Articles | Volume 24, issue 18
https://doi.org/10.5194/acp-24-10245-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-10245-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Glaciation of mixed-phase clouds: insights from bulk model and bin-microphysics large-eddy simulation informed by laboratory experiment
Pacific Northwest National Laboratory, Richland, WA, USA
Steve Krueger
The University of Utah, Salt Lake City, UT, USA
Sisi Chen
National Center for Atmospheric Research, Boulder, CO, USA
Mikhail Ovchinnikov
Pacific Northwest National Laboratory, Richland, WA, USA
Will Cantrell
Michigan Technological University, Houghton, MI, USA
Raymond A. Shaw
Michigan Technological University, Houghton, MI, USA
Related authors
Fan Yang, Hamed Fahandezh Sadi, Raymond A. Shaw, Fabian Hoffmann, Pei Hou, Aaron Wang, and Mikhail Ovchinnikov
Atmos. Chem. Phys., 25, 3785–3806, https://doi.org/10.5194/acp-25-3785-2025, https://doi.org/10.5194/acp-25-3785-2025, 2025
Short summary
Short summary
Large-eddy simulations of a convection cloud chamber show two new microphysics regimes, cloud oscillation and cloud collapse, due to haze–cloud interactions. Our results suggest that haze particles and their interactions with cloud droplets should be considered especially in polluted conditions. To properly simulate haze–cloud interactions, we need to resolve droplet activation and deactivation processes, instead of using Twomey-type activation parameterization.
Zeen Zhu, Fan Yang, Steven Krueger, and Yangang Liu
EGUsphere, https://doi.org/10.5194/egusphere-2025-3489, https://doi.org/10.5194/egusphere-2025-3489, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
To better understand cloud behavior, we used computer simulations to study how the air mix in clouds. Our results show that the pattern of mixing seen from aircraft measurements may not reflect the true mixing process happening inside clouds. This result suggests that care is needed when using aircraft data to study the cloud mixing process and that new ways of observing clouds could offer clearer insights.
Sisi Chen, Lulin Xue, Sarah A. Tessendorf, Thomas Chubb, Andrew Peace, Suzanne Kenyon, Johanna Speirs, Jamie Wolff, and Bill Petzke
Atmos. Chem. Phys., 25, 6703–6724, https://doi.org/10.5194/acp-25-6703-2025, https://doi.org/10.5194/acp-25-6703-2025, 2025
Short summary
Short summary
This study aims to investigate how cloud seeding affects snowfall in Australia's Snowy Mountains. By running simulations with different setups, we found that seeding impact varies greatly with weather conditions. Seeding increased snow in stable weather but sometimes reduced it in stormy weather. This helps us to better understand when seeding works best to boost water supplies.
Robert Grosz, Kamal Kant Chandrakar, Raymond A. Shaw, Jesse C. Anderson, Will Cantrell, and Szymon P. Malinowski
Atmos. Meas. Tech., 18, 2619–2638, https://doi.org/10.5194/amt-18-2619-2025, https://doi.org/10.5194/amt-18-2619-2025, 2025
Short summary
Short summary
Our objective was to enhance understanding of thermally driven convection in terms of small-scale variations in the temperature scalar field. We conducted a small-scale study of the temperature field in the Π Chamber using three different temperature differences (10 K, 15 K, and 20 K). Measurements were carried out using a miniaturized UltraFast Thermometer operating at 2 kHz, allowing undisturbed vertical temperature profiling from 8 cm above the floor to 5 cm below the ceiling.
Fan Yang, Hamed Fahandezh Sadi, Raymond A. Shaw, Fabian Hoffmann, Pei Hou, Aaron Wang, and Mikhail Ovchinnikov
Atmos. Chem. Phys., 25, 3785–3806, https://doi.org/10.5194/acp-25-3785-2025, https://doi.org/10.5194/acp-25-3785-2025, 2025
Short summary
Short summary
Large-eddy simulations of a convection cloud chamber show two new microphysics regimes, cloud oscillation and cloud collapse, due to haze–cloud interactions. Our results suggest that haze particles and their interactions with cloud droplets should be considered especially in polluted conditions. To properly simulate haze–cloud interactions, we need to resolve droplet activation and deactivation processes, instead of using Twomey-type activation parameterization.
