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The near-surface boundary layer over patchy snow is highly heterogeneous and dynamic. Layers of opposing stability coexist within only a few horizontal meters. Conventional experimental methods to investigate this layer suffer from limitations related to the fixed positions of eddy covariance sensors. To overcome these difficulties, we set up a centimeter-resolution large eddy simulation of flow across an idealised transition from bare ground to snow. We force the simulation with high-frequency eddy covariance data recorded during a field campaign. We show that the model can represent the real flow by comparing it to independent eddy covariance data. However, the simulation underestimates vertical wind speed fluctuations, especially at high frequencies. Sensitivity analyses show that this is influenced by grid resolution and surface roughness representation but not much by subgrid-scale parameterization. Nevertheless, the model can reproduce the experimentally observed plumes of warm air intermittently detaching from bare ground and being advected over snow. This process is highly dynamic, with time scales of only a few seconds. We can show that the growth of a stable internal boundary layer adjacent to the snow surface can be approximated by a power law. With low wind speeds, deeper stable layers develop, while strong wind speeds limit the growth. Even close to the surface, the buoyancy fluxes are heterogeneous and driven by terrain variations, which also induce the frequent decoupling of a thin layer adjacent to the snow surface. Our simulations point the path towards generalizing point-based and aerial measurements to three dimensions. [ABSTRACT FROM AUTHOR]

Over the past two decades, the joint manifestation of global warming and rapid urbanization has significantly increased the occurrence of heatwaves and the formation of urban heat islands in temperate cities. Consequently, this synergy has amplified the frequency and duration of periods with tropical nights (TNs) in these urban areas. While the occurrences of such extreme events demonstrate irregular and nonlinear annual patterns, they consistently manifest a discernible rising decadal trend in local or regional climatic data. In urban regions situated amidst hilly or mountainous landscapes, changing wind directions--often associated with uphill or downhill thermal flows--profoundly impact the spread and dispersion of heat-related pollution, creating unique natural ventilation patterns. Using the Lausanne/Pully urban area in Switzerland as examples of hilly and lakeshore temperate cities, this study explores the influence of wind patterns and natural urban ventilation on the nonlinearity of recorded climatic data within an urban environment. This study integrates a mesoscale numerical weather prediction model (COSMO-1), a microscale Computational Fluid Dynamics (CFD) model, field observations, variational mode decomposition technique, and statistical analysis to investigate how wind speed and direction critically influence the nonlinearity of recorded long-term trends of extreme events, specifically focusing on the frequency and duration of TNs in lakeshore and hilly cities. The results strongly indicate a direct correlation between the frequency of TNs and the occurrence of specific moderate wind patterns. These wind patterns are exclusively captured by the microscale CFD model, unlike the mesoscale model, which neglects both urban morphology and complex hilly terrains. The impact of temporal and spatial variability of the wind field on long-term observations at fixed measurement stations suggests that caution should be exercised when relying on limited spatial measurement points to monitor and quantify long-term urban climate trends, particularly in cities located in complex terrains. [ABSTRACT FROM AUTHOR]

The Mountain snowpack stores months of winter precipitation at high elevations, supplying snowmelt to lowland areas in drier seasons for agriculture and human consumption worldwide. Accurate seasonal predictions of the snowpack are thus of great importance, but such forecasts suffer from major challenges such as resolving interactions between forcing variables at high spatial resolutions. To test novel approaches to resolve these processes, seasonal snowpack simulations are run at different grid resolutions (50 m, 100 m, 250 m) and with variable forcing data for the water year 2016/2017. COSMO-1E data is either dynamically downscaled with the High-resolution Intermediate Complexity Atmospheric Research (HICAR) model or statistically downscaled to provide forcing data for snowpack simulations with the Flexible Snowpack Model (FSM2oshd). Simulations covering complex terrain in the Swiss Alps are carried out with the operational settings of the FSM2oshd model or with a model extension including windand gravitational-induced snow transport (FSM2trans). The simulated snow height is evaluated against observed snow height collected during LiDAR flights in spring 2017. Observed spatial snow accumulation patterns and snow height distribution are best matched with simulations using dynamically downscaled data and the FSM2trans model extension, indicating the importance of both accurate meteorological forcing data and snow transport schemes. This study demonstrates for the first time the effects of applying dynamical downscaling schemes to snowpack simulations at the seasonal and catchment scale. [ABSTRACT FROM AUTHOR]

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]

Wind-blown snow is a frequent phenomenon in high-elevation and polar regions which impacts the surface energy and mass balance of these areas. Loose surface snow gets eroded and transported by wind, which influences the snow particles' physical properties (size, shape, optical properties) that determine the characteristics of the emerging wind-impacted snowpack layer. During airborne snow transport, the governing processes happen on the micro-scale while the particles are transported over long distances. The unfolding processes and the evolution of the particles' physical properties are thus difficult to observe in situ. Here, we used cold-laboratory ring wind tunnel experiments as an interim solution to study the governing processes during airborne snow transport with stable water isotopes as tracers for these micro-scale processes. Repeated analysis of airborne-sampled snow by micro-computed tomography (µ CT) documented a growing and rounding of snow particles with transport time, with a concurrent decrease in specific surface area. Stable water isotope analysis of airborne snow and water vapour allowed us to attribute this evolution to the process of airborne snow metamorphism. The changes observed in the snow isotopic composition showed a clear isotopic signature of metamorphic deposition, which requires particle–air temperature gradients. These results question the validity of the thermal-equilibrium assumption between particles and air inside the saltation layer of wind-blown snow events, where the conditions are similar to the ones found in the wind tunnel. Our results thus refine the understanding of the governing processes in the saltation layer and suggest that the snow's isotopic composition can inform on local wind-blown snow events as the original snow isotope signal gets overprinted by airborne snow metamorphism. Within transport times of 3 h, we observed changes in the isotope signal of airborne snow of up to +1.47 ‰ in δ18 O, ±5.7 ‰ in δ D, and -6.1 ‰ in d-excess. Thus, airborne snow metamorphism has the potential to influence the climate signal stored in snow and ice core stable water isotope records. [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]

