SNOWPACK: where do we stand today?

The Swiss snow-cover model SNOWPACK is presently used in many applications from snow sports and engineering to climate change assessment but also for avalanche warning. The core routines are packed in a library that also serves as the basic module for the land surface scheme Alpine3D. The separate application MeteoIO handles all input data in both applications. These components, including a visualization tool, are available as open source packages (models.slf.ch). Since 2002, the year three papers describing the model in detail appeared (for example, see Lehning et al., 2002), SNOWPACK evolved in many respects. Based on newly acquired data sets, we updated the parameterizations of the density of new snow (see Schmucki et al., submitted) or of the albedo. We also revisited some concepts of the model such as snow settlement: we now divide the stress applied to the snow into a purely static overburden and a stress rate dependent term that allows mimicking the relaxation behavior of new and older snow. In addition, we adapted the temperature dependence of viscosity to cover a large temperature range from about -70 °C up to the melting point (Groot et al., 2013). Finally, we maximized the accuracy of both mass and energy balance. This is necessary for implementing advanced water transport equations such as the recent solver for the Richards equation (Wever et al., 2013)

ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks

This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local- and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP)

Improving stream flow discharge modelling during snow melt

The importance of the snow cover for the hydrological cycle is well known but the understanding is still limited. For example, the effect of rain-on-snow on melt water runoff and the coupling between spring snow melt and stream flow discharge are difficult to describe quantitatively due to the complex nature of natural snow covers. Snow height can very over short distances and processes influencing the snow cover development, such as solar radiation and wind, are spatially highly variable in complex alpine terrain. These effects influence the layering of the snow cover and because layers with different snow properties also have different hydraulic properties, the relation between snow melt and snow cover runoff gets rather complex. However, it has already been shown that describing melt water flow through a snow cover using Richards equation, that takes into account the snow stratigraphy, is improving snow cover runoff estimations locally. In this study, an advanced physical based snow cover model that solves Richards equation (SNOWPACK) is used in a distributed way in a spatially explicit model for alpine terrain (Alpine3D). The model setup simulates the snow cover development and stream discharge over a snow season for the Dischma catchment in Switzerland. A comparison between modelled and observed discharge of the catchment outlet shows that solving Richards equation for snow yields better agreement than simpler (bucket) methods for liquid water flow in snow. The simulations also show a strong variation in contribution of snow cover runoff between areas, depending on slope exposition. This can be associated with different shortwave radiation input for snow melt. The results show that important improvements in estimating the contribution of snow cover runoff to the hydrological cycle can be achieved by solving Richards equation for snow. However, future research should also focus on a better estimation of hydraulic properties for a wider range of snow types and the understanding of lateral and preferential flow in snow

Antarctic-wide ice-shelf firn emulation reveals robust future firn air depletion signal for the Antarctic Peninsula

Antarctic firn is critical for ice-shelf stability because it stores meltwater that would otherwise pond on the surface. Ponded meltwater increases the risk of hydrofracture and subsequent potential ice-shelf collapse. Here, we use output from a firn model to build a computationally simpler emulator that uses a random forest to predict ice-shelf effective firn air content, which considers impermeable ice layers that make deeper parts of the firn inaccessible to meltwater, based on climate conditions. We find that summer air temperature and precipitation are the most important climatic features for predicting firn air content. Based on the climatology from an ensemble of Earth System Models, we find that the Larsen C Ice Shelf is most at risk of firn air depletion during the 21st century, while the larger Ross and Ronne-Filchner ice shelves are unlikely to experience substantial firn air content change. This work demonstrates the utility of emulation for computationally efficient estimations of complicated ice sheet processes. &nbsp;</p

Distributed snow and rock temperature modelling in steep rock walls using Alpine3D

In this study we modelled the influence of the spatially and temporally heterogeneous snow cover on the surface energy balance and thus on rock temperatures in two rugged, steep rock walls on the Gemsstock ridge in the central Swiss Alps. The heterogeneous snow depth distribution in the rock walls was introduced to the distributed, process-based energy balance model Alpine3D with a precipitation scaling method based on snow depth data measured by terrestrial laser scanning. The influence of the snow cover on rock temperatures was investigated by comparing a snow-covered model scenario (precipitation input provided by precipitation scaling) with a snow-free (zero precipitation input) one. Model uncertainties are discussed and evaluated at both the point and spatial scales against 22 near-surface rock temperature measurements and high-resolution snow depth data from winter terrestrial laser scans.In the rough rock walls, the heterogeneously distributed snow cover was moderately well reproduced by Alpine3D with mean absolute errors ranging between 0.31 and 0.81 m. However, snow cover duration was reproduced well and, consequently, near-surface rock temperatures were modelled convincingly. Uncertainties in rock temperature modelling were found to be around 1.6 °C. Errors in snow cover modelling and hence in rock temperature simulations are explained by inadequate snow settlement due to linear precipitation scaling, missing lateral heat fluxes in the rock, and by errors caused by interpolation of shortwave radiation, wind and air temperature into the rock walls.Mean annual near-surface rock temperature increases were both measured and modelled in the steep rock walls as a consequence of a thick, long-lasting snow cover. Rock temperatures were 1.3–2.5 °C higher in the shaded and sunny rock walls, while comparing snow-covered to snow- free simulations. This helps to assess the potential error made in ground temperature modelling when neglecting snow in steep bedrock

