CCN Activation and Droplet Growth in Pi Chamber Simulations with Lagrangian Particle–Based Microphysics

Wojciech W. Grabowski aNSF NCAR, Boulder, Colorado

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Yongjoon Kim bGlocal M&S Co., Ltd., Seoul, South Korea

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Seong Soo Yum cDepartment of Atmospheric Sciences, Yonsei University, Seoul, South Korea
dClimate and Environmental Research Institute, KIST, Seoul, South Korea

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Abstract

Numerical simulations of turbulent moist Rayleigh–Bénard convection driving CCN activation and droplet growth in the laboratory Pi chamber are discussed. Supersaturation fluctuations come from isobaric mixing of warm and humid air rising from the lower boundary with colder air featuring lower water vapor concentrations descending from the upper boundary. Lagrangian particle–based microphysics is used to represent the growth of haze CCN and cloud droplets with kinetic, solute, and surface tension effects included. Dry CCN spectra in the range between 2- and 200-nm radii from field observations are considered. Increasing the total CCN concentration from pristine to polluted conditions results in an increase in the droplet concentration and reduction in the mean droplet radius and spectral width. These are in agreement with Pi chamber observations and numerical simulations, as well as with numerous past studies of CCN cloud-base activation in natural clouds. The key result is that a relatively small fraction of the available CCN is activated in the Pi chamber fluctuating supersaturations, from about a half in the pristine case to only a 10th in the polluted case. The activation fraction as a function of the dry CCN radius is similar in all simulations, close to zero at the CCN small end, increasing to a maximum at CCN radius around 50 nm, and decreasing to close to zero at the large CCN end. This is explained as too small supersaturations to activate small CCN as in natural clouds and insufficient time to allow large CCN reaching the critical radius.

Significance Statement

Impact of turbulence on the formation and growth of cloud droplets is an important cloud physics problem. Laboratory experiments in the Michigan Technological University cloud chamber provide key insights into this problem. Numerical simulations of cloud chamber processes discussed in this paper complement laboratory experiments by providing insights difficult or impossible to obtain in the laboratory. The study contrasts the formation and growth of cloud droplets in the laboratory cloud chamber with processes taking place in natural clouds. The differences documented in this paper pose questions concerning the impact of turbulence on the formation and growth of cloud droplets as a result of interactions of clouds with their environment.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wojciech W. Grabowski, grabow@ucar.edu

Abstract

Numerical simulations of turbulent moist Rayleigh–Bénard convection driving CCN activation and droplet growth in the laboratory Pi chamber are discussed. Supersaturation fluctuations come from isobaric mixing of warm and humid air rising from the lower boundary with colder air featuring lower water vapor concentrations descending from the upper boundary. Lagrangian particle–based microphysics is used to represent the growth of haze CCN and cloud droplets with kinetic, solute, and surface tension effects included. Dry CCN spectra in the range between 2- and 200-nm radii from field observations are considered. Increasing the total CCN concentration from pristine to polluted conditions results in an increase in the droplet concentration and reduction in the mean droplet radius and spectral width. These are in agreement with Pi chamber observations and numerical simulations, as well as with numerous past studies of CCN cloud-base activation in natural clouds. The key result is that a relatively small fraction of the available CCN is activated in the Pi chamber fluctuating supersaturations, from about a half in the pristine case to only a 10th in the polluted case. The activation fraction as a function of the dry CCN radius is similar in all simulations, close to zero at the CCN small end, increasing to a maximum at CCN radius around 50 nm, and decreasing to close to zero at the large CCN end. This is explained as too small supersaturations to activate small CCN as in natural clouds and insufficient time to allow large CCN reaching the critical radius.

