Impacts of Drop Clustering and Entrainment‐Mixing on Mixed Phase Shallow Cloud Properties Over the Southern Ocean: Results From SOCRATES

Abstract

Entrainment and associated mixing (i.e., entrainment‐mixing) have been shown to impact drop size distributions. However, most past studies have focused on warm clouds and have not considered the impacts on mixed phase clouds (i.e., those containing liquid and ice particles). This study characterizes the impacts of entrainment‐mixing on mixed phase cloud properties over the Southern Ocean using in situ observations. By taking advantage of strong correlations between droplet clustering and entrainment‐mixing, a clustering metric is used as a proxy to assess the degree of mixing. This maximizes the available sample size for a statistical analysis of entrainment‐mixing impacts on mixed phase properties. A positive relationship is found between the magnitude of droplet clustering and large ice concentrations (those with maximum dimensions greater than ∼300 μm), suggesting entrainment‐mixing enhances the Wegener‐Bergeron‐Findeisen (WBF) process. Particle size distributions are averaged over different ranges of liquid (liquid water content (LWC)) to total water content (TWC) ratio. Since the ratio is expected to transition from 1 to 0 during glaciation, differences in the distributions provide insight into the relation of entrainment‐mixing to mixed phase cloud evolution. Mixed phase samples with the greatest large ice concentrations occur at LWC/TWC < 0.4 in low clustering regions. However, these samples are relatively few, whereas high clustering regions have a greater frequency of samples with LWC/TWC < 0.4. This suggests sublimation/vapor sinks associated with entrainment can counteract the enhanced WBF. In high clustering regions, distributions of small droplets are relatively constant and large droplets (>30 μm) are preferentially removed as LWC/TWC transitions from 1 to 0.

On the Scale of Particle Clustering Induced by Inhomogeneous Entrainment‐Mixing in Mixed‐Phase Cumulus Clouds

“…Since lower droplet concentration is typically found near the cloud edges, where the cloud parcels are more diluted by entrained air, the AVAPC can be used as an indicator of particle clustering induced by inhomogeneous mixing. This is consistent with previous studies (D'Alessandro & McFarquhar, 2023). Note that the AVAPC is calculated at 1 Hz, so the counts of gray points are less than that in Figures 2d–2f.…”

“…Clustering of particles induced by inhomogeneous mixing have been demonstrated in some previous studies (D'Alessandro & McFarquhar, 2023; A. Korolev & Milbrandt, 2022). However, the scales of the particle clusters are not well understood.…”

“…According to D'Alessandro and McFarquhar (2023) and Shaw et al. (2002), the CI can be improved by using the volume average pairwise correlation (VAPC) parameter.…”

“…VAPC can be optimized to the altered volume average pairwise correlation (AVAPC, D'Alessandro & McFarquhar, 2023): $$$\text{AVAPC}={\log }_{10}\left(\frac{1}{\overline{x}}(\text{CI}+1)\right)={\log }_{10}\left(\frac{{\sigma }^{2}}{{\overline{x}}^{2}}\right)$$$ …”