As snowpacks are largely governed by thermodynamics, special care is usually given as to ensure the first principle, i.e. energy conservation, in their mathematical and numerical descriptions. On the other hand, the second principle, i.e. entropy production, has received less attention. However, the second principle, and its numerical translation, has proven to be a powerful tool in applied mathematics to ensure the stability of mathematical and numerical models. The goal of this work is thus to present the derivation of thermodynamically consistent numerical snowpack models. This rigorous approach restricts the number of acceptable numerical schemes that unconditionally comply with the second principle, and which are thus free of spurious oscillations, overshoots, or divergence. As examples, we consider some regularly encountered cases of numerical instabilities in snowpack models, and re-visit them based on the second principle point of view.
How to cite: Fourteau, K., Jondeau, K., and Cances, C.: Some insights from the second principle for snow modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5827, https://doi.org/10.5194/egusphere-egu25-5827, 2025.
Snow physical properties result from the complex 3D arrangement of ice and air at the micrometre scale, referred to as snow microstructure. Describing snow microstructure and predicting its temporal evolution are keys for snowpack models, such as CROCUS or SNOWPACK. Currently, the evolution laws of density and SSA in both models are not fully satisfactory, as shown by some model errors when compared to observations. For example, SSA of new snow simulated on CROCUS tends to decrease faster than what is observed experimentally, while the inverted density profile due to strong gradient metamorphism observed in arctic snowpacks is not captured by CROCUS. These limitations result partly from the fact that evolution laws were empirically derived from experimental time series covering a limited number of snow evolution scenarios, and whose temporal and spatial resolutions could be enhanced.
X-ray tomography has brought new insights into snow microstructure observation, enabling a quantitative assessment of its variations and a deeper understanding of the physical processes at the micrometer scale. While first measurements were made at room temperature and required to fix the microstructure evolution with impregnation, the use of micro-CT directly inside a cold lab offers the possibility to conduct extensive measurements of snow samples in a cold environment. In this work, we use micro-CT measurements to characterize the temporal evolution of microstructural properties of snow under dry snow metamorphism. To do so, we designed a snow-metamorphism cell to control the temperature at the upper and lower boundaries of a cylindrical snow sample of size 1.8 cm x 2 cm2. This cell can operate directly inside the tomograph and offers the possibility to conduct in-situ monitoring under various experimental conditions. We explored temporal evolutions for different initial snow types, mean temperatures, and temperature gradients ranging from isothermal condition up to 200 K/m. From the micro-CT measurements, we calculate the microstructure properties and analyze their temporal evolution. We also explore the relationships between characteristic lengths, such as ssa, correlation length, mean chord length, and curvature length. In this work, we present the preliminary results from a selection of experiments. The long-term objective is to produce highly resolved time-series with systematic variations of the experimental conditions, and to monitor the evolution of the snow microstructural properties in order to compare them to existing evolution laws and suggest improvements if needed.
How to cite: Dick, O., Calonne, N., Hagenmuller, P., and Laurent, B.: Monitoring dry snow metamorphism from in-situ tomographic measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16255, https://doi.org/10.5194/egusphere-egu25-16255, 2025.
Snowflakes usually have different shapes for different formation environments. When drifting snow happens, fragmentation makes snowflakes transform into rounder shapes and releases more small particles. This is important because it changes airborne snow particles' size distribution(SPSD) and concentration, affecting blowing snow mass flux and sublimation rate. However, current drifting and blowing snow models ignore this, increasing uncertainty in predicting snow mass and energy balance. Here, we develop a drifting and blowing snow model considering the snow fragmentation process during particle-bed interaction and investigate the effects of fragmentation on drifting and blowing snow. The results show that compared to not considering fragmentation, fragmentation changes the SPSD, resulting in an enhancement of mass flux and sublimation rate. The sublimation rate of blowing snow increases by 75% on average under a moderate wind speed ( with a friction velocity between 0.3 and 0.5 m/s). Initial SPSD also affects the final sublimation rate, which indicates that SPSD is an important factor for blowing snow modeling.
How to cite: Li, G., Bao, J., Yu, H., and Huang, N.: Drifting Snow Particle Fragmentation Enhances Blowing Snow Sublimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3069, https://doi.org/10.5194/egusphere-egu25-3069, 2025.