Wind energy is one of the potential options to fill the gap in renewable energy production in Switzerland during the winter season when the energy demand exceeds local production capacities. With likely further rising energy consumption in the future, the winter energy deficit may further increase. However, a reliable assessment of wind energy potential in complex terrain remains challenging. To obtain such information, numerical simulations are performed using a combination of the "Consortium for Small-scale Modeling" and "Weather Research and Forecasting" (COSMO-WRF) models initialized and driven by COSMO-1E model, which allows us to simulate the influence of topography at a horizontal resolution of 300 m. Two LiDAR measurement campaigns were conducted in the regions of Lukmanier Pass and Les Diablerets, Switzerland. Observational LiDAR data and measurements from nearby wind sensor networks are used to validate the COSMO-WRF simulations. The simulations show an improved representation of wind speed and direction near the ground compared to COSMO-1E. However, with increasing height and less effect of the terrain, COSMO-WRF tends to overestimate the wind speeds, following the bias that is already present in COSMO-1E. We investigate two characteristic mountain-terrain flow features, namely waves and Foehn. The effect of mountain-induced waves of the flow is investigated through an event that occurred in the area of Diablerets. One-year analysis for the frequency of conditions that are favorable for mountain wave formation is estimated. The Foehn impact on wind was observed in the Lukmanier domain. We attempt quantification of the probability of occurrence using the Foehnix model. The result shows a high probability of Foehn occurrence during the winter and early spring seasons. Our study highlights the importance of incorporating complex terrain-related meteorological events into the wind energy assessment. Furthermore, for an accurate assessment of wind speed in complex terrain, our study suggests the necessity to have a better representation of the topography compared to COSMO-1E. [ABSTRACT FROM AUTHOR]

Rain-on-snow events can cause severe flooding in snow-dominated regions. These are expected to become more frequent in the future as climate change shifts the precipitation from snowfall to rainfall. However, little is known about how winter rainfall interacts with an evergreen canopy and affects the underlying snowpack. In this study, we document 5 years of rain-on-snow events and snowpack observations at two boreal forested sites of eastern Canada. Our observations show that rain-on-snow events over a boreal canopy lead to the formation of melt–freeze layers as rainwater refreezes at the surface of the sub-canopy snowpack. They also generate frozen percolation channels, suggesting that preferential flow is favoured in the sub-canopy snowpack during rain-on-snow events. We then used the multi-layer snow model SNOWPACK to simulate the sub-canopy snowpack at both sites. Although SNOWPACK performs reasonably well in reproducing snow height (RMSE = 17.3 cm), snow surface temperature (RMSE = 1.0 °C), and density profiles (agreement score = 0.79), its performance declines when it comes to simulating snowpack stratigraphy, as it fails to reproduce many of the observed melt–freeze layers. To correct for this, we implemented a densification function of the intercepted snow in the canopy module of SNOWPACK. This new feature allows the model to reproduce 33 % more of the observed melt–freeze layers that are induced by rain-on-snow events. This new model development also delays and reduces the snowpack runoff. In fact, it triggers the unloading of dense snow layers with small rounded grains, which in turn produces fine-over-coarse transitions that limit percolation and favour refreezing. Our results suggest that the boreal vegetation modulates the sub-canopy snowpack structure and runoff from rain-on-snow events. Overall, this study highlights the need for canopy snow property measurements to improve hydrological models in forested snow-covered regions. [ABSTRACT FROM AUTHOR]

In absence of the high-frequency measurements of wind components, sonic temperature and water vapour required by the eddy covariance (EC) method, Monin–Obukhov similarity theory (MOST) is often used to calculate heat fluxes. However, MOST requires assumptions of stability corrections and roughness lengths. In most environments and weather situations, roughness length and stability corrections have high uncertainty. Here, we revisit the modified Bowen-ratio method, which we call C-method, to calculate the latent heat flux over snow. In the absence of high-frequency water vapour measurements, we use sonic anemometer data, which have become much more standard. This method uses the exchange coefficient for sensible heat flux to estimate latent-heat flux. Theory predicts the two exchange coefficients to be equal and the method avoids assuming roughness lengths and stability corrections. We apply this method to two datasets from high mountain (Alps) and polar (Antarctica) environments and compare it with MOST and the three-layer model (3LM). We show that roughness length has a great impact on heat fluxes calculated using MOST and that different calculation methods over snow lead to very different results. Instead, the 3LM leads to good results, in part due to the fact that it avoids roughness length assumptions to calculate heat fluxes. The C-method presented performs overall better or comparable to established MOST with different stability corrections and provides results comparable to the direct EC method. An application of this method is provided for a new station installed in the Pamir mountains. [ABSTRACT FROM AUTHOR]

Wind-blown snow is a frequent phenomenon in high-elevation and polar regions which impacts the surface energy and mass balance of these areas. Loose surface snow gets eroded and transported by wind which influences the snow particle's physical properties (size, shape, optical properties) that determine the characteristics of the emerging wind-impacted snowpack layer. During airborne snow transport, the governing processes are happening on the micro-scale, while the particles are transported over long distances. The unfolding processes and the evolution of the particle's physical properties are thus difficult to observe in-situ. Here we used cold-laboratory ring wind tunnel experiments as an interim solution to study the governing processes during airborne snow transport with stable water isotopes as tracers for these micro-scale processes. Repeated analysis of airborne-sampled snow by micro-computed tomography (μCT) documented a growing and rounding of snow particles with transport time with a concurrent decrease in specific surface area. Stable water isotope analysis of airborne snow and water vapour allowed us to attribute this evolution to the process of airborne snow metamorphism. The changes observed in the snow isotopic composition showed a clear isotopic signature of metamorphic deposition, which requires particle-air temperature gradients. These results question the validity of the thermal equilibrium assumption between particles and air inside the saltation layer of wind-blown snow events where the conditions are similar to the ones found in the wind tunnel. Our results thus refine the understanding of the governing processes in the saltation layer and suggest that the snow's isotopic composition can inform on local wind-blown snow events as the original snow isotope signal gets overprinted by airborne snow metamorphism. Thus, airborne snow metamorphism has the potential to influence the climate signal stored in snow and ice core stable water isotope records. [ABSTRACT FROM AUTHOR]

Mountain snowpack forecasting relies on accurate mass and energy input information 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 extent 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 time scales. 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 output suggesting that parameterizations of the process using surface wind fields only may 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 output 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 studying the representation of snow accumulation and ablation processes. [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]

