Arctic Cloud‐Base Ice Precipitation Properties Retrieved Using Bayesian Inference

Journal Article · 11 August 2023 · Journal of Geophysical Research: Atmospheres

DOI: https://doi.org/10.1029/2022JD038202 · OSTI ID:1994890

Silber, Israel  ^[1]

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1. Department of Meteorology and Atmospheric Science Pennsylvania State University, University Park University Park PA USA, Now at Atmospheric Sciences and Global Change Division Pacific Northwest National Laboratory Richland WA USA

Abstract Cloud‐climate feedbacks are still the greatest source of uncertainty in current climate projections. Arctic clouds, which are predominantly stratiform and supercooled, often long‐lived, and nearly continuously precipitate ice particles, contribute roughly 10% of the uncertainty attributed to the global cloud feedback. This Arctic cloud uncertainty is driven by incomplete observational and theoretical knowledge required to estimate and explain the state and active processes occurring in those clouds. A focus on ice precipitation properties at Arctic cloud base rather than the surface deconfounds the product of cloud condensate sink processes from the influence of the atmospheric thermodynamic state below cloud base, rendering cloud‐base properties a more appealing target for inference and evaluation of model simulations. Here I describe an inverse model for the estimation of cloud base ice precipitation properties over Utqiagvik, North Slope of Alaska, using the synthesis of ground‐based radar and lidar measurements. By leveraging a Markov Chain Monte Carlo algorithm as the core of the inverse model, a wide range of particle size distributions are sampled, and different combinations of ice habit models are examined, both of which are typically fixed in other retrieval methods. Results show intriguing links between different cloud base thermodynamic and ice precipitation properties. Apparent ice number concentration enhancements at temperatures of −5 and −15°C suggest possible secondary ice production (SIP). The analysis alludes to an overestimation of SIP occurrence and intensity, especially in studies relying only on radar or lidar measurements. Finally, reflectivity‐dependent ice precipitation rate and ice water content parameterizations are presented.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Archive; Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Pennsylvania State University, University Park, PA (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC05-76RL01830; SC0021004; SC0021034
• OSTI ID: 1994890
• Report Number(s): PNNL-SA-187529; e2022JD038202
• Journal Information: Journal of Geophysical Research: Atmospheres, Journal Name: Journal of Geophysical Research: Atmospheres Journal Issue: 16 Vol. 128; ISSN 2169-897X
• Publisher: American Geophysical Union (AGU)Copyright Statement
• Country of Publication: United States
• Language: English