CR5.1

In the Northern Hemisphere, the seasonal snow cover plays a major role in the climate system via its effect on albedo and surface fluxes, influencing the variations in near surface temperature. Across climate models, the parameterization of the snow-albedo relationship remains a source of high uncertainty, often leading to large biases in the representation of local and global climate.

In this work, we analyze regional climate model outputs from the flagship pilot study (FPS) Land Use and Climate Across Scales (LUCAS) of the European branch of the Coordinated Downscaling Experiments EURO-CORDEX. These experiments include land use change forcing to identify robust biophysical impacts of land use changes on climate across regional to local spatial scales and at various time scales from extreme events to multi-decadal trends.

Here, we evaluate the ability of this ensemble of regional climate models combined with different land surface models to capture the climate forcing from the snow albedo effect in Europe, by comparing their representation of the Snow Atmosphere Sensitivity Index (SASI) with reanalyses and satellite observations. A specific focus is given to three sub-regions: Scandinavia, East Baltic and East Europe. For all regions, during the accumulation period, the models tend to largely agree on the representation of SASI. However, during the ablation period, there are large disparities, which are related to differences in the representation of the snow cover fraction in the models. This suggests that the choice of the land model is more critical for the representation of the climate forcing from the snow albedo effect than the atmospheric model. These differences in SASI leads to discrepancies in the simulated surface temperature. 

How to cite: Daloz, A. S., Schwingshackl, C., Mooney, P., Strada, S., T. Lund, M., Breil, M., M. Cardoso, R., Davin, E., Hoffmann, P., Katragkou, E., C.A. Lima, D., Meier, R., de Noblet-Ducoudre, N., Rechid, D., M. M. Soares, P., Sofiadis, G., Strandberg, G., and H. Toelle, M.: Climate forcing due to the snow albedo effect in the regional climate models from the CORDEX Flagship Pilot study LUCAS., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2120, https://doi.org/10.5194/egusphere-egu21-2120, 2021.

The temporal evolution of the snowpack is controlled by the surface temperature, which plays a key role in physical processes such as snowmelt. It shows large spatial variations in mountainous areas, where the illumination conditions are variable and depend on the topography. The surface energy budget is affected by the particular processes that occur in these areas, such as the modulation of the illumination by local slope and the re-illumination of the surface from surrounding slopes. These topography effects are often neglected in models, considering the surface as flat and smooth. Here we aim at estimating the surface temperature and the radiation budget of snow-covered complex terrains, in order to evaluate the role of the different processes that control their spatial variations. For this, a modelling chain is implemented to derive surface temperature from in-situ measurements. The main component is the Rough Surface Ray-Tracing (RSRT) model, based on a photon transport algorithm to quantify the impact of surface roughness in snow-covered areas. It is coupled to a surface scheme in order to estimate the radiation budget. To validate the model, we use in-situ measurements and satellite thermal observations (TIRS sensor aboard Landsat-8) in the Col du Lautaret area, in the French Alps. The satellite images are corrected from atmospheric effects with a single-channel algorithm. The results of the simulations show (i) an agreement between the simulated and observed surface temperature for a diurnal cycle in winter; (ii) the spatial variations of surface temperature are on the order of 5 to 10°C between opposed slope orientations; (iii) the agreement with satellite observations is improved when considering topography effects. It is therefore necessary to account for these effects to estimate the spatial variations of the radiation budget and surface temperature over snow-covered complex terrain. 

How to cite: Robledano, A., Picard, G., Arnaud, L., Larue, F., and Ollivier, I.: Surface temperature and radiation budget of snow-covered complex terrains, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10029, https://doi.org/10.5194/egusphere-egu21-10029, 2021.

Proper modelling of heat and mass transfer in snow is a prerequisite for understanding snow metamorphism and simulating the mass and energy budget of a snowpack and the underlying ground. The transfer of heat and water vapor in snow can be described with macroscopic conservation equations, which include effective coefficients such as the snow thermal conductivity or the snow water vapor diffusion coefficient. Here, we investigate the impact of the surface kinetics of water vapor sublimation and deposition at the microscopic scale on these macroscopic equations, restraining ourselves to the limiting cases of slow and fast kinetics. In particular, we show that under the assumption of fast kinetics the thermal behavior of snow is similar to that of a regular inert medium, but with an enhanced conduction in the pores, due to latent heat transported with water vapor. Besides, faster kinetics increases the effective water vapor diffusion coefficient, which nonetheless remains less than that in free air. Most (but not all) available experimental investigations suggest that in snow, fast surface kinetics prevails, so that our results have numerous implications for the proper simulation of heat and mass transfer in snow.

How to cite: Fourteau, K., Domine, F., and Hagenmuller, P.: The Influence of Vapor Attachment Kinetics on Snow Effective Properties, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2193, https://doi.org/10.5194/egusphere-egu21-2193, 2021.

Seasonal snowpack significantly influences the catchment runoff and thus represents an important input for the hydrological cycle. Changes in the precipitation distribution and intensity, as well as a shift from snowfall to rain is expected in the future due to climate changes. As a result, rain-on-snow events, which are considered to be one of the main causes of floods in winter and spring, may occur more frequently. Heat from liquid precipitation constitutes one of the snowpack energy balance components. Consequently, snowmelt and runoff may be strongly affected by these temperature and precipitation changes.