The state of research on snow mass balance over sea ice has advanced in recent years, with significant progress in understanding the complex snow-ice interactions. However, challenges remain in accurately assessing the snow depth variability over sea ice in both space and time, particularly when considering the effect of snow transport by wind. In Antarctica, the calving of ice shelves generates icebergs that get trapped in landfast sea ice and act as obstacles to drifting snow. By accumulating snow around them, icebergs may influence the dynamics of land-fast ice in coastal areas but their precise impact on the mass balance and spatial distribution of snow remains uncertain. Drifting snow models are valuable for isolating the geometric properties of obstacles and independently examining their impact on snowdrifts. In our study, we investigate the effect of iceberg geometry on snowdrift quantities by combining aerial laser scanner observations and numerical Euler-Lagrange simulations. Properties such as iceberg size, roundness and elongation were evaluated and the model outcome was compared to the observations. Results show that the size of icebergs governs the snowdrift quantities, while other shape characteristics mostly affect the snow distribution across the iceberg sides. A new scaling law has been discovered, revealing a clear power-law relationship between the size of snowdrifts and icebergs. Our work improves the understanding of drifting snow processes over Antarctic land-fast ice, particularly the impact of large-scale features on the snow distribution. It can offer deeper insights into the comparison of regions with small and large icebergs, along with their associated land-fast ice characteristics and help to quantitatively predict sea ice dynamics.
How to cite: Hames, O., Bouzdine, I., Haas, C., and Lehning, M.: Drifting Snow around Icebergs: Understanding the Role of Iceberg Size and Shape Through Modeling and Observational Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16197, https://doi.org/10.5194/egusphere-egu25-16197, 2025.
In mountains, wind- and gravity-driven transport of snow affects the overall distribution of snow and can have a significant effect on snowmelt dynamics. In the context of the Swiss operational snow melt forecasting, a compromise must be found to enable the representation of such small-scale processes over the entire Swiss Alps while maintaining viable computational costs.
To this end, the snow redistribution modules SNOWTRAN-3D and SnowSlide were implemented and adapted within the FSM2oshd physics-based snow cover model. In an earlier study we showed the added value of snow redistribution representations on a 1180 km2 domain within the Eastern Swiss Alps when running simulations at 25, 50 and 100 m spatial resolutions. Here, we present the challenges and developments that are needed to apply this research model successfully over the whole Swiss Alps at 100 m resolution in an operational setting. In particular, we discuss the following issues:
- The Swiss Alps include very high elevations, with summits above 4000 m.a.s.l. and glaciers. Transport parameters that were shown to be suitable for terrain at 2500 m.a.s.l. are not applicable in more extreme conditions and need diversification.
- Wind fields, although dynamically downscaled, need further post-processing to mitigate biases that became evident in comparison to wind station measurements, particularly on exposed ridges.
- The representation of snow redistribution and of forest snow processes have to be integrated as both types of processes coexist wherever open alpine terrain interfaces with subalpine forest.
- The snow cover fraction scheme has to be adapted to better account for snow transport processes and sub-grid variability in simulations at high spatial resolution.
How to cite: Quéno, L., Jonas, T., Mazzotti, G., and Magnusson, J.: Including snow redistribution in snow hydrology modelling: challenges and developments to make a research model operational at nation-scale , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17090, https://doi.org/10.5194/egusphere-egu25-17090, 2025.
Current snow models – including the most sophisticated ones, such as CROCUS and SNOWPACK – struggle to properly simulate Arctic snowpack characteristics such as density profiles. Indeed, those models have been developed and designed for Alpine snowpacks, which evolve differently from Arctic ones due to higher wind speeds, increasing the compaction of the upper snowpack layers, and stronger temperature gradients, inducing upward water vapor fluxes within the snowpack and influencing the compaction and metamorphism. Both phenomena – combined with complex interactions with the vegetation – are at the origin of the wind-slab and depth hoar formation in Arctic snowpacks. The Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC) – being the Canadian Earth System Model (CanESM) land surface component – uses a medium-complexity single-layer snow scheme. Whether correctly representing Arctic snowpack bulk characteristics requires a multilayer approach over a single-layer snow scheme is still an open question. To assess the model skills, 1D simulations were performed at ten sites – including three Arctic sites. Improvements in the snow model scheme were carried out, including three new parameterizations to better represent Arctic snow: (1) blowing snow sublimation losses, (2) wind inclusion in the computation of fresh snow density, and (3) increased wind compaction. Those improvements allow most of the current model skills to be improved at the Arctic sites. Uncertainties related to the meteorological forcing, variable measurements, snow drift, and model bias compensations are a perpetual challenge in those model assessments. Future studies will involve spatial evaluation of those model developments in addition to implementing new snow cover fraction parameterization in CLASSIC. The influence of these new developments will be assessed against the ESA Snow CCI variables for different land types and for the simulated surface energy and carbon fluxes.
How to cite: Lalande, M., Kinnard, C., and Roy, A.: Improvement of the CLASSIC Snow Model to Better Simulate Arctic Snowpacks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-45, https://doi.org/10.5194/egusphere-egu25-45, 2025.
