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Discussion papers
https://doi.org/10.5194/acp-2018-697
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2018-697
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 27 Aug 2018

Research article | 27 Aug 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Subgrid Variations of the Cloud Water and Droplet Number Concentration Over Tropical Ocean: Satellite Observations and Implications for Warm Rain Simulation in Climate Models

Zhibo Zhang1,2, Hua Song2, Po-Lun Ma3, Vincent E. Larson4, Minghuai Wang5, Xiquan Dong6, and Jianwu Wang7 Zhibo Zhang et al.
  • 1Physics Department, University of Maryland Baltimore County (UMBC), Baltimore, MD, USA
  • 2Joint Center for Earth Systems Technology, UMBC, Baltimore, MD, USA
  • 3Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
  • 4Department of Mathematical Sciences, University of Wisconsin – Milwaukee, Milwaukee, WI, USA
  • 5Institute for Climate and Global Change Research & School of Atmospheric Sciences, Nanjing University, Nanjing, China
  • 6Department of Hydrology & Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
  • 7Department of Information System, UMBC, Baltimore, MD, USA

Abstract. One of the difficulties of simulating the warm rain process in global climate models (GCM) is how to account for the impact of subgrid variations of cloud properties, such as cloud water and cloud droplet number concertation, on the nonlinear precipitation processes such as autoconversion. In practice, this impact is often treated by adding a so-called enhancement factor term to the parameterization scheme. In this study, we derive the subgrid variations of liquid-phase cloud properties over the tropical ocean using the satellite remote sensing products from MODIS (Moderate Resolution Imaging Spectroradiometer) and investigate the corresponding enhancement factors for the GCM parameterization of autoconversion rate. The wide spatial coverage of the MODIS product enables us to depict a detailed quantitative picture of the enhancement factor Eq due to the subgrid variation of cloud water, which shows a clear cloud regime dependence, namely a significant increase from the stratocumulus (Sc) to cumulus (Cu) cloud regions. Assuming a constant Eq=3.2 would overestimate the observed Eq in the Sc regions and underestimate it in the Cu regions. We also found that the Eq based on the Lognormal PDF assumption performs slightly better than that based on the Gamma PDF assumption. A simple parameterization scheme is provided to relate the Eq to the grid-mean liquid cloud fraction, which can be readily used in GCMs. For the first time, the enhancement factor EN due to the subgrid variation of CDNC is derived from satellite observation, and results reveal several regions downwind of biomass burning aerosols (e.g., Gulf of Guinea, East Coast of South Africa), air pollution (i.e., Eastern China Sea), and active volcanos (e.g., Kilauea Hawaii and Ambae Vanuatu), where the EN is comparable, or even larger than Eq, even after the optically thin clouds are screened out.

Zhibo Zhang et al.
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Status: final response (author comments only)
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Zhibo Zhang et al.
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