented in Fig. 2.

Fig. 3.
Fig. 3.

Vertical profiles of mean temperature and water vapor mixing ratio for the various DNS cases.

Citation: Journal of the Atmospheric Sciences 79, 4; 10.1175/JAS-D-21-0250.1

Figure 4 shows vertical profiles of water vapor and potential temperature fluctuations, their covariance, and supersaturation fluctuations. The temperature and water vapor fluctuations peak near the top and bottom boundaries and decrease toward the center, similar to past studies of dry Rayleigh–Bénard convection and Rayleigh–Bénard convection with water vapor (e.g., Ahlers et al. 2012; Chandrakar et al. 2020a, and references therein). They are also consistent with the scaling presented in Chandrakar et al. (2020a) to explain the profile of scalar fluctuations outside the viscous layer. The temperature fluctuation profiles are nearly vertically symmetric and overlap for all cases. The water vapor fluctuations are also symmetric for DNS-AD. However, the magnitude of water vapor fluctuations is higher near the bottom boundary for other sidewall conditions. This vertical asymmetry also increases with a decrease in the sidewall saturation level; fluctuations near the bottom boundary increase and near the top boundary decrease with a reduction in the sidewall saturation. In turbulent convection, scalar variance is directly proportional to net scalar flux. The variance of a scalar field obeys a scaling, ϕnet/w* (Wyngaard 2010), where ϕnet is the net scalar flux, and w* is the characteristic velocity scale. For a fixed turbulent intensity, w* is expected to be the same. Since the bottom moisture flux increases with a decrease in the sidewall saturation, the magnitude of the water vapor fluctuations near the bottom surface increases and vice versa for the fluctuation magnitude near the top boundary.

Fig. 4.
Fig. 4.

Vertical profiles of (a) standard deviation of water vapor mixing ratio fluctuations, (b) standard deviation of potential temperature fluctuations, (c) covariance between potential temperature and water vapor mixing ratio fluctuations, and (d) standard deviation of supersaturation fluctuations for the various DNS cases.

Citation: Journal of the Atmospheric Sciences 79, 4; 10.1175/JAS-D-21-0250.1

In convection with saturated top and bottom boundaries, as is the case here, the normalized covariance is expected to be close to unity. However, the different diffusivities of water vapor and heat decorrelate fluctuations in the water vapor and temperature fields near boundaries. Still, the mean normalized covariance increases away from the boundaries due to greater mixing from turbulence (Chandrakar et al. 2020a). A similar profile of the normalized covariance is observed in the current study for DNS-AD (it is not clearly visible in Fig. 4c since its value is close to unity). As mentioned earlier, natural convection, whether in the atmospheric boundary layer/clouds or the Pi Chamber, is not like this idealized scenario since relative fluxes vary for different scalars. As can be seen in Fig. 4c, the normalized covariance value decreases with a decrease in the sidewall saturation level, even in the regions away from all boundaries. For all the cases with sidewall fluxes, the covariance decreases away from the top and boundaries. It is relatively flat in the center region, 20 cm away from both boundaries. The decrease in the sidewall saturation causes an increase in water vapor flux relative to the heat flux. This relative flux from the side decreases the covariance away from the top and bottom boundaries. At the top and bottom boundaries, heat and water vapor fluxes are highly correlated, producing a normalized covariance close to unity (slight deviation due to relative diffusivities of both scalars).

Supersaturation fluctuations strongly depend on the water vapor and temperature covariance (Kulmala et al. 1997; Siebert and Shaw 2017; Chandrakar et al. 2020a; Thomas et al. 2021), which is seen here in Fig. 4d. Supersaturation fluctuations increase with a decrease in the sidewall saturation level consistent with the covariance profiles and Eq. (2). Contributions from differences in the water vapor and temperature variances are relatively small since they do not change significantly for the various sidewall conditions. Nonetheless, the increase in the asymmetry of water vapor fluctuations with a decrease in the sidewall saturation also affects the asymmetry of the supersaturation fluctuation profiles. S(qυ¯,θ¯)=qυ¯/qυs(θ¯) also affects the fluctuation magnitude as suggested by Eq. (2). But in this case, it acts opposite to the covariance contribution. This decreases the magnitude of supersaturation fluctuations since S(qυ¯, θ¯) decreases with a reduction in the sidewall saturation.

Effects of sidewall fluxes are further investigated by analyzing the scalar fluctuation statistics similar to Fig. 4 but in a horizontal direction (Fig. 5). For DNS-AD, the horizontal profiles of temperature and water vapor standard deviation are the largest near side boundaries and decrease toward the center because the LSC carries plumes from the top and bottom boundaries that are not well mixed along sidewalls. Moreover, the no-slip boundary condition for velocity causes variation in turbulent mixing normal to sidewalls. The presence of nonadiabatic sidewalls causes nonzero heat and water vapor fluxes depending on the updraft and downdraft branches of the LSC. On the updraft side, a higher temperature and water vapor induce negative fluxes of both scalars. However, colder and less moist fluid from the top boundary drives positive fluxes on the downdraft side. These fluxes cause an immediate decrease of the fluctuations right at the sidewall boundaries with a sharp increase nearby and a peak within 5 cm of the boundaries. The fluctuation magnitudes then decrease from these peak values near the boundaries toward the center due to greater mixing at the center. Since the sidewall temperature is at the mean of the top and bottom boundaries and water vapor is not, the magnitude of the heat flux is the same on both sides but not the water vapor flux. This asymmetry produces larger fluctuations of water vapor on one side than the other. The peak water vapor fluctuations on the right side also increase with a decrease in the sidewall saturation.

Fig. 5.
Fig. 5.

Profiles of turbulence statistics as in Fig. 4 but in the Y direction. The statistics presented here exclude grid cells near the boundaries (6 cm on each side) to limit direct wall effects.

Citation: Journal of the Atmospheric Sciences 79, 4; 10.1175/JAS-D-21-0250.1

Apart from the difference in sidewall heat and water vapor fluxes due to the LSC, the sidewall induces a water vapor flux but not heat flux for a well-mixed fluid since the sidewalls are at the mean temperature. This flux of water vapor relative to heat decreases the correlation of water vapor and temperature along the sidewalls. The correlation increases toward the center due to better mixing in the convection core (away from the boundaries). As explained above, the covariance profile is also asymmetric, more for DNS-S90, due to the asymmetry in the relative flux. In the convection core, covariance decreases with sidewall saturation due to the mixing of fluid from the sidewall boundaries similar to entrainment and mixing of dry environmental air inside atmospheric clouds. The standard deviation of supersaturation fluctuations has a relatively flat horizontal profile for DNS-AD even though water vapor and temperature fluctuations peak near the side boundaries. This is due to a higher value of the covariance and a lower value of the saturation ratio near the side boundaries (not shown). For other sidewall cases, supersaturation fluctuations peak near the sidewalls similar to water vapor and temperature fluctuations. In the cases with nonadiabatic sidewalls, a sharp decrease in the covariance near the boundaries causes supersaturation fluctuations to increase. Similar to the vertical profiles, the magnitude of supersaturation fluctuations in the convection core increases with a decrease in the sidewall saturation. The effects of sidewall fluxes on scalar fluctuations and covariance are analogous to the impact one would expect in the presence of entrainment and mixing of dry environmental air into clouds.

5. Evaluation of the subgrid-scale model: LES of Rayleigh–Bénard convection

The SGS model introduced in section 2 was implemented in CM1 for the LES configuration and evaluated here by comparing the scalar fluctuation statistics with the reference DNS case (DNS-S95). To evaluate the performance of an SGS scheme, traditionally, there are two methods: “a posteriori” and “a priori” tests (Meneveau and Katz 2000). In a posteriori test, the benchmark DNS or measurement data are compared with the model output. We use the a posteriori method to evaluate the performance of the LES-SGS scheme proposed here by comparing net scalar statistics with corresponding reference DNS output. The a priori test compares subfiltered components of DNS data with modeled SGS components at the length scale of LES. Although the a priori test is left here for future investigation, it may shed some light on regimes where the proposed SGS model may not perform well. In the spirit of the a priori method, we compared filtered and subfiltered parts2 of the scalar statistics with the LES resolved and SGS output statistics at different grid spacing. It is not precisely an a priori test since the model output is compared instead of the modeled SGS terms before running the model. However, it can further elucidate the SGS model performance compared to the a posteriori test. For this comparison, the DNS output is filtered with a spatial box filter using different filter widths of the size of LES grid spacings (10, 16, and 25 DNS grid points).