The snow depth and the increase of snow depth after three consecutive days of snowfall, hereinafter referred to as ds and DH3gg, respectively, are typically chosen for avalanche protection and avalanche hazard assessment purposes. With specific reference to the Central Apennines (Central Italy), the preferable provider of observations for avalanche related applications is MeteoMont, which supplies ds observations at 34 manual stations, measured between 1978 and 2023. The area of interest is also covered by ERA5-Land, over a period of 73 years, from 1950 to 2023. In terms of temporal, spatial and quantitative availability of snow information, ERA5-Land consists in a more appealing choice as most manual weather stations set up in the Central Apennines are located at lower altitudes compared to where avalanches are likely to occur. Moreover, data recorded at manual stations appears to be incomplete, especially during extreme snowfall events. However, it is necessary to stress that ERA5-Land is affected by biases (e.g. underestimation or overestimation of extremes) and the use of uncorrected data in all applications might lead to unreasonable results. Therefore, in order to overcome the listed limitations, the suggested approach consists in the regionalisation of both ERA5-Land and MeteoMont ds and DH3gg and in the subsequent bias correction and downscaling of the regionalised ERA5-Land variables by means of the regionalised MeteoMont ones. With regards to ERA5-Land, 51 nodes have been considered as their grids intersect recorded and reconstructed avalanche paths in the Abruzzo Region (extracted from the Avalanche Record and the Map of Probabilistic Location of Avalanches provided by the Abruzzo Region). This ensures that the selected nodes are solely representative of areas where avalanches are most likely to occur. The regionalisation of both ERA5-Land and MeteoMont ds and DH3gg is performed by applying the index value regional method before the bias correction and the downscaling of ERA5-Land data as, in terms of computational efforts, only 2 bias corrections and downscalings for each couple of best-matched ERA5-Land and MeteoMont homogeneous areas would be required instead of 102 (2 for each couple of nodes and stations). The bias correction and downscaling of the ERA5-Land regionalised variables are then performed by means of a statistical transformation based on the assumption that said variables are described by one of the distributions belonging to the GEV family. This work is of particular relevance as, on the one hand, it overcomes the limited availability of snow information in the Central Apennines, especially in relation to avalanche related applications. In fact, it provides a tool that quantifies ds and DH3gg quantiles at elevations and sites that are not supplied with observations. On the other hand, it provides realistic initial and boundary conditions for simulating avalanche dynamics, drawing up hazard and risk maps, and designing active and/or passive defence structures.
How to cite: Fontana, S., Pasquali, D., and Di Risio, M.: Regionalisation, Bias Correction and Downscaling of ERA5-Land Snow Variables by Means of Local Observations Recorded in Central Italy , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19323, https://doi.org/10.5194/egusphere-egu25-19323, 2025.
The flow of meltwater through snow, acknowledged as a porous medium, is a crucial hydrological process essential for predicting the cryosphere’s response to climate change. This work aims to model the intricate coupling between meltwater infiltration and the non-equilibrium thermodynamics of ice-melt phase change at the Darcy scale. The proposed model consists of the Richards’ equation for infiltration, and evolution equations for ice and water temperature fields, which account for the thermal budget resulting from melt refreezing. Additionally, the model takes into account variations in porosity within the ice structure. The study presents numerical results from simulations conducted on 2D models of snowpacks with distinct initial levels of dryness and varying physical setups, which examine the mechanics of infiltration and alteration of the porosity structure due to refreezing. The implementation employs the PorePy and PyGeoN Python libraries.
How to cite: Crippa, C., Fumagalli, A., Scotti, A., Papini, M., and Longoni, L.: Modelling Meltwater Infiltration and Refreezing in Snow under Non-Isothermal Conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9709, https://doi.org/10.5194/egusphere-egu25-9709, 2025.
How to cite: Grinsted, A., Rathmann, N. M., and Hvidberg, C.: Calibrating a compressible firn rheology and application to firn in shear zones, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17643, https://doi.org/10.5194/egusphere-egu25-17643, 2025.
A melting snowpack initiates runoff production after cold content has been eliminated and the pore liquid water content has grown to overcome capillary resistance, a process called ripening. Here, we quantify the time-space distribution of ripening within a 4341 km² mountain basin in Montana, USA. Using model output for a 19 year period we compute a time-series of the energy needed for ripening, termed the Runoff Energy Hurdle (REH). The REH is associated with snowpack mass but is variably influenced by cold content, peaks earlier than mass, and is typically eliminated in days. We show that individual locations have complex year-to-year histories of REH growth and loss. Through K-means clustering, we identify four distinct ripening behaviors across high year-to-year variability. One cluster has ripening events throughout the snow season and can include 7-92 % of the basin depending on the year. Three additional clusters ripen progressively later in the spring season within narrowing time windows. We test machine learning methods for predicting the major spring ripening event at each location, based solely on snowpack state. The predictability is proportional to the magnitude of REH, with runoff activation of the highest REH locations predictable within an 18-day window eight weeks in advance.
