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Arctic cloud simulations of the polar-optimized version of the Weather Research and Forecasting model (Polar WRF) were compared with retrievals using the CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation measurements. For the period from 1 December 2015 to 31 January 2016, a series of 24-to 48-hr simulations initialized daily at 00 UTC were examined. In particular, two cloud microphysics schemes, the Morrison double moment and the WRF single-moment 6-class (WSM6), were tested. The modeled cloud top heights had a correlation coefficient (r) of 0.69-0.72 with those from satellite retrievals, and a mean bias of less than 400 m. For the mean ice water content profile and mixed-phase cloud occurrence, the Morrison scheme's clouds were in better agreement with satellite retrievals than the WSM6. However, the use of the Morrison scheme resulted in underestimates of outgoing longwave radiation by -11.7 W m(-2) compared to satellite observations. The bias was reduced to -0.4 W m(-2) with the WSM6 which produced a stronger precipitation rate (by 10%) resulting in a drier and less-cloudy atmosphere. This also leads to the 7-Wm(-2) mean difference in the surface downward longwave radiation (DLR) between the schemes, which is large enough to explain the spread of the Arctic DLR in the current climate models. However, as the temporal variation in DLR showed good agreement with ground observations (r: 0.68-0.92), it is concluded that the Polar WRF can be useful for studying cloud effects on the winter Arctic surface climate.

Plain Language Summary Clouds are important for the Arctic climate, but simulating such clouds with numerical models is still challenging. The accuracy of model clouds has not been sufficiently examined due to the harsh Arctic environment obstructing cloud observations, especially during Arctic winters experiencing polar nights. This study compares the Arctic winter clouds simulated by a weather forecast model to cloud observations from active (lidar and radar) satellite instruments. The model successfully produced cloud patterns similar to the satellite observations. However, the choice of the cloud physics module in the model can modify the amount of cloud water significantly enough to affect the simulated surface climate.