Yu Yao, Po-Lun Ma, Yi Qin, Matthew W. Christensen, Hui Wan, Kai Zhang, Balwinder Singh, Meng Huang, and Mikhail Ovchinnikov
EGUsphere, https://doi.org/10.5194/egusphere-2024-523, https://doi.org/10.5194/egusphere-2024-523, 2024
Preprint withdrawn
Short summary
Short summary
Giant aerosols have substantial effects on warm rain formation. However, it remains challenging to quantify the impact of giant particles at global scale. In this work, we applied earth system model to investigate its impacts by implementing new giant aerosol treatments to consider its physical process. We found this approach substantially affect liquid cloud and improved model's precipitation response to aerosols. Our findings demonstrate the significant impact of giant aerosols on climate.
Zeen Zhu, Fan Yang, Pavlos Kollias, Raymond A. Shaw, Alex B. Kostinski, Steve Krueger, Katia Lamer, Nithin Allwayin, and Mariko Oue
Atmos. Meas. Tech., 17, 1133–1143, https://doi.org/10.5194/amt-17-1133-2024, https://doi.org/10.5194/amt-17-1133-2024, 2024
Short summary
Short summary
In this article, we demonstrate the feasibility of applying advanced radar technology to detect liquid droplets generated in the cloud chamber. Specifically, we show that using radar with centimeter-scale resolution, single drizzle drops with a diameter larger than 40 µm can be detected. This study demonstrates the applicability of remote sensing instruments in laboratory experiments and suggests new applications of ultrahigh-resolution radar for atmospheric sensing.
Elise Rosky, Will Cantrell, Tianshu Li, Issei Nakamura, and Raymond A. Shaw
Atmos. Chem. Phys., 23, 10625–10642, https://doi.org/10.5194/acp-23-10625-2023, https://doi.org/10.5194/acp-23-10625-2023, 2023
Short summary
Short summary
Using computer simulations of water, we find that water under tension freezes more easily than under normal conditions. A linear equation describes how freezing temperature increases with tension. Accordingly, simulations show that naturally occurring tension in water capillary bridges leads to higher freezing temperatures. This work is an early step in determining if atmospheric cloud droplets freeze due to naturally occurring tension, for example, during processes such as droplet collisions.
Sisi Chen, Lulin Xue, Sarah Tessendorf, Kyoko Ikeda, Courtney Weeks, Roy Rasmussen, Melvin Kunkel, Derek Blestrud, Shaun Parkinson, Melinda Meadows, and Nick Dawson
Atmos. Chem. Phys., 23, 5217–5231, https://doi.org/10.5194/acp-23-5217-2023, https://doi.org/10.5194/acp-23-5217-2023, 2023
Short summary
Short summary
The possible mechanism of effective ice growth in the cloud-top generating cells in winter orographic clouds is explored using a newly developed ultra-high-resolution cloud microphysics model. Simulations demonstrate that a high availability of moisture and liquid water is critical for producing large ice particles. Fluctuations in temperature and moisture down to millimeter scales due to cloud turbulence can substantially affect the growth history of the individual cloud particles.
Walter Hannah, Kyle Pressel, Mikhail Ovchinnikov, and Gregory Elsaesser
Geosci. Model Dev., 15, 6243–6257, https://doi.org/10.5194/gmd-15-6243-2022, https://doi.org/10.5194/gmd-15-6243-2022, 2022
Short summary
Short summary
An unphysical checkerboard signal is identified in two configurations of the atmospheric component of E3SM. The signal is very persistent and visible after averaging years of data. The signal is very difficult to study because it is often mixed with realistic weather. A method is presented to detect checkerboard patterns and compare the model with satellite observations. The causes of the signal are identified, and a solution for one configuration is discussed.
Istvan Geresdi, Lulin Xue, Sisi Chen, Youssef Wehbe, Roelof Bruintjes, Jared A. Lee, Roy M. Rasmussen, Wojciech W. Grabowski, Noemi Sarkadi, and Sarah A. Tessendorf
Atmos. Chem. Phys., 21, 16143–16159, https://doi.org/10.5194/acp-21-16143-2021, https://doi.org/10.5194/acp-21-16143-2021, 2021
Short summary
Short summary
By releasing soluble aerosols into the convective clouds, cloud seeding potentially enhances rainfall. The seeding impacts are hard to quantify with observations only. Numerical models that represent the detailed physics of aerosols, cloud and rain formation are used to investigate the seeding impacts on rain enhancement under different natural aerosol backgrounds and using different seeding materials. Our results indicate that seeding may enhance rainfall under certain conditions.