How to cite: Harper, J., Cherblanc, C., Pérez Álvaro, J., and Johnson, J.: Characterizing and Predicting Watershed-Wide Snowpack Ripening Patterns with Machine Learning Methods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3784, https://doi.org/10.5194/egusphere-egu25-3784, 2025.
Assessing snow melt and the liquid water content (LWC) of snow is crucial for understanding the hydrological cycle for predicting water resources, hydroelectric power generation, runoff, and potential flooding. It is also essential for correcting remote sensing signals (RADAR) and forecasting wet snow avalanches, for which snow stability is closely linked to its water content. Various methods exist to measure snow LWC, including calorimetry techniques, centrifugal separation, and dielectric methods based on permittivity differences between ice, air, and water. While these methods are well established, they are limited to low sampling resolutions and do not capture the typically high spatial variability of liquid water within the snowpack. However, Donahue et al. recently (2022) demonstrated the potential of near-infrared (NIR) spectral imaging for visualizing the 2D spatial variability of snow wetness in their study, Mapping Liquid Water Content in Snow at the Millimeter Scale: An Intercomparison of Mixed-Phase Optical Property Models Using Hyperspectral Imaging and In Situ Measurements (The Cryosphere).
The SnowImager instrument (snowimager.ch), recently developed at the Institute for Snow and Avalanche Research (WSL/SLF) together with a local start-up (Davos Instruments), allows for measuring the 2D spatial NIR diffuse-reflectance of snow stratigraphies at wavelengths of 850 nm and 940 nm. Leveraging the fact that reflectance at 850 nm is less influenced by liquid water than at 940 nm, we explore the application of NIR diffuse-reflectance imaging for measuring 2D LWC distribution with the SnowImager. As a first step, we developed a wetness index based on the reflectance measurements, and which is proportional to the LWC. Because the NIR diffuse-reflectance also depends on the optical equivalent grain diameter, a baseline dry reflectance ratio was determined using dry snow samples collected over the winter season 2023/2024. In addition, field measurements (in Weissfluhjoch test site and in Tschuggen during the melt season) were carried out to compare the wetness index against conventional liquid water content measurements obtained with a capacitive sensor.
Results from the Tschuggen campaign exhibit good agreement between the wetness index and the LWC measurements with the capacitive sensor for the snowpack wetness evolution. Furthermore, the imaging approach demonstrates the ability of capturing high resolution 2D variability of the LWC within a snowpack. Although the findings are promising, limitations were identified at snow microstructure regions of high textural contrasts. Further research is required to validate the wetness index method comprehensively, particularly concerning the characterization of the baseline reflectance ratio.
How to cite: Philippe, V., Mewes, L., and Walter, B.: Investigating the potential of snow liquid water content retrieval from near-infrared reflectance measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15329, https://doi.org/10.5194/egusphere-egu25-15329, 2025.
The Arctic snowpack covers a large portion of the Earth’s surface, yet detailed snow observations in these areas are sparse compared to observations in alpine environments. The Arctic presents unique environmental conditions, leading to thin snowpacks undergoing high-temperature gradients. These conditions lead to specific evolutions of the snow microstructure, which results in peculiar snowpack properties. To improve our understanding and description of the Arctic snowpack, an eight-month-long field campaign (IVORI) was conducted in Cambridge Bay at the Canadian High Arctic Research Station, Nunavut, Canada (69°N) during the 2023-2024 winter. The campaign is based on daily acquisitions of the 3D snow microstructure at 10 μm using a cold laboratory X-ray tomograph located next to the field site, along with extensive monitoring of the meteorological conditions and traditional snow characterizations. This dataset notably contains 200 tomographic samples and 50 snow stratigraphic profiles covering the full snow depth.
Here we present the specific climatic context of the 2023-2024 winter at Cambridge Bay, along with an analysis of the evolution of the vertical profiles of density and specific surface area. Finally, a preliminary overview of the performance of snow models at this Arctic location is given, highlighting potential areas for improvement.
How to cite: Bouvet, L., Calonne, N., Hagenmuller, P., Arnaud, L., Dick, O., Fourteau, K., Fructus, M., Kramer, D., Langlois, A., Lejeune, Y., Meloche, J., Roulle, J., Silis, A., Védrine, L., Vionnet, V., and Dumont, M.: Insights of the seasonal evolution of an arctic snowpack from an intensive field campaign, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9995, https://doi.org/10.5194/egusphere-egu25-9995, 2025.
