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
EGUsphere, https://doi.org/10.5194/egusphere-2024-2051, https://doi.org/10.5194/egusphere-2024-2051, 2024
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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 on 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.
Robert Grosz, Kamal Kant Chandrakar, Raymond A. Shaw, Jesse C. Anderson, Will Cantrell, and Szymon P. Malinowski
EGUsphere, https://doi.org/10.5194/egusphere-2024-2051, https://doi.org/10.5194/egusphere-2024-2051, 2024
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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 on 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.
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.
Fan Yang, Hamed Fahandezh Sadi, Raymond A. Shaw, Fabian Hoffmann, Pei Hou, Aaron Wang, and Mikhail Ovchinnikov
EGUsphere, https://doi.org/10.5194/egusphere-2024-1693, https://doi.org/10.5194/egusphere-2024-1693, 2024
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, 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.
Dennis Niedermeier, Jens Voigtländer, Silvio Schmalfuß, Daniel Busch, Jörg Schumacher, Raymond A. Shaw, and Frank Stratmann
Atmos. Meas. Tech., 13, 2015–2033, https://doi.org/10.5194/amt-13-2015-2020, https://doi.org/10.5194/amt-13-2015-2020, 2020
Short summary
Short summary
In this paper, we present the new moist-air wind tunnel LACIS-T (Turbulent Leipzig Aerosol Cloud Interaction Simulator). It is used to study cloud physical processes in general and interactions between turbulence and cloud microphysical processes in particular. The operating principle of LACIS-T is explained, and the first results are depicted from deliquescence and droplet formation experiments observing clear indications on the effect of turbulence on these microphysical processes.
Katarzyna Karpińska, Jonathan F. E. Bodenschatz, Szymon P. Malinowski, Jakub L. Nowak, Steffen Risius, Tina Schmeissner, Raymond A. Shaw, Holger Siebert, Hengdong Xi, Haitao Xu, and Eberhard Bodenschatz
Atmos. Chem. Phys., 19, 4991–5003, https://doi.org/10.5194/acp-19-4991-2019, https://doi.org/10.5194/acp-19-4991-2019, 2019
Short summary
Short summary
Observations of clouds at a mountain-top laboratory revealed for the first time the presence of “voids”, i.e., elongated volumes inside a cloud that are devoid of droplets. Theoretical and numerical analyses suggest that these voids are a result of strong and long-lasting vortex presence in turbulent air. If this is confirmed in further investigation, the effect may become an important part of models describing cloud evolution and rain formation.
Michael L. Larsen and Raymond A. Shaw
Atmos. Meas. Tech., 11, 4261–4272, https://doi.org/10.5194/amt-11-4261-2018, https://doi.org/10.5194/amt-11-4261-2018, 2018
Short summary
Short summary
A statistical tool frequently utilized to measure scale-dependent departures from perfect randomness is the radial distribution function. This tool has many strengths, but it is not easy to calculate for particle detections within a three-dimensional sample volume. In this manuscript, we introduce and test a new method to estimate the three-dimensional radial distribution function in realistic measurement volumes.
Fan Yang, Pavlos Kollias, Raymond A. Shaw, and Andrew M. Vogelmann
Atmos. Chem. Phys., 18, 7313–7328, https://doi.org/10.5194/acp-18-7313-2018, https://doi.org/10.5194/acp-18-7313-2018, 2018
Short summary
Short summary
Cloud droplet size distribution (CDSD), which is related to cloud albedo and lifetime, is usually observed broader than predicted from adiabatic parcel calculations. Results in this study show that the CDSD can be broadened during condensational growth as a result of Ostwald ripening amplified by droplet deactivation and reactivation. Our results suggest that it is important to consider both curvature and solute effects before and after cloud droplet activation in a 3-D cloud model.