As mentioned in section 3, only the average heat flux at the bottom surface and turbulent kinetic energy are matched with the DNS data to determine the exchange coefficient for scalars (heat and moisture) and momentum. Therefore, the water vapor flux at the bottom boundary in LES is slightly (less than 5%) different than DNS-S95. We choose the bottom heat flux instead of the water vapor flux to tune the scalar exchange coefficient since it is the main driver of the convection in the current case. The mean profiles of water vapor and temperature from LES-CTL matches well with the DNS-S95 mean profiles (see Fig. 6). A minor difference in the mean water vapor profile slope is related to the differences in the water vapor flux. Figures 7 and 8 show the water vapor and temperature standard deviations, their covariance, and supersaturation standard deviation profiles for the resolved and SGS scales and their total magnitude. The total magnitudes of the water vapor and temperature fluctuations follow the DNS data very well, even near the boundaries. Here, the total magnitude of fluctuations is defined as the square root of the sum of SGS and resolved-scale variance (or as the sum of the covariance contributions for the covariance profile). Although the numerical dissipation due to the discretization on the coarse LES grid is expected to reduce scalar fluctuations, the LES fluctuation magnitudes of individual scalars are slightly higher than DNS. Additionally, the normalized covariance is slightly smaller in LES. These results are most likely due to a somewhat higher value of the bottom water vapor flux in the LES. The scalar and momentum exchange coefficients are tuned based on the bottom boundary. Thus, sidewall fluxes could be slightly different from DNS, which could also cause deviation of the LES result from DNS. Moreover, the numerical Prandtl and Schmidt numbers are the same in LES. This could also affect the scalar covariance and individual scalar fluctuations.

Fig. 6.
Fig. 6.

Vertical profiles of (a) mean temperature and (b) water vapor mixing ratio for LES-CTL compared with DNS-S95 results.

Citation: Journal of the Atmospheric Sciences 79, 4; 10.1175/JAS-D-21-0250.1

Fig. 7.
Fig. 7.

Vertical profiles of (a) standard deviation of water vapor mixing ratio fluctuations, (b) standard deviation of potential temperature fluctuations, (c) covariance between potential temperature and water vapor mixing ratio fluctuations, and (d) standard deviation of supersaturation fluctuations for LES-CTL. Profiles from DNS-S95 are also plotted as dashed lines for comparison. The net fluctuation magnitude is calculated by taking the square root of the sum of resolved and SGS variances [(σres2+σSGS2)1/2] or by summing the covariance contributions for the covariance profile. These profiles are obtained from excluding data points near the sidewall boundaries (see text for details).

Citation: Journal of the Atmospheric Sciences 79, 4; 10.1175/JAS-D-21-0250.1

Figure 8 compares the resolved and SGS scalar statistics from LES to filtered and subfiltered DNS statistics. The LES-SGS water vapor and temperature statistics very well capture the subfiltered-scale DNS statistics. They are slightly smaller than DNS output near the boundaries, and the covariance value is somewhat larger in the convection core. The SGS supersaturation standard deviation is also moderately larger for LES-SGS, especially near the boundaries. Similarly, the resolved-scale LES statistics of water vapor and temperature are close to the DNS filtered-scale statistics (slight deviation near the convection core).

Fig. 8.
Fig. 8.

As in Fig. 7, but showing the resolved (solid blue) and SGS (solid red) LES scalar statistics and the filtered (blue dashed) and subfiltered (red dashed) DNS statistics. The DNS data are filtered with a spatial box filter using a filter width of 16 DNS grid cells.

Citation: Journal of the Atmospheric Sciences 79, 4; 10.1175/JAS-D-21-0250.1

The water vapor standard deviation from the LES is also able to capture the vertical asymmetry in the DNS profile as explained in the previous section. SGS scalar fluctuations near the boundaries are larger than the resolved fluctuations. However, SGS fluctuations are significantly smaller in the convection core than the resolved fluctuations, especially for temperature and water vapor. The total normalized covariance from the SGS model also captures the DNS profile reasonably well quantitatively. The SGS covariance profile decreases away from the boundaries and is nearly flat in the convection core, similar to the DNS data. Thus, it can simulate the decrease in covariance due to relative sidewall fluxes of scalars. The supersaturation standard deviation calculated from the scalar variances and covariance at resolved and SGS scales [Eq. (2)] is also able to quantitatively well match the supersaturation standard deviation from the DNS. However, the LES results do not fully capture the asymmetry in the supersaturation standard deviation profile. The most likely reason for this dissimilarity is the use of the same diffusivity and exchange coefficient for both water vapor and temperature in the LES. A slight deviation of the mean water vapor profile compared to the DNS run is also evident in Fig. 6 because of the use of the same exchange coefficient for water vapor and temperature. Similar to the water vapor and temperature standard deviation profiles, the magnitude of SGS supersaturation is also larger than the resolved-scale fluctuations near the boundaries. In the convection core, SGS fluctuations in supersaturation are smaller than the resolved-scale fluctuations, but the difference is less than the differences in the temperature and water vapor standard deviations. This smaller difference is a result of a smaller SGS covariance magnitude [see Eq. (2)].

The sensitivity of the LES-SGS results to the model grid spacing is also tested here by performing two additional simulations with a smaller (2 cm in LES-HI) and larger (5 cm in LES-LO) grid spacing. LES-HI and LES-LO both show a good agreement with the DNS results similar to LES-CTL (Figs. 9–12). The resolved and SGS LES scalar statistics consistently match filtered and subfiltered DNS statistics across a wide range of filter scales. However, the resolved supersaturation standard deviation profile varies more from the filtered DNS magnitude at a lower resolution, likely due to deviation in sidewall fluxes. SGS fluctuations in water vapor, temperature, and supersaturation relative to resolved-scale fluctuations for LES-LO are larger than in the LES-HI and LES-CTL cases as expected. The change in relative SGS fluctuations with a change in grid spacing is more apparent for supersaturation than the individual scalars. For supersaturation fluctuations, the small-scale mixing of scalars is important. Thus, it scales more with the grid spacing. The normalized covariance of SGS temperature and water vapor fluctuations in the convection core is also smaller for LES-LO than LES-HI. In the LES here, SGS water vapor and temperature fluctuations are small compared to those at the resolved scale in the current Pi Chamber simulations owing to a small inertial range that is limited by the small length scale of the domain. The situation would be different in cloud-scale simulations with typical LES grid spacing due to a greater range of length scales involved. Overall, these results suggest that the SGS supersaturation scheme is appropriately scaling across different LES grid spacings. These grid sensitivity test results build confidence in the SGS supersaturation model introduced here.

Fig. 9.
Fig. 9.

As in Fig. 7, but for LES-HI.

Citation: Journal of the Atmospheric Sciences 79, 4; 10.1175/JAS-D-21-0250.1

Fig. 10.
Fig. 10.

As in Fig. 8, but for LES-HI. The DNS data are filtered using a spatial box filter with a filter width of 10 DNS grid cells.

Citation: Journal of the Atmospheric Sciences 79, 4; 10.1175/JAS-D-21-0250.1

Fig. 11.
Fig. 11.

As in Fig. 7, but for LES-LO.

Citation: Journal of the Atmospheric Sciences 79, 4; 10.1175/JAS-D-21-0250.1

Fig. 12.
Fig. 12.

As in Fig. 8, but for LES-LO. The DNS data are filtered with a spatial box filter using a filter width of 25 DNS grid cells.

Citation: Journal of the Atmospheric Sciences 79, 4; 10.1175/JAS-D-21-0250.1

6. Implications for applying the SGS supersaturation model to bulk and Lagrangian microphysics schemes

The SGS supersaturation variance obtained from the model described in section 2 can be applied to parameterize the activation of cloud droplets driven by turbulent fluctuations (e.g., Prabhakaran et al. 2020; Shawon et al. 2021) with an assumption about the shape of the joint PDF of supersaturation fluctuations and aerosols. Additionally, bulk microphysics schemes commonly assume a constant shape parameter (or spectral width) of the droplet spectrum. The current SGS scheme can be used in bulk schemes to solve the prognostic equation of droplet spectral width [see Eq. (16) in Chandrakar et al. 2018] that in turn can be used to calculate the effective radius and microphysical process rates (e.g., autoconversion). Another application of the new SGS model is in bin microphysics schemes. It can be applied for modeling spectral broadening due to turbulence as a diffusion term (Saito et al. 2019). In this case, the diffusivity (D∝ξ2σs2τs2/τt) is a function of SGS supersaturation variance, the turbulent mixing time scale, and the system time scale (τs: a combination the phase relaxation and turbulence time scale) [see Eq. (2) in Chandrakar et al. 2020b].