Snow cover and seasonally frozen ground (SFG) are the key cryospheric elements on the southern edge of Altai Mountains (SEAM). Quantifying the thermal effect of snow cover on the frozen ground remains challenging. Utilizing the datasets observed at Altai Kuwei Snow Station (AKSS) and by National Meteorological Stations of China Meteorological Administration (CMA), we evaluated the thermal effect of snow cover on SFG regime. The results observed by AKSS indicated that the energy exchange between the ground and atmosphere was significantly insulated by snow cover, resulting in a considerable temperature offset between the snow surface and the ground below. This offset reached a maximum of 12.8 °C for a snow depth of 50 cm, but decreased for snowpack depths of >70 cm, whereas the snow temperature lapse rate was systematically steeper in the upper snowpack than at depth. Snow cover was the dominating driver of inter-annual differences in the SFG regime, as represented by the annual maximum freezing depth and soil heat flux. The observed average soil heat loss rate increased from 2.68 to 5.86 W/m2 on two occasions when the average snow depth decreased from 61.2 cm to 13.7 cm, resulting in an increase in maximum freezing depth of SFG from 69 cm to >250 cm soil depth. The results observed by CMA also demonstrate how snow cover controlled the SFG regime by warming the ground and inhibiting freezing of the soil column. Snow cover caused a 44.5-cm decline of annual maximum freezing depth during 1961-2015 period. SFG degradation between 1961 and 2015 was accompanied by increases in both air temperature and snow cover, with the former playing the dominant role. The correlation between snow cover and the ground–atmosphere temperature offset provides a new empirical method of evaluating the effective thermal effect of snow cover on SFG.
How to cite: zhang, W.: Observations on snow cover and frozen ground in the Chinese Altai Mountains, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2196, https://doi.org/10.5194/egusphere-egu25-2196, 2025.
The Antarctic and Greenland ice sheets are particularly vulnerable to global warming. Surface melt and runoff are increasing over Greenland, inducing a decrease in surface mass balance. Projections suggest that this process will accelerate in the future and could also affect the Antarctic ice sheet. Over ice sheets, snowpacks can reach several tens of meters and have the capacity to store and refreeze liquid water. This process directly impacts the amount of runoff and is strongly dependent on the physical characteristics of the snowpack, particularly the snow density governed by metamorphism and overburden pressure. Consequently, understanding and modelling the evolution of ice sheets requires an accurate representation of surface and internal snowpack processes. However, many Earth system models have simplified snowpack schemes, often evaluated and adapted for seasonal snow but not for polar snow conditions.
Here we present an automatic method for initialization and calibration of densification in snowpack models, applied to the ORCHIDEE model, the land surface scheme of the IPSL-CM Earth system model. ORCHIDEE includes an intermediate complexity representation of the snowpack with 12 snow layers and 8 ice layers. In this work, we use ORCHIDEE in offline conditions with atmospheric forcings from the polar-oriented regional atmospheric model MAR. We develop a snowpack initialization method adaptable to any snowpack thickness and model. To address the limitations of densification parameterizations for polar regions identified in ORCHIDEE, we use an automatic tuning method known as History Matching to calibrate free parameters of the densification formulations. Calibration of 1D simulations over two characteristic dry-snow locations in Greenland and Antarctica enable us to improve densification across the rest of the ice sheets. We apply this method for two different types of density parameterizations and obtain similar good agreement with observed density profiles from the SUMup database. In the future, this methodology can be extended to other free parameters of the model, such as those associated with the albedo parameterization.
How to cite: Conesa, P., Agosta, C., Charbit, S., Beylat, S., and Dumas, C.: Dry snow densification over ice sheets in the ORCHIDEE land surface model , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3774, https://doi.org/10.5194/egusphere-egu25-3774, 2025.
As a form of solid precipitation, snow plays a crucial role in climate regulation by reflecting solar radiation and insulating the ground. Additionally, it serves as a vital water resource, influencing hydrological cycles through its seasonal melting process. So, accurate predictions of snowfall and the subsequent evolution of the snowpack are essential. In this study, some investigations are made to reveal the impact of multi-strategically assimilating Global Precipitation Measurement (GPM) precipitation and Himawari-8/Advanced Himawari Imager (AHI) water vapor radiances (WVR) on forecasting a heavy snowfall event and snow properties on the ground over the Eastern Qinghai-Tibet Plateau employing the Weather Research and Forecast model (WRF) and the Four-Dimensional Variational assimilation system. DA strategies includes two aspects: the initial time of Reg_NWPs runs and the type of observations used. The initial times of Reg_NWPs are 0000 UTC, 0600 UTC, and 1200 UTC on October 28, 2022. Separate and combined DA tests are conducted to forecast. For the process of snowfall, the joint assimilation of the two not only yields multi-dimensional atmospheric insights but also addresses the limitations of individual assimilation. Assimilation GPM and AHI are respective sensitivity to the lower layers (about 800hpa) and upper layers (about 400hpa) of model. The individual assimilation GPM has the greatest effect on near-surface humidity field, and AHI plays a dominant role in the joint assimilation. In addition, we further compare the 12-hourly cumulative snowfall with in-situ meteorological station observations. The predictions of snowfall from DA_G&A perform much better with the correlation coefficient and root-mean-square error 0.36 and 3.14mm, respectively. As for different initial times of NWPs, the best snowfall forecast is 0600 UTC on October 28, 2022, and the CC is 0.4. For the snow properties on theground, the results indicate that the predictions of snow properties, such as snow depth (SD), snow cover fraction (SCF) and snow albedo (SAL), are influenced by both the initial time of Reg_NWPs and the type of observations. DA_G&A showed a significant increase in deep snow area (SD >15cm), and a decrease in shallow snow area (SD<5cm). Comparing with some reanalyzed and remote sensing inversion datasets, the predictions exhibit good physical consistency between snow parameters and fine temporal-spatial resolution. However, the land surface scheme of Reg_NWPs tends to overestimate SCF and SAL. So, in the future, the integration of a land surface DA system (LDAS) into Reg_NWPs will be considered for on-line coupling.