Fan Yang, Raymond Shaw, and Huiwen Xue
Atmos. Chem. Phys., 16, 9421–9433, https://doi.org/10.5194/acp-16-9421-2016, https://doi.org/10.5194/acp-16-9421-2016, 2016
Short summary
Short summary
When dry air is mixed into a cloud, droplets evaporate. If the diluted cloud mixture continues to rise, the remaining droplets will grow. In this work we show theoretically and computationally that a critical height exists, above which the droplets in a mixed, diluted cloud volume become larger than those in an undiluted volume. An environment that is humid and aerosol free is most favorable for producing such large droplets, which may contribute to the onset of precipitation formation.
B. Wehner, F. Werner, F. Ditas, R. A. Shaw, M. Kulmala, and H. Siebert
Atmos. Chem. Phys., 15, 11701–11711, https://doi.org/10.5194/acp-15-11701-2015, https://doi.org/10.5194/acp-15-11701-2015, 2015
Short summary
Short summary
During the CARRIBA campaign on Barbados, 91 cases with increased aerosol particle number concentrations near clouds were detected from helicopter-borne measurements. Most of these cases are correlated with enhanced irradiance in the ultraviolet range. The events have a mean length of 100m, corresponding to a lifetime of 300s, meaning a growth of several nm/h. Such high values cannot be explained by sulfuric acid alone; thus extremely low volatility organic compounds are probably involved here.
S. Risius, H. Xu, F. Di Lorenzo, H. Xi, H. Siebert, R. A. Shaw, and E. Bodenschatz
Atmos. Meas. Tech., 8, 3209–3218, https://doi.org/10.5194/amt-8-3209-2015, https://doi.org/10.5194/amt-8-3209-2015, 2015
Related subject area
Subject: Clouds | Technique: Laboratory Measurement | Topic: Instruments and Platforms
Calibration of Optical Particle Spectrometers Using Mounted Fibres
Comment on “A universally applicable method of calculating confidence bands for ice nucleation spectra derived from droplet freezing experiments” by Fahy et al. (2022)
Icing wind tunnel measurements of supercooled large droplets using the 12 mm total water content cone of the Nevzorov probe
The Microfluidic Ice Nuclei Counter Zürich (MINCZ): a platform for homogeneous and heterogeneous ice nucleation
Photoacoustic hygrometer for icing wind tunnel water content measurement: design, analysis, and intercomparison
SPIN modification for low-temperature experiments
Characterization and first results from LACIS-T: a moist-air wind tunnel to study aerosol–cloud–turbulence interactions
Low-temperature triple-capillary cryostat for ice crystal growth studies
A high-speed particle phase discriminator (PPD-HS) for the classification of airborne particles, as tested in a continuous flow diffusion chamber
The SPectrometer for Ice Nuclei (SPIN): an instrument to investigate ice nucleation
BINARY: an optical freezing array for assessing temperature and time dependence of heterogeneous ice nucleation
Experimental quantification of contact freezing in an electrodynamic balance
Application of linear polarized light for the discrimination of frozen and liquid droplets in ice nucleation experiments
Jessica Girdwood, Harry Ballington, Chris Stopford, Rob Lewis, and Evelyn Hesse
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-55, https://doi.org/10.5194/amt-2024-55, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
Optical particle spectrometers (OPSs) are a class of instruments, commonly used for measurement of particle size distributions, which require calibration. Conventionally, this is performed using a known aerosol source, which has reliability issues. In this paper, we present a technique for OPS calibration which involves placing objects in the instrument, which generate a known response. The fibre calibration was more reliable when the technique was compared with a conventional calibration.
Gabor Vali
Atmos. Meas. Tech., 16, 4303–4306, https://doi.org/10.5194/amt-16-4303-2023, https://doi.org/10.5194/amt-16-4303-2023, 2023
Short summary
Short summary
Different methods for the calculation of nucleation spectra from drop-freezing experiments are discussed as the choice of data processing reflects on underlying principles.