It is straightforward to apply the SGS supersaturation scheme in Lagrangian particle-based models using Lagrangian stochastic differential equations. There are two different approaches to do that. The first is to use separate equations for SGS water vapor and temperature along individual particle trajectories with the assumption of Gaussian Wiener forcing and neglecting any deviation from the Lagrangian fluid trajectories (Paoli and Shariff 2009). In that case, a part of the Wiener forcing terms for both the scalars would be correlated depending on the correlation coefficient between the two (qυ′T′¯ σqυ−1 σT−1). This approach does not require an assumption about the shape of supersaturation fluctuation PDF; instead, PDF shapes of water vapor and temperature fluctuation forcing are assumed. However, a problem with this approach is that it requires solving for the time tendencies of two Lagrangian variables apart from the velocity fluctuations. The second approach is to treat the SGS supersaturation fluctuations along Lagrangian trajectories as a separate stochastic process. The assumption of Gaussian supersaturation fluctuations in the Wiener forcing term is the topic of an ongoing investigation.

7. Discussion and conclusions

Processes involving multiscalar interactions in turbulent convection are interesting from the standpoint of fundamental fluid dynamics and have several atmospheric science applications (e.g., transport of reactive species, wave propagation, cloud formation and growth). The focus here is on multiscalar interactions governing the supersaturation—a nonlinear function of water vapor and temperature. Supersaturation fluctuations play a critical role in cloud particle activation and growth in turbulent moist convection. This article introduces a new SGS model for supersaturation fluctuations based on scalar variance and covariance. Conservation equations for prognostic water vapor and temperature variances and their covariance based on Deardorff (1974a) were implemented in CM1 for this purpose. The new SGS model was tested in LES and compared to benchmark DNS of turbulent Rayleigh–Bénard convection with water vapor (simulations of the Pi Cloud Chamber). This setup, without cloud droplets, provided an ideal framework for testing the SGS model without complications from external feedbacks (such as atmospheric radiation). DNS results were found to compare favorably with the Pi Chamber measurements reported in Anderson et al. (2021) and Thomas et al. (2019) for similar conditions. DNS was also conducted with different sidewall conditions to investigate the influence of external fluxes on turbulent statistics for the two-scalar Rayleigh–Bénard convection. A follow-up study will investigate the role of cloud droplet growth.

Comparison of the DNS results with measurements in the Pi Chamber at similar conditions (e.g., Anderson et al. 2021; Thomas et al. 2019) showed a good agreement. There are some limitations of the scalar and turbulence statistics measurements in the Pi Chamber, e.g., a large sample volume of instruments, Eulerian point measurements. There are also challenges in obtaining the supersaturation mean and fluctuations from measurements because collocated temperature and water vapor measurements are required. The current DNS overcomes those limitations and provides more detailed information about scalar and turbulence statistics to complement the measurements.

Comparison of the DNS of Pi Chamber with adiabatic sidewalls to DNS with other sidewalls conditions showed that the net sidewall heat flux is not significant if the sidewalls are at the mean temperature of the top and bottom boundaries. The water vapor flux increased at the bottom and decreased at the top boundary as the sidewall saturation was decreased, influencing the profiles of the mean and standard deviation of water vapor. The presence of relative scalar fluxes from the side boundaries decorrelated scalars, and this was not just limited to the layer near the boundaries but also affected the convection core. The decrease in covariance between water vapor and temperature fluctuations increased the supersaturation fluctuations. An analogy can be made between the production of supersaturation fluctuations in the current simplified setting and entrainment mixing in atmospheric clouds. In both cases, external water vapor and thermal fluxes are present, and will affect supersaturation fluctuations in a similar way. However, in atmospheric clouds, condensation/evaporation and dynamical feedbacks make this process more complicated.

The new SGS model is a significant advancement over existing schemes since it captures supersaturation fluctuations generated from scalar mixing apart from vertical velocity fluctuations. By treating water vapor and temperature fluctuations separately, it accounts for SGS supersaturation variability during turbulent mixing. It can be used with bulk, bin, or Lagrangian microphysics schemes to represent cloud–turbulence interactions at subgrid scales. Additionally, the model is compatible with higher-order turbulence closure schemes since it solves prognostic equations for scalar variances and covariance. Comparison of the Pi Chamber LES incorporating the new SGS model with the benchmark DNS showed a close match of profiles of different scalar statistics. The SGS model well reproduced profiles of water vapor and temperature variance and their covariance. It also captured asymmetry in the vertical profile of σqυ due to the sidewall fluxes. The SGS supersaturation variance was also consistent with the DNS run and appropriately scaled with the LES grid spacing.

The SGS supersaturation variability in the Pi Chamber case is driven by scalar mixing. However, vertical velocity fluctuations are also an important contributor to supersaturation variability for atmospheric convection in a stratified environment, and the new SGS model also accounts for this effect. The diabatic contributions of condensation/evaporation to the scalar variance and covariance are also zero in the current simulations. These terms will reduce the magnitude of supersaturation or subsaturation fluctuations in simulations with cloud. In our SGS model, the effect of condensation can be introduced as an additional sink term (−s′/τc), and it would reduce the supersaturation variance at the rate of −2σs2/τc. This contribution will be explored further using DNS and LES of the Pi Chamber case with droplets in a subsequent study.

Overall, the SGS model presented here showed promising results compared to benchmark DNS in the current idealized setting of turbulent Rayleigh–Bénard convection. Further investigation of the SGS model and terms associated with condensation in cloudy conditions is a subject of future work. Application of the model to realistic atmospheric cloud conditions will also be a part of the subsequent studies.

Acknowledgments.

This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement 1852977. This work was also supported in part by U.S. Department of Energy Atmospheric System Research Grant DE-SC0020118. KKC was supported by the NCAR ASP postdoctoral fellowship program. We would like to acknowledge high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation. RAS acknowledges support from NSF Grant AGS-2133229. We also thank three anonymous reviewers for their constructive comments and suggestions that improved the paper.

Data availability statement.

The current study used CM1 (https://www2.mmm.ucar.edu/people/bryan/cm1/) for the simulations presented here. The model outputs used in this study are too large to archive. Instead, the input and namelist files for the simulation runs will be made available upon request to the first author.

