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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]

Abstract: Understanding the dynamics driving the transformation of snowfall crystals into blowing snow particles is critical to correctly account for the energy and mass balances in polar and alpine regions. Here we propose a fragmentation theory of fractal snow crystals that explicitly links the size distribution of blowing snow particles to that of falling snow crystals. We use discrete element modeling of the fragmentation process to support the assumptions made in our theory. By combining this fragmentation model with a statistical mechanics model of blowing snow, we are able to reproduce the characteristic features of blowing snow size distributions measured in the field and in a wind tunnel. In particular, both model and measurements show the emergence of a self‐similar scaling for large particle sizes and a systematic deviation from this scaling for small particle sizes.

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]

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]

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]

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]

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]