Vertical Dependence of Horizontal Variation of Cloud Microphysics: Observations from theACE-ENA field campaign and implications for warm rain simulation in climate models
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2020-08-11
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Zhang, Z., Song, Q., Mechem, D., Larson, V., Wang, J., Liu, Y., Witte, M., Dong, X., and Wu, P.: Vertical Dependence of Horizontal Variation of Cloud Microphysics: Observations from the ACE-ENA field campaign and implications for warm rain simulation in climate models, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-788, in review, 2020.
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law
Public Domain Mark 1.0
This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law
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.