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Mountain snowpack forecasting relies on accurate mass and energy input information in relation to the snowpack. For this reason, coupled snow–atmosphere models, which downscale input fields to the snow model using atmospheric physics, have been developed. These coupled models are often limited in the spatial and temporal extents of their use by computational constraints. In addressing this challenge, we introduce HICARsnow, an intermediate-complexity coupled snow–atmosphere model. HICARsnow couples two physics-based models of intermediate complexity to enable basin-scale snow and atmospheric modeling at seasonal timescales. To showcase the efficacy and capability of HICARsnow, we present results from its application to a high-elevation basin in the Swiss Alps. The simulated snow depth is compared throughout the snow season to aerial lidar data. The model shows reasonable agreement with observations from peak accumulation through late-season melt-out, representing areas of high snow accumulation due to redistribution processes, as well as melt patterns caused by interactions between radiation and topography. HICARsnow is also found to resolve preferential deposition, with model outputs suggesting that parameterizations of the process using surface wind fields may only be inappropriate under certain atmospheric conditions. The two-way coupled model also improves surface air temperatures over late-season snow, demonstrating added value for the atmospheric model as well. Differences between observations and model outputs during the accumulation season indicate a poor representation of redistribution processes away from exposed ridges and steep terrain and a low bias in albedo at high elevations during the ablation season. Overall, HICARsnow shows great promise for applications in operational snow forecasting and in studying the representation of snow accumulation and ablation processes. [ABSTRACT FROM AUTHOR]

Dynamic downscaling of atmospheric forcing data to the hectometer resolution has shown increases in accuracy for landsurface models, but at great computational cost. Here we present a validation of a novel intermediate complexity atmospheric model, HICAR, developed for hectometer scale applications. HICAR can run more than 500x faster than conventional atmospheric models, while containing many of the same physics parameterizations. Station measurements of air temperature, wind speed, and radiation, in combination with data from a scanning Doppler wind LiDAR, are compared to 50 m resolution HICAR output during late spring. We examine the model's performance over bare ground and melting snow. The model shows a smaller root mean squared error in 2 m air temperature than the driving model, and approximates the 3D flow features present around ridges and along slopes. Timing and magnitude of changes in shortwave and longwave radiation also show agreement with measurements. Nocturnal cooling during clear nights is overestimated at the snow covered site. Additionally, the thermal wind parameterization employed by the model typically produces excessively strong surface winds, driven in part by this excessive nocturnal cooling over snow. These findings highlight the utility of HICAR as a tool for dynamically downscaling forcing datasets, and expose the need for improvements to the snow model used in HICAR. [ABSTRACT FROM AUTHOR]

Drifting and blowing snow are important features in polar and high mountain regions. They control the surface mass balance in windy conditions and influence sublimation of snow and ice surfaces. Despite their importance, model representations in weather and climate assessments have high uncertainties because the associated physical processes are complex and highly variable in space and time. This contribution investigates the saltation system, which is the lower boundary condition for drifting and blowing snow models. Using a combination of (previous) measurements and new physics-based modeling with large-eddy simulation (LES), we show that the prevailing parameterizations that describe the saltation system in atmospheric models are based on contradictory assumptions: while some scaling laws are typical of a saltation system dominated by aerodynamic entrainment, others represent a saltation system controlled by splash. We show that both regimes can exist, depending on the friction velocity. Contrary to sand saltation, aerodynamic entrainment of surface particles is not negligible. It is important at low wind speeds, leading to a saltation height and near-surface particle velocity which increase with the friction velocity. In a splash-dominated saltation regime at higher friction velocities, the saltation height and near-surface particle velocity become invariant with the friction velocity and closer to what is observed with sand. These findings are accompanied by a detailed description of the theoretical, experimental and numerical arguments behind snow saltation parameterizations. This work offers a comprehensive understanding of the snow saltation system and its scaling laws, useful for both modelers and experimentalists. [ABSTRACT FROM AUTHOR]

One of the primary causes of non-uniform snowfall deposition on the ground in mountainous regions is the preferential deposition of snow, which results from the interaction of near-surface winds with topography and snow particles. However, producing high-resolution snowfall deposition patterns can be computationally expensive due to the need to run full atmospheric models. To address this, we developed two statistical downscaling schemes that can efficiently downscale near-surface, low-resolution snowfall data to fine-scale snow deposition accounting for the effect of preferential deposition in mountainous regions. Our approach relies on a comprehensive, model database generated using 3D wind fields from an atmospheric model and a preferential deposition model on several thousand simulated topographies covering a broad range in terrain characteristics. Both snowfall downscaling schemes rely on fine-scale topographic scaling parameters and low-resolution wind speed as input. While one scheme, referred to as the "wind scheme", further necessitates fine-scale vertical wind components, a second scheme, referred to as the "aspect scheme", does not require fine-scale temporal input. We achieve this by additionally downscaling near-surface vertical wind speed solely using topographic scaling parameters and low-resolution wind direction. We assess the performance of our downscaling schemes using an independent subset of the model database on simulated topographies, model data on actual terrain, and spatially measured new snow depth obtained through a photogrammetric drone survey following a snowfall event on previously snow-free ground. While the assessments show that our downscaling schemes perform well (relative errors <±3% with modeled and <±6% with measured snowfall deposition), they also demonstrate comparable results to benchmark downscaling models. However, our schemes notably outperform the benchmark models in representing fine- scale patterns. Our downscaling schemes possess several key features, including high computational efficiency, versatility enabled by the comprehensive model database, and independence from fine-scale temporal input data (aspect scheme), indicating their potential for widespread applicability. Therefore, our downscaling schemes for near-surface snowfall and vertical wind speed can be beneficial for various applications at fine grid resolutions such as in atmospheric and climate sciences, snow hydrology, glaciology, remote sensing, and avalanche sciences. [ABSTRACT FROM AUTHOR]