Johannes Lucke, Tina Jurkat-Witschas, Romy Heller, Valerian Hahn, Matthew Hamman, Wolfgang Breitfuss, Venkateshwar Reddy Bora, Manuel Moser, and Christiane Voigt
Atmos. Meas. Tech., 15, 7375–7394, https://doi.org/10.5194/amt-15-7375-2022, https://doi.org/10.5194/amt-15-7375-2022, 2022
Short summary
Short summary
Flight testing in icing conditions requires instruments that are able to accurately measure the liquid water content of supercooled large droplets (SLDs). This work finds that the 12 mm cone of the Nevzorov hot-wire probe has excellent collection properties for SLDs. We also derive a correction to compensate for the low collision efficiency of small droplets with the cone. The results provide a procedure to evaluate LWC measurements of the 12 mm cone during wind tunnel and airborne experiments.
Florin N. Isenrich, Nadia Shardt, Michael Rösch, Julia Nette, Stavros Stavrakis, Claudia Marcolli, Zamin A. Kanji, Andrew J. deMello, and Ulrike Lohmann
Atmos. Meas. Tech., 15, 5367–5381, https://doi.org/10.5194/amt-15-5367-2022, https://doi.org/10.5194/amt-15-5367-2022, 2022
Short summary
Short summary
Ice nucleation in the atmosphere influences cloud properties and lifetimes. Microfluidic instruments have recently been used to investigate ice nucleation, but these instruments are typically made out of a polymer that contributes to droplet instability over extended timescales and relatively high temperature uncertainty. To address these drawbacks, we develop and validate a new microfluidic instrument that uses fluoropolymer tubing to extend droplet stability and improve temperature accuracy.
Benjamin Lang, Wolfgang Breitfuss, Simon Schweighart, Philipp Breitegger, Hugo Pervier, Andreas Tramposch, Andreas Klug, Wolfgang Hassler, and Alexander Bergmann
Atmos. Meas. Tech., 14, 2477–2500, https://doi.org/10.5194/amt-14-2477-2021, https://doi.org/10.5194/amt-14-2477-2021, 2021
Short summary
Short summary
This work describes the design, calibration, and application of a hygrometer and sampling system, which have been developed and used for water content measurement in experimentally simulated atmospheric icing conditions with relevance in fundamental icing research as well as aviation testing and certification. Together with a general description of water content measurement and accompanying uncertainties, the results of a comparison to reference instruments in an icing wind tunnel are presented.
André Welti, Kimmo Korhonen, Pasi Miettinen, Ana A. Piedehierro, Yrjö Viisanen, Annele Virtanen, and Ari Laaksonen
Atmos. Meas. Tech., 13, 7059–7067, https://doi.org/10.5194/amt-13-7059-2020, https://doi.org/10.5194/amt-13-7059-2020, 2020
Short summary
Short summary
We describe a modification of the SPectrometer for Ice Nuclei (SPIN) chamber to study ice nucleation at low temperatures, relevant for ice formation in cirrus clouds. Validation experiments of homogeneous freezing of aqueous ammonium sulfate droplets and heterogeneous ice nucleation on silver iodide particles are included to demonstrate the advantages of the modified SPIN chamber for the investigation of ice nucleation in the extended temperature range.
Dennis Niedermeier, Jens Voigtländer, Silvio Schmalfuß, Daniel Busch, Jörg Schumacher, Raymond A. Shaw, and Frank Stratmann
Atmos. Meas. Tech., 13, 2015–2033, https://doi.org/10.5194/amt-13-2015-2020, https://doi.org/10.5194/amt-13-2015-2020, 2020
Short summary
Short summary
In this paper, we present the new moist-air wind tunnel LACIS-T (Turbulent Leipzig Aerosol Cloud Interaction Simulator). It is used to study cloud physical processes in general and interactions between turbulence and cloud microphysical processes in particular. The operating principle of LACIS-T is explained, and the first results are depicted from deliquescence and droplet formation experiments observing clear indications on the effect of turbulence on these microphysical processes.
