Articles | Volume 25, issue 6
https://doi.org/10.5194/acp-25-3785-2025
© Author(s) 2025. 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-25-3785-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Apr 2025
Research article |
| 01 Apr 2025
| 01 Apr 2025
Microphysics regimes due to haze–cloud interactions: cloud oscillation and cloud collapse
Brookhaven National Laboratory, Upton, New York, USA
Hamed Fahandezh Sadi
Department of Physics, Michigan Technological University, Houghton, Michigan, USA
Raymond A. Shaw
Department of Physics, Michigan Technological University, Houghton, Michigan, USA
Fabian Hoffmann
Meteorologisches Institut, Ludwig-Maximilians-Universität München, Munich, Germany
Department of Physics, Michigan Technological University, Houghton, Michigan, USA
Department of Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, Michigan, USA
Aaron Wang
Pacific Northwest National Laboratory, Richland, Washington, USA
Mikhail Ovchinnikov
Pacific Northwest National Laboratory, Richland, Washington, USA
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Zeen Zhu, Pavlos Kollias, and Fan Yang
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Walter Hannah, Kyle Pressel, Mikhail Ovchinnikov, and Gregory Elsaesser
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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.
Zeen Zhu, Pavlos Kollias, Edward Luke, and Fan Yang
Atmos. Chem. Phys., 22, 7405–7416, https://doi.org/10.5194/acp-22-7405-2022, https://doi.org/10.5194/acp-22-7405-2022, 2022
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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
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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.
Simon Unterstrasser, Fabian Hoffmann, and Marion Lerch
Geosci. Model Dev., 13, 5119–5145, https://doi.org/10.5194/gmd-13-5119-2020, https://doi.org/10.5194/gmd-13-5119-2020, 2020
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Particle-based cloud models use simulation particles for the representation of cloud particles like droplets or ice crystals. The collision and merging of cloud particles (i.e. collisional growth a.k.a. collection in the case of cloud droplets and aggregation in the case of ice crystals) was found to be a numerically challenging process in such models. The study presents verification exercises in a 1D column model, where sedimentation and collisional growth are the only active processes.
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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.
Large-eddy simulations of a convection cloud chamber show two new microphysics regimes, cloud...
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