Global climate models (GCMs) are challenged by difficulties in simulating cloud phase and cloud radiative effect over the Southern Ocean (SO). Some of the new‐generation GCMs predict too much liquid and too little ice in mixed‐phase clouds. This misrepresentation of cloud phase in GCMs results in weaker negative cloud feedback over the SO and a higher climate sensitivity. Based on a model comparison with observational data obtained during the Southern Ocean Cloud Radiation and Aerosol Transport Experimental Study, this study addresses a key uncertainty in the Community Earth System Model version 2 (CESM2) related to cloud phase, namely ice formation in pristine remote SO clouds. It is found that sea spray organic aerosols (SSOAs) are the most important type of ice nucleating particles (INPs) over the SO with concentrations 1 order of magnitude higher than those of dust INPs based on measurements and CESM2 simulations. Secondary ice production (SIP) which includes riming splintering, rain droplet shattering, and ice‐ice collisional fragmentation as implemented in CESM2 is the dominant ice production process in moderately cold clouds with cloud temperatures greater than −20°C. SIP enhances the in‐cloud ice number concentrations (Ni) by 1–3 orders of magnitude and predicts more mixed‐phase (with percentage occurrence increased from 15% to 21%), in better agreement with the observations. This study highlights the importance of accurately representing the cloud phase over the pristine remote SO by considering the ice nucleation of SSOA and SIP processes, which are currently missing in most GCM cloud microphysics parameterizations.
- NSF-PAR ID:
- 10214658
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Bulletin of the American Meteorological Society
- ISSN:
- 0003-0007
- Page Range / eLocation ID:
- 1 to 92
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The persistent Southern Ocean (SO) shortwave radiation biases in climate models and reanalyses have been associated with the poor representation of clouds, precipitation, aerosols, the atmospheric boundary layer, and their intrinsic interactions. Capitalizing on shipborne observations collected during the Clouds Aerosols Precipitation Radiation and atmospheric Composition Over the Southern Ocean 2016 and 2018 field campaigns, this research investigates and characterizes cloud and precipitation processes from synoptic to micro scales. Distinct cloud and precipitation regimes are found to correspond to the seven thermodynamic clusters established using a K‐means clustering technique, while less distinctions are evident using the cyclone and (cold) front compositing methods. Cloud radar and disdrometer data reveal that light precipitation is common over the SO with higher intensities associated with cyclonic and warm frontal regions. Multiple lines of evidence suggest the presence of diverse microphysical features in several cloud regimes, including the likely dominance of ice aggregation in deep precipitating clouds. Signatures of mixed phase, and in some cases, riming were detected in shallow convective clouds away from the frontal conditions. Two of the K‐means clusters with contrasting cloud and precipitation properties are observed over the high‐latitude SO and coastal Antarctica, suggesting distinct physical processes therein. Through a single case study, in‐situ and remote‐sensing data collected by an overflight of the Southern Ocean Clouds Radiation Aerosol Transport Experimental Study were also evaluated and complement the ship‐based analysis.
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Abstract This study presents the first numerical simulations of seeded clouds over the Snowy Mountains of Australia. WRF-WxMod, a novel glaciogenic cloud-seeding model, was utilized to simulate the cloud response to winter orographic seeding under various meteorological conditions. Three cases during the 2018 seeding periods were selected for model evaluation, coinciding with an intensive ground-based measurement campaign. The campaign data were used for model validation and evaluation. Comparisons between simulations and observations demonstrate that the model realistically represents cloud structures, liquid water path, and precipitation. Sensitivity tests were performed to pinpoint key uncertainties in simulating natural and seeded clouds and precipitation processes. They also shed light on the complex interplay between various physical parameters/processes and their interaction with large-scale meteorology. Our study found that in unseeded scenarios, the warm and cold biases in different initialization datasets can heavily influence the intensity and phase of natural precipitation. Secondary ice production via Hallett–Mossop processes exerts a secondary influence. On the other hand, the seeding impacts are primarily sensitive to aerosol conditions and the natural ice nucleation process. Both factors alter the supercooled liquid water availability and the precipitation phase, consequently impacting the silver iodide (AgI) nucleation rate. Furthermore, model sensitivities were inconsistent across cases, indicating that no single model configuration optimally represents all three cases. This highlights the necessity of employing an ensemble approach for a more comprehensive and accurate assessment of the seeding impact.
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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 (0.17more » « less
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1175/BAMS-D-20-0132.1