Brian D. Swanson and Jon Nelson
Atmos. Meas. Tech., 12, 6143–6152, https://doi.org/10.5194/amt-12-6143-2019, https://doi.org/10.5194/amt-12-6143-2019, 2019
Short summary
Short summary
We have built a triple-capillary cryostat designed to reduce potential instrumental effects that may have influenced earlier measurements and to improve our understanding of the processes responsible for ice crystal shapes and sizes. In this cryostat, a crystal forms on one of three well-separated and ultrafine capillaries. In this paper we describe the new instrument and present several observations made using the instrument to illustrate the instrument's advantages.
Fabian Mahrt, Jörg Wieder, Remo Dietlicher, Helen R. Smith, Chris Stopford, and Zamin A. Kanji
Atmos. Meas. Tech., 12, 3183–3208, https://doi.org/10.5194/amt-12-3183-2019, https://doi.org/10.5194/amt-12-3183-2019, 2019
Short summary
Short summary
A new instrument, the High Speed Particle Phase Discriminator (PPD-HS), is presented, with the goal of quantifying liquid and ice fraction in conditions relevant for mixed-phase clouds. PPD-HS captures the near-forward spatial intensity distribution of scattered light on a single particle basis. Symmetry analysis of the scattering pattern is used to determine the shape of the particles, with cloud droplets and ice crystals producing symmetrical and asymmetrical scattering patterns, respectively.
Sarvesh Garimella, Thomas Bjerring Kristensen, Karolina Ignatius, Andre Welti, Jens Voigtländer, Gourihar R. Kulkarni, Frank Sagan, Gregory Lee Kok, James Dorsey, Leonid Nichman, Daniel Alexander Rothenberg, Michael Rösch, Amélie Catharina Ruth Kirchgäßner, Russell Ladkin, Heike Wex, Theodore W. Wilson, Luis Antonio Ladino, Jon P. D. Abbatt, Olaf Stetzer, Ulrike Lohmann, Frank Stratmann, and Daniel James Cziczo
Atmos. Meas. Tech., 9, 2781–2795, https://doi.org/10.5194/amt-9-2781-2016, https://doi.org/10.5194/amt-9-2781-2016, 2016
Short summary
Short summary
The SPectrometer for Ice Nuclei (SPIN) is a commercially available ice nuclei counter manufactured by Droplet Measurement Technologies in Boulder, CO. This study characterizes the SPIN chamber, reporting data from laboratory measurements and quantifying uncertainties. Overall, we report that the SPIN is able to reproduce previous CFDC ice nucleation measurements.
C. Budke and T. Koop
Atmos. Meas. Tech., 8, 689–703, https://doi.org/10.5194/amt-8-689-2015, https://doi.org/10.5194/amt-8-689-2015, 2015
Short summary
Short summary
A new optical freezing array for the study of heterogeneous ice nucleation in microliter-sized droplets is introduced, tested and applied to the study of immersion freezing in aqueous Snomax suspensions. Using different cooling rates, a small time dependence of ice nucleation induced by two different classes of ice nucleators was detected and the corresponding heterogeneous ice nucleation rate coefficient was quantified.
N. Hoffmann, A. Kiselev, D. Rzesanke, D. Duft, and T. Leisner
Atmos. Meas. Tech., 6, 2373–2382, https://doi.org/10.5194/amt-6-2373-2013, https://doi.org/10.5194/amt-6-2373-2013, 2013
T. Clauss, A. Kiselev, S. Hartmann, S. Augustin, S. Pfeifer, D. Niedermeier, H. Wex, and F. Stratmann
Atmos. Meas. Tech., 6, 1041–1052, https://doi.org/10.5194/amt-6-1041-2013, https://doi.org/10.5194/amt-6-1041-2013, 2013
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...
