On the Role of Macrophysics and Microphysics in Km‐Scale Simulations of Mixed‐Phase Clouds During Cold Air Outbreaks

Regional atmospheric models struggle to maintain supercooled liquid in mixed-phase clouds during polar cold-air outbreaks (CAOs). Previous studies focused on the parameterization of aerosol, microphysics and turbulence to understand the origin of this widespread model bias. This study investigates the role of macrophysics parameterizations (MacP) in the simulation of mixed-phase clouds. Km-scale simulations are performed for a large number of CAO cases over Norway, for which continuous ground observations were collected at one site over 6 months. We use a novel analysis that attributes the cloud-radiative errors to deficiencies in specific cloud regimes. We show that the MacP matters for cloud-radiative effects in CAOs, but that it is probably not the primary cause of the lack of liquid water in simulated mixed-phase clouds. Of all the MacP sensitivities explored in this study, the prognostic representation of both liquid and ice shows most promise in increasing the liquid water path. A newly proposed hybrid MacP with prognostic frozen and diagnostic liquid cloud fraction reproduces some of the benefits of the prognostic scheme at reduced cost and complexity. The two-moment microphysics scheme in this study produces too large precipitation particles. Reducing the snow deposition rate decreases the precipitation particle sizes and largely improves the liquid water path. Simulations are less sensitive to reduced riming rates.