The near-surface boundary layer above patchy snow cover in mountainous terrain is characterized by a highly complex interplay of various flows on multiple scales. In this study, we present data from a comprehensive field campaign that cover a period of 21 days of the ablation season in an alpine valley, from continuous snow cover until complete melt out. We recorded near-surface eddy covariance data at different heights and investigated spectral decompositions. The topographic setting led to the categorisation of flows into up and down valley flows, with a down valley Föhn event in the middle of the observation period. Our findings reveal that the snow cover fraction is a major driver for the structure and dynamics of the atmospheric layer adjacent to the snow surface. With bare ground emerging, stable internal boundary layers (SIBL) developed over the snow. As the snow coverage decreased, the depth of the SIBL decreased below 1 m and spectra of air temperature variance showed a transition towards turbulent time scales, which were caused by the intermittent advection of shallow plumes of warm air over the snow surface. The intermittent advection could also be observed visually with high spatio-temporal resolution measurements using a thermal infrared camera. While the shallow advection only affected the lowest measurement level at 0.3 m, the measurements above at 1 m, 2 m, and 3 m indicate that the distribution of eddy size and, thus, the turbulence structure, did not distinctly change with height. [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]

Rain-on-snow events can cause severe flooding in snow–dominated regions. These are expected to become more frequent in the future as climate change shifts the precipitation from snowfall to rainfall. However, little is known about how winter rainfall interacts with an evergreen canopy and affects the underlying snowpack. In this study, we document 5 years of rain-on-snow events and snowpack observations at two boreal forested sites of eastern Canada. Our observations show that rain-on-snow events over a boreal canopy leads to the formation of melt–freeze layers as rainwater refreezes at the surface of the sub–canopy snowpack. They also generate frozen percolation channels, suggesting that preferential flow is favored in the sub–canopy snowpack during rain-on-snow events. We then used the multi–layer snow model SNOWPACK to simulate the sub–canopy snowpack at both sites. Although SNOWPACK performs reasonably well in reproducing snow height (RMSE = 17.3 cm), snow surface temperature (RMSE = 1.0 °C), and density profiles (agreement score = 0.79), its performance declines when it comes to simulating snowpack stratigraphy, as it fails to reproduce many of the observed melt–freeze layers. To correct for this, we implemented a densification function of the intercepted snow in the canopy module of SNOWPACK. This new feature allows 27 of the 32 observed melt–freeze layers induced by rain-on-snow events to be formed by the model, instead of only 18 with the original canopy module. This new model development also delays and reduces the snowpack runoff. Indeed, it triggers the unloading of dense unloaded snow layers with small rounded grains, which in turn produces fine–over–coarse transitions that limit percolation and favor refreezing. Our results show that the boreal vegetation modulates the sub–canopy snowpack structure and runoff from rain-on-snow events. Overall, this study highlights the need for canopy snow properties measurements to improve hydrological models in forested snow–covered regions. [ABSTRACT FROM AUTHOR]

Rain-on-snow events can cause severe flooding in snow-dominated regions. These are expected to become more frequent in the future as climate change shifts the precipitation from snowfall to rainfall. However, little is known about how winter rainfall interacts with an evergreen canopy and affects the underlying snowpack. In this study, we document 5 years of rain-on-snow events and snowpack observations at two boreal forested sites of eastern Canada. Our observations show that rain-on-snow events over a boreal canopy leads to the formation of melt-freeze layers as rainwater refreezes at the surface of the sub-canopy snowpack. They also generate frozen percolation channels, suggesting that preferential flow is favored in the sub-canopy snowpack during rain-on-snow events. We then used the multi-layer snow model SNOWPACK to simulate the sub-canopy snowpack at both sites. Although SNOWPACK performs reasonably well in reproducing snow height (RMSE = 17.3 cm), snow surface temperature (RMSE = 1.0℃), and density profiles (agreement score = 0.79), its performance declines when it comes to simulating snowpack stratigraphy, as it fails to reproduce many of the observed melt-freeze layers. To correct for this, we implemented a densification function of the intercepted snow in the canopy module of SNOWPACK. This new feature allows 27 of the 32 observed melt-freeze layers induced by rain-on-snow events to be formed by the model, instead of only 18 with the original canopy module. This new model development also delays and reduces the snowpack runoff. Indeed, it triggers the unloading of dense unloaded snow layers with small rounded grains, which in turn produces fine-over-coarse transitions that limit percolation and favor refreezing. Our results show that the boreal vegetation modulates the sub-canopy snowpack structure and runoff from rain-on-snow events. Overall, this study highlights the need for canopy snow properties measurements to improve hydrological models in forested snow-covered regions. [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]

Owing to drifting snow processes, snow accumulation and surface density in polar environments are variable in space and time. We present new field data of manual measurements, repeat terrestrial laser scanning and snow micro-penetrometry from Dronning Maud Land, Antarctica, showing the density of new snow accumulations. We combine these data with published drifting snow mass flux observations, to evaluate the performance of the 1-D, detailed, physics-based snow cover model SNOWPACK in representing drifting snow and surface density. For two sites in East Antarctica with multiple years of data, we found a coefficient of determination for the simulated drifting snow of r 2 = 0.42 and r 2 = 0.50, respectively. The field observations show the existence of low-density snow accumulations during low wind conditions. Successive high wind speed events generally erode these low-density layers while producing spatially variable erosion/deposition patterns with typical length scales of a few metres. We found that a model setup that is able to represent low-density snow accumulating during low wind speed conditions, as well as subsequent snow erosion and redeposition at higher densities during drifting snow events was mostly able to describe the observed temporal variability of surface density in the field. [ABSTRACT FROM AUTHOR]

Snow plays an essential role in the Arctic as the interface between the sea ice and the atmosphere. Optical properties, thermal conductivity and mass distribution are critical to understanding the complex Arctic sea ice system's energy balance and mass distribution. By conducting measurements from October 2019 to September 2020 on the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have produced a dataset capturing the year-long evolution of the physical properties of the snow and surface scattering layer, a highly porous surface layer on Arctic sea ice that evolves due to preferential melt at the ice grain boundaries. The dataset includes measurements of snow during MOSAiC. Measurements included profiles of depth, density, temperature, snow water equivalent, penetration resistance, stable water isotope, salinity and microcomputer tomography samples. Most snowpit sites were visited and measured weekly to capture the temporal evolution of the physical properties of snow. The compiled dataset includes 576 snowpits and describes snow conditions during the MOSAiC expedition. [ABSTRACT FROM AUTHOR]

Wind energy potential in complex terrain is still poorly understood and difficult to quantify. With Switzerland's current efforts to shift to renewable energy resources, it is now becoming even more crucial to investigate the hidden potential of wind energy. However, the country's topography makes the assessment very challenging. We present two measurement campaigns at Lukmanier and Les Diablerets, as representative areas of the complex terrain of the Swiss Alps. A general understanding of local wind flow characteristics is achieved by comparing wind speed measurements from a near-surface ultra-sonic anemometer and from light detection and ranging (LiDAR) measurements further aloft. The measurements show how the terrain modifies synoptic wind for example through katabatic flows and effects of local topography. We use an artificial neural network (ANN) to combine the data from the measurement campaign with wind speed measured by weather stations in the surrounding area of the study sites. The ANN approach is validated against a set of LiDAR measurements which were not used for model calibration and also against wind speed measurements from a 25-meter mast, previously installed at Lukmanier. The statistics of the ANN output obtained from multi-year time series of nearby weather stations match accurately the ones of the mast data. However, for the rather short validation periods from the LiDAR, the ANN has difficulties in predicting lowest wind speeds at both sites, and highest wind speeds at Les Diablerets. [ABSTRACT FROM AUTHOR]

