Low-level stratiform clouds containing both liquid and ice tend to persist locally for 5-10 days at a time in the Arctic, with many of them lightly precipitating. Their longevity has important consequences for the frequency of occurrence of low-level clouds in the Arctic which strongly determines the surface energy budget by modulating the net radiative energy at the surface. A number of hypotheses have been put forth to explain the extraordinary resilience of these clouds including the lack of available ice-forming nuclei, strong static stability which limits turbulent mixing, the presence of moisture inversions which limits the drying usually associated with entrainment, and enhanced cloud-top radiative cooling and liquid water production through condensation due to limit solar heating through much of the year. In addition, local moisture sources and large-scale dynamics likely play an important role in the lifetime of these clouds.

The relative importance of cloud-aerosol interactions, local moisture sources and large-scale dynamics in the evolution of Arctic mixed-phase clouds are determined using a the NCAR/Penn State Mesoscale Model (MM5) and a new 2-moment microphysics scheme that predicts both mass and number concentration of four cloud species (cloud liquid, cloud ice, rain and snow). Mixed-phase cloud systems observed at the SHEBA and the Barrow NSA sites are simulated with the aim of better understanding the processes that result in their longevity. Sensitivity studies in which the assumed properties of the aerosols are varied will be performed to determine the importance of cloud-aerosol interactions in the evolution of Arctic mixed-phase clouds. A statistical comparison of the modeled and observed properties of mixed-phase clouds and the dynamical regimes in which they thrive will be given. Trajectory analyses are performed to determine the importance of air mass origins in determining the cloud microphysical properties. The importance of local moisture sources in the evolution of these cloud layers is also studied by varying the surface boundary conditions in the model.