Articles | Volume 14, issue 8
https://doi.org/10.5194/amt-14-5473-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-5473-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Aug 2021
Research article |
| 12 Aug 2021
| 12 Aug 2021
Effects of the large-scale circulation on temperature and water vapor distributions in the Π Chamber
Jesse C. Anderson
Department of Physics and Atmospheric Sciences Program, Michigan Technological University, 1400 Townsend Drive, Houghton, MI, USA
Subin Thomas
Department of Physics and Atmospheric Sciences Program, Michigan Technological University, 1400 Townsend Drive, Houghton, MI, USA
Prasanth Prabhakaran
Department of Physics and Atmospheric Sciences Program, Michigan Technological University, 1400 Townsend Drive, Houghton, MI, USA
Raymond A. Shaw
Department of Physics and Atmospheric Sciences Program, Michigan Technological University, 1400 Townsend Drive, Houghton, MI, USA
Will Cantrell
CORRESPONDING AUTHOR
Department of Physics and Atmospheric Sciences Program, Michigan Technological University, 1400 Townsend Drive, Houghton, MI, USA
Related authors
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.
Prasanth Prabhakaran, Timothy A. Myers, Fabian Hoffmann, and Graham Feingold
EGUsphere, https://doi.org/10.5194/egusphere-2025-2935, https://doi.org/10.5194/egusphere-2025-2935, 2025
Short summary
Short summary
We explore how climate change and aerosol affect the evolution of marine low-clouds. Using high-resolution simulations, we find that warming has a stronger impact on these clouds, but aerosol becomes more important after the clouds form precipitation. Our results suggest that attempts to brighten these clouds using aerosol may become less effective in a warmer future due to the decrease in cloud cover.
Yao-Sheng Chen, Prasanth Prabhakaran, Fabian Hoffmann, Jan Kazil, Takanobu Yamaguchi, and Graham Feingold
Atmos. Chem. Phys., 25, 6141–6159, https://doi.org/10.5194/acp-25-6141-2025, https://doi.org/10.5194/acp-25-6141-2025, 2025
Short summary
Short summary
Injecting sea salt aerosols into marine stratiform clouds can distribute the cloud water over more droplets in smaller sizes. This process is expected to make the clouds brighter, allowing them to reflect more sunlight back to space. However, it may also cause the clouds to lose water over time, reducing their ability to reflect sunlight. We use a computer model to show that the loss of cloud water occurs relatively quickly and does not completely offset the initial brightening.
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.
Aaron Wang, Steve Krueger, Sisi Chen, Mikhail Ovchinnikov, Will Cantrell, and Raymond A. Shaw
Atmos. Chem. Phys., 24, 10245–10260, https://doi.org/10.5194/acp-24-10245-2024, https://doi.org/10.5194/acp-24-10245-2024, 2024
Short summary
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.
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.
Prasanth Prabhakaran, Fabian Hoffmann, and Graham Feingold
Atmos. Chem. Phys., 24, 1919–1937, https://doi.org/10.5194/acp-24-1919-2024, https://doi.org/10.5194/acp-24-1919-2024, 2024
Short summary
Short summary
In this study, we explore the impact of deliberate aerosol perturbation in the northeast Pacific region using large-eddy simulations. Our results show that cloud reflectivity is sensitive to the aerosol sprayer arrangement in the pristine system, whereas in the polluted system it is largely proportional to the total number of aerosol particles injected. These insights would aid in assessing the efficiency of various aerosol injection strategies for climate intervention applications.
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.