Stable water isotopes (SWIs) contain valuable information on the past climate and phase changes in the hydrologic cycle. Recently, vapor measurements in the polar regions have provided new insights into the effects of snow‐related and atmospheric processes on SWIs. The purpose of this study is to elucidate the drivers of the particularly depleted vapor isotopic composition measured on a ship close to the East Antarctic coast during the Antarctic Circumnavigation Expedition in 2017. Reanalysis data and backward trajectories are used to model the isotopic composition of air parcels arriving in the atmospheric boundary layer (ABL) above the ship. A simple model is developed to account for moisture exchanges with the snow surface. The model generally reproduces the observed trend with strongly depleted vapor δ18O values in the middle of the 6‐day study period. This depletion is caused by direct air mass advection from the ice sheet where the vapor is more depleted in heavy SWIs due to distillation during cloud formation. The time spent by the air masses in the marine ABL shortly before arrival at the ship is crucial as ocean evaporation typically leads to an abrupt change in the isotopic signature. Snow sublimation is another important driver when the isotopic composition of the sublimation flux differs substantially from that of the advected air mass, for example, marine air arriving at the coast or free‐tropospheric air descending from high altitudes. Despite strong simplifications, our model is a useful and computationally efficient method for understanding SWI dynamics at polar sites. Plain Language Summary: Stable water isotopes are useful to reconstruct historical temperature conditions from ice cores. This method is possible because phase changes of water alter the isotopic composition. For example, if an air mass cools down, forms clouds, and produces rain or snowfall, the water vapor preferentially loses heavy water molecules. This study aims to explain a remarkable vapor isotopic signal measured on a ship close to the East Antarctic coast during 6 days in 2017. We model the isotopic composition of air parcels along their pathways to the ship and develop a novel approach to represent moisture exchange with the snow surface. The modeled vapor isotopic composition at the ship reaches a distinct minimum, similar to the measurements, when the air parcels move directly from the ice sheet to the ship. As expected, the vapor isotopic composition is lower over the ice sheet than over the ocean, largely due to cloud formation. However, moisture uptake from the snow surface and from the ocean shortly before arrival at the ship can strongly and abruptly influence the isotopic signature of the air masses. Although our model is not perfect, it helps to improve the interpretation of isotope measurements at polar sites. Key Points: Direct air mass advection from the ice sheet leads to strongly depleted vapor isotopic compositions at a ship close to the Mertz glacierBoth isotopic distillation due to cloud formation and sublimation of surface snow drive the vapor isotopic composition over the ice sheetOcean evaporation can quickly overwrite the isotopic signature of air masses shortly before arrival at the ship [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]

Snow plays an essential role in the Arctic as the interface between the sea ice and the atmosphere. Optical properties, thermal conductivity and mass distribution are critical to understanding the complex Arctic sea ice system's energy balance and mass distribution. By conducting measurements from October 2019 to September 2020 on the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have produced a dataset capturing the year-long evolution of the physical properties of the snow and surface scattering layer, a highly porous surface layer on Arctic sea ice that evolves due to preferential melt at the ice grain boundaries. The dataset includes measurements of snow during MOSAiC. Measurements included profiles of depth, density, temperature, snow water equivalent, penetration resistance, stable water isotope, salinity and microcomputer tomography samples. Most snowpit sites were visited and measured weekly to capture the temporal evolution of the physical properties of snow. The compiled dataset includes 576 snowpits and describes snow conditions during the MOSAiC expedition. [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 Simulations (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]

High resolution (< 1km) 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 time-scales 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 (50m), 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 118x fewer computational resources than the WRF atmospheric model. This gain in efficiency makes dynamic downscaling accessible to ecohydrological research, where downscaled data is often required at hectometer resolution for whole basins at seasonal time scales. 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]

Snow cornices growing on the leeward side of mountain ridges are common in alpine and polar regions during snow seasons. These structures may crack and fall, leading to an increase in avalanche danger. Although cornice formation has been observed in wind tunnel tests and the field, knowledge gaps still exist regarding the formation mechanism. This is particularly true with respect to wind conditions which favor cornice formation. To characterize the wind effects as the main factor for cornice growth, we carried out ring wind tunnel (RWT) experiments in a cold laboratory under various wind conditions. We quantitatively investigated the growth rate of the cornice in the horizontal and vertical direction as well as the airborne particle concentration. The results show that cornices only appear under a moderate wind speed range (1–2 times the threshold wind speed). The cornice growth rates in length and thickness are mainly determined by the combined effects of mass accumulation and erosion. The lower-limit wind speed for cornice growth is approximately equal to the threshold wind speed for snow transport. The upper limit of wind speed is when the erosion rate is higher than the deposition rate. The length growth rate of the cornices reaches a maximum for wind speeds approximately 40 % higher than the threshold wind speed. Moreover, a conceptual model for interpreting the cornice accretion mechanism is proposed based on the mass conservation and the results of the RWT experiments. The estimated suitable wind condition for cornice growth and formation are in good agreement with field observations in Gruvefjellet, Svalbard. Based on the physics of drifting snow, our results provide new insights into snow cornice formation and improve understanding of cornice processes that can influence avalanche activity. The experimental results and the conceptual model can be used in future snow cornice simulation and prediction work for cornice-induced avalanches. [ABSTRACT FROM AUTHOR]