Jesse C. Anderson, Subin Thomas, Prasanth Prabhakaran, Raymond A. Shaw, and Will Cantrell
Atmos. Meas. Tech., 14, 5473–5485, https://doi.org/10.5194/amt-14-5473-2021, https://doi.org/10.5194/amt-14-5473-2021, 2021
Short summary
Short summary
Fluctuations due to turbulence in Earth's atmosphere can play a role in how many droplets a cloud has and, eventually, whether that cloud rains or evaporates. We study such processes in Michigan Tech's cloud chamber. Here, we characterize the turbulent and large-scale motions of air in the chamber, measuring the spatial and temporal distributions of temperature and water vapor, which we can combine to get the distribution of relative humidity, which governs cloud formation and dissipation.
Sisi Chen, Lulin Xue, and Man-Kong Yau
Atmos. Chem. Phys., 20, 10111–10124, https://doi.org/10.5194/acp-20-10111-2020, https://doi.org/10.5194/acp-20-10111-2020, 2020
Short summary
Short summary
This study employs a parcel–DNS (direct numerical simulation) modeling framework to accurately resolve the aerosol–droplet–turbulence interactions in an ascending air parcel. The effect of turbulence, aerosol hygroscopicity, and aerosol mass loading on droplet growth and rain formation is investigated through a series of in-cloud seeding experiments in which hygroscopic particles were seeded near the cloud base.
Cited articles
Anderson, J. C., Thomas, S., Prabhakaran, P., Shaw, R. A., and Cantrell, W.: 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, 2021. a
Arakawa, A. and Lamb, V. R.: Computational design of the basic dynamical processes of the UCLA general circulation model, Methods in Computational Physics: Advances in Research and Applications, 17, 173–265, 1977. a
Bergeron, T.: Über die dreidimensional verknüpfende Wetteranalyse, Geophys. Norv., 5, 1–111, 1928. a
Chandrakar, K. K., Cantrell, W., Chang, K., Ciochetto, D., Niedermeier, D., Ovchinnikov, M., Shaw, R. A., and Yang, F.: Aerosol indirect effect from turbulence-induced broadening of cloud-droplet size distributions, P. Natl. Acad. Sci. USA, 113, 14243–14248, https://doi.org/10.1073/pnas.1612686113, 2016. a, b
Chang, K., Bench, J., Brege, M., Cantrell, W., Chandrakar, K., Ciochetto, D., Mazzoleni, C., Mazzoleni, L. R., Niedermeier, D., and Shaw, R. A.: A Laboratory Facility to Study Gas-Aerosol-Cloud Interactions in a Turbulent Environment: The Π Chamber, B. Am. Meteorol. Soc., 97, 2343–2358, https://doi.org/10.1175/BAMS-D-15-00203.1, 2016. a
Chen, S., Xue, L., and Yau, M.: Hygroscopic seeding effects of giant aerosol particles simulated by the Lagrangian-particle-based direct numerical simulation, Geophys. Res. Lett., 48, e2021GL094621, https://doi.org/10.1029/2021GL094621, 2021. a
Chen, S., Krueger, S., Dziekan, P., MacMillan, T., Richter, D., Schmalfuss, S., Shima, S., Yang, F., Anderson, J. C., Cantrell, W. H., and Shaw, R. A.: Intercomparison of model simulations of cloudy Rayleigh-Bénard convection in a laboratory chamber, J. Adv. Model. Earth Sy., in preparation, 2024. a, b
Curry, J., Pinto, J., Benner, T., and Tschudi, M.: Evolution of the cloudy boundary layer during the autumnal freezing of the Beaufort Sea, J. Geophys. Res.-Atmos., 102, 13851–13860, 1997. a
