Bert Kruyt, Rebecca Mott, Dylan Reynolds, Ethan Gutmann.
Mountain processes such as runoff, snow and ice melt, avalanches or permafrost degradation are strongly driven by atmospheric processes. Often physical-based process modeling is used to predict snow water storage in mountain and runoff in large mountain catchments or to assess climate change impacts on mountain processes. Typically process and energy balance model applications require meteorological input with i) correct representation of inter-variable dependencies, ii) high temporal resolutions (hourly) and iii) very high spatial resolutions (below 1 km). To bridge the gap between local and global circulation model scales, a variety of downscaling techniques exist which can be roughly separated into dynamic downscaling and statistical downscaling methods. Dynamical downscaling with complex atmospheric models can satisfy the above requirements, but are expensive computationally, limiting applications to rather small model domains and short time periods. This constraint also makes dynamical downscaling incapable of providing atmospheric forcing over an ensemble of climate scenarios. In this study we will use the Intermediate Complexity Atmospheric Research model (ICAR) to dynamically downscale atmospheric processes in complex terrain to the sub-kilometer scale. Initial and boundary conditions are provided by Consortium for Small-Scale Modeling (COSMO1) reanalysis data at 1.1 km horizontal resolution. ICAR permits three-dimensional atmospheric simulations with transient and spatially variable boundary conditions and wind fields, maintaining a complete three-dimensional grid of pressure, wind, temperature and various hydrometeors (e.g. water vapour, snow, graupel). Contrary to a Numerical Weather Prediction model, ICAR does not solve the Navier-Stokes equations but uses linear mountain wave theory to calculate disturbances in the windfield. This significantly reduces the computational demands, thereby allowing the model to be run at higher resolutions. It is the first time ICAR is used at a sub-kilometer spatial resolution, as well as in the very complex terrain of the Swiss Alps.
In this work, we will address the model’s capability to reproduce small-scale processes that play a governing role in the mountain climate. Such processes are local-scale cloud formation processes and complex flow phenomena such as speed up effects over ridges and mountain crests, channeling effects in alpine valleys as well as down- and updrafts strongly affecting precipitation patterns in complex terrain. Applying ICAR in very complex terrain requires new parameterizations of orographic drag, flow deflection by orographic barriers and the implementation of a higher-order advection scheme. An improved interpolation scheme was also implemented to assure a smooth transition from the low-resolution forcing data to the high-resolution model grid. We will discuss how the model’s simplified flow field calculations can be modified to mimic the behavior of more complex models. Including all relevant processes in the model physics while still allowing for relatively low computational demands requires careful balancing, and the challenges faced here are presented together with first results. These model results are validated by a variety of measured data sets, ranging from point-measurements to more detailed information such as wind Lidar data. Modeled solid precipitation distribution is validated against high-resolution snow depth maps obtained from LIDAR and ADS flights. These comparisons allow to test the performance of ICAR in reproducing small-scale processes that play a governing role in local-scale mountain climate. We will further present limitations of the model framework to reproduce the spatial variability of precipitation in very complex terrain.