The lateral transport of heat above abrupt (sub-)metre-scale steps in land surface temperature influences the local surface energy balance. We present a novel experimental method to investigate the stratification and dynamics of the near-surface atmospheric layer over a heterogeneous land surface. Using a high-resolution thermal infrared camera pointing at synthetic screens, a 30 Hz sequence of frames is recorded. The screens are deployed upright and horizontally aligned with the prevailing wind direction. The screen's surface temperature serves as a proxy for the local air temperature. We developed a method to estimate near-surface two-dimensional wind fields at centimetre resolution from tracking the air temperature pattern on the screens. Wind field estimations are validated with near-surface three-dimensional short-path ultrasonic data. To demonstrate the capabilities of the screen method, we present results from a comprehensive field campaign at an alpine research site during patchy snow cover conditions. The measurements reveal an extremely heterogeneous near-surface atmospheric layer. Vertical profiles of horizontal and vertical wind reflect multiple layers of different static stability within 2 m above the surface. A dynamic, thin stable internal boundary layer (SIBL) develops above the leading edge of snow patches protecting the snow surface from warmer air above. During pronounced gusts, the warm air from aloft entrains into the SIBL and reaches down to the snow surface adding energy to the snow pack. Measured vertical turbulent sensible heat fluxes are shown to be consistent with air temperature and wind profiles obtained using the screen method and confirm its capabilities to investigate complex in situ near-surface heat exchange processes. [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]

The remoteness and extreme conditions of the Arctic make it a very difficult environment to investigate. In these polar regions covered by sea ice, the wind is relatively strong due to the absence of obstructions and redistributes a large part of the deposited snow mass, which complicates estimates for precipitation hardly distinguishable from blowing or drifting snow. Moreover, the snow mass balance in the sea ice system is still poorly understood, notably due to the complex structure of its surface. Quantitatively assessing the snow distribution on sea ice and its connection to the sea ice surface features is an important step to remove the snow mass balance uncertainties (i.e., snow transport contribution) in the Arctic environment. In this work we introduce snowBedFoam 1.0., a physics-based snow transport model implemented in the open-source fluid dynamics software OpenFOAM. We combine the numerical simulations with terrestrial laser scan observations of surface dynamics to simulate snow deposition in a MOSAiC (Multidisciplinary Drifting Observatory for the Study of Arctic Climate) sea ice domain with a complicated structure typical for pressure ridges. The results demonstrate that a large fraction of snow accumulates in their vicinity, which compares favorably against scanner measurements. However, the approximations imposed by the numerical framework, together with potential measurement errors (precipitation), give rise to quantitative inaccuracies, which should be addressed in future work. The modeling of snow distribution on sea ice should help to better constrain precipitation estimates and more generally assess and predict snow and ice dynamics in the Arctic. [ABSTRACT FROM AUTHOR]

This study compares the ability of two degree-day models (Poli-Hydro and a hybrid degree-day implementation of Alpine3D) and one full energy-balance melt model (Alpine3D) to predict the discharge on two partly glacierized Alpine catchments of different size and intensity of exploitation, under present conditions and climate change as projected at the end of the century. For the present climate, the magnitude of snowmelt predicted by Poli-Hydro is sensibly lower than the one predicted by the other melt schemes, and the melting season is delayed by 1 month. This difference can be explained by the combined effect of the reduced complexity of the melting scheme and the reduced computational temporal resolution. The degree-day implementation of Alpine3D reproduces a melt season closer to the one obtained with its full solver; in fact, the onset of the degree-day mode still depends upon the full energy-balance solver, thus not bringing any particular benefit in terms of inputs and computational load, unlike with Poli-Hydro. Under climate change conditions, Alpine3D is more sensitive than Poli-Hydro, reproducing discharge curves and volumes shifted by 1 month earlier as a consequence of the earlier onset of snowmelt. Despite their benefits, the coarser temporal computational resolution and the fixed monthly degree days of simpler melt models like Poli-Hydro make them controversial to use for climate change applications with respect to energy-balance ones. Nevertheless, under strong river regulation, the influence of calibration might even overshadow the benefits of a full energy-balance scheme. [ABSTRACT FROM AUTHOR]

Predicting wind flow in highly complex terrain like the Alps is a challenge for all models. When physical processes need to be resolved in a spatially explicit manner, grids with high horizontal resolution of a few hundred meters are often required and drastically limit, in many cases, the extent and duration of the simulations. Many surface process models, like the simulation of heterogeneous snow cover across a season, however, need long time series on large domains as inputs. Statistical downscaling can provide the required data, but no model can reach the desired resolutions effectively and provide temporally resolved wind speed and direction on highly complex topography. The assessment of the potential for wind energy in the Alps, a promising player in the energy transition, is an example where the current shortcomings cause strong limitations. We present "Wind‐Topo", a novel approach based on deep learning that discovers some of the interactions between high‐resolution topography and coarser‐resolution states of the atmosphere to generate near‐surface wind fields with a 50‐m resolution. In our test case, we use a large number of measurement stations in Switzerland to train the model and an operational weather prediction model (COSMO‐1) as predictor. Wind‐Topo employs a custom architecture that analyses the state of the atmosphere on various scales and associates it with high‐resolution topography. A dedicated loss function leads to good scoring metrics as well as accurate wind‐speed distributions at 60 independent stations used for a thorough validation. 50‐m resolution wind fields are generated efficiently and exhibit several expected orographic effects like ridge acceleration, sheltering, and deflection. Furthermore, the bias and mean absolute error from COSMO‐1 at the alpine validation stations, which are 0.72 and 1.77 m·s−1 respectively, are reduced to −0.07 and 1.21 m·s−1. [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]

River ecosystems are highly sensitive to climate change and projected future increase in air temperature is expected to increase the stress for these ecosystems. Rivers are also an important socio-economic factor impacting, amongst others, agriculture, tourism, electricity production, and drinking water supply and quality. In addition to changes in water availability, climate change will impact river temperature. This study presents a detailed analysis of river temperature and discharge evolution over the 21st century in Switzerland. In total, 12 catchments are studied, situated both on the lowland Swiss Plateau and in the Alpine regions. The impact of climate change is assessed using a chain of physics-based models forced with the most recent climate change scenarios for Switzerland including low-, mid-, and high-emission pathways. The suitability of such models is discussed in detail and recommendations for future improvements are provided. The model chain is shown to provide robust results, while remaining limitations are identified. These are mechanisms missing in the model to correctly simulate water temperature in Alpine catchments during the summer season. A clear warming of river water is modelled during the 21st century. At the end of the century (2080–2090), the median annual river temperature increase ranges between +0.9 ∘ C for low-emission and +3.5 ∘ C for high-emission scenarios for both lowland and Alpine catchments. At the seasonal scale, the warming on the lowland and in the Alpine regions exhibits different patterns. For the lowland the summer warming is stronger than the one in winter but is still moderate. In Alpine catchments, only a very limited warming is expected in winter. The period of maximum discharge in Alpine catchments, currently occurring during mid-summer, will shift to earlier in the year by a few weeks (low emission) or almost 2 months (high emission) by the end of the century. In addition, a noticeable soil warming is expected in Alpine regions due to glacier and snow cover decrease. All results of this study are provided with the corresponding source code used for this paper. [ABSTRACT FROM AUTHOR]