de Roode, S. R., Frederikse, T., Siebesma, A. P., Ackerman, A. S., Chylik, J., Field, P. R., Fricke, J., Gryschka, M., Hill, A., Honnert, R., Krueger, S. K., Lac, C., Lesage, A. T., and Tomassini, L.: Turbulent transport in the gray zone: A large eddy model intercomparison study of the CONSTRAIN cold air outbreak case, J. Adv. Model. Earth Sy., 11, 597–623, 2019. a
Deardorff, J. W.: Stratocumulus-capped mixed layers derived from a three-dimensional model, Bound.-Lay. Meteorol., 18, 495–527, 1980. a
Dong, X. and Mace, G. G.: Arctic stratus cloud properties and radiative forcing derived from ground-based data collected at Barrow, Alaska, J. Climate, 16, 445–461, 2003. a
Fan, J., Leung, L. R., Rosenfeld, D., and DeMott, P. J.: Effects of cloud condensation nuclei and ice nucleating particles on precipitation processes and supercooled liquid in mixed-phase orographic clouds, Atmos. Chem. Phys., 17, 1017–1035, https://doi.org/10.5194/acp-17-1017-2017, 2017. a
Field, P. and Heymsfield, A.: Importance of snow to global precipitation, Geophys. Res. Lett., 42, 9512–9520, 2015. a
Findeisen, W.: Kolloid-meteorologische Vorgänge bei Neiderschlags-bildung, Meteorol. Z., 55, 121–133, 1938. a
Fridlind, A. M., Ackerman, A., McFarquhar, G., Zhang, G., Poellot, M., DeMott, P., Prenni, A., and Heymsfield, A.: Ice properties of single-layer stratocumulus during the Mixed-Phase Arctic Cloud Experiment: 2. Model results, J. Geophys. Res.-Atmos., 112, D24202, https://doi.org/10.1029/2007JD008646, 2007. a
Fridlind, A. M., Van Diedenhoven, B., Ackerman, A. S., Avramov, A., Mrowiec, A., Morrison, H., Zuidema, P., and Shupe, M. D.: A FIRE-ACE/SHEBA case study of mixed-phase Arctic boundary layer clouds: Entrainment rate limitations on rapid primary ice nucleation processes, J. Atmos. Sci., 69, 365–389, 2012. a
Fu, S. and Xue, H.: The effect of ice nuclei efficiency on Arctic mixed-phase clouds from large-eddy simulations, J. Atmos. Sci., 74, 3901–3913, 2017. a
Fu, S., Deng, X., Shupe, M. D., and Xue, H.: A modelling study of the continuous ice formation in an autumnal Arctic mixed-phase cloud case, Atmos. Res., 228, 77–85, 2019. a
Furtado, K., Field, P., Boutle, I., Morcrette, C., and Wilkinson, J.: A physically based subgrid parameterization for the production and maintenance of mixed-phase clouds in a general circulation model, J. Atmos. Sci., 73, 279–291, 2016. a
Hill, A., Field, P., Furtado, K., Korolev, A., and Shipway, B.: Mixed-phase clouds in a turbulent environment. Part 1: Large-eddy simulation experiments, Q. J. Roy. Meteorol. Soc., 140, 855–869, 2014. a
Huang, J. M. and Zhang, J.: Rayleigh–Bénard thermal convection perturbed by a horizontal heat flux, J. Fluid Mech., 954, R2, https://doi.org/10.1017/jfm.2022.1035, 2023. a
Kays, W. M., Crawford, M. E., and Weigand, B.: Convective heat and mass transfer, Vol. 4, McGraw-Hill New York, ISBN: 9780070334571, 1980. a
Khain, A., Pokrovsky, A., Pinsky, M., Seifert, A., and Phillips, V.: Simulation of Effects of Atmospheric Aerosols on Deep Turbulent Convective Clouds Using a Spectral Microphysics Mixed-Phase Cumulus Cloud Model. Part I: Model Description and Possible Applications, J. Atmos. Sci., 61, 2963–2982, https://doi.org/10.1175/JAS-3350.1, 2004. a
Khairoutdinov, M. F. and Randall, D. A.: Cloud Resolving Modeling of the ARM Summer 1997 IOP: Model Formulation, Results, Uncertainties, and Sensitivities, J. Atmos. Sci., 60, 607–625, https://doi.org/10.1175/1520-0469(2003)060<0607:CRMOTA>2.0.CO;2, 2003 (code available at: http://rossby.msrc.sunysb.edu/SAM.html, last access: 16 February 2024). a, b
