This study focusses on the role of turbulent mixing, surface coupling and radiation in a polar boundary layer. The goal is to gain insight in the relative role of these small scale processes and how these processes can compensate each other. As such, we extend the GABLS1 model intercomparison for turbulent mixing (Cuxart et al., 2006) with the other relevant physical processes in the SBL over ice. We use the Single Column Model (SCM) version of the Weather Research and Forecasting (WRF) mesoscale meteorological model and run different combinations of boundary-layer and radiation schemes (using one state of the art surface scheme). As such, an intercomparison of schemes within a single model is obtained. We confirm a wide variety in the state of the atmosphere and the surface variables for the selected parameterization schemes.
Subsequently, a sensitivity analysis for one particular combination of parameterization schemes is performed for the governing processes of turbulent mixing, surface coupling and radiation. Using a novel analysis method based on time-integrated SBL development, the variation between the sensitivity runs indicates the relative orientation of model sensitivities to variations in governing processes. Furthermore, this sensitivity can explain the variety of model results obtained in the intercomparison of different parameterization schemes.
We apply the same method for several geostrophic wind speeds to represent a large range of synoptic conditions. Our preliminary results indicate a shift in process significance for different wind regimes. For low wind regimes, the model sensitivity is larger for coupling and radiation, while for high wind speeds, not surprisingly, the largest sensitivity is found for the turbulent mixing process. Additionally, for typical wind speeds we find that for the 2m temperature and net radiation budget, the orientations of the turbulent mixing and surface coupling overlap. This implies that compensating errors in the boundary-layer scheme and land-surface model can remain hidden and this may explain the relatively slow progress in model development.