On the Ability of Convection-Permitting E3SM Global Atmosphere Model in Simulating Arctic Mixed Phase Clouds During Cold-Air Outbreaks

To assess the model capabilities in representing the Arctic cloud regime transition, we investigate the Lagrangian evolution of boundary layer mixed-phase clouds during an intense cold-air outbreak over the Norwegian Sea. Specifically, we use a doubly periodic mode of the Simplified Cloud Resolving E3SM Atmosphere Model (SCREAM), an emerging global kilometer-scale model developed by Department of Energy (DOE). Model simulations of cloud properties are compared against large-eddy simulations, satellite and ground-based observations as captured during the DOE Atmospheric Radiation Measurement (ARM) program Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign.

These boundary layer mixed-phase clouds are driven by strong surface forcings that are relatively well constrained by the model configuration, rendering that biases in simulated cloud properties primarily arise from the parameterization of physical processes. In addition to the marine boundary layer clouds, the default model setting simulates a persistent high-level ice cloud during the whole simulation period, which is absent in observations. Sensitivity experiment demonstrates that the high-level cloud is produced by the falsely triggered ice nucleation parameterization which considers temperature dependence alone. Moreover, liquid water paths are found to be too low in both the stratiform and convective cloud regions due to the overly strong Wegener–Bergeron–Findeisen (WBF) process, linked to inconsistent treatments of the subgrid-scale liquid-ice cloud overlapping and microphysical processes. By correcting the subgrid-scale cloud overlapping assumption, we address the low bias in supercooled liquid water and greatly improve cloud-top liquid-ice phase partitioning based on CALIOP observations.

This study identifies key issues that contribute to model biases in simulating mixed-phase clouds. It improves our understanding of these related physical processes and their model representations, paving the way for advancements in next-generation convection-permitting modeling.