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Vertical structure and ice production processes of shallow convective post-frontal clouds over the Southern Ocean in MARCUS, Part II: Modeling study

Yishi HuaUniversity of Wyoming, Laramie, Wyoming, USA

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Zachary J. LeboaUniversity of Wyoming, Laramie, Wyoming, USA

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Bart GeertsaUniversity of Wyoming, Laramie, Wyoming, USA

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Yonggang WangbState University of New York at Oswego, Oswego, New York, USA

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Yazhe HuaUniversity of Wyoming, Laramie, Wyoming, USA

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Abstract

Part I of this series presented a detailed overview of post-frontal mixed-phase clouds observed during the Measurements of Aerosols, Radiation and CloUds over the Southern Ocean (MARCUS) field campaign. In Part II, we focus on a multi-day (February 23-26, 2018) case with the aim of understanding ice production as well as model sensitivity to ice process parameterizations using the Weather Research and Forecast (WRF) model. Interestingly, the control simulation with the Predicted Particle Properties (P3) microphysics scheme underestimates the ice content and overestimates the supercooled liquid water content, contrary to the bias common in global climate models. The simulations targeted at ice production processes show negligible sensitivity to cloud droplet number concentrations. Further, neither increasing ice nuclei particle (INP) concentrations to an unrealistic level nor adjusting it to MARCUS field estimations alone guarantees more ice production in the model. However, the simulated clouds are found to be highly sensitive to the implementation of immersion freezing, the thresholding of condensation/deposition freezing initiation, and the propensity of rime splintering process. By increasing immersion freezing of cloud droplets, relaxing thresholds for condensation/deposition freezing, or removing rime splintering thresholds, the model significantly improves its performance in producing ice. The relaxation of temperature threshold to observed cloud top temperature suggests an in-cloud seeder-feeder mechanism. The results of this work call for an increase in observations of INP, especially over the remote Southern Ocean and at relatively high temperatures, and measurements of ice particle size distributions to better constrain ice nucleating processes in models.

Corresponding author: Zachary J. Lebo, zlebo@uwyo.edu

Abstract

Part I of this series presented a detailed overview of post-frontal mixed-phase clouds observed during the Measurements of Aerosols, Radiation and CloUds over the Southern Ocean (MARCUS) field campaign. In Part II, we focus on a multi-day (February 23-26, 2018) case with the aim of understanding ice production as well as model sensitivity to ice process parameterizations using the Weather Research and Forecast (WRF) model. Interestingly, the control simulation with the Predicted Particle Properties (P3) microphysics scheme underestimates the ice content and overestimates the supercooled liquid water content, contrary to the bias common in global climate models. The simulations targeted at ice production processes show negligible sensitivity to cloud droplet number concentrations. Further, neither increasing ice nuclei particle (INP) concentrations to an unrealistic level nor adjusting it to MARCUS field estimations alone guarantees more ice production in the model. However, the simulated clouds are found to be highly sensitive to the implementation of immersion freezing, the thresholding of condensation/deposition freezing initiation, and the propensity of rime splintering process. By increasing immersion freezing of cloud droplets, relaxing thresholds for condensation/deposition freezing, or removing rime splintering thresholds, the model significantly improves its performance in producing ice. The relaxation of temperature threshold to observed cloud top temperature suggests an in-cloud seeder-feeder mechanism. The results of this work call for an increase in observations of INP, especially over the remote Southern Ocean and at relatively high temperatures, and measurements of ice particle size distributions to better constrain ice nucleating processes in models.

Corresponding author: Zachary J. Lebo, zlebo@uwyo.edu
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