Datensatz

Zusammenfassung

Simulations of snow-atmosphere interactions are often the domain of complex, modern atmospheric models. These models have assisted our understanding of the physical processes behind snow-atmosphere interactions and have demonstrated capability of simulating atmospheric processes which affect seasonal snow. However, computational limitations have limited the use of fully coupled snow-atmosphere models to case studies. The High-resolution Intermediate Complexity Atmospheric Research (HICAR) model presents a computationally efficient platform through which some snow-atmosphere process can be simulated at the hectometer scale and at seasonal time scales. In particular, ridge-scale snow depth patterns influenced by preferential deposition and leeside eddies are resolved by the model. This is done while still utilizing 804x fewer computational resources than the Weather Research and Forecasting (WRF) model. Here we present a validation of the HICAR model using snow depth data and distributed wind data collected during Winter 2022/2023 in complex terrain in the Swiss Alps. These results show that HICAR can downscale both winds and precipitation to within the same accuracy as WRF. Thus, HICAR offers the ability to dynamically downscale forcing data to the target resolution of snow model simulations or for detailed studies under future climate scenarios. To this point, results from a study using a one-way coupling strategy between HICAR and an intermediate complexity snow model with snow redistribution will also be presented, showing the importance of different accumulation processes across scales. Remaining challenges and caveats of this modeling strategy, including the representation of turbulent mixing and dependency on input data, will also be discussed.