REFERENCES

  • Abade, G. C., W. W. Grabowski, and H. Pawlowska, 2018: Broadening of cloud droplet spectra through eddy hopping: Turbulent entraining parcel simulations. J. Atmos. Sci., 75, 3365–3379, https://doi.org/10.1175/JAS-D-18-0078.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ahlers, G., 2000: Effect of sidewall conductance on heat-transport measurements for turbulent Rayleigh-Bénard convection. Phys. Rev. E, 63, 015303, https://doi.org/10.1103/PhysRevE.63.015303.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ahlers, G., E. Bodenschatz, D. Funfschilling, S. Grossmann, X. He, D. Lohse, R. J. Stevens, and R. Verzicco, 2012: Logarithmic temperature profiles in turbulent Rayleigh-Bénard convection. Phys. Rev. Lett., 109, 114501, https://doi.org/10.1103/PhysRevLett.109.114501.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Anderson, J. C., S. Thomas, P. Prabhakaran, R. A. Shaw, and W. Cantrell, 2021: Effects of the large-scale circulation on temperature and water vapor distributions in the Π Chamber. Atmos. Meas. Tech., 14, 5473–5485, https://doi.org/10.5194/amt-14-5473-2021.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bryan, G. H., and J. M. Fritsch, 2002: A benchmark simulation for moist nonhydrostatic numerical models. Mon. Wea. Rev., 130, 2917–2928, https://doi.org/10.1175/1520-0493(2002)130<2917:ABSFMN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bryan, G. H., and R. Rotunno, 2014: Gravity currents in confined channels with environmental shear. J. Atmos. Sci., 71, 1121–1142, https://doi.org/10.1175/JAS-D-13-0157.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chandrakar, K. K., W. Cantrell, K. Chang, D. Ciochetto, D. Niedermeier, M. Ovchinnikov, R. A. Shaw, and F. Yang, 2016: Aerosol indirect effect from turbulence-induced broadening of cloud-droplet size distributions. Proc. Natl. Acad. Sci. USA, 113, 14 243–14 248, https://doi.org/10.1073/pnas.1612686113.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chandrakar, K. K., W. Cantrell, D. Ciochetto, S. Karki, G. Kinney, and R. Shaw, 2017: Aerosol removal and cloud collapse accelerated by supersaturation fluctuations in turbulence. Geophys. Res. Lett., 44, 4359–4367, https://doi.org/10.1002/2017GL072762.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chandrakar, K. K., W. Cantrell, and R. A. Shaw, 2018: Influence of turbulent fluctuations on cloud droplet size dispersion and aerosol indirect effects. J. Atmos. Sci., 75, 3191–3209, https://doi.org/10.1175/JAS-D-18-0006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chandrakar, K. K., W. Cantrell, S. Krueger, R. A. Shaw, and S. Wunsch, 2020a: Supersaturation fluctuations in moist turbulent Rayleigh–Bénard convection: A two-scalar transport problem. J. Fluid Mech., 884, A19, https://doi.org/10.1017/jfm.2019.895.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chandrakar, K. K., I. Saito, F. Yang, W. Cantrell, T. Gotoh, and R. A. Shaw, 2020b: Droplet size distributions in turbulent clouds: Experimental evaluation of theoretical distributions. Quart. J. Roy. Meteor. Soc., 146, 483–504, https://doi.org/10.1002/qj.3692.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chandrakar, K. K., W. W. Grabowski, H. Morrison, and G. H. Bryan, 2021: Impact of entrainment mixing and turbulent fluctuations on droplet size distributions in a cumulus cloud: An investigation using Lagrangian microphysics with a subgrid-scale model. J. Atmos. Sci., 78, 2983–3005, https://doi.org/10.1175/JAS-D-20-0281.1.

    • Search Google Scholar
    • Export Citation

  • Chang, K., and Coauthors, 2016: A laboratory facility to study gas–aerosol–cloud interactions in a turbulent environment: The Π Chamber. Bull. Amer. Meteor. Soc., 97, 2343–2358, https://doi.org/10.1175/BAMS-D-15-00203.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Cooper, W. A., 1989: Effects of variable droplet growth histories on droplet size distributions. Part I: Theory. J. Atmos. Sci., 46, 1301–1311, https://doi.org/10.1175/1520-0469(1989)046<1301:EOVDGH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Deardorff, J. W., 1974a: Three-dimensional numerical study of the height and mean structure of a heated planetary boundary layer. Bound.-Layer Meteor., 7, 81–106, https://doi.org/10.1007/BF00224974.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Deardorff, J. W., 1974b: Three-dimensional numerical study of turbulence in an entraining mixed layer. Bound.-Layer Meteor., 7, 199–226, https://doi.org/10.1007/BF00227913.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Deardorff, J. W., 1980: Stratocumulus-capped mixed layers derived from a three-dimensional model. Bound.-Layer Meteor., 18, 495–527, https://doi.org/10.1007/BF00119502.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • De Rooy, W. C., and Coauthors, 2013: Entrainment and detrainment in cumulus convection: An overview. Quart. J. Roy. Meteor. Soc., 139, 1–19, https://doi.org/10.1002/qj.1959.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Desai, N., K. K. Chandrakar, K. Chang, W. Cantrell, and R. A. Shaw, 2018: Influence of microphysical variability on stochastic condensation in a turbulent laboratory cloud. J. Atmos. Sci., 75, 189–201, https://doi.org/10.1175/JAS-D-17-0158.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Devenish, B., and Coauthors, 2012: Droplet growth in warm turbulent clouds. Quart. J. Roy. Meteor. Soc., 138, 1401–1429, https://doi.org/10.1002/qj.1897.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Feingold, G., I. Koren, H. Wang, H. Xue, and W. A. Brewer, 2010: Precipitation-generated oscillations in open cellular cloud fields. Nature, 466, 849–852, https://doi.org/10.1038/nature09314.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Field, P., A. Hill, K. Furtado, and A. Korolev, 2014: Mixed-phase clouds in a turbulent environment. Part 2: Analytic treatment. Quart. J. Roy. Meteor. Soc., 140, 870–880, https://doi.org/10.1002/qj.2175.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gao, W., and M. Wesely, 1994: Numerical modeling of the turbulent fluxes of chemically reactive trace gases in the atmospheric boundary layer. J. Appl. Meteor., 33, 835–847, https://doi.org/10.1175/1520-0450(1994)033<0835:NMOTTF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gerber, H., 1991: Supersaturation and droplet spectral evolution in fog. J. Atmos. Sci., 48, 2569–2588, https://doi.org/10.1175/1520-0469(1991)048<2569:SADSEI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grabowski, W. W., and L.-P. Wang, 2013: Growth of cloud droplets in a turbulent environment. Annu. Rev. Fluid Mech., 45, 293–324, https://doi.org/10.1146/annurev-fluid-011212-140750.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grabowski, W. W., and G. C. Abade, 2017: Broadening of cloud droplet spectra through eddy hopping: Turbulent adiabatic parcel simulations. J. Atmos. Sci., 74, 1485–1493, https://doi.org/10.1175/JAS-D-17-0043.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grötzbach, G., 1983: Spatial resolution requirements for direct numerical simulation of the Rayleigh-Bénard convection. J. Comput. Phys., 49, 241–264, https://doi.org/10.1016/0021-9991(83)90125-0.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hoffmann, F., and G. Feingold, 2019: Entrainment and mixing in stratocumulus: Effects of a new explicit subgrid-scale scheme for large-eddy simulations with particle-based microphysics. J. Atmos. Sci., 76, 1955–1973, https://doi.org/10.1175/JAS-D-18-0318.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hoffmann, F., T. Yamaguchi, and G. Feingold, 2019: Inhomogeneous mixing in Lagrangian cloud models: Effects on the production of precipitation embryos. J. Atmos. Sci., 76, 113–133, https://doi.org/10.1175/JAS-D-18-0087.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Jiménez, C., F. Ducros, B. Cuenot, and B. Bédat, 2001: Subgrid scale variance and dissipation of a scalar field in large eddy simulations. Phys. Fluids, 13, 1748–1754, https://doi.org/10.1063/1.1366668.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Katul, G. G., A. M. Sempreviva, and D. Cava, 2008: The temperature–humidity covariance in the marine surface layer: A one-dimensional analytical model. Bound.-Layer Meteor., 126, 263–278, https://doi.org/10.1007/s10546-007-9236-z.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Katul, G. G., D. Cava, S. Launiainen, and T. Vesala, 2009: An analytical model for the two-scalar covariance budget inside a uniform dense canopy. Bound.-Layer Meteor., 131, 173–192, https://doi.org/10.1007/s10546-009-9361-y.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kerstein, A. R., 1988: A linear-eddy model of turbulent scalar transport and mixing. Combust. Sci. Technol., 60, 391–421, https://doi.org/10.1080/00102208808923995.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Korolev, A. V., and G. A. Isaac, 2000: Drop growth due to high supersaturation caused by isobaric mixing. J. Atmos. Sci., 57, 1675–1685, https://doi.org/10.1175/1520-0469(2000)057<1675:DGDTHS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Korolev, A. V., and I. P. Mazin, 2003: Supersaturation of water vapor in clouds. J. Atmos. Sci., 60, 2957–2974, https://doi.org/10.1175/1520-0469(2003)060<2957:SOWVIC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Krishnamurti, R., and L. N. Howard, 1981: Large-scale flow generation in turbulent convection. Proc. Natl. Acad. Sci. USA, 78, 1981–1985, https://doi.org/10.1073/pnas.78.4.1981.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kulmala, M., Ü. Rannik, E. L. Zapadinsky, and C. F. Clement, 1997: The effect of saturation fluctuations on droplet growth. J. Aerosol Sci., 28, 1395–1409, https://doi.org/10.1016/S0021-8502(97)00015-3.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kurowski, M. J., W. W. Grabowski, and P. K. Smolarkiewicz, 2014: Anelastic and compressible simulation of moist deep convection. J. Atmos. Sci., 71, 3767–3787, https://doi.org/10.1175/JAS-D-14-0017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lanotte, A. S., A. Seminara, and F. Toschi, 2009: Cloud droplet growth by condensation in homogeneous isotropic turbulence. J. Atmos. Sci., 66, 1685–1697, https://doi.org/10.1175/2008JAS2864.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • MacMillan, T., R. A. Shaw, W. H. Cantrell, and D. H. Richter, 2021: Direct numerical simulation of turbulence and microphysics in the Pi Chamber. Phys. Rev. Fluids, 7, 020501, https://doi.org/10.1103/PhysRevFluids.7.020501.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mellado, J. P., 2017: Cloud-top entrainment in stratocumulus clouds. Annu. Rev. Fluid Mech., 49, 145–169, https://doi.org/10.1146/annurev-fluid-010816-060231.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mellado, J. P., M. Puche, and C. C. van Heerwaarden, 2017: Moisture statistics in free convective boundary layers growing into linearly stratified atmospheres. Quart. J. Roy. Meteor. Soc., 143, 2403–2419, https://doi.org/10.1002/qj.3095.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Meneveau, C., and J. Katz, 2000: Scale-invariance and turbulence models for large-eddy simulation. Annu. Rev. Fluid Mech., 32, 1–32, https://doi.org/10.1146/annurev.fluid.32.1.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Moeng, C.-H., and R. Rotunno, 1990: Vertical-velocity skewness in the buoyancy-driven boundary layer. J. Atmos. Sci., 47, 1149–1162, https://doi.org/10.1175/1520-0469(1990)047<1149:VVSITB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Morrison, H., and Coauthors, 2020: Confronting the challenge of modeling cloud and precipitation microphysics. J. Adv. Model. Earth Syst., 12, e2019MS001689, https://doi.org/10.1029/2019MS001689.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Niedermeier, D., K. Chang, W. Cantrell, K. K. Chandrakar, D. Ciochetto, and R. A. Shaw, 2018: Observation of a link between energy dissipation rate and oscillation frequency of the large-scale circulation in dry and moist Rayleigh-Bénard turbulence. Phys. Rev. Fluids, 3, 083501, https://doi.org/10.1103/PhysRevFluids.3.083501.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Paoli, R., and K. Shariff, 2009: Turbulent condensation of droplets: Direct simulation and a stochastic model. J. Atmos. Sci., 66, 723–740, https://doi.org/10.1175/2008JAS2734.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Pauluis, O., and J. Schumacher, 2011: Self-aggregation of clouds in conditionally unstable moist convection. Proc. Natl. Acad. Sci. USA, 108, 12 623–12 628, https://doi.org/10.1073/pnas.1102339108.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Politovich, M. K., and W. A. Cooper, 1988: Variability of the supersaturation in cumulus clouds. J. Atmos. Sci., 45, 1651–1664, https://doi.org/10.1175/1520-0469(1988)045<1651:VOTSIC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Prabhakaran, P., A. S. M. Shawon, G. Kinney, S. Thomas, W. Cantrell, and R. A. Shaw, 2020: The role of turbulent fluctuations in aerosol activation and cloud formation. Proc. Natl. Acad. Sci. USA, 117, 16 831–16 838, https://doi.org/10.1073/pnas.2006426117.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Saito, I., T. Gotoh, and T. Watanabe, 2019: Broadening of cloud droplet size distributions by condensation in turbulence. J. Meteor. Soc. Japan, 97, 867–891, https://doi.org/10.2151/jmsj.2019-049.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sardina, G., F. Picano, L. Brandt, and R. Caballero, 2015: Continuous growth of droplet size variance due to condensation in turbulent clouds. Phys. Rev. Lett., 115, 184501, https://doi.org/10.1103/PhysRevLett.115.184501.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shaw, R. A., 2003: Particle-turbulence interactions in atmospheric clouds. Annu. Rev. Fluid Mech., 35, 183–227, https://doi.org/10.1146/annurev.fluid.35.101101.161125.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shawon, A. S. M., P. Prabhakaran, G. Kinney, R. A. Shaw, and W. Cantrell, 2021: Dependence of aerosol-droplet partitioning on turbulence in a laboratory cloud. J. Geophys. Res. Atmos., 126, e2020JD033799, https://doi.org/10.1029/2020JD033799.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shi, X., H. L. Hagen, F. K. Chow, G. H. Bryan, and R. L. Street, 2018: Large-eddy simulation of the stratocumulus-capped boundary layer with explicit filtering and reconstruction turbulence modeling. J. Atmos. Sci., 75, 611–637, https://doi.org/10.1175/JAS-D-17-0162.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shi, X., R. M. Enriquez, R. L. Street, G. H. Bryan, and F. K. Chow, 2019: An implicit algebraic turbulence closure scheme for atmospheric boundary layer simulation. J. Atmos. Sci., 76, 3367–3386, https://doi.org/10.1175/JAS-D-18-0375.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shishkina, O., R. J. Stevens, S. Grossmann, and D. Lohse, 2010: Boundary layer structure in turbulent thermal convection and its consequences for the required numerical resolution. New J. Phys., 12, 075022, https://doi.org/10.1088/1367-2630/12/7/075022.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Siebert, H., and R. A. Shaw, 2017: Supersaturation fluctuations during the early stage of cumulus formation. J. Atmos. Sci., 74, 975–988, https://doi.org/10.1175/JAS-D-16-0115.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stevens, R. J., D. Lohse, and R. Verzicco, 2014: Sidewall effects in Rayleigh–Bénard convection. J. Fluid Mech., 741, 1–27, https://doi.org/10.1017/jfm.2013.664.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tennekes, H., and J. L. Lumley, 2018: A First Course in Turbulence. MIT Press, 320 pp.