How to cite: Ren, J. and Huang, C.: Impact of the Snowfall and Snow Properties Predictions with Multiple Data Assimilation Strategies Digesting GPM Precipitation and Himawari-8/AHI water vapor radiance into Reg_NWPs over TP plateau , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6026, https://doi.org/10.5194/egusphere-egu25-6026, 2025.
How to cite: Mewes, L., Philippe, V., Schneebeli, M., Löwe, H., and Walter, B.: Spatial-variability of snow surface and snowpack properties characterized by near-infrared diffuse reflectance imaging, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18277, https://doi.org/10.5194/egusphere-egu25-18277, 2025.
Forests influence the inside-canopy snow dynamics in various ways depending on topography and the prevailing climate. Understanding how forest effects on snow change with climate variability and climate change is essential for predicting the future role of forests for seasonal snow dynamics. Thereby location-specific studies, such as the one presented here, provide valuable insights into forest-snow interactions within particular regions. In this study, the physically-based and fully distributed snow model openAMUNDSEN, was used to simulate the seasonal snow cover evolution in the Berchtesgaden National Park, Bavaria, Germany. This area is characterized by significant elevation differences (ranging up to 2000 meters within a 3.5 km distance) and offers an ideal setting to examine how forest-snow interactions vary across complex mountain terrain. The model is forced with meteorological data collected from 20 automatic weather stations located in open areas and distributed across different elevations. Simulations were conducted at a spatial resolution of 50 x 50 meters. The temperature at 10 cm ground was measured by 150 temperature-moisture sensors positioned within the forest. These sensors are deployed across various elevations and forest densities. Using these measurements, snow cover duration and snow disappearance date were derived for forested plots and used to evaluate the simulated snow cover. The results indicate that observed and simulated snow metrics generally show consistent patterns within the forested regions of the study area, though some deviations were observed at specific locations. The presented investigations contribute to a more detailed understanding of forest-snow interactions in mountainous environments.
How to cite: Storebakken, B., Rottler, E., Warscher, M., and Strasser, U.: Evaluation of simulated snow inside forests using measured ground temperature, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8377, https://doi.org/10.5194/egusphere-egu25-8377, 2025.
Dataset of snowpack properties combined with atmospheric forcing are necessary to evaluate snow models. Here, we followed the evolution of the snowpack at Col de Porte, a regular snow observation site located near Grenoble at 1350 m, with detailed measurements of the snow microstructure and related properties. The goals were 1/ to test the feasibility of using X-ray tomography for regular snowpack monitoring, 2/ to carry out an inter-comparison of different instruments for density and specific surface area (SSA) measurements, and 3/ to provide new dataset of snow properties including snow microstructure and meteorological forcing for model driving and evaluation for a low-elevation alpine environment. Over the winters 2021-2022 and 2022-2023, the standard observation program was complemented by SnowMicroPen measurements, SSA measurements with two optical instruments (DUFISSS and HISSGraS), and 3D imaging using a cold laboratory X-ray tomograph located next to the snow field. Measurements were performed weekly to bi-weekly. For tomography, snow were collected in cylinders of 4 cm diameter and 15 cm height. The scans were performed at two resolutions: 10 microns (50 min scan per cm) and 42 microns (3 min scan per cm). We present the evolution of the snowpack in relation to the weather conditions. Snow heights were well below average for the second winter, with several total snowpack disappearances, from mid-February on. Both winters showed regular rain-on-snow and melt events throughout the winter, offering suited data to evaluate wet snow and liquid water flow in models, especially. An inter-comparison of density and SSA estimates from tomography, SnowMicroPen and optical instruments is provided. Finally, we present a preliminary comparison of the snowpack evolution between measurements and the snowpack model Crocus.