Version 1 of a sea ice module for the physics-based, detailed, multi-layer SNOWPACK model

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

Contrasting regional variability of buried meltwater extent over 2 years across the Greenland Ice Sheet

The Greenland Ice Sheet (GrIS) rapid mass loss is primarily driven by an increase in meltwater runoff, which highlights the importance of understanding the formation, evolution, and impact of meltwater features on the ice sheet. Buried lakes are meltwater features that contain liquid water and exist under layers of snow, firn, and/or ice. These lakes are invisible in optical imagery, challenging the analysis of their evolution and implication for larger GrIS dynamics and mass change. Here, we present a method that uses a convolutional neural network, a deep learning method, to automatically detect buried lakes across the GrIS. For the years 2018 and 2019 (which represent low- and high-melt years, respectively), we compare total areal extent of both buried and surface lakes across six regions, and we use a regional climate model to explain the spatial and temporal differences. We find that the total buried lake extent after the 2019 melt season is 56&thinsp;% larger than after the 2018 melt season across the entire ice sheet. Northern Greenland has the largest increase in buried lake extent after the 2019 melt season, which we attribute to late-summer surface melt and high autumn temperatures. We also provide evidence that different processes are responsible for buried lake formation in different regions of the ice sheet. For example, in southwest Greenland, buried lakes often appear on the surface during the previous melt season, indicating that these meltwater features form when surface lakes partially freeze and become insulated as snowfall buries them. Conversely, in southeast Greenland, most buried lakes never appear on the surface, indicating that these features may form due to downward percolation of meltwater and/or subsurface penetration of shortwave radiation. We provide support for these processes via the use of a physics-based snow model. This study provides additional perspective on the potential role of meltwater on GrIS dynamics and mass loss. &nbsp;</p

The Impact of Diffusive Water Vapor Transport on Snow Profiles in Deep and Shallow Snow Covers and on Sea Ice

Water vapor transport has been highlighted as a critical process in Arctic snowpacks, shaping the snow cover structure in terms of density, thermal conductivity, and temperature profile among others. Here, we present an attempt to describe the thermally-induced vertical diffusion of water vapor in the snow cover and its effects of the snowpack structure using the SNOWPACK model. Convection, that may also constitute a significant part of vapor transport, is not addressed. Assuming saturated conditions at the upper boundary of the snowpack and as initial condition, the vapor flux between snow layers is expressed by a 1-dimensional transient diffusion equation, which is solved with a finite difference routine. The implications on the snowpack of this vertical diffusive flux, are analyzed using metrics such as the cumulative density change due to diffusive vapor transport, the degree of over- or undersaturation, the instantaneous snow density change rate, and the percentage of snow density change. We present results for four different regions sampling the space of natural snow cover variability: Alpine, Subarctic, Arctic, and Antarctic sea ice. The largest impact of diffusive water vapor transport is observed in snow on sea ice in the Weddell Sea and the shallow Arctic snowpack. The simulations show significant density reductions upon inclusion of diffusive water vapor transport: cumulative density changes from diffusive vapor transport can reach �62 and �66 kg m�3 for the bottom layer in the shallow Arctic snowpack and snow on sea ice, respectively. For comparison, in deeper snow covers, they rarely exceed �40 kg m�3. This leads to changes in density for shallow snowpacks at the soil-snow interface in the range of �5 to �21%. Mirroring the density decease at depth is a thicker deposition layer above it with increase in density around 7.5%. Similarly, for the sea ice, the density decreased at the sea ice-snow interface by �20%. We acknowledge that vapor transport by diffusion may in some snow covers�such as in thin tundra snow�be small compared to convective transport, which will have to be addressed in future work

Deep ice layer formation in an alpine snowpack: monitoring and modeling

Ice layers may form deep in the snowpack due to preferential water flow, with impacts on the snowpack mechanical, hydrological and thermodynamical properties. This detailed study at a high-altitude alpine site aims to monitor their formation and evolution thanks to the combined use of a comprehensive observation dataset at a daily frequency and state-of-the-art snow-cover modeling with improved ice formation representation. In particular, daily SnowMicroPen penetration resistance profiles enabled us to better identify ice layer temporal and spatial heterogeneity when associated with traditional snowpack profiles and measurements, while upward-looking ground penetrating radar measurements enabled us to detect the water front and better describe the snowpack wetting when associated with lysimeter runoff measurements. A new ice reservoir was implemented in the one-dimensional SNOWPACK model, which enabled us to successfully represent the formation of some ice layers when using Richards equation and preferential flow domain parameterization during winter 2017. The simulation of unobserved melt-freeze crusts was also reduced. These improved results were confirmed over 17 winters. Detailed snowpack simulations with snow microstructure representation associated with a high-resolution comprehensive observation dataset were shown to be relevant for studying and modeling such complex phenomena despite limitations inherent to one-dimensional modeling.</p