Significance Statement

Impact of turbulence on the formation and growth of cloud droplets is an important cloud physics problem. Laboratory experiments in the Michigan Technological University cloud chamber provide key insights into this problem. Numerical simulations of cloud chamber processes discussed in this paper complement laboratory experiments by providing insights difficult or impossible to obtain in the laboratory. The study contrasts the formation and growth of cloud droplets in the laboratory cloud chamber with processes taking place in natural clouds. The differences documented in this paper pose questions concerning the impact of turbulence on the formation and growth of cloud droplets as a result of interactions of clouds with their environment.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wojciech W. Grabowski, grabow@ucar.edu
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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, 33653379, https://doi.org/10.1175/JAS-D-18-0078.1.

    • 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, 54735485, https://doi.org/10.5194/amt-14-5473-2021.

    • Search Google Scholar
    • Export Citation
  • Anderson, J. C., P. Beeler, M. Ovchinnikov, W. Cantrell, S. Krueger, R. A. Shaw, F. Yang, and L. Fierce, 2023: Enhancements in cloud condensation nuclei activity from turbulent fluctuations in supersaturation. Geophys. Res. Lett., 50, e2022GL102635, https://doi.org/10.1029/2022GL102635.

    • Search Google Scholar
    • Export Citation
  • Anderson, J. C., I. Helman, R. A. Shaw, and W. Cantrell, 2024: Droplet growth or evaporation does not buffer the variability in supersaturation in clean clouds. J. Atmos. Sci., 81, 225233, https://doi.org/10.1175/JAS-D-23-0104.1.

    • Search Google Scholar
    • Export Citation
  • Andrejczuk, M., W. W. Grabowski, S. P. Malinowski, and P. K. Smolarkiewicz, 2004: Numerical simulation of cloud–clear air interfacial mixing. J. Atmos. Sci., 61, 17261739, https://doi.org/10.1175/1520-0469(2004)061<1726:NSOCAI>2.0.CO;2.

    • 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 24314 248, https://doi.org/10.1073/pnas.1612686113.

    • 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, 29833005, 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, 23432358, https://doi.org/10.1175/BAMS-D-15-00203.1.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., 2014: Extracting microphysical impacts in large-eddy simulations of shallow convection. J. Atmos. Sci., 71, 44934499, https://doi.org/10.1175/JAS-D-14-0231.1.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., 2015: Untangling microphysical impacts on deep convection applying a novel modeling methodology. J. Atmos. Sci., 72, 24462464, https://doi.org/10.1175/JAS-D-14-0307.1.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., 2020: Comparison of Eulerian bin and Lagrangian particle-based schemes in simulations of Pi chamber dynamics and microphysics. J. Atmos. Sci., 77, 11511165, https://doi.org/10.1175/JAS-D-19-0216.1.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., 2024: Pi chamber simulation with superdroplets. Version 1.0. UCAR/NCAR–GDEX, accessed 11 January 2024, https://doi.org/10.5065/7e21-j337.

  • Grabowski, W. W., and D. Jarecka, 2015: Modeling condensation in shallow nonprecipitating convection. J. Atmos. Sci., 72, 46614679, https://doi.org/10.1175/JAS-D-15-0091.1.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., and H. Morrison, 2016: Untangling microphysical impacts on deep convection applying a novel modeling methodology. Part II: Double-moment microphysics. J. Atmos. Sci., 73, 37493770, https://doi.org/10.1175/JAS-D-15-0367.1.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., and H. Morrison, 2017: Modeling condensation in deep convection. J. Atmos. Sci., 74, 22472267, https://doi.org/10.1175/JAS-D-16-0255.1.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., and H. Pawlowska, 2023: Adiabatic evolution of cloud droplet spectral width: A new look at an old problem. Geophys. Res. Lett., 50, e2022GL101917, https://doi.org/10.1029/2022GL101917.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., M. Andrejczuk, and L.-P. Wang, 2011: Droplet growth in a bin warm-rain scheme with Twomey CCN activation. Atmos. Res., 99, 290301, https://doi.org/10.1016/j.atmosres.2010.10.020.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., H. Morrison, S.-I. Shima, G. C. Abade, P. Dziekan, and H. Pawlowska, 2019: Modeling of cloud microphysics: Can we do better? Bull. Amer. Meteor. Soc., 100, 655672, https://doi.org/10.1175/BAMS-D-18-0005.1.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., L. Thomas, and B. Kumar, 2022a: Impact of cloud-base turbulence on CCN activation: Single-size CCN. J. Atmos. Sci., 79, 551566, https://doi.org/10.1175/JAS-D-21-0184.1.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., L. Thomas, and B. Kumar, 2022b: Impact of cloud-base turbulence on CCN activation: CCN distribution. J. Atmos. Sci., 79, 29652981, https://doi.org/10.1175/JAS-D-22-0075.1.