High-resolution (< 1 km) atmospheric modeling is increasingly used to study precipitation distributions in complex terrain and cryosphere–atmospheric processes. While this approach has yielded insightful results, studies over annual timescales or at the spatial extents of watersheds remain unrealistic due to the computational costs of running most atmospheric models. In this paper we introduce a high-resolution variant of the Intermediate Complexity Atmospheric Research (ICAR) model, HICAR. We detail the model development that enabled HICAR simulations at the hectometer scale, including changes to the advection scheme and the wind solver. The latter uses near-surface terrain parameters which allow HICAR to simulate complex topographic flow features. These model improvements clearly influence precipitation distributions at the ridge scale (50 m), suggesting that HICAR can approximate processes dependent on particle–flow interactions such as preferential deposition. A 250 m HICAR simulation over most of the Swiss Alps also shows monthly precipitation patterns similar to two different gridded precipitation products which assimilate available observations. Benchmarking runs show that HICAR uses 594 times fewer computational resources than the Weather Research and Forecasting (WRF) atmospheric model. This gain in efficiency makes dynamic downscaling accessible to ecohydrological research, where downscaled data are often required at hectometer resolution for whole basins at seasonal timescales. These results motivate further development of HICAR, including refinement of parameterizations used in the wind solver and coupling of the model with an intermediate-complexity snow model. [ABSTRACT FROM AUTHOR]

The surface mass balance (SMB) of large polar ice sheets and of snow and ice surfaces in general are incompletely understood because of the complexity of processes involved. One such process, drifting and blowing snow, has only been considered in a very simplified way in current meteorological and climatological models. To address this problem, the CRYOWRF model has been developed, a coupled model between the meteorological Weather Research and Forecasting model (WRF) and the snow model SNOWPACK, augmented by a detailed treatment of drifting and blowing snow. Applying CRYOWRF to the SMB of Antarctica, we find that the model reproduces measurements of SMB with similar errors than current models. Drifting and blowing snow and its sublimation play a particularly important role, especially in regions of strong katabatic winds. The CRYOWRF simulations are also in line with satellite estimates of blowing snow frequency. There is a need to further consolidate results by simulations with a higher grid resolution and by including more measurements of SMB contributions from snow fall to transport and sublimation. Plain Language Summary: Assessing current and predicting future sea level rise in connection with the general fate of our snow and ice masses on Earth requires understanding snow precipitation in extreme environments and the dynamics of snow on the surface. Over large parts of Antarctica, drifting and blowing snow and sublimation, which is the phase change of ice back to atmospheric vapor, are the only surface ablation processes and need therefore to be well quantified. With a new model, that shows similar performance to other models, we find that drifting and blowing snow and its sublimation play an important role for the snow mass balance especially in regions with strong winds. This has consequences not only for the snow mass balance alone but for the whole ice sheet dynamics as well as for estimating precipitation in these extreme environments. Key Points: Evaluation of the coupled Weather Research and Forecasting (WRF) and SNOWPACK model CRYOWRF, including a blowing snow module, shows reasonable representation of blowing snowBetter representation of atmospheric conditions at a station level by CRYOWRF compared to WRFBlowing snow is an important component of the Antarctic surface mass balance, especially in the katabatic regions of the continent [ABSTRACT FROM AUTHOR]

Accurately simulating snow cover dynamics and the snow–atmosphere coupling is of major importance for topics as wide-ranging as water resources, natural hazards, and climate change impacts with consequences for sea level rise. We present a new modelling framework for atmospheric flow simulations for cryospheric regions called CRYOWRF. CRYOWRF couples the state-of-the-art and widely used atmospheric model WRF (the Weather Research and Forecasting model) with the detailed snow cover model SNOWPACK. CRYOWRF makes it feasible to simulate the dynamics of a large number of snow layers governed by grain-scale prognostic variables with online coupling to the atmosphere for multiscale simulations from the synoptic to the turbulent scales. Additionally, a new blowing snow scheme is introduced in CRYOWRF and is discussed in detail. CRYOWRF's technical design goals and model capabilities are described, and the performance costs are shown to compare favourably with existing land surface schemes. Three case studies showcasing envisaged use cases for CRYOWRF for polar ice sheets and alpine snowpacks are provided to equip potential users with templates for their research. Finally, the future roadmap for CRYOWRF's development and usage is discussed. [ABSTRACT FROM AUTHOR]

