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 Zhang, Qianqian Song, David Mechem, Vincent Larson, Jian Wang, Yangang Liu, Mikael K. Witte, Xiquan Dong, Peng Wu

Research output: Contribution to journalArticlepeer-review

Abstract

In the current global climate models (GCMs), 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-rain 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 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 is significantly smaller than its counterpart that accounts only for the subgrid variation of qc, especially at cloud top. Although this study is based on limited cases, it suggests that the subgrid variations of Nc and its correlation with qc both need to be considered for an accurate simulation of the autoconversion process in GCMs.

Original languageEnglish (US)
Pages (from-to)3103-3121
Number of pages19
JournalAtmospheric Chemistry and Physics
Volume21
Issue number4
DOIs
StatePublished - Mar 2 2021

ASJC Scopus subject areas

  • Atmospheric Science

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