Investigating the Representation of Microphysical Processes in Arctic Mixed-Phase Clouds in ICON-LEM

The Arctic shows an increased climate warming rate. There still exist uncertainties around the role that cloud feedback mechanisms play in this. In our study, we want to address these uncertainties. For that, we created a semi operational setup with daily cloud-resolving simulations over Svalbard. For these simulations with 600 m resolution, we use the ICOsahedral Non-hydrostatic model in the large eddy mode (ICON-LEM). The setup uses a two-moment microphysical parameterization and can handle heterogeneous surfaces. We apply the operational forecasts from the global ICON model as lateral boundary conditions. The advantage of this setup is that we can step away from focussing on single cases and instead look at many different types of large scale and local conditions.We created and evaluated several months of these simulations using observations from Ny-Ålesund (Svalbard) for comparison. The supersite “AWIPEV” is located there. It includes a microwave radiometer, daily radiosondes, a rain gauge and other remote sensing instruments. In addition, we were able to use the Cloudnet data set that provides a classification of the hydrometeors. We found that the model captures general features such as the wind flow, integrated water vapour, temperature and relative humidity profiles very well. The cloud occurrence was overestimated by the model but still lies in a climatologically realistic range.As the large scale dynamics are accurately simulated, this gives us the foundation to scrutinize the details of the cloud microphysical parameterization. As the next step, we investigate the shortcomings we could see in greater detail. One example is the more efficient production of cloud ice in the model than what was observed. For the analysis of specific microphysical processes, we are working on a software package which should enable the independent running of certain microphysical processes. This will make entangling the contributions of each process simpler and clearer while saving computational resources. Further, we show that for the Arctic, we must consider that standard nuclei concentrations, as used in models developed for the mid-latitudes do not represent the Arctic state. The goal in the long term is to improve the microphysical parameterization so that the model can better represent Arctic mixed-phase clouds.