Evaluation of Two Cloud Parameterizations and Their Possible Adaptation to Arctic Climate Conditions

• Published in: Atmosphere
• Main Authors: Daniel Klaus, Wolfgang Dorn, Klaus Dethloff, Annette Rinke, Moritz Mielke
• Format: Text
• Language: English
• Published: Molecular Diversity Preservation International 2012
• Subjects: cloud parameterization; single-column climate model; model evaluation; sensitivity study; Arctic; North Pole
• Online Access: https://doi.org/10.3390/atmos3030419

Based on the atmospheric regional climate model HIRHAM5, the single-column model version HIRHAM5-SCM was developed and applied to investigate the performance of a relative humidity based (RH-Scheme) and a prognostic statistical cloud scheme (PS-Scheme) in the central Arctic. The surface pressure as well as dynamical tendencies of temperature, specific humidity, and horizontal wind were prescribed from the ERA-Interim data set to enable the simulation of a realistic annual cycle. Both modeled temperature and relative humidity profiles were validated against radio soundings carried out on the 35th North Pole drifting station (NP-35). Simulated total cloud cover was evaluated with NP-35 and satellite-based ISCCP-D2 and MODIS observations. The more sophisticated PS-Scheme was found to perform more realistically and matched the observations better. Nevertheless, the model systematically overestimated the monthly averaged total cloud cover. Sensitivity studies were conducted to assess the effect of modified “tuning” parameters on cloud-related model variables. Two tunable parameters of the PS-Scheme and six tuning parameters contained in the cloud microphysics were analyzed. Lower values of the PS-Scheme adjustment parameter q0, which defines the shape of the symmetric beta distribution (acting as probability density function), as well as higher values of the cloud water threshold CWmin or autoconversion rate γ1 are able to reduce the overestimation of Arctic clouds. Furthermore, a lower cloud ice threshold γthr, which controls the Bergeron–Findeisen process, improves model cloudiness and the ratio of liquid to solid water content.