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1. Ice in Southern Ocean Clouds With Cloud Top Temperatures Exceeding −5°C

   https://doi.org/10.1029/2021JD034574  

   Zaremba, Troy J.; Rauber, Robert M.; McFarquhar, Greg M.; DeMott, Paul J.; D’Alessandro, John J.; Wu, Wei (July 2021, Journal of Geophysical Research: Atmospheres)

   Abstract 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. 

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2. The University of Washington Ice–Liquid Discriminator (UWILD) improves single-particle phase classifications of hydrometeors within Southern Ocean clouds using machine learning

   https://doi.org/10.5194/amt-14-7079-2021  

   Atlas, Rachel; Mohrmann, Johannes; Finlon, Joseph; Lu, Jeremy; Hsiao, Ian; Wood, Robert; Diao, Minghui (January 2021, Atmospheric Measurement Techniques)

   Abstract. Mixed-phase Southern Ocean clouds are challenging to simulate, and theirrepresentation in climate models is an important control on climatesensitivity. In particular, the amount of supercooled water and frozen massthat they contain in the present climate is a predictor of their planetaryfeedback in a warming climate. The recent Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) vastly increased theamount of in situ data available from mixed-phase Southern Ocean clouds usefulfor model evaluation. Bulk measurements distinguishing liquid and ice watercontent are not available from SOCRATES, so single-particle phaseclassifications from the Two-Dimensional Stereo (2D-S) probe are invaluablefor quantifying mixed-phase cloud properties. Motivated by the presence oflarge biases in existing phase discrimination algorithms, we develop a noveltechnique for single-particle phase classification of binary 2D-S images usinga random forest algorithm, which we refer to as the University of WashingtonIce–Liquid Discriminator (UWILD). UWILD uses 14 parameters computed frombinary image data, as well as particle inter-arrival time, to predict phase.We use liquid-only and ice-dominated time periods within the SOCRATES datasetas training and testing data. This novel approach to model training avoidsmajor pitfalls associated with using manually labeled data, including reducedmodel generalizability and high labor costs. We find that UWILD is wellcalibrated and has an overall accuracy of 95 % compared to72 % and 79 % for two existing phase classificationalgorithms that we compare it with. UWILD improves classifications of smallice crystals and large liquid drops in particular and has more flexibilitythan the other algorithms to identify both liquid-dominated and ice-dominatedregions within the SOCRATES dataset. UWILD misclassifies a small percentageof large liquid drops as ice. Such misclassified particles are typicallyassociated with model confidence below 75 % and can easily befiltered out of the dataset. UWILD phase classifications show that particleswith area-equivalent diameter (Deq)  < 0.17 mm are mostlyliquid at all temperatures sampled, down to −40 ∘C. Largerparticles (Deq>0.17 mm) are predominantly frozen at alltemperatures below 0 ∘C. Between 0 and 5 ∘C,there are roughly equal numbers of frozen and liquid mid-sized particles (0.17<0.33 mm), and larger particles (Deq>0.33 mm) are mostly frozen. We also use UWILD's phaseclassifications to estimate sub-1 Hz phase heterogeneity, and we showexamples of meter-scale cloud phase heterogeneity in the SOCRATES dataset. 

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3. Stratocumulus Precipitation Properties Over the Southern Ocean Observed From Aircraft During the SOCRATES Campaign

   https://doi.org/10.1029/2023JD039831  

   Kang, L; Marchand, R T; Wood, R (March 2024, Journal of Geophysical Research: Atmospheres)

   Abstract Precipitation plays an important role in cloud and aerosol processes over the Southern Ocean (SO). The main objective of this study is to characterize SO precipitation properties associated with SO stratocumulus clouds. We use data from the Southern Ocean Clouds Radiation Aerosol Transport Experimental Study (SOCRATES), and leverage observations from airborne radar, lidar, and in situ probes. We find that for the cold‐topped clouds (cloud‐top‐temperature <0°C), the phase of precipitation with reflectivity >0 dBZ is predominantly ice, while reflectivity < −10 dBZ is predominantly liquid. Liquid‐phase precipitation properties are retrieved where radar and lidar are zenith‐pointing. Power‐law relationships between reflectivity (Z) and rain rate (R) are developed, and the derived Z–R relationships show vertical dependence and sensitivity to the presence of droplets with diameters between 10 and 40 μm. Using derived Z–R relationships, a reflectivity‐velocity (ZV) retrieval method, and a radar‐lidar retrieval method, we derive rain rate and other precipitation properties. The retrieved rain rate from all three methods shows good agreement with in‐situ aircraft estimates, with rain rates typically being quite light (<0.1 mm hr⁻¹). We examine the vertical distribution of precipitation properties, and find that rain rate, precipitation number concentration, and precipitation liquid water all decrease as one gets closer to the surface, while precipitation size and distribution width increases. We also examine how cloud base rain rate (R_CB) depends on cloud depth (H) and aerosol concentration (N_a) for particles with a diameter greater than 70 nm, and find thatR_CBis proportional to . 