  • Thomas, L., W. W. Grabowski, and B. Kumar, 2020: Diffusional growth of cloud droplets in homogeneous isotropic turbulence: DNS, scaled-up DNS, and stochastic model. Atmos. Chem. Phys., 20, 9087–9100, https://doi.org/10.5194/acp-20-9087-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Thomas, S., M. Ovchinnikov, F. Yang, D. van der Voort, W. Cantrell, S. K. Krueger, and R. A. Shaw, 2019: Scaling of an atmospheric model to simulate turbulence and cloud microphysics in the Pi Chamber. J. Adv. Model. Earth Syst., 11, 1981–1994, https://doi.org/10.1029/2019MS001670.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Thomas, S., P. Prabhakaran, W. Cantrell, and R. A. Shaw, 2021: Is the water vapor supersaturation distribution Gaussian? J. Atmos. Sci., 78, 2385–2395, https://doi.org/10.1175/JAS-D-20-0388.1.

    • Search Google Scholar
    • Export Citation

  • Vaillancourt, P., M. Yau, P. Bartello, and W. W. Grabowski, 2002: Microscopic approach to cloud droplet growth by condensation. Part II: Turbulence, clustering, and condensational growth. J. Atmos. Sci., 59, 3421–3435, https://doi.org/10.1175/1520-0469(2002)059<3421:MATCDG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Warhaft, Z., 1981: The use of dual heat injection to infer scalar covariance decay in grid turbulence. J. Fluid Mech., 104, 93–109, https://doi.org/10.1017/S0022112081002838.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Williams, A. G., J. M. Hacker, and H. Kraus, 1997: Transport processes in the tropical warm pool boundary layer. Part II: Vertical structure and variability. J. Atmos. Sci., 54, 2060–2082, https://doi.org/10.1175/1520-0469(1997)054<2060:TPITTW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wyngaard, J. C., 2010: Turbulence in the Atmosphere. Cambridge University Press, 393 pp.

  • Wyngaard, J. C., and M. LeMone, 1980: Behavior of the refractive index structure parameter in the entraining convective boundary layer. J. Atmos. Sci., 37, 1573–1585, https://doi.org/10.1175/1520-0469(1980)037<1573:BOTRIS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wyngaard, J. C., W. Pennell, D. Lenschow, and M. A. LeMone, 1978: The temperature-humidity covariance budget in the convective boundary layer. J. Atmos. Sci., 35, 47–58, https://doi.org/10.1175/1520-0469(1978)035<0047:TTHCBI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zilitinkevich, S., T. Elperin, N. Kleeorin, and I. Rogachevskii, 2007: Energy- and flux-budget (EFB) turbulence closure model for stably stratified flows. Part I: Steady-state, homogeneous regimes. Atmospheric Boundary Layers, Springer, 11–35.

