Vertical dependence of horizontal variation of cloud microphysics: observations from the ACE-ENA field campaign and implications for warm-rain simulation in climate models

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 (q(c)) and cloud droplet number concentration (N-c) 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 q(c) and N-e variations that tends to make EF > 1, the strong positive correlation between q(c) and N-e results in a suppressing effect that tends to make EF < 1. This effect is especially strong at cloud top, where the q(c) and N-e correlation can be as high as 0.95. We also found that the physically complete EF that accounts for the covariation of q(c) and N-e is significantly smaller than its counterpart that accounts only for the subgrid variation of q(c) , especially at cloud top. Although this study is based on limited cases, it suggests that the subgrid variations of N-e and its correlation with q(c) both need to be considered for an accurate simulation of the autoconversion process in GCMs.