This study describes and evaluates a new downscaling scheme that specifically addresses the need for hillslope-scale atmospheric-forcing time series for modelling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope in that it does not rely directly on surface observations and is able to generate the full suite of model forcing variables required for hydrological and land surface modelling in hourly time steps. It achieves this by utilizing the previously published TopoSCALE scheme to generate synthetic observations of the current climate at the hillslope scale while accounting for a broad range of surface–atmosphere interactions. These synthetic observations are then used to debias (downscale) CORDEX climate variables using the quantile mapping method. A further temporal disaggregation step produces sub-daily fields. This approach has the advantages of other empirical–statistical methods, including speed of use, while it avoids the need for ground data, which are often limited. It is therefore a suitable method for a wide range of remote regions where ground data is absent, incomplete, or not of sufficient length. The approach is evaluated using a network of high-elevation stations across the Swiss Alps, and a test application in which the impacts of climate change on Alpine snow cover are modelled. [ABSTRACT FROM AUTHOR]

Accurately simulating snow-cover dynamics and the snow-atmosphere coupling is of major importance for topics as wide-ranging as water resources, natural hazards and climate change impacts with consequences for sea-level rise. We present a new modelling framework for atmospheric flow simulations for cryospheric regions called CRYOWRF. CRYOWRF couples the state-of-the-art and widely used atmospheric model WRF, with the detailed snow-cover model SNOWPACK. CRYOWRF makes it feasible to simulate dynamics of a large number of snow layers governed by grain-scale prognostic variables with online coupling to the atmosphere for multiscale simulations from the synoptic to the turbulent scales. Additionally, a new blowing snow scheme is introduced in CRYOWRF and is discussed in detail. CRYOWRF's technical design goals and model capabilities are described and performance costs are shown to compare favourably with existing land surface schemes. Three case studies showcasing envisaged use-cases for CRYOWRF for polar ice sheets and alpine snowpacks are provided to equip potential users with templates for their research. Finally, the future road-map for CRYOWRF's development and usage is discussed. [ABSTRACT FROM AUTHOR]

This study describes and evaluates a new downscaling scheme that specifically addresses the need for hillslope scale atmospheric forcing time-series for modeling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope and is able to generate the full suite of model forcing variables required for hydrological and land surface modeling at hourly timesteps. It achieves this by utilising the previously published TopoSCALE scheme (Fiddes et al. 2014) to generate a synthetic observation of current climate at hillslope scale while accounting for a broad range of surface-atmosphere interactions. These synthetic observations are then used to debias (downscale) CORDEX climate variables using the quantile mapping method. A further temporal disaggregation step produces sub-daily fields. This approach has the advantages of other empirical-statistical methods, namely speed of use while avoiding the need for ground data, which is often limited. It is therefore a suitable method for a wide range of remote regions where ground data is absent, incomplete, or not of sufficient length. The approach is evaluated using a network of high elevation stations across the Swiss Alps and a test application of modelling climate change impacts on Alpine snow cover is given. [ABSTRACT FROM AUTHOR]

ABSTRACT Twenty-first century snow depth and snow water equivalent ( SWE) changes are assessed for three time periods (2020-2049, 2045-2079 and 2070-2099) at 11 stations in Switzerland with the physics-based snow model SNOWPACK and meteorological input data perturbed by the output from ten regional climate models ( RCMs) through the delta change method. Unlike in previous studies, incoming long-wave radiation has also been modified for future climatic conditions. We thus show the range of future snow simulations assuming different RCM projections. Model validation yields satisfying results for simulating snow depth and SWE for the reference period with errors in the order of 9% and 15%, respectively. For the end of the century, the stations between 1000-1700 m a.s.l. show no pronounced elevation dependence but surprisingly react quite similarly in terms of the relative magnitude of snow cover decrease, which may reach 90%. The projected small increase in winter precipitation has almost no effect at these stations, but incoming long-wave radiation has an important effect. At the high-elevation station Weissfluhjoch (2540 m a.s.l.) however, the precipitation increase is partly able to compensate for the increased temperature. This would imply that the snow cover at mid-elevation stations becomes temperature and radiation dominated and will react similarly to the spatially small differences in the projected temperature change. The low-elevation stations already show a strong decrease in the near future, and the inclusion of modified incoming long-wave radiation has almost no effect on the decrease of future snow depth and SWE because the temperatures are already close to the melting point in the reference period. At the end of the century, mean snow depth/ SWE are reduced by 35/32%, 83/86% and 96/97% at high-, mid- and low-elevations, respectively. [ABSTRACT FROM AUTHOR]

The article focuses on the application of the Alpine 3D model to the Goldbergkees basin in Austria for glacier mass balance, and glacier runoff studies. Alpine 3D is a distributed model for land surface-atmosphere interaction. The objective of the study is to enable a detailed evaluation of the model that provides model performance statistics and make the model originally designed for snow science. The strengths and weaknesses of the model, likewise its applicability to glaciological problems, and glacier hydrology are also explored.