How to cite: Calonne, N., Hagenmuller, P., Granger, R., Bouvet, L., Fourteau, K., Brondex, J., Tuzet, F., Lejeune, Y., Dufour, A., Fructus, M., and Dumont, M.: Intensive field campaign on snow microstructure evolution at a low-elevation alpine site, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10482, https://doi.org/10.5194/egusphere-egu25-10482, 2025.
Sophisticated snowpack models are required to provide accurate estimation of snowpack properties across the forest-tundra ecotone where in situ measurements are rare. As snowpack properties strongly influence radar scattering signals, accurate simulation is crucial for the success of spaceborne radar missions to retrieve snow water equivalent (SWE). In this study, we evaluate the ability of default and Arctic Crocus embedded within the Soil, Vegetation and Snow version 2 (SVS2-Crocus) land surface model to simulate snowpack properties (e.g. depth, density, SWE, specific surface area) across a 40-km transect of the Northwest Territories, Canada, using two winter seasons (2021-22 & 2022-23) of in situ measurements. An ensemble of simulated snowpack properties (120 members from default and Arctic SVS2-Crocus) were used in the Snow Microwave Radiative Transfer (SMRT) model to simulate Ku-band (13.5 GHz) backscatter. SMRT backscatter using multi-layer SVS2-Crocus snowpack simulations were compared to backscatter using a simplified 3-layer radar-equivalent snowpack. Results highlight that Arctic SVS2-Crocus wind-induced compaction modifications were spatially transferable across the forest-tundra ecotone and lead to an improvement in the simulation of surface snow density at all sites, reducing the RMSE of surface density by an average of 29%. The parameterisation of below-canopy wind speed limits the ability of SVS2-Crocus to increase surface density to match measurements, despite the inclusion of Arctic modifications and should be revised for sparse (e.g. canopy densities < 15 %) canopy environments. Basal vegetation modifications were less effective in simulating low-density basal snow layers at all sites (default RMSE: 67 kg m-3; Arctic RMSE: 69 kg m-3) but were necessary to simulate a physically representative Arctic density profile. SVS2-Crocus underestimated snow specific surface area (SSA) leading to high errors in the simulation of snow backscatter (default RMSE: 3.5 dB; Arctic RMSE: 5.3 dB). RMSE of backscatter was reduced by implementing a minimum SSA value (8.7 m2 kg-1; default RMSE: 1.4 dB; Arctic RMSE: 1.3 dB) or by scaling the scattering effects of the snowpack (polydispersity: 0.63; default RMSE: 1.6 dB; Arctic RMSE: 2.6 dB). Utilising a radar-equivalent snowpack was effective in retaining the scattering behaviour of the multi-layer snowpack (RMSE < 1 dB) providing a means to monitor SWE with reduced computational complexity.
How to cite: Woolley, G., Rutter, N., Wake, L., Vionnet, V., Derksen, C., Meloche, J., Montpetit, B., Hould Gosselin, G., Essery, R., and Marsh, P.: Simulating snow properties and Ku-band backscatter across the forest-tundra ecotone, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10619, https://doi.org/10.5194/egusphere-egu25-10619, 2025.
Precipitation and snowmelt from the Andes Cordillera are vital water resources for downstream communities and ecosystems, particularly in Central Chile, where agricultural water demands peak during hot, dry summers—out of sync with the winter precipitation regime. The snowpack serves as a natural reservoir, delaying water release; however, warmer temperatures are shifting precipitation patterns from snow to rain and accelerating snowmelt, potentially undermining the snowpack’snatural storage capacity. Understanding the vulnerability of this natural reservoir to climate warming is critical. In this study, we employ the Weather Research and Forecasting (WRF) model, configured for convective-permitting simulations over South America (WRF-SAAG), to analyze snowpack dynamics under current and future climate conditions. We simulate a moderate-to-high socioeconomic scenario (SSP3.7.0) over a 22-year period and compare model outputs with observations from high-elevation hydrometeorological stations in Chile and Argentina. Results show reasonable agreement in snow water equivalent (SWE) timing and magnitude, though mean monthly precipitation is overestimated by ~20%. We calculate the Snow Storage Index (Hale et al., 2023) for both historical (2000–2021) and future (2060–2080) periods, assessing its temporal and spatial variability at both grid (4 km) and catchment scales. We also analyze key snowpack characteristics, including peak SWE, duration, and melt rates, highlighting projected reductions in natural storage capacity across the Southern Andes. This research enhances our understanding of snow dynamics in a region with complex topography and varying climatic conditions. Findings are crucial for policymakers and water managers, providing essential insights for developing climate adaptation strategies in the Southern Andes foothills, an area of growing societal importance yet relatively understudied.
How to cite: Scaff, L. and Krogh, S.: Quantifying the vulnerability of the natural storage capacity of the Andes Cordillera snowpack using a 4-km convection-permitting regional climate model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14003, https://doi.org/10.5194/egusphere-egu25-14003, 2025.