Cited articles
Ahlers, G., Grossmann, S., and Lohse, D.: Heat transfer and large scale
dynamics in turbulent Rayleigh-Bénard convection, Rev. Mod. Phys., 81,
2095–2102, https://doi.org/10.1103/RevModPhys.81.503, 2009. a
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness,
Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. a
Anderson, J., Thomas, S., Prabhakaran, P., Shaw, R., and Cantrell, W.: Data supporting the paper “Effects of the Large-Scale Circulation on Temperature and Water Vapor Distributions in the Π Chamber”, Michigan Tech Research Data [data set], https://doi.org/10.37099/mtu.dc.all-datasets/3, 2021. a
Belmonte, A. and Libchaber, A.: Thermal signature of plumes in turbulent
convection: the skewness of the derivative, Phys. Rev. E, 53, 4893,
https://doi.org/10.1103/PhysRevE.53.4893, 1996. a
Brown, E. and Ahlers, G.: Rotations and cessations of the large-scale
circulation in turbulent Rayleigh-Bénard convection, J. Fluid Mech., 568,
351–386, https://doi.org/10.1017/S0022112006002540, 2006. a, b
Brown, E. and Ahlers, G.: Large-scale circulation model for turbulent
Rayleigh-Bénard convection, Phys. Rev. Lett., 98, 134501,
https://doi.org/10.1103/PhysRevLett.98.134501, 2007a. a
Brown, E. and Ahlers, G.: Temperature gradients, and search for non-Boussinesq effects, in the interior of turbulent Rayleigh-Bénard convection, Europhys. Lett., 80, 14001, https://doi.org/10.1209/0295-5075/80/14001, 2007b. a, b
Brown, E. and Ahlers, G.: The origin of oscillations of the large-scale
circulation of turbulent Rayleigh–Bénard convection, J. Fluid Mech.,
638, 383–400, https://doi.org/10.1017/S0022112009991224, 2009. a
Brown, E., Nikolaenko, A., and Ahlers, G.: Reorientation of the large-scale
circulation in turbulent Rayleigh-Bénard convection, Phys. Rev. Lett.,
95, 084503, https://doi.org/10.1103/PhysRevLett.95.084503, 2005. 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, Proc.
Natl. Acad. Sci., 113, 14243–14248, https://doi.org/10.1073/pnas.1612686113, 2016. a, b, c, d
Chandrakar, K. K., Cantrell, W., Ciochetto, D., Karki, S., Kinney, G., and Shaw, R.: Aerosol Removal and Cloud Collapse Accelerated by Supersaturation
Fluctuations in Turbulence, Geophys. Res. Lett., 44, 4359–4367,
https://doi.org/10.1002/2017GL072762, 2017. a
Chandrakar, K. K., Cantrell, W., Krueger, S., Shaw, R. A., and Wunsch, S.:
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, 2020a. a, b, c
Chandrakar, K. K., Saito, I., Yang, F., Cantrell, W., Gotoh, T., and Shaw,
R. A.: Droplet size distributions in turbulent clouds: experimental
evaluation of theoretical distributions, Q. J. Roy. Meteor. Soc., 146, 483–504, https://doi.org/10.1002/qj.3692,
2020b. a
Chang, K., Bench, J., Brege, M., Cantrell, W., Chandrakar, K., Ciochetto, D.,
Mazzoleni, C., Mazzoleni, L., Niedermeier, D., and Shaw, R.: A laboratory
facility to study gas–aerosol–cloud interactions in a turbulent
environment: The Π chamber, Bull. Am. Meteor. Soc., 97, 2343–2358,
https://doi.org/10.1175/BAMS-D-15-00203.1, 2016. a, b
Chillà, F. and Schumacher, J.: New perspectives in turbulent
Rayleigh-Bénard convection, Eur. Phys. J. E, 35, 1–25,
https://doi.org/10.1140/epje/i2012-12058-1, 2012. a
Desai, N., Chandrakar, K., Chang, K., Cantrell, W., and Shaw, R.: 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, 2018. a
Funfschilling, D., Brown, E., and Ahlers, G.: Torsional oscillations of the
large-scale circulation in turbulent Rayleigh-Bénard convection, J. Fluid Mech., 607, 119–139, https://doi.org/10.1017/S0022112008001882, 2008. a
Gerber, H.: 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,
1991. a
Grabowski, W. W. and Wang, L.-P.: Growth of cloud droplets in a turbulent
environment, Ann. Rev. Fluid Mech., 45, 293–324,
https://doi.org/10.1146/annurev-fluid-011212-140750, 2013. a, b
