Comparing the Atmospheric Boundary Layer in a High-Resolution Model with Ground-Based Observations: A Detailed Look at Boundary Layer Clouds

Boundary layer cloud represent one of the largest sources of uncertainties in climate models. In particular, the climate change predictions are strongly dependent on the parametrization of the cloud processes and on the representation of convective and boundary layer processes.

The goal of this work is to evaluate the representation of boundary layer clouds in the high-resolution (150 - 300 m) icosahedral non-hydrostatic model (ICON-LEM) by means of a direct comparison to ground-based observations. We exploit a new PBL classification based on wind lidar, ceilometer and tower measurements applied to observations of the JOYCE supersite in Germany. ICON is developed by the Max Planck Institute for Meteorology (MPI-M) and the German Weather Service (DWD); the goal is to understand if the mechanisms for cloud formation are properly described in the ICON-LEM model and in which conditions differences and biases are observed.

We identify different cloud regimes based on their degree of coupling to the surface; to estimate the degree of coupling, we apply different methodologies presented in literature as well as a PBL classification analogous to the one used for the observations, which has been developed for the large eddy model output (ICON-LEM). We then characterize cloud regimes in terms of liquid water path, mean lifting condensation level, mean vertical velocity at cloud base, cloud thickness, cloud base and cloud top heights and Rayleigh radar reflectivity and we compare the results to the corresponding ground-based observations.

Preliminary results show that PBL clouds formation does not always coincide with the ground-based observations even if a good match in the thermodynamic conditions is found; Cloud base is higher than in the observations and some dependency on domain size is found.

PBL clouds in general circulation models (GCMs) are heavily depending on several parameterizations. To evaluate, understand and improve these parameterizations, high resolution models are often necessary. This study bridges the gap between observations and high resolution models. The evaluation of the PBL cloud representation in ICON-LEM and the gained understanding is an essential step to improve the representation of the PBL in GCMs in the future.