In snow-dominated regions, today’s snow is tomorrow’s water, making reliable estimates of snow water equivalent (SWE) and snow depth crucial for water resource management. In this context, data assimilation is a powerful tool to optimally combine models and measurements, enhancing accuracy and reliability. Ensemble-based techniques like the Ensemble Kalman Filter (EnKF) and Particle Filter (PF) are often used but their deployment in real-time applications can make it challenging to ensure timely and accurate results. To address these challenges, we propose an innovative data assimilation framework for snow hydrology that leverages Long Short-Term Memory (LSTM) networks. Using data from seven diverse study sites across the Northern Hemisphere, our framework is trained on the outputs of an EnKF, persuing a balance between computational efficiency and model complexity to advance data assimilation applications in snow hydrology. This LSTM-based framework achieves performance comparable to the EnKF in improving open-loop estimates, with only minor increases in root-mean-square error (RMSE): +6 mm for SWE and +6 cm for snow depth on average. Adding a memory component enhances stability and accuracy, especially under sparse data conditions. When trained on long-term datasets spanning 25 years, the LSTM framework demonstrated promising spatial transferability, with accuracy reductions of less than 20% for snow water equivalent and snow depth estimation. After training, the LSTM approach significantly outperformed a parallelized EnKF in computational efficiency, reducing runtime by 70% while maintaining comparable accuracy. Training on multi-site data further ensured robust performance across diverse climate regimes and during both dry and average water years, with a modest RMSE increase compared to the EnKF (+6 mm for SWE and +18 cm for snow depth). By combining the strengths of traditional ensemble methods and modern machine learning, this framework offers a scalable, computationally efficient, and reliable alternative for operational snow hydrology data assimilation.
How to cite: Blandini, G., Avanzi, F., Campo, L., Gabellani, S., Aalstad, K., Girotto, M., Yamaguchi, S., Hirashima, H., and Ferraris, L.: Learning to filter: Snow data assimilation using a Long Short-Term Memory network, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17427, https://doi.org/10.5194/egusphere-egu25-17427, 2025.
How to cite: Dadic, R., Martin, J., Pirazzini, R., Anderson, B., Schneebeli, M., Jaggi, M., Macfarlane, A., Lehning, M., Wever, N., and Heil, P.: Snow Microstructure over Antarctic Landfast Ice, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20052, https://doi.org/10.5194/egusphere-egu25-20052, 2025.
The IMAU Firn Densification Model (IMAU-FDM) is a 1D, semi-empirical model that simulates the evolution of snow grain size, firn density, firn air content, temperature, and liquid water content. It has been used primarily to investigate future surface changes over both Greenland and Antarctica, as well as for continent-wide estimates of mass change from satellite altimetry. Here, we will present a streamlined, updated IMAU-FDM with results for the Greenland Ice Sheet extended back to 1940 and through to 2023. IMAU-FDM is driven by ERA5, dynamically downscaled by the regional climate model RACMO 2.3p2 to 5.5 km^2 resolution. We will present timeseries of firn air content, liquid water content, and ice slab presence across the Greenland Ice Sheet, and initial results of future runs through 2100.
How to cite: Case, E., Kuipers-Munneke, P., Brils, M., van de Berg, W.-J., Tijm-Reijmer, C., and van den Broeke, M.: Firn densification across the Greenland Ice Sheet from the IMAU-FDM (1940-2023), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21697, https://doi.org/10.5194/egusphere-egu25-21697, 2025.
We investigated the effect of formed snowdrifts in advance on the turbulent flow and subsequent snowdrift distribution in a numerical simulation. We conducted an ideal numerical simulation for snowdrift distribution around three types of snow fences: two-dimensional fence, three-dimensional fence, and two-dimensional fence with a bottom gap. Snowdrifts resulting from an 8-hour drifting snow event were estimated by dynamically updating the bottom boundary conditions every 2 hours to reflect the developed snowdrift structures. Compared to simulation without boundary updates, snowdrift height on windward side of the two-dimensional fence was higher in the updated simulation. This increase was attributed to the weakened wind speed and modified snow particle trajectories around the previous snowdrifts. For the three-dimensional and bottom-gap fences, significant differences of snowdrift height were observed on the leeward areas between the updated and no-updated simulations. Snowdrifts on the leeward side of these fences were formed further downstream in the no-updated simulation. In contrast, the updated simulations generated snowdrifts closer to the fence on the leeward side. These findings suggested that neglecting the impact of the previous snowdrift structures in numerical simulation could lead to an overestimation of snowdrift development on the leeward side of obstacles.
How to cite: Tanji, S.: Estimating the effect of pre-existing snowdrift on turbulent airflow and subsequent snowdrift in the numerical simulation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14098, https://doi.org/10.5194/egusphere-egu25-14098, 2025.