Korolev, A., Strapp, J., Isaac, G., and Nevzorov, A.: The Nevzorov airborne hot-wire LWC–TWC probe: Principle of operation and performance characteristics, J. Atmos. Ocean. Tech., 15, 1495–1510, 1998. a
Korolev, A., McFarquhar, G., Field, P. R., Franklin, C., Lawson, P., Wang, Z., Williams, E., Abel, S. J., Axisa, D., Borrmann, S., Crosier, J., Fugal, J., Krämer, M., Lohmann, U., Schlenczek, O., Schnaiter, M., and Wendisch, M.: Mixed-phase clouds: Progress and challenges, Meteor. Mon., 58, 5–1, 2017. a, b, c, d
Krueger, S. K.: Technical note: Equilibrium droplet size distributions in a turbulent cloud chamber with uniform supersaturation, Atmos. Chem. Phys., 20, 7895–7909, https://doi.org/10.5194/acp-20-7895-2020, 2020. a
Li, X.-Y., Brandenburg, A., Haugen, N. E. L., and Svensson, G.: Eulerian and Lagrangian approaches to multidimensional condensation and collection, J. Adv. Model. Earth Sy., 9, 1116–1137, 2017. a
Li, X.-Y., Mehlig, B., Svensson, G., Brandenburg, A., and Haugen, N. E.: Collision fluctuations of lucky droplets with superdroplets, J. Atmos. Sci., 79, 1821–1835, 2022. a
Lukas, M., Schwidetzky, R., Eufemio, R. J., Bonn, M., and Meister, K.: Toward understanding bacterial ice nucleation, J. Phys. Chem. B, 126, 1861–1867, 2022. a
Morrison, H., Zuidema, P., Ackerman, A. S., Avramov, A., de Boer, G., Fan, J. W., Fridlind, A. M., Hashino, T., Harrington, J. Y., Luo, Y. L., Ovchinnikov, M., and Shipway, B.: Intercomparison of cloud model simulations of Arctic mixed-phase boundary layer clouds observed during SHEBA/FIRE-ACE, J. Adv. Model. Earth Sy., 3, M05001, https://doi.org/10.1029/2011ms000066, 2011. a
Morrison, H., de Boer, G., Feingold, G., Harrington, J., Shupe, M. D., and Sulia, K.: Resilience of persistent Arctic mixed-phase clouds, Nat. Geosci., 5, 11–17, https://doi.org/10.1038/ngeo1332, 2012. a
Mülmenstädt, J., Sourdeval, O., Delanoë, J., and Quaas, J.: Frequency of occurrence of rain from liquid-, mixed-, and ice-phase clouds derived from A-Train satellite retrievals, Geophys. Res. Lett., 42, 6502–6509, https://doi.org/10.1002/2015GL064604, 2015. a
Niemela, J., Skrbek, L., Sreenivasan, K., and Donnelly, R.: Turbulent convection at very high Rayleigh numbers, Nature, 404, 837–840, 2000. a
Norgren, M. S., de Boer, G., and Shupe, M. D.: Observed aerosol suppression of cloud ice in low-level Arctic mixed-phase clouds, Atmos. Chem. Phys., 18, 13345–13361, https://doi.org/10.5194/acp-18-13345-2018, 2018. a
Ovchinnikov, M., Korolev, A., and Fan, J.: Effects of ice number concentration on dynamics of a shallow mixed-phase stratiform cloud, J. Geophys. Res.-Atmos., 116, D00T06, https://doi.org/10.1029/2011jd015888, 2011. a
Ovchinnikov, M., Ackerman, A. S., Avramov, A., Cheng, A., Fan, J., Fridlind, A. M., Ghan, S., Harrington, J., Hoose, C., Korolev, A., McFarquhar, G. M., Morrison, H., Paukert, M., Savre, J., Shipway, B. J., Shupe, M. D., Solomon, A., and Sulia, K.: Intercomparison of large-eddy simulations of Arctic mixed-phase clouds: Importance of ice size distribution assumptions, J. Adv. Model. Earth Sy., 6, 223–248, 2014. a, b
Pinsky, M., Khain, A., and Korolev, A.: Theoretical analysis of liquid–ice interaction in the unsaturated environment with application to the problem of homogeneous mixing, J. Atmos. Sci., 75, 1045–1062, 2018. a