    • Search Google Scholar
    • Export Citation
1

The gridscale magnitude is represented as a¯, (a2¯−a¯2) shows SGS variance, and (ab¯−a¯ b¯) shows SGS fluctuation covariance.

2

If 〈f〉 represents a filtered scalar data, the subfiltered scalar field is defined as f − 〈f〉.

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  • Abade, G. C., W. W. Grabowski, and H. Pawlowska, 2018: Broadening of cloud droplet spectra through eddy hopping: Turbulent entraining parcel simulations. J. Atmos. Sci., 75, 3365–3379, https://doi.org/10.1175/JAS-D-18-0078.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ahlers, G., 2000: Effect of sidewall conductance on heat-transport measurements for turbulent Rayleigh-Bénard convection. Phys. Rev. E, 63, 015303, https://doi.org/10.1103/PhysRevE.63.015303.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ahlers, G., E. Bodenschatz, D. Funfschilling, S. Grossmann, X. He, D. Lohse, R. J. Stevens, and R. Verzicco, 2012: Logarithmic temperature profiles in turbulent Rayleigh-Bénard convection. Phys. Rev. Lett., 109, 114501, https://doi.org/10.1103/PhysRevLett.109.114501.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Anderson, J. C., S. Thomas, P. Prabhakaran, R. A. Shaw, and W. Cantrell, 2021: Effects of the large-scale circulation on temperature and water vapor distributions in the Π Chamber. Atmos. Meas. Tech., 14, 5473–5485, https://doi.org/10.5194/amt-14-5473-2021.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bryan, G. H., and J. M. Fritsch, 2002: A benchmark simulation for moist nonhydrostatic numerical models. Mon. Wea. Rev., 130, 2917–2928, https://doi.org/10.1175/1520-0493(2002)130<2917:ABSFMN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bryan, G. H., and R. Rotunno, 2014: Gravity currents in confined channels with environmental shear. J. Atmos. Sci., 71, 1121–1142, https://doi.org/10.1175/JAS-D-13-0157.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chandrakar, K. K., W. Cantrell, K. Chang, D. Ciochetto, D. Niedermeier, M. Ovchinnikov, R. A. Shaw, and F. Yang, 2016: Aerosol indirect effect from turbulence-induced broadening of cloud-droplet size distributions. Proc. Natl. Acad. Sci. USA, 113, 14 243–14 248, https://doi.org/10.1073/pnas.1612686113.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chandrakar, K. K., W. Cantrell, D. Ciochetto, S. Karki, G. Kinney, and R. Shaw, 2017: Aerosol removal and cloud collapse accelerated by supersaturation fluctuations in turbulence. Geophys. Res. Lett., 44, 4359–4367, https://doi.org/10.1002/2017GL072762.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chandrakar, K. K., W. Cantrell, and R. A. Shaw, 2018: Influence of turbulent fluctuations on cloud droplet size dispersion and aerosol indirect effects. J. Atmos. Sci., 75, 3191–3209, https://doi.org/10.1175/JAS-D-18-0006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chandrakar, K. K., W. Cantrell, S. Krueger, R. A. Shaw, and S. Wunsch, 2020a: Supersaturation fluctuations in moist turbulent Rayleigh–Bénard convection: A two-scalar transport problem. J. Fluid Mech., 884, A19, https://doi.org/10.1017/jfm.2019.895.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chandrakar, K. K., I. Saito, F. Yang, W. Cantrell, T. Gotoh, and R. A. Shaw, 2020b: Droplet size distributions in turbulent clouds: Experimental evaluation of theoretical distributions. Quart. J. Roy. Meteor. Soc., 146, 483–504, https://doi.org/10.1002/qj.3692.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chandrakar, K. K., W. W. Grabowski, H. Morrison, and G. H. Bryan, 2021: Impact of entrainment mixing and turbulent fluctuations on droplet size distributions in a cumulus cloud: An investigation using Lagrangian microphysics with a subgrid-scale model. J. Atmos. Sci., 78, 2983–3005, https://doi.org/10.1175/JAS-D-20-0281.1.

