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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.

Single‐ and multi‐layer clouds are commonly observed over the Southern Ocean in varying synoptic settings, yet few studies have characterized and contrasted their properties. This study provides a statistical analysis of the microphysical properties of single‐ and multi‐layer clouds using in‐situ observations acquired during the Southern Ocean Cloud‐Radiation Aerosol Transport Experimental Study. The relative frequencies of ice‐containing samples (i.e., mixed and ice phase) for multi‐layer clouds are 0.05–0.25 greater than for single‐layer clouds, depending on cloud layer height. In multi‐layer clouds, the lowest cloud layers have the highest ice‐containing sample frequencies, which decrease with increasing cloud layer height up to the third highest cloud layer. This suggests a prominent seeder‐feeder mechanism over the region. Ice nucleating particle (cloud condensation nuclei) concentrations are positively (negatively) correlated with ice‐containing sample frequencies in select cases. Differences in microphysical properties are observed for single‐ and multi‐layer clouds. Drop concentrations (size distributions) are greater (narrower) for single‐layer clouds compared with the lowest multi‐layer clouds. When differentiating cloud layers by top (single‐ and highest multi‐layer clouds) and non‐top layers (underlying multi‐layer clouds), total particle size distributions (including liquid and ice) are similarly broader for non‐top cloud layers. Additionally, drop concentrations in coupled environments are approximately double those in decoupled environments.

This study documents the presence of ice in stratocumulus clouds with cloud top temperatures (CTT) > −5 °C in the cold sector of extratropical cyclones over the Southern Ocean (SO) during ten SO Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) research flights. Case studies are presented showing ice signatures within clouds when CTT were between −2 and −5°C, evidenced in Doppler radar radial velocity changes observed during high‐altitude flight legs as ice particles melted across the 0°C isotherm. Ice on these legs was found to contribute to precipitation 3.8% of the time from clouds with −5°C < CTT <0°C. Clouds observed with a distinct melting level on high‐altitude flight legs overall had greater cloud depths, tops with higher reflectivities, and higher linear depolarization ratios, compared to clouds without a melting level. In situ flight legs were also analyzed when Himawari‐8 CTT were between 0 and −5°C and the aircraft was sampling in cloud within that temperature range. It was found that 3% of clouds sampled in situ with −5°C < CTT <0°C were mixed phase with a mean number concentration of 2.35 L⁻¹for nonspherical particles with maximum diameters >100 μm and 1.13 L⁻¹for nonspherical particles with maximum diameters >200 μm.

Maritime boundary‐layer clouds over the Southern Ocean (SO) have a large shortwave radiative effect. Yet, climate models have difficulties in representing these clouds and, especially, their phase in this observationally sparse region. This study aims to increase the knowledge of SO cloud phase by presenting in‐situ cloud microphysical observations from the Southern Ocean Clouds, Radiation, Aerosol, Transport Experimental Study (SOCRATES). We investigate the occurrence of ice in summertime marine stratocumulus and cumulus clouds in the temperature range between 6 and −25°C. Our observations show that in ice‐containing clouds, maximum ice number concentrations of up to several hundreds per liter were found. The observed ice crystal concentrations were on average one to two orders of magnitude higher than the simultaneously measured ice nucleating particle (INP) concentrations in the temperature range below −10°C and up to five orders of magnitude higher than estimated INP concentrations in the temperature range above −10°C. These results highlight the importance of secondary ice production (SIP) in SO summertime marine boundary‐layer clouds. Evidence for rime splintering was found in the Hallett‐Mossop (HM) temperature range but the exact SIP mechanism active at lower temperatures remains unclear. Finally, instrument simulators were used to assess simulated co‐located cloud ice concentrations and the role of modeled HM rime‐splintering. We found that CAM6 is deficient in simulating number concentrations across the HM temperature range with little sensitivity to the model HM process, which is inconsistent with the aforementioned observational evidence of highly active SIP processes in SO low‐level clouds.

Cloud phase and relative humidity (RH) distributions at −67° to 0°C over the Southern Ocean during austral summer are compared between in situ airborne observations and global climate simulations. A scale-aware comparison is conducted using horizontally averaged observations from 0.1 to 50 km. Cloud phase frequencies, RH distributions, and liquid mass fraction are found to be less affected by horizontal resolutions than liquid and ice water content (LWC and IWC, respectively), liquid and ice number concentrations (Nc_liqand Nc_ice, respectively), and ice supersaturation (ISS) frequency. At −10° to 0°C, observations show 27%–34% and 17%–37% of liquid and mixed phases, while simulations show 60%–70% and 3%–4%, respectively. Simulations overestimate (underestimate) LWC and Nc_liqin liquid (mixed) phase, overestimate Nc_icein mixed phase, underestimate IWC in ice and mixed phases, and underestimate (overestimate) liquid mass fraction below (above) −5°C, indicating that observational constraints are needed for different cloud phases. RH frequently occurs at liquid saturation in liquid and mixed phases for all datasets, yet the observed RH in ice phase can deviate from liquid saturation by up to 20%–40% at −20° to 0°C, indicating that the model assumption of liquid saturation for coexisting ice and liquid is inaccurate for low liquid mass fractions (<0.1). Simulations lack RH variability for partial cloud fractions (0.1–0.9) and underestimate (overestimate) ISS frequency for cloud fraction <0.1 (≥0.6), implying that improving RH subgrid-scale parameterizations may be a viable path to account for small-scale processes that affect RH and cloud phase heterogeneities. Two sets of simulations (nudged and free-running) show very similar results (except for ISS frequency) regardless of sample sizes, corroborating the statistical robustness of the model–observation comparisons.