ALBERT

Description: 〉Regional atmospheric models struggle to maintain super-cooled 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 in the simulation of mixed-phase clouds. We perform kilometer-scale simulations for all CAO cases observed during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) over Norway, for which continuous ground-based observations were collected over 6 months. A novel analysis is used that attributes the cloud-radiative errors to deficiencies in specific cloud regimes. We show that the macrophysics parameterization 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 macrophysics sensitivities explored in this study, the prognostic representation of both liquid and ice fraction shows most promise in increasing the liquid water path. A newly proposed hybrid macrophysics parameterization 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. This study confirms that uncertainties in mixed-phase microphysics are a major bottleneck to capturing observed cold-air outbreak cloud properties.