Snow cornices growing on the lee of mountain ridges are a common feature in alpine and polar regions during snow seasons. They can result in potential avalanche risk when they crack and fall. Current studies of cornices mainly focus on their deformation, collapsing, and avalanche risk via field observations. Few studies have paid attention to the accretion process of cornices, especially on their horizontal growth which enhances the instability of cornices. In this work, experiments in a cold laboratory under various 5 wind conditions are carried out to investigate the environmental conditions and the internal physical mechanism of cornice formation. The results show thatâ€"for the specific settings in our wind tunnelâ€"cornices appear only under moderate wind speeds which lead to necessary net mass flux divergence near the edge. The fastest growth rate is with winds approximately 40% higher than the rebound threshold wind speed for snow transport because then the snow mass supply to the cornice edge is sufficient. Mass collection efficiency on the cornice surface decreases with the increasing wind speed. This work improves our understanding of cornice formation. [ABSTRACT FROM AUTHOR]

This paper studies numerically the convection of water vapour in snowpacks using an Eulerian-Eulerian two-phase approach. The convective water vapour transport in snow and its effects on snow density are often invoked to explain observed density profiles, e.g. of thin Arctic snow covers, but this process has never been numerically simulated and analysed in a systematic manner. Here, the impact of convection on the thermal and phase change regimes as a function of different snowpack depths, thermal boundary conditions and Rayleigh numbers is analysed. We find considerable impact of natural convection on the snow density distribution with a layer of significantly lower density at the bottom of the snowpack and a layer of higher density located higher in the snowpack or at the surface. We find that emergent heterogeneity in the snow porosity results in a feedback effect on the spatial organization of convection cells causing their horizontal displacement. Regions where the snowpack is most impacted by phase changes are found to be horizontally extended and vertically thin, 'pancake'-like layers at the top and bottom of the snowpack. We further demonstrate that among the parameters important for natural convection, the snowpack depth has the strongest influence on the heat and mass transfer. Despite our simplifying assumptions, our study represents a significant improvement over the state of the art and a first step to accurately simulate snowpack dynamics in diverse regions of the cryosphere. [ABSTRACT FROM AUTHOR]

The influence of drifting and blowing snow on surface mass and energy exchange is difficult to quantify due to limitations in both measurements and models, but is still potentially very important over large areas with seasonal or perennial snow cover. We present a unique set of measurements that make possible the calculation of turbulent moisture, heat, and momentum fluxes during conditions of drifting and blowing snow. From the data, Monin–Obukhov estimation of bulk fluxes is compared to eddy-covariance-derived fluxes. In addition, large-eddy simulations with sublimating particles are used to more completely understand the vertical profiles of the fluxes. For a storm period at the Syowa S17 station in East Antarctica, the bulk parametrization severely underestimates near-surface heat and moisture fluxes. The large-eddy simulations agree with the eddy-covariance fluxes when the measurements are minimally disturbed by the snow particles. We conclude that overall exchange over snow surfaces is much more intense than current models suggest, which has implications for the total mass balance of the Antarctic ice sheet and the cryosphere. [ABSTRACT FROM AUTHOR]

This study compares the ability of two degree-day models (Poli-Hydro and a degree-day implementation of Alpine3D) and one full energy-balance melt model (Alpine3D) to predict the discharge on two partly glacierized Alpine catchments of different size and intensity of exploitation, under present conditions and climate change as projected at the end of the century. For present climate, the magnitude of snow melt predicted by Poli-Hydro is sensibly lower than the one predicted by the other melt schemes, and the melting season is delayed by one month. This difference can be explained by the combined effect of the reduced complexity of the melting scheme and the reduced computational temporal resolution. The degree-day implementation of Alpine3D reproduces a melt season closer to the one obtained with its full solver; in fact, the onset of the degree-day mode still depends upon the full energy-balance solver, thus not bringing any particular benefit in terms of inputs and computational load, unlike with Poli-Hydro. Under climate change conditions, Alpine3D is more sensitive than Poli-Hydro, reproducing discharge curves and volumes shifted by one month earlier as a consequence of the earlier onset of snow melt. Despite their benefits, the coarser temporal computational resolution and the fixed monthly degree-days of simpler melt models like Poli-Hydro make them controversial to use for climate change applications with respect to energy-balance ones. Nevertheless, under strong river regulation, the influence of calibration might even overshadow the benefits of a full energy-balance scheme. [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]

The remoteness and extreme conditions of the Arctic make it a very difficult environment to investigate. In these regions, the wind has a substantial effect and redistributes a large part of the snow, which complicates precipitation estimates. Moreover, the snow mass balance in the sea ice system is still poorly understood, notably due to the complex structure of its surface. Quantitatively assessing the snow distribution on sea ice and its connection to the sea ice surface features is an important step to remove these uncertainties. In this work we introduce snowBedFoam 1.0., a physics-based snow transport model implemented in the open source fluid dynamics software OpenFOAM. We combine the numerical simulations with terrestrial lidar observations of surface dynamics to simulate snow deposition on a piece of MOSAiC sea ice with a complicated structure typical for pressure ridges. The results demonstrate that a large fraction of snow accumulates in their vicinity, which compares favorably against terrestrial laser scans. However, the approximations imposed by the numerical framework together with potential measurement errors (precipitation) give rise to quantitative inaccuracies. The modelling of snow distribution on sea ice should help to better constrain precipitation estimates and more generally assess and predict snow and ice dynamics in the Arctic. [ABSTRACT FROM AUTHOR]

The evaluation of snowpack models capable of accounting for snow management in ski resorts is a major step towards acceptance of such models in supporting the daily decision-making process of snow production managers. In the framework of the EU Horizon 2020 (H2020) project PROSNOW, a service to enable real-time optimization of grooming and snow-making in ski resorts was developed. We applied snow management strategies integrated in the snowpack simulations of AMUNDSEN, Crocus, and SNOWPACK–Alpine3D for nine PROSNOW ski resorts located in the European Alps. We assessed the performance of the snow simulations for five winter seasons (2015–2020) using both ground-based data (GNSS-measured snow depth) and spaceborne snow maps (Copernicus Sentinel-2). Particular attention has been devoted to characterizing the spatial performance of the simulated piste snow management at a resolution of 10 m. The simulated results showed a high overall accuracy of more than 80 % for snow-covered areas compared to the Sentinel-2 data. Moreover, the correlation to the ground observation data was high. Potential sources for local differences in the snow depth between the simulations and the measurements are mainly the impact of snow redistribution by skiers; compensation of uneven terrain when grooming; or spontaneous local adaptions of the snow management, which were not reflected in the simulations. Subdividing each individual ski resort into differently sized ski resort reference units (SRUs) based on topography showed a slight decrease in mean deviation. Although this work shows plausible and robust results on the ski slope scale by all three snowpack models, the accuracy of the results is mainly dependent on the detailed representation of the real-world snow management practices in the models. As snow management assessment and prediction systems get integrated into the workflow of resort managers, the formulation of snow management can be refined in the future. [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]