He, Y.-H. and Xia, K.-Q.: Temperature fluctuation profiles in turbulent
thermal convection: a logarithmic dependence versus a power-law dependence,
Phys. Rev. Lett., 122, 014503, https://doi.org/10.1103/PhysRevLett.122.014503, 2019. 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. a
Korolev, A. V. and Isaac, G. A.: 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, 2000. a
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
Lamb, D. and Verlinde, J.: Physics and chemistry of clouds, Cambridge
University Press, https://doi.org/10.1017/CBO9780511976377, 2011. a, b
Liu, Y. and Ecke, R. E.: Local temperature measurements in turbulent rotating
Rayleigh-Bénard convection, Phys. Rev. E, 84, 016311,
https://doi.org/10.1103/PhysRevE.84.016311, 2011. a
Niedermeier, D., Chang, K., Cantrell, W., Chandrakar, K. K., Ciochetto, D., and Shaw, R. A.: 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, 2018. a, b, c, d
Pincus, R. and Baker, M. B.: Effect of precipitation on the albedo
susceptibility of clouds in the marine boundary layer, Nature, 372, 250–252,
https://doi.org/10.1038/372250a0, 1994. 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, https://doi.org/10.1073/pnas.2006426117, 2020. a
Pruppacher, H. and Klett, J.: Microphysics of Clouds and Precipitation, 2nd edn., Kluwer Academic, https://doi.org/10.1007/978-94-009-9905-3, 1997. a
Qiu, X.-L. and Tong, P.: Large-scale velocity structures in turbulent thermal
convection, Phys. Rev. E, 64, 036304,
https://doi.org/10.1103/PhysRevE.64.036304, 2001. a
Qiu, X.-L., Shang, X.-D., Tong, P., and Xia, K.-Q.: Velocity oscillations in
turbulent Rayleigh–Bénard convection, Phys. Fluids, 16, 412–423,
https://doi.org/10.1063/1.1637350, 2004. a
Sakievich, P., Peet, Y., and Adrian, R.: Large-scale thermal motions of
turbulent Rayleigh–Bénard convection in a wide aspect-ratio cylindrical
domain, Int. J. Heat Fluid Fl., 61, 183–196,
https://doi.org/10.1016/j.ijheatfluidflow.2016.04.011, 2016.
a
Shang, X.-D., Qiu, X.-L., Tong, P., and Xia, K.-Q.: Measured local heat
transport in turbulent Rayleigh-Bénard convection, Phys. Rev. Lett., 90, 074501, https://doi.org/10.1103/PhysRevLett.90.074501, 2003. 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., Neidermeier, D., and Xue, L:
Cloud-aerosol-turbulence interactions: Science priorities and concepts for
a large-scale laboratory facility, Bull. Am. Meteor. Soc., 101, E1026–E1035, https://doi.org/10.1175/BAMS-D-20-0009.1, 2020. a
Siebert, H. and Shaw, R. A.: 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, 2017. 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 Syst., 11, 1981–1994, https://doi.org/10.1029/2019MS001670, 2019. a
Twomey, S.: The influence of pollution on the shortwave albedo of clouds, J.
Atmos. Sci., 34, 1149–1152, https://doi.org/10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2, 1977. a
Wang, L.-P., Ayala, O., Rosa, B., and Grabowski, W. W.: Turbulent collision
efficiency of heavy particles relevant to cloud droplets, New J. Phys., 10, 075013, https://doi.org/10.1088/1367-2630/10/7/075013, 2008. a
Xi, H.-D., Zhou, Q., and Xia, K.-Q.: Azimuthal motion of the mean wind in
turbulent thermal convection, Phys. Rev. E, 73, 056312,
https://doi.org/10.1103/PhysRevE.73.056312, 2006. a, b
Xi, H.-D., Zhou, S.-Q., Zhou, Q., Chan, T.-S., and Xia, K.-Q.: Origin of the
temperature oscillation in turbulent thermal convection, Phys. Rev. Lett.,
102, 044503, https://doi.org/10.1103/PhysRevE.73.056312, 2009. a, b, c
Xie, Y.-C., Hu, Y.-B., and Xia, K.-Q.: Universal fluctuations in the bulk of
Rayleigh–Bénard turbulence, J. Fluid Mech., 878, R1,
https://doi.org/10.1017/jfm.2019.667, 2019. a
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.
Fluctuations due to turbulence in Earth's atmosphere can play a role in how many droplets a...
