Arctic clouds are one of the largest uncertainties in climate modeling. Based on the atmospheric regional climate model HIRHAM5, a single-column model version was developed and applied to investigate the performance of a relative humidity (RH-Scheme) and a prognostic statistical (PS-Scheme) cloud scheme in the central Arctic. The simulated total cloud cover was evaluated with satellite-based MODIS observations. The more sophisticated PS-Scheme, with the beta distribution as probability density function, was identified to perform more realistically and match the observations better than the RH-Scheme. Nevertheless, a systematic overestimation of monthly averaged total cloud cover was found. Thus, sensitivity studies were conducted to assess the effect of changing model “tuning” parameters. Lower values of the cloud ice threshold g_thr, which controls the Bergeron-Findeisen process, show here the most significant reduction of simulated Arctic cloudiness. Furthermore, the combined effect of lower g_thr and a modified PS-Scheme (permitting negatively skewed beta distributions) can be used to minimize biases relative to MODIS. The height of the planetary boundary layer (PBL) is related to various subgrid-scale processes in the PBL and can be used to describe e.g. cloud characteristics, like cloud-top entrainment rate or the evolution of stratocumulus clouds, and connections between the surface and free troposphere. This study evaluates simulated Arctic PBL heights with ERA-Interim reanalysis and GPS-RO as well as CALIOP satellite data. HIRHAM5, ERA-Interim, and CALIOP spatial patterns agree fairly well associated with the same annual cycle, but GPS-RO seems to be biased in the JJA and SON seasons. While the low bias of ERA-Interim PBL heights was reconfirmed, relative differences between HIRHAM5 and the satellite datasets show in part contrary spatial patterns due to significant differences between GPS-RO and CALIOP.