Pinto, J. O.: Autumnal mixed-phase cloudy boundary layers in the Arctic, J. Atmos. Sci., 55, 2016–2038, 1998. a
Prabhakaran, P., Shawon, A. S. M., Kinney, G., Thomas, S., Cantrell, W., and Shaw, R. A.: The role of turbulent fluctuations in aerosol activation and cloud formation, P. Natl. Acad. Sci. USA, 117, 16831–16838, 2020. a
Shaw, R. A., Cantrell, W., Chen, S., Chuang, P., Donahue, N., Feingold, G., Kollias, P., Korolev, A., Kreidenweis, S., Krueger, S., Mellado, J. P., Niedermeier, D., and Xue, L.: Cloud-Aerosol-Turbulence Interactions: Science Priorities and Concepts for a Large-Scale Laboratory Facility, B. Am. Meteorol. Soc., 101, E1026–E1035, https://doi.org/10.1175/BAMS-D-20-0009.1, 2020. a
Shaw, R. A., Thomas, S., Prabhakaran, P., Cantrell, W., Ovchinnikov, M., and Yang, F.: 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, 2023. a
Shima, S., Kusano, K., Kawano, A., Sugiyama, T., and Kawahara, S.: 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, Q. J. Roy. Meteor. Soc., 135, 1307–1320, 2009. a
Smolarkiewicz, P. K. and Grabowski, W. W.: The multidimensional positive definite advection transport algorithm: nonoscillatory option, J. Comput. Phys., 86, 355–375, https://doi.org/10.1016/0021-9991(90)90105-A, 1990. a
Solomon, A., de Boer, G., Creamean, J. M., McComiskey, A., Shupe, M. D., Maahn, M., and Cox, C.: The relative impact of cloud condensation nuclei and ice nucleating particle concentrations on phase partitioning in Arctic mixed-phase stratocumulus clouds, Atmos. Chem. Phys., 18, 17047–17059, https://doi.org/10.5194/acp-18-17047-2018, 2018. a
Storelvmo, T., Kristjánsson, J. E., Lohmann, U., Iversen, T., Kirkevåg, A., and Seland, Ø.: Modeling of the Wegener–Bergeron–Findeisen process—Implications for aerosol indirect effects, Environ. Res. Lett., 3, 045001, https://doi.org/10.1088/1748-9326/3/4/045001, 2008. a
Tao, W.-K., Chen, J.-P., Li, Z., Wang, C., and Zhang, C.: Impact of aerosols on convective clouds and precipitation, Rev. Geophys., 50, RG2001, https://doi.org/10.1029/2011RG000369, 2012. a
Thomas, S., Ovchinnikov, M., Yang, F., van der Voort, D., Cantrell, W., Krueger, S. K., and Shaw, R. A.: Scaling of an Atmospheric Model to Simulate Turbulence and Cloud Microphysics in the Pi Chamber, J. Adv. Model. Earth Sy., 11, 1981–1994, https://doi.org/10.1029/2019MS001670, 2019. a, b, c, d, e, f
Thomas, S., Yang, F., Ovchinnikov, M., Cantrell, W. H., and Shaw, R. A.: Scaling of turbulence and microphysics in a convection–cloud chamber of varying height, J. Adv. Model. Earth Sy., 15, e2022MS003304, https://doi.org/10.1029/2022MS003304, 2023. a, b
Vergara-Temprado, J., Miltenberger, A. K., Furtado, K., Grosvenor, D. P., Shipway, B. J., Hill, A. A., Wilkinson, J. M., Field, P. R., Murray, B. J., and Carslaw, K. S.: Strong control of Southern Ocean cloud reflectivity by ice-nucleating particles, P. Natl. Acad. Sci. USA, 115, 2687–2692, 2018. a, b
Verlinde, J., Harrington, J. Y., McFarquhar, G., Yannuzzi, V., Avramov, A., Greenberg, S., Johnson, N., Zhang, G., Poellot, M., Mather, J. H., Turner, D. D., Eloranta, E. W., Zak, B. D., Prenni, A. J., Daniel, J. S., Kok, G. L., Tobin, D. C., Holz, R., Sassen, K., Spangenberg, D., Minnis, P., Tooman, T. P., Ivey, M. D., Richardson, S. J., Bahrmann, C. P., Shupe, M., DeMott, P. J., Heymsfield, A. J., and Schofield, R.: The mixed-phase Arctic cloud experiment, B. Am. Meteorol. Soc., 88, 205–222, 2007. a
