A Performance Baseline for the Representation of Clouds and Humidity in Cloud-Resolving ICON-LEM Simulations in the Arctic

In the context of Arctic amplification many of the feedback mechanisms, decreasing or enhancing the warming, involve clouds and water vapor. Currently, there is a gap in understanding the role of clouds which leads to uncertainties in climate simulations. Modeling frameworks such as the ICOsahedral Non-hydrostatic model (ICON) are used to understand the Arctic atmospheric processes as well as predict future changes. In this study, we challenge ICON in the large-eddy setup (ICON-LEM) by performing cloud-resolving simulations over parts of Svalbard, including Ny-angstrom lesund. We ran daily simulations over 5 months and analyzed the column above Ny-angstrom lesund. The local supersite's observations enabled us to create a baseline for the model performance focusing on the representation of liquid water and water vapor. We narrow in on possibilities to improve the cloud microphysical representation based on statistical evaluations, not just single cases. We found an astonishing agreement between most of the analyzed variables. For instance, the model integrated water vapor showed only a low bias of 0.21 kg m(-2). The number of cloudy days is slightly higher in the model (+4%). Further, we found that the model produces an unrealistically high number of pure ice clouds. Small to medium precipitation events are similar in amount and time but the number of strong precipitation events is underestimated. Further results are discussed and show that ICON-LEM is a useful tool to study the Arctic. With this thorough analysis, we highlight the value of local cloud-resolving simulations to understand changes in the Arctic atmosphere.