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 (0.170.33 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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Characterization of Southern Ocean Boundary Layer Clouds Using Airborne Radar, Lidar, and In Situ Cloud Data: Results From SOCRATES
Climate and numerical weather prediction models struggle to accurately predict radiative forcing over the Southern Ocean (SO), as the amount of clouds and their phases are poorly represented in such models due to a lack of observations upon which to base parameterizations. To address this, a novel particle identification (PID) scheme, based upon airborne radar and lidar data, was applied to data collected during the Southern Ocean Clouds, Aerosol, Radiation Transport Experimental Study (SOCRATES) to assess the vertical structure of SO boundary layer clouds. A comparison between the PID scheme and in situ phase data from SOCRATES showed relatively good agreement between the two data types. The convectivity of the clouds sampled during SOCRATES was determined using the novel Echo Classification from COnvectivity for Vertically pointing radars product. The PID and in situ data were then used synergistically to identify the following features of cloud vertical structure: (a) Supercooled liquid water was very common (Probability,
- NSF-PAR ID:
- 10380145
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- Volume:
- 127
- Issue:
- 21
- ISSN:
- 2169-897X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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