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4. Observations of clouds, aerosols, precipitation, and surface radiation over the Southern Ocean: An overview of CAPRICORN, MARCUS, MICRE and SOCRATES

   https://doi.org/10.1175/BAMS-D-20-0132.1  

   McFarquhar, Greg M.; Bretherton, Chris; Marchand, Roger; Protat, Alain; DeMott, Paul J.; Alexander, Simon P.; Roberts, Greg C.; Twohy, Cynthia H.; Toohey, Darin; Siems, Steve; et al (November 2020, Bulletin of the American Meteorological Society)

   null (Ed.)

   Abstract Weather and climate models are challenged by uncertainties and biases in simulating Southern Ocean (SO) radiative fluxes that trace to a poor understanding of cloud, aerosol, precipitation and radiative processes, and their interactions. Projects between 2016 and 2018 used in-situ probes, radar, lidar and other instruments to make comprehensive measurements of thermodynamics, surface radiation, cloud, precipitation, aerosol, cloud condensation nuclei (CCN) and ice nucleating particles over the SO cold waters, and in ubiquitous liquid and mixed-phase cloudsnucleating particles over the SO cold waters, and in ubiquitous liquid and mixed-phase clouds common to this pristine environment. Data including soundings were collected from the NSF/NCAR G-V aircraft flying north-south gradients south of Tasmania, at Macquarie Island, and on the RV Investigator and RSV Aurora Australis. Synergistically these data characterize boundary layer and free troposphere environmental properties, and represent the most comprehensive data of this type available south of the oceanic polar front, in the cold sector of SO cyclones, and across seasons. Results show a largely pristine environments with numerous small and few large aerosols above cloud, suggesting new particle formation and limited long-range transport from continents, high variability in CCN and cloud droplet concentrations, and ubiquitous supercooled water in thin, multi-layered clouds, often with small-scale generating cells near cloud top. These observations demonstrate how cloud properties depend on aerosols while highlighting the importance of confirmed low clouds were responsible for radiation biases. The combination of models and observations is examining how aerosols and meteorology couple to control SO water and energy budgets. 

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5. Evaluation of Cloud and Precipitation Simulations in CAM6 and AM4 Using Observations Over the Southern Ocean

   https://doi.org/10.1029/2020EA001241  

   Zhou, Xiaoli; Atlas, Rachel; McCoy, Isabel L.; Bretherton, Christopher S.; Bardeen, Charles; Gettelman, Andrew; Lin, Pu; Ming, Yi (February 2021, Earth and Space Science)

   Abstract This study uses cloud and radiative properties collected from in situ and remote sensing instruments during two coordinated campaigns over the Southern Ocean between Tasmania and Antarctica in January–February 2018 to evaluate the simulations of clouds and precipitation in nudged‐meteorology simulations with the CAM6 and AM4 global climate models sampled at the times and locations of the observations. Fifteen SOCRATES research flights sampled cloud water content, cloud droplet number concentration, and particle size distributions in mixed‐phase boundary layer clouds at temperatures down to −25°C. The 6‐week CAPRICORN2 research cruise encountered all cloud regimes across the region. Data from vertically pointing 94 GHz radars deployed was compared with radar simulator output from both models. Satellite data were compared with simulated top‐of‐atmosphere (TOA) radiative fluxes. Both models simulate observed cloud properties fairly well within the variability of observations. Cloud base and top in both models are generally biased low. CAM6 overestimates cloud occurrence and optical thickness while cloud droplet number concentrations are biased low, leading to excessive TOA reflected shortwave radiation. In general, low clouds in CAM6 precipitate at the same frequency but are more homogeneous compared to observations. Deep clouds are better simulated but produce snow too frequently. AM4 underestimates cloud occurrence but overestimates cloud optical thickness even more than CAM6, causing excessive outgoing longwave radiation fluxes but comparable reflected shortwave radiation. AM4 cloud droplet number concentrations match observations better than CAM6. Precipitating low and deep clouds in AM4 have too little snow. Further investigation of these microphysical biases is needed for both models. 

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