    • Search Google Scholar
    • Export Citation
  • Grinstein, F. F., L. G. Margolin, and W. J. Rider, 2007: Implicit Large Eddy Simulation: Computing Turbulent Fluid Dynamics. Cambridge University Press, 578 pp.

  • Margolin, L. G., and W. J. Rider, 2002: A rationale for implicit turbulence modelling. Int. J. Numer. Methods Fluids, 39, 821841, https://doi.org/10.1002/fld.331.

    • Search Google Scholar
    • Export Citation
  • Margolin, L. G., W. J. Rider, and F. F. Grinstein, 2006: Modeling turbulent flow with implicit LES. J. Turbul., 7, N15, https://doi.org/10.1080/14685240500331595.

    • Search Google Scholar
    • Export Citation
  • Park, M., S. S. Yum, P. Seo, N. Kim, and C. Ahn, 2023a: A new CCN number concentration prediction method based on multiple linear regression and non-negative matrix factorization: 1. Development, validation, and comparison using the measurement data over the Korean Peninsula. J. Geophys. Res. Atmos., 128, e2023JD039189, https://doi.org/10.1029/2023JD039189.

    • Search Google Scholar
    • Export Citation
  • Park, M., S. S. Yum, P. Seo, C. Ahn, N. Kim, B. E. Anderson, and K. L. Thornhill, 2023b: A new CCN number concentration prediction method based on multiple linear regression and non-negative matrix factorization: 2. Application to obtain CCN spectra in and around the Korean Peninsula. J. Geophys. Res. Atmos., 128, e2023JD039234, https://doi.org/10.1029/2023JD039234.

    • 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 83116 838, https://doi.org/10.1073/pnas.2006426117.

    • Search Google Scholar
    • Export Citation
  • Shaw, R. A., S. Thomas, P. Prabhakaran, W. Cantrell, M. Ovchinnikov, and F. Yang, 2023: Fast and slow microphysics regimes in a minimalist model of cloudy Rayleigh-Bénard convection. Phys. Rev. Res., 5, 043018, https://doi.org/10.1103/PhysRevResearch.5.043018.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Shima, S., K. Kusano, A. Kawano, T. Sugiyama, and S. Kawahara, 2009: The super-droplet method for the numerical simulation of clouds and precipitation: A particle-based and probabilistic microphysics model coupled with a non-hydrostatic model. Quart. J. Roy. Meteor. Soc., 135, 13071320, https://doi.org/10.1002/qj.441.

    • Search Google Scholar
    • Export Citation
  • Slawinska, J., W. W. Grabowski, H. Pawlowska, and H. Morrison, 2012: Droplet activation and mixing in large-eddy simulation of a shallow cumulus field. J. Atmos. Sci., 69, 444462, https://doi.org/10.1175/JAS-D-11-054.1.

    • 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, 19811994, https://doi.org/10.1029/2019MS001670.

    • Search Google Scholar
    • Export Citation
  • Yang, F., F. Hoffmann, R. A. Shaw, M. Ovchinnikov, and A. M. Vogelmann, 2023: An intercomparison of large-eddy simulations of a convection cloud chamber using haze-capable bin and Lagrangian cloud microphysics schemes. J. Adv. Model. Earth Syst., 15, e2022MS003270, https://doi.org/10.1029/2022MS003270.

    • Search Google Scholar
    • Export Citation
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