    • Search Google Scholar
    • Export Citation

  • Chang, K., and Coauthors, 2016: A laboratory facility to study gas–aerosol–cloud interactions in a turbulent environment: The Π Chamber. Bull. Amer. Meteor. Soc., 97, 2343–2358, https://doi.org/10.1175/BAMS-D-15-00203.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Cooper, W. A., 1989: Effects of variable droplet growth histories on droplet size distributions. Part I: Theory. J. Atmos. Sci., 46, 1301–1311, https://doi.org/10.1175/1520-0469(1989)046<1301:EOVDGH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Deardorff, J. W., 1974a: Three-dimensional numerical study of the height and mean structure of a heated planetary boundary layer. Bound.-Layer Meteor., 7, 81–106, https://doi.org/10.1007/BF00224974.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Deardorff, J. W., 1974b: Three-dimensional numerical study of turbulence in an entraining mixed layer. Bound.-Layer Meteor., 7, 199–226, https://doi.org/10.1007/BF00227913.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Deardorff, J. W., 1980: Stratocumulus-capped mixed layers derived from a three-dimensional model. Bound.-Layer Meteor., 18, 495–527, https://doi.org/10.1007/BF00119502.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • De Rooy, W. C., and Coauthors, 2013: Entrainment and detrainment in cumulus convection: An overview. Quart. J. Roy. Meteor. Soc., 139, 1–19, https://doi.org/10.1002/qj.1959.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Desai, N., K. K. Chandrakar, K. Chang, W. Cantrell, and R. A. Shaw, 2018: Influence of microphysical variability on stochastic condensation in a turbulent laboratory cloud. J. Atmos. Sci., 75, 189–201, https://doi.org/10.1175/JAS-D-17-0158.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Devenish, B., and Coauthors, 2012: Droplet growth in warm turbulent clouds. Quart. J. Roy. Meteor. Soc., 138, 1401–1429, https://doi.org/10.1002/qj.1897.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Feingold, G., I. Koren, H. Wang, H. Xue, and W. A. Brewer, 2010: Precipitation-generated oscillations in open cellular cloud fields. Nature, 466, 849–852, https://doi.org/10.1038/nature09314.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Field, P., A. Hill, K. Furtado, and A. Korolev, 2014: Mixed-phase clouds in a turbulent environment. Part 2: Analytic treatment. Quart. J. Roy. Meteor. Soc., 140, 870–880, https://doi.org/10.1002/qj.2175.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gao, W., and M. Wesely, 1994: Numerical modeling of the turbulent fluxes of chemically reactive trace gases in the atmospheric boundary layer. J. Appl. Meteor., 33, 835–847, https://doi.org/10.1175/1520-0450(1994)033<0835:NMOTTF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gerber, H., 1991: Supersaturation and droplet spectral evolution in fog. J. Atmos. Sci., 48, 2569–2588, https://doi.org/10.1175/1520-0469(1991)048<2569:SADSEI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grabowski, W. W., and L.-P. Wang, 2013: Growth of cloud droplets in a turbulent environment. Annu. Rev. Fluid Mech., 45, 293–324, https://doi.org/10.1146/annurev-fluid-011212-140750.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grabowski, W. W., and G. C. Abade, 2017: Broadening of cloud droplet spectra through eddy hopping: Turbulent adiabatic parcel simulations. J. Atmos. Sci., 74, 1485–1493, https://doi.org/10.1175/JAS-D-17-0043.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grötzbach, G., 1983: Spatial resolution requirements for direct numerical simulation of the Rayleigh-Bénard convection. J. Comput. Phys., 49, 241–264, https://doi.org/10.1016/0021-9991(83)90125-0.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hoffmann, F., and G. Feingold, 2019: Entrainment and mixing in stratocumulus: Effects of a new explicit subgrid-scale scheme for large-eddy simulations with particle-based microphysics. J. Atmos. Sci., 76, 1955–1973, https://doi.org/10.1175/JAS-D-18-0318.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hoffmann, F., T. Yamaguchi, and G. Feingold, 2019: Inhomogeneous mixing in Lagrangian cloud models: Effects on the production of precipitation embryos. J. Atmos. Sci., 76, 113–133, https://doi.org/10.1175/JAS-D-18-0087.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Jiménez, C., F. Ducros, B. Cuenot, and B. Bédat, 2001: Subgrid scale variance and dissipation of a scalar field in large eddy simulations. Phys. Fluids, 13, 1748–1754, https://doi.org/10.1063/1.1366668.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Katul, G. G., A. M. Sempreviva, and D. Cava, 2008: The temperature–humidity covariance in the marine surface layer: A one-dimensional analytical model. Bound.-Layer Meteor., 126, 263–278, https://doi.org/10.1007/s10546-007-9236-z.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Katul, G. G., D. Cava, S. Launiainen, and T. Vesala, 2009: An analytical model for the two-scalar covariance budget inside a uniform dense canopy. Bound.-Layer Meteor., 131, 173–192, https://doi.org/10.1007/s10546-009-9361-y.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kerstein, A. R., 1988: A linear-eddy model of turbulent scalar transport and mixing. Combust. Sci. Technol., 60, 391–421, https://doi.org/10.1080/00102208808923995.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Korolev, A. V., and G. A. Isaac, 2000: Drop growth due to high supersaturation caused by isobaric mixing. J. Atmos. Sci., 57, 1675–1685, https://doi.org/10.1175/1520-0469(2000)057<1675:DGDTHS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Korolev, A. V., and I. P. Mazin, 2003: Supersaturation of water vapor in clouds. J. Atmos. Sci., 60, 2957–2974, https://doi.org/10.1175/1520-0469(2003)060<2957:SOWVIC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Krishnamurti, R., and L. N. Howard, 1981: Large-scale flow generation in turbulent convection. Proc. Natl. Acad. Sci. USA, 78, 1981–1985, https://doi.org/10.1073/pnas.78.4.1981.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kulmala, M., Ü. Rannik, E. L. Zapadinsky, and C. F. Clement, 1997: The effect of saturation fluctuations on droplet growth. J. Aerosol Sci., 28, 1395–1409, https://doi.org/10.1016/S0021-8502(97)00015-3.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kurowski, M. J., W. W. Grabowski, and P. K. Smolarkiewicz, 2014: Anelastic and compressible simulation of moist deep convection. J. Atmos. Sci., 71, 3767–3787, https://doi.org/10.1175/JAS-D-14-0017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lanotte, A. S., A. Seminara, and F. Toschi, 2009: Cloud droplet growth by condensation in homogeneous isotropic turbulence. J. Atmos. Sci., 66, 1685–1697, https://doi.org/10.1175/2008JAS2864.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • MacMillan, T., R. A. Shaw, W. H. Cantrell, and D. H. Richter, 2021: Direct numerical simulation of turbulence and microphysics in the Pi Chamber. Phys. Rev. Fluids, 7, 020501, https://doi.org/10.1103/PhysRevFluids.7.020501.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mellado, J. P., 2017: Cloud-top entrainment in stratocumulus clouds. Annu. Rev. Fluid Mech., 49, 145–169, https://doi.org/10.1146/annurev-fluid-010816-060231.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mellado, J. P., M. Puche, and C. C. van Heerwaarden, 2017: Moisture statistics in free convective boundary layers growing into linearly stratified atmospheres. Quart. J. Roy. Meteor. Soc., 143, 2403–2419, https://doi.org/10.1002/qj.3095.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Meneveau, C., and J. Katz, 2000: Scale-invariance and turbulence models for large-eddy simulation. Annu. Rev. Fluid Mech., 32, 1–32, https://doi.org/10.1146/annurev.fluid.32.1.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Moeng, C.-H., and R. Rotunno, 1990: Vertical-velocity skewness in the buoyancy-driven boundary layer. J. Atmos. Sci., 47, 1149–1162, https://doi.org/10.1175/1520-0469(1990)047<1149:VVSITB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Morrison, H., and Coauthors, 2020: Confronting the challenge of modeling cloud and precipitation microphysics. J. Adv. Model. Earth Syst., 12, e2019MS001689, https://doi.org/10.1029/2019MS001689.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Niedermeier, D., K. Chang, W. Cantrell, K. K. Chandrakar, D. Ciochetto, and R. A. Shaw, 2018: Observation of a link between energy dissipation rate and oscillation frequency of the large-scale circulation in dry and moist Rayleigh-Bénard turbulence. Phys. Rev. Fluids, 3, 083501, https://doi.org/10.1103/PhysRevFluids.3.083501.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Paoli, R., and K. Shariff, 2009: Turbulent condensation of droplets: Direct simulation and a stochastic model. J. Atmos. Sci., 66, 723–740, https://doi.org/10.1175/2008JAS2734.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Pauluis, O., and J. Schumacher, 2011: Self-aggregation of clouds in conditionally unstable moist convection. Proc. Natl. Acad. Sci. USA, 108, 12 623–12 628, https://doi.org/10.1073/pnas.1102339108.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Politovich, M. K., and W. A. Cooper, 1988: Variability of the supersaturation in cumulus clouds. J. Atmos. Sci., 45, 1651–1664, https://doi.org/10.1175/1520-0469(1988)045<1651:VOTSIC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Prabhakaran, P., A. S. M. Shawon, G. Kinney, S. Thomas, W. Cantrell, and R. A. Shaw, 2020: The role of turbulent fluctuations in aerosol activation and cloud formation. Proc. Natl. Acad. Sci. USA, 117, 16 831–16 838, https://doi.org/10.1073/pnas.2006426117.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Saito, I., T. Gotoh, and T. Watanabe, 2019: Broadening of cloud droplet size distributions by condensation in turbulence. J. Meteor. Soc. Japan, 97, 867–891, https://doi.org/10.2151/jmsj.2019-049.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sardina, G., F. Picano, L. Brandt, and R. Caballero, 2015: Continuous growth of droplet size variance due to condensation in turbulent clouds. Phys. Rev. Lett., 115, 184501, https://doi.org/10.1103/PhysRevLett.115.184501.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shaw, R. A., 2003: Particle-turbulence interactions in atmospheric clouds. Annu. Rev. Fluid Mech., 35, 183–227, https://doi.org/10.1146/annurev.fluid.35.101101.161125.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shawon, A. S. M., P. Prabhakaran, G. Kinney, R. A. Shaw, and W. Cantrell, 2021: Dependence of aerosol-droplet partitioning on turbulence in a laboratory cloud. J. Geophys. Res. Atmos., 126, e2020JD033799, https://doi.org/10.1029/2020JD033799.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shi, X., H. L. Hagen, F. K. Chow, G. H. Bryan, and R. L. Street, 2018: Large-eddy simulation of the stratocumulus-capped boundary layer with explicit filtering and reconstruction turbulence modeling. J. Atmos. Sci., 75, 611–637, https://doi.org/10.1175/JAS-D-17-0162.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shi, X., R. M. Enriquez, R. L. Street, G. H. Bryan, and F. K. Chow, 2019: An implicit algebraic turbulence closure scheme for atmospheric boundary layer simulation. J. Atmos. Sci., 76, 3367–3386, https://doi.org/10.1175/JAS-D-18-0375.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shishkina, O., R. J. Stevens, S. Grossmann, and D. Lohse, 2010: Boundary layer structure in turbulent thermal convection and its consequences for the required numerical resolution. New J. Phys., 12, 075022, https://doi.org/10.1088/1367-2630/12/7/075022.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Siebert, H., and R. A. Shaw, 2017: Supersaturation fluctuations during the early stage of cumulus formation. J. Atmos. Sci., 74, 975–988, https://doi.org/10.1175/JAS-D-16-0115.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stevens, R. J., D. Lohse, and R. Verzicco, 2014: Sidewall effects in Rayleigh–Bénard convection. J. Fluid Mech., 741, 1–27, https://doi.org/10.1017/jfm.2013.664.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tennekes, H., and J. L. Lumley, 2018: A First Course in Turbulence. MIT Press, 320 pp.