Many physically‐based models for climate change impact studies require sub‐daily temporal resolution of the forcing data to provide meaningful predictions. However, climate scenarios are typically available at daily time step, severely limiting the application of such physically‐based models. In this study, we propose an enhanced delta‐change method for downscaling climate change scenarios from daily to hourly resolution. The approach presented provides objective criteria for assessing the quality of the determined delta and downscaled time series, while also fixing issues of common quantile mapping methods used for spatial downscaling related to the decrease of correlation between different variables. However, this new approach has limitations in correctly representing statistically extreme events and changes in the frequency of discontinuous events such as precipitation. Smoothing of historical and future data is required prior to applying the delta‐change method, and the related parameters are found to have a subtle impact on the correctness of the representation of the seasonal means as well as the resulting (artificial) variability in the scenario data product. This new method is universal and can be applied with smoothing approaches apart from the harmonic fitting used in this work and in the past. In this study, the assessment suggested the use of seven harmonics for the smoothing of the input data as a best choice of this parameter for the data used. The method is applied to a Swiss climate change scenario data set, CH2018, and to a complement of this set to a Swiss alpine measurement network obtained by spatial transfer of CH2018, resulting in a set of 68 climate change scenarios at hourly resolution for 188 stations over Switzerland significantly expanding upon the spatial and temporal resolution of the CH2018 data set. All source code to perform such an analysis and the complete data product are provided open access. [ABSTRACT FROM AUTHOR]

Rivers are ecosystems highly sensitive to climate change and projected future increase in air temperature is expected to increase the stress for these ecosystems. Rivers are also an important socio-economical factor. In addition to changes in water availability, climate change will impact the temperature of rivers. This study presents a detailed analysis of river temperature and discharge evolution over the 21st century in Switzerland, a country covering a wide range of Alpine and lowland hydrolog- ical regimes. In total, 12 catchments are studied. They are situated both in the lowland Swiss Plateau and the Alpine regions and cover overall 10% of the country's area. This represents the so far largest study of climate change impacts on river temperature in Switzerland. The impact of climate change is assessed using a chain of physics-based models forced with the most recent climate change scenarios for Switzerland including low, mid, and high emissions pathways. A clear warming of river water is modelled during the 21st century, more pronounced for the high emission scenarios and toward the end of the century. For the period 2030-2040, median warming in river temperature of +1.1°C for Swiss Plateau catchments and of +0.8°C for Alpine catchments are expected compared to the reference period 1990-2000 (similar for all emission scenarios). At the end of the century (2080-2090), the median annual river temperature increase ranges between +0.9°C for low emission and +3.5°C for high emission scenarios for both Swiss Plateau and Alpine catchments. At the seasonal scale, the warming on the Swiss Plateau and in the Alpine regions exhibits different patterns. For the Swiss Plateau, the spring and fall warming is comparable to the warming in winter, while the summer warming is stronger but still moderate. In Alpine catchments, only a very limited warming is expected in winter. A marked discharge increase in winter and spring is expected in these catchments due to enhanced snowmelt and a larger fraction of liquid precipitation. Accordingly, the period of maximum discharge in Alpine catchments, currently occurring during mid-summer, will shift to earlier in the year by a few weeks (low emission) or almost two months (high emission) by the end of the century. In summer, the marked discharge reduction in Alpine catchments for high emission scenarios leads to an increase in sensitivity of water temperature to low discharge, which is not observed in the Swiss Plateau catchments. In addition, an important soil warming is expected due to glacier and snow cover decrease. These effects combined lead to a summertime river warming of +6.0°C in Alpine catchments by the end of the century for high emission scenarios. Two metrics are used to show the adverse effects of river temperature increase both on natural and human systems. All results of this study along with the necessary source code are provided with this manuscript. [ABSTRACT FROM AUTHOR]

Modelling and forecasting wind-driven redistribution of snow in mountainous regions with its implications on avalanche danger, mountain hydrology or flood hazard is still a challenging task often lacking in essential details. Measurements of drifting and blowing snow for improving process understanding and model validation are typically limited to point measurements at meteorological stations, providing no information on the spatial variability of horizontal mass fluxes or even the vertically integrated mass flux. We present a promising application of a compact and low-cost radar system for measuring and characterizing larger-scale (hundreds of metres) snow redistribution processes, specifically blowing snow off a mountain ridge. These measurements provide valuable information of blowing snow velocities, frequency of occurrence, travel distances and turbulence characteristics. Three blowing snow events are investigated, two in the absence of precipitation and one with concurrent precipitation. Blowing snow velocities measured with the radar are validated by comparison against wind velocities measured with a 3D ultra-sonic anemometer. A minimal blowing snow travel distance of 60–120 m is reached 10–20 % of the time during a snow storm, depending on the strength of the storm event. The relative frequency of transport distances decreases exponentially above the minimal travel distance, with a maximum measured distance of 280 m. In a first-order approximation, the travel distance increases linearly with the wind velocity, allowing for an estimate of a threshold wind velocity for snow particle entrainment and transport of 7.5–8.8 m s -1 , most likely depending on the prevailing snow cover properties. Turbulence statistics did not allow a conclusion to be drawn on whether low-level, low-turbulence jets or highly turbulent gusts are more effective in transporting blowing snow over longer distances, but highly turbulent flows are more likely to bring particles to greater heights and thus influence cloud processes. Drone-based photogrammetry measurements of the spatial snow height distribution revealed that increased snow accumulation in the lee of the ridge is the result of the measured local blowing snow conditions. [ABSTRACT FROM AUTHOR]

