Assessing spatially distributed snow simulations with MEB-Crocus in subalpine forests through modelling experiments

Abstract. The growing use of physics-based snow models at sub-kilometric resolution for scientific and operational applications calls for spatially distributed model evaluations. Such are particularly challenging in forested areas, where suitable ground truth data is largely lacking. This study assesses the first spatially distributed simulations of the forest snow scheme MEB-Crocus at 250 m resolution through comparison with a benchmark model, FSM2oshd. MEB-Crocus will be integrated in Météo-France's operational snow modelling chain in the near future. Its canopy implementation is based on principles typical for land surface models intended for coarse-resolution large-scale applications, while the canopy implementation in FSM2oshd was specifically developed and validated for simulations in alpine terrain and at sub-kilometric resolution. FSM2oshd has already been successfully evaluated against spatial observations and vegetation parameters were upscaled from hyper-resolution snow-vegetation simulations, providing confidence for using it as a benchmark. A suite of modelling experiments with varying combinations of vegetation datasets and grid cell tiling was performed to enable assessment of different aspects of MEB-Crocus. In the default operational configuration, MEB-Crocus was found to overestimate snow water equivalent (SWE) at peak of winter especially at elevations where forest is present, but to simulate shorter snow cover durations than FSM2. Land cover datasets and process parametrizations accounted for a similar share of model discrepancies. With identical forest structure information, differences in canopy snow processes were the main driver of model discrepancies, with MEB-Crocus generally overestimating peak SWE especially in denser forests. Simulations with MEB-Crocus including recent enhancements to the parametrizations of canopy snow interception and unloading led to strongly reduced differences in peak SWE. Insights from these model comparisons inform future model development efforts and encourage the evaluation of spatially distributed models across a range of forest structures, topographic settings, and meteorological conditions.