  • Thomas, L., W. W. Grabowski, and B. Kumar, 2020: Diffusional growth of cloud droplets in homogeneous isotropic turbulence: DNS, scaled-up DNS, and stochastic model. Atmos. Chem. Phys., 20, 9087–9100, https://doi.org/10.5194/acp-20-9087-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Thomas, S., M. Ovchinnikov, F. Yang, D. van der Voort, W. Cantrell, S. K. Krueger, and R. A. Shaw, 2019: Scaling of an atmospheric model to simulate turbulence and cloud microphysics in the Pi Chamber. J. Adv. Model. Earth Syst., 11, 1981–1994, https://doi.org/10.1029/2019MS001670.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Thomas, S., P. Prabhakaran, W. Cantrell, and R. A. Shaw, 2021: Is the water vapor supersaturation distribution Gaussian? J. Atmos. Sci., 78, 2385–2395, https://doi.org/10.1175/JAS-D-20-0388.1.

    • Search Google Scholar
    • Export Citation

  • Vaillancourt, P., M. Yau, P. Bartello, and W. W. Grabowski, 2002: Microscopic approach to cloud droplet growth by condensation. Part II: Turbulence, clustering, and condensational growth. J. Atmos. Sci., 59, 3421–3435, https://doi.org/10.1175/1520-0469(2002)059<3421:MATCDG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Warhaft, Z., 1981: The use of dual heat injection to infer scalar covariance decay in grid turbulence. J. Fluid Mech., 104, 93–109, https://doi.org/10.1017/S0022112081002838.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Williams, A. G., J. M. Hacker, and H. Kraus, 1997: Transport processes in the tropical warm pool boundary layer. Part II: Vertical structure and variability. J. Atmos. Sci., 54, 2060–2082, https://doi.org/10.1175/1520-0469(1997)054<2060:TPITTW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wyngaard, J. C., 2010: Turbulence in the Atmosphere. Cambridge University Press, 393 pp.

  • Wyngaard, J. C., and M. LeMone, 1980: Behavior of the refractive index structure parameter in the entraining convective boundary layer. J. Atmos. Sci., 37, 1573–1585, https://doi.org/10.1175/1520-0469(1980)037<1573:BOTRIS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wyngaard, J. C., W. Pennell, D. Lenschow, and M. A. LeMone, 1978: The temperature-humidity covariance budget in the convective boundary layer. J. Atmos. Sci., 35, 47–58, https://doi.org/10.1175/1520-0469(1978)035<0047:TTHCBI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zilitinkevich, S., T. Elperin, N. Kleeorin, and I. Rogachevskii, 2007: Energy- and flux-budget (EFB) turbulence closure model for stably stratified flows. Part I: Steady-state, homogeneous regimes. Atmospheric Boundary Layers, Springer, 11–35.

    • Search Google Scholar
    • Export Citation
  • <sc>Fig</sc>. 1.
    View in gallery
    Fig. 1.

    Isosurfaces of supersaturated regions (S = 1.001) for DNS-S95. Here, the axis labels represent the domain size (in m).

  • <sc>Fig</sc>. 2.
    View in gallery
    Fig. 2.

    Nusselt (squares) and Sherwood (circles) numbers at the top (black) and bottom (red) boundaries for different DNS cases as listed in Table 1. The error bars show the standard deviation of the flux data (the standard errors are approximately the size of the symbols). The sign of the top fluxes is negative but they are displayed as positive nondimensional values.

  • <sc>Fig</sc>. 3.
    View in gallery
    Fig. 3.

    Vertical profiles of mean temperature and water vapor mixing ratio for the various DNS cases.

  • <sc>Fig</sc>. 4.
    View in gallery
    Fig. 4.

    Vertical profiles of (a) standard deviation of water vapor mixing ratio fluctuations, (b) standard deviation of potential temperature fluctuations, (c) covariance between potential temperature and water vapor mixing ratio fluctuations, and (d) standard deviation of supersaturation fluctuations for the various DNS cases.

  • <sc>Fig</sc>. 5.
    View in gallery
    Fig. 5.

    Profiles of turbulence statistics as in Fig. 4 but in the Y direction. The statistics presented here exclude grid cells near the boundaries (6 cm on each side) to limit direct wall effects.

  • <sc>Fig</sc>. 6.
    View in gallery
    Fig. 6.

    Vertical profiles of (a) mean temperature and (b) water vapor mixing ratio for LES-CTL compared with DNS-S95 results.

  • <sc>Fig</sc>. 7.
    View in gallery
    Fig. 7.

    Vertical profiles of (a) standard deviation of water vapor mixing ratio fluctuations, (b) standard deviation of potential temperature fluctuations, (c) covariance between potential temperature and water vapor mixing ratio fluctuations, and (d) standard deviation of supersaturation fluctuations for LES-CTL. Profiles from DNS-S95 are also plotted as dashed lines for comparison. The net fluctuation magnitude is calculated by taking the square root of the sum of resolved and SGS variances [(σres2+σSGS2)1/2] or by summing the covariance contributions for the covariance profile. These profiles are obtained from excluding data points near the sidewall boundaries (see text for details).

  • <sc>Fig</sc>. 8.
    View in gallery
    Fig. 8.

    As in Fig. 7, but showing the resolved (solid blue) and SGS (solid red) LES scalar statistics and the filtered (blue dashed) and subfiltered (red dashed) DNS statistics. The DNS data are filtered with a spatial box filter using a filter width of 16 DNS grid cells.

  • <sc>Fig</sc>. 9.
    View in gallery
    Fig. 9.

    As in Fig. 7, but for LES-HI.

  • <sc>Fig</sc>. 10.
    View in gallery
    Fig. 10.

    As in Fig. 8, but for LES-HI. The DNS data are filtered using a spatial box filter with a filter width of 10 DNS grid cells.

  • <sc>Fig</sc>. 11.
    View in gallery
    Fig. 11.

    As in Fig. 7, but for LES-LO.

  • <sc>Fig</sc>. 12.
    View in gallery
    Fig. 12.

    As in Fig. 8, but for LES-LO. The DNS data are filtered with a spatial box filter using a filter width of 25 DNS grid cells.

Fig. 1.

Isosurfaces of supersaturated regions (S = 1.001) for DNS-S95. Here, the axis labels represent the domain size (in m).
Fig. 2.

Nusselt (squares) and Sherwood (circles) numbers at the top (black) and bottom (red) boundaries for different DNS cases as listed in Table 1. The error bars show the standard deviation of the flux data (the standard errors are approximately the size of the symbols). The sign of the top fluxes is negative but they are displayed as positive nondimensional values.
Fig. 3.

Vertical profiles of mean temperature and water vapor mixing ratio for the various DNS cases.
Fig. 4.

Vertical profiles of (a) standard deviation of water vapor mixing ratio fluctuations, (b) standard deviation of potential temperature fluctuations, (c) covariance between potential temperature and water vapor mixing ratio fluctuations, and (d) standard deviation of supersaturation fluctuations for the various DNS cases.
Fig. 5.

Profiles of turbulence statistics as in Fig. 4 but in the Y direction. The statistics presented here exclude grid cells near the boundaries (6 cm on each side) to limit direct wall effects.
Fig. 6.

Vertical profiles of (a) mean temperature and (b) water vapor mixing ratio for LES-CTL compared with DNS-S95 results.
Fig. 7.

Vertical profiles of (a) standard deviation of water vapor mixing ratio fluctuations, (b) standard deviation of potential temperature fluctuations, (c) covariance between potential temperature and water vapor mixing ratio fluctuations, and (d) standard deviation of supersaturation fluctuations for LES-CTL. Profiles from DNS-S95 are also plotted as dashed lines for comparison. The net fluctuation magnitude is calculated by taking the square root of the sum of resolved and SGS variances [(σres2+σSGS2)1/2] or by summing the covariance contributions for the covariance profile. These profiles are obtained from excluding data points near the sidewall boundaries (see text for details).
Fig. 8.

As in Fig. 7, but showing the resolved (solid blue) and SGS (solid red) LES scalar statistics and the filtered (blue dashed) and subfiltered (red dashed) DNS statistics. The DNS data are filtered with a spatial box filter using a filter width of 16 DNS grid cells.
Fig. 9.

As in Fig. 7, but for LES-HI.
Fig. 10.

As in Fig. 8, but for LES-HI. The DNS data are filtered using a spatial box filter with a filter width of 10 DNS grid cells.
Fig. 11.

As in Fig. 7, but for LES-LO.
Fig. 12.

As in Fig. 8, but for LES-LO. The DNS data are filtered with a spatial box filter using a filter width of 25 DNS grid cells.
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