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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  11 Aug 2020

11 Aug 2020

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This preprint is currently under review for the journal ACP.

Vertical Dependence of Horizontal Variation of Cloud Microphysics: Observations from the ACE-ENA field campaign and implications for warm rain simulation in climate models

Zhibo Zhang1,2, Qianqian Song1,2, David Mechem3, Vincent Larson4, Jian Wang5, Yangang Liu6, Mikael Witte7,8, Xiquan Dong9, and Peng Wu9,10 Zhibo Zhang et al.
  • 1Physics Department, University of Maryland Baltimore County (UMBC), Baltimore, 21250, USA
  • 2Joint Center for Earth Systems Technology, UMBC, Baltimore, 21250, USA
  • 3Department of Geography and Atmospheric Science, University of Kansas, Lawrence, 66045, USA
  • 4Department of Mathematical Sciences, University of Wisconsin – Milwaukee, Milwaukee, 53201, USA
  • 5Center for Aerosol Science and Engineering, Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, 63130, USA
  • 6Environmental and Climate Science Department, Brookhaven National Laboratory, Upton, 11973, USA
  • 7Joint Institute for Regional Earth System Science and Engineering, University of California Los Angeles, Los Angeles, 90095, USA
  • 8Jet Propulsion Laboratory, California Institute of Technology, Pasadena, 91011, USA
  • 9Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, 85721, USA
  • 10Pacific Northwest National Laboratory, Richland, WA 99354, USA

Abstract. In the current global climate models (GCM), the nonlinearity effect of subgrid cloud variations on the parameterization of warm rain process, e.g., the autoconversion rate, is often treated by multiplying the resolved-scale warm ran process rates by a so-called enhancement factor (EF). In this study, we investigate the subgrid-scale horizontal variations and covariation of cloud water content (qc) and cloud droplet number concentration (Nc) in marine boundary layer (MBL) clouds based on the in-situ measurements from a recent field campaign, and study the implications for the autoconversion rate EF in GCMs. Based on a few carefully selected cases from the field campaign, we found that in contrast to the enhancing effect of qc and Nc variations that tends to make EF > 1, the strong positive correlation between qc and Nc results in a suppressing effect that makes tends to make EF < 1. This effect is especially strong at cloud top where the qc and Nc correlation can be as high as 0.95. We also found that the physically complete EF that accounts for the covariation of qc and Nc has a robust decreasing trend from cloud base to cloud top. Because the autoconversion process is most important at the cloud top, this vertical dependence of EF should be taken into consideration in the GCM parametrization scheme.

Zhibo Zhang et al.

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Zhibo Zhang et al.

Zhibo Zhang et al.


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Latest update: 29 Sep 2020
Publications Copernicus
Short summary
Tis study investigates the small-scale variations and covariations of cloud microphysical properties, namly, cloud liquid water content and cloud droplet number concentration, in marine boundary layer clouds based on in situ observation from the ACE-ENA campaign. We discuss the dependence of cloud variations on vertical location in cloud and the implications for warm rain simulations in the global climate models.
Tis study investigates the small-scale variations and covariations of cloud microphysical...