Sea ice is an important component of the global climate system. The presence of a snowpack covering sea ice can strongly modify the thermodynamic behavior of the sea ice, due to the low thermal conductivity and high albedo of snow. The snowpack can be stratified and change properties (density, water content, grain size and shape) throughout the seasons. Melting snow provides freshwater which can form melt ponds or cause flushing of salt out of the underlying sea ice, while flooding of the snow layer by saline ocean water can strongly impact both the ice mass balance and the freezing point of the snow. To capture the complex dynamics from the snowpack, we introduce modifications to the physics-based, multi-layer SNOWPACK model to simulate the snow–sea-ice system. Adaptations to the model thermodynamics and a description of water and salt transport through the snow–sea-ice system by coupling the transport equation to the Richards equation were added. These modifications allow the snow microstructure descriptions developed in the SNOWPACK model to be applied to sea ice conditions as well. Here, we drive the model with data from snow and ice mass-balance buoys installed in the Weddell Sea in Antarctica. The model is able to simulate the temporal evolution of snow density, grain size and shape, and snow wetness. The model simulations show abundant depth hoar layers and melt layers, as well as superimposed ice formation due to flooding and percolation. Gravity drainage of dense brine is underestimated as convective processes are so far neglected. Furthermore, with increasing model complexity, detailed forcing data for the simulations are required, which are difficult to acquire due to limited observations in polar regions. [ABSTRACT FROM AUTHOR]

Stream temperature and discharge are key hydrological variables for ecosystem and water resource management and are particularly sensitive to climate warming. Despite the wealth of meteorological and hydrological data, few studies have quantified observed stream temperature trends in the Alps. This study presents a detailed analysis of stream temperature and discharge in 52 catchments in Switzerland, a country covering a wide range of alpine and lowland hydrological regimes. The influence of discharge, precipitation, air temperature, and upstream lakes on stream temperatures and their temporal trends is analysed from multi-decadal to seasonal timescales. Stream temperature has significantly increased over the past 5 decades, with positive trends for all four seasons. The mean trends for the last 20 years are +0.37±0.11 ∘ C per decade for water temperature, resulting from the joint effects of trends in air temperature (+0.39±0.14 ∘ C per decade), discharge (-10.1±4.6 % per decade), and precipitation (-9.3±3.4 % per decade). For a longer time period (1979–2018), the trends are +0.33±0.03 ∘ C per decade for water temperature, +0.46±0.03°C per decade for air temperature, -3.0±0.5 % per decade for discharge, and -1.3±0.5 % per decade for precipitation. Furthermore, we show that snow and glacier melt compensates for air temperature warming trends in a transient way in alpine streams. Lakes, on the contrary, have a strengthening effect on downstream water temperature trends at all elevations. Moreover, the identified stream temperature trends are shown to have critical impacts on ecological and economical temperature thresholds (the spread of fish diseases and the usage of water for industrial cooling), especially in lowland rivers, suggesting that these waterways are becoming more vulnerable to the increasing air temperature forcing. Resilient alpine rivers are expected to become more vulnerable to warming in the near future due to the expected reductions in snow- and glacier-melt inputs. A detailed mathematical framework along with the necessary source code are provided with this paper. [ABSTRACT FROM AUTHOR]

Cellular-automata-based modelling for simulating snow bedforms and snow deposition is introduced in this study. The well-known ReSCAL model, previously used for sand bedforms, is adapted for this purpose by implementing a simple sintering mechanism. The effect of sintering is first explored for solitary barchan dunes of different sizes and flow conditions. Three types of behaviour are observed: small barchans continue their motion without any perceptible difference while large barchans sinter immediately. Barchans of intermediate size split, leaving behind a sintered core and a smaller barchan is formed. It is found that sintering introduces an upper limit to the size of bedforms that can remain mobile. The concept of "maximum streamwise length" (MSL) is introduced and MSL is identified for different wind speeds using the solitary dune scenario. Simulations of the full evolution from an initially flat snow layer to a complex dune field are performed next. It is found that the largest bedforms lie below the MSL threshold. Additionally, it is found that shallow snow layers are most susceptible to mechanical destabilization by the wind. [ABSTRACT FROM AUTHOR]

Stream temperature is a key hydrological variable for ecosystem and water resources management and is particularly sensitive to climate warming. Despite the wealth of meteorological and hydrological data, few studies have quantified observed stream temperature trends in the Alps. This study presents a detailed analysis of stream temperatures in 52 catchments in Switzerland, a country covering a wide range of alpine and lowland hydrological regimes. The influence of discharge, precipitation, air temperature and upstream lakes on stream temperatures and their temporal trends is analysed from multi-decade to seasonal time scales. Stream temperature has significantly increased over the past 5 decades, with positive trends for all four seasons. The mean trends for the last 20 years are +0.37 °C per decade for water temperature, resulting from joint effects of trends in air temperature (+0.39 °C per decade) in discharge (-10.1 % per decade) and in precipitation (-9.3 % per decade). For a longer time period (1979-2018), the trends are +0.33 °C per decade for water temperature, +0.46 °C per decade for air temperature, -3.0 % per decade for discharge and -1.3 % per decade for precipitation. We furthermore show that in alpine streams, snow and glacier melt compensates air temperature warming trends in a transient way. Lakes, on the contrary have a strengthening effect on downstream water temperature trends at all elevations. The identified stream temperature trends are furthermore shown to have critical impacts on ecological temperature thresholds, especially in lowland rivers, suggesting that these are becoming more vulnerable to the increasing air temperature forcing. Resilient alpine rivers are expected to become more vulnerable to warming in the near future due to the expected reductions in snow- and glacier melt inputs. [ABSTRACT FROM AUTHOR]

Sea ice is an important component of the global climate system. The presence of a snowpack covering sea ice can strongly modify the thermodynamic behaviour of the sea ice, due to the low thermal conductivity and high albedo of snow. The snowpack can be strongly stratified and change properties (density, water content, grain size and shape) throughout the seasons. Fresh water percolation during snow melt can decrease the salinity of the underlying ice, while flooding of the snow layer by saline ocean water can strongly impact both the ice mass balance and the freezing point of the snow. To capture the complex dynamics from the snowpack, we introduce modifications to the physics-based, multi-layer SNOWPACK model to simulate the snow-sea ice system. This involves modifications to the model thermodynamics and to describe water and salt transport through the snow - sea ice system by coupling the transport equation to the Richards equation. These modifications allow the snow microstructure descriptions developed in the SNOWPACK model to be applied to sea ice conditions as well. Here, we drive the model with data from Snow and Ice Mass-balance Buoys installed in the Weddell Sea in Antarctica. The model is able to simulate the temporal evolution of snow density, grain size and shape and snow wetness. The model simulations show abundant depth hoar layers and melt layers, as well as superimposed ice formation due to flooding and percolation. Gravity drainage of dense brine is underestimated as convective processes are so far neglected. Furthermore, with increasing model complexity, detailed forcing data for the simulations is required, which is difficult to acquire due to limited observations in polar regions. [ABSTRACT FROM AUTHOR]