Vignon, É., Alexander, S. P., DeMott, P. J., Sotiropoulou, G., Gerber, F., Hill, T. C. J., Marchand, R., Nenes, A., and Berne, A.: Challenging and Improving the Simulation of Mid-Level Mixed-Phase Clouds Over the High-Latitude Southern Ocean, J. Geophys. Res.-Atmos., 126, e2020JD033490, https://doi.org/10.1029/2020JD033490, 2021. a
Wang, A.: Bulk model and LES results of glaciation in a chamber, NERSC Science Gateways [data set], https://portal.nersc.gov/archive/home/w/wang1202/www/Wang2024ACP_Glaciation/, last access: 16 February 2024. a
Wang, A., Pan, Y., and Markowski, P. M.: The Influence of Turbulence Memory on Idealized Tornado Simulations, Mon. Weather Rev., 148, 4875–4892, https://doi.org/10.1175/MWR-D-20-0031.1, 2020. a
Wang, A., Pan, Y., Bryan, G. H., and Markowski, P. M.: Modeling near-surface turbulence in large-eddy simulations of a tornado: An application of thin boundary layer equations, Mon. Weather Rev., 151, 1587–1607, 2023. a
Wang, A., Ovchinnikov, M., Yang, F., Cantrell, W., Yeom, J., and Shaw, R. A.: The Dual Nature of Entrainment-Mixing Signatures Revealed Through Large-Eddy Simulations of a Convection-Cloud Chamber, J. Atmos. Sci., in review, 2024a. a
Wegener, A.: Thermodynamik der atmosphäre, JA Barth, ISBN 9781015973084, 1911. a
Westbrook, C. and Illingworth, A.: The formation of ice in a long-lived supercooled layer cloud, Q. J. Roy. Meteorol. Soc., 139, 2209–2221, 2013. a
Yang, F., Ovchinnikov, M., and Shaw, R. A.: Minimalist model of ice microphysics in mixed-phase stratiform clouds, Geophys. Res. Lett., 40, 3756–3760, 2013. a
Yang, F., Ovchinnikov, M., Thomas, S., Khain, A., McGraw, R., Shaw, R. A., and Vogelmann, A. M.: Large-eddy simulations of a convection cloud chamber: Sensitivity to bin microphysics and advection, J. Adv. Model. Earth Sy., 14, e2021MS002895, https://doi.org/10.1029/2021MS002895, 2022. a, b
Yang, F., Hoffmann, F., Shaw, R. A., Ovchinnikov, M., and Vogelmann, A. M.: An Intercomparison of Large-Eddy Simulations of a Convection Cloud Chamber Using Haze-Capable Bin and Lagrangian Cloud Microphysics Schemes, J. Adv. Model. Earth Sy., 15, e2022MS003270, https://doi.org/10.1029/2022MS003270, 2023. a, b
Yeom, J. M., Helman, I., Prabhakaran, P., Anderson, J. C., Yang, F., Shaw, R. A., and Cantrell, W.: Cloud microphysical response to entrainment and mixing is locally inhomogeneous and globally homogeneous: Evidence from the lab, P. Natl. Acad. Sci. USA, 120, e2307354120, https://doi.org/10.1073/pnas.230735412, 2023. a
Short summary
We employ two methods to examine a laboratory experiment on clouds with both ice and liquid phases. The first assumes well-mixed properties; the second resolves the spatial distribution of turbulence and cloud particles. Results show that while the trends in mean properties generally align, when turbulence is resolved, liquid droplets are not fully depleted by ice due to incomplete mixing. This underscores the threshold of ice mass fraction in distinguishing mixed-phase clouds from ice clouds.
We employ two methods to examine a laboratory experiment on clouds with both ice and liquid...
Altmetrics
Final-revised paper
Preprint