The objective of this study is 1) to evaluate the frequency, inter-annual variability and extremity of rain-on-snow events in the past based on existing measurements together with an analysis of changes in the snowpack energy balance, and 2) to simulate the effect of predicted increase in air temperature on the occurrence of rain-on-snow events in the future. We selected 40 near-natural mountain catchments in Czechia with significant snow influence on runoff and with available long-time series (>35 years) of daily hydrological and meteorological variables. A semi-distributed conceptual model, HBV-light, was used to simulate the individual components of the water cycle at a catchment scale. The model was calibrated for each of study catchments by using 100 calibration trials which resulted in respective number of optimized parameter sets. The model performance was evaluated against observed runoff and snow water equivalent. Rain-on-snow events definition by threshold values for air temperature, snow depth, rain intensity and snow water equivalent decrease allowed us to analyze inter-annual variations and trends in rain-on-snow events during the study period 1965-2019 and to explain the role of different catchment attributes.

The preliminary results show that a significant change of rain-on-snow events related to increasing air temperature is not clearly evident. Since both air temperature and elevation seem to be an important rain-on-snow drivers, there is an increasing rain-on-snow events occurrence during winter season due to a decrease in snowfall fraction. In contrast, a decrease in total number of events was observed due to the shortening of the period with existing snow cover on the ground. Modelling approach also opened further questions related to model structure and parameterization, specifically how individual model procedures and parameters represent the real natural processes. To understand potential model artefacts might be important when using HBV or similar bucket-type models for impact studies, such as modelling the impact of climate change on catchment runoff.

How to cite: Hotovy, O. and Jenicek, M.: Snowpack energy balance and changes in the frequency and extremity of rain-on-snow events in the warming climate, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-73, https://doi.org/10.5194/egusphere-egu21-73, 2021.

Mountainous catchments play an important role in water regulation as they store water along winter time and release it when snow melts several months after precipitations. Mid-altitude catchments are more prone to respond to climate changes as snow cover dynamics is directly impacted by temperature changes. Along with large scale precipitation events snow cover dynamics is also driven by small scale terrain characteristics which impact deposition, wind transport, melting through distributed solar insolation along slopes. These highly characteristic patterns impact snow cover dynamics significantly and the hydrological response even in small catchments.

This study focus on a small mid altitude alpine catchment at Col du Lautaret (France) which is a 15.28 ha subalpine catchment with the elevation range between 2000-2200 meter, typically 5-6 months period of full snow coverage over a grass dominated vegetation. Over this catchment, we simulated the impact of small scale snow spatial variability on the water cycle with the surface-subsurface coupled hyper-resolution distributed hydrological model ParFLOW/CLM. It consisted in several meteorological forcing scenarios prescribed to the model including distributed (2D) or non-distributed (1D) precipitation, solar radiation and wind. The model is able to simulate the snow cover distribution through the CLM energy balance module according to a combination of these forcings. The water transfers are then calculated through the Richards and kinematic wave equations following the ParFLOW formulation.

2D forcings induce a more spatially heterogeneous snowpack, which becomes patchy at the melt season. This asynchronous melt results in a longer melt period and a smoother hydrological response. However, 1D forcings do not generate such patchiness. Amongst the mechanisms responsible for the 2D distribution of the forcings, precipitation redistribution is the most important. Solar insolation distribution ads to the differential melting and wind distribution is not very important as a primary agent on the surface energy budget, but is important as it impacts precipitation redistribution in the watershed, which we treated separately.

How to cite: Gupta, A., Voisin, D., and Cohard, J.-M.: Sensitivity of snow cover spatio-temporal dynamics to the spatial distribution of meteorological forcings in a mid altitude alpine catchment: model analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16320, https://doi.org/10.5194/egusphere-egu21-16320, 2021.

The simulation of firn densification, although first models were already developed in the 1960s, is still a work in progress. Various models and variants of earlier models developed throughout the decades testify for this (e.g. Lundin et al. 2017, Stevens et al, 2020). Here we focus on the first stage of firn densification up to the density of 550 kg m⁻³, hence the first few meters of the firn column. Describing the early stage of the process well is crucial as it proceeds fastest and influences further densification. Alley first applied the process of grain boundary sliding to firn in 1987 and thereby provided a physics based material model for the densification of firn at low densities. Despite being used in many firn densification models, it is sometimes debated if grain boundary sliding is governing the densification at low densities as there are very few observations of intra-crystalline deformation in firn.

We aim to test to which extent grain boundary sliding can be used to reproduce measured firn density profiles and to constrain the parameter range in the constitutive relation. To this end, we conduct a high number of simulations for various locations, stepping through the parameter space and select the best match with corresponding measured density profiles. By doing so, we are following Alley’s original approach, but we make use of a much larger firn density dataset provided by the SUMup working group (Koenig & Montgomery, 2020).

Forcing data provided by the regional climate model RACMO (van Wessem et al., 2014, Noël et al., 2015) allows not only to simulate steady state solutions but transient simulations. Our model implementation provides a very fast, complete and flexible simulation environment, allowing to test wide parameter ranges in short time and hence enables us to cover a great amount of firn properties. The broad testing approach allows to evaluate if and in which ways grain boundary sliding might play a role in firn densification at low densities.

How to cite: Schultz, T., Humbert, A., Müller, R., and Gross, D.: How Well Does Grain Boundary Sliding Represent Densification Rates in the Upper Firn?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2966, https://doi.org/10.5194/egusphere-egu21-2966, 2021.

The site level component of the Earth System Model – Snow Model Intercomparison Project has 28 participating model variants. We summarize model performance at the Boreal Ecosystem Research and Monitoring Sites (BERMS) Old Aspen (OAS), Old Black Spruce (OBS) and Old Jack Pine (OJP) forests in Saskatchewan.

Many CMIP5 models have been previously shown to overestimate the winter albedo in the boreal forest due to errors in plant functional type (PFT) and leaf area index (LAI). In this project provided values for PFT and LAI were not implemented in a few models, but many models show a positive albedo bias in excess of 0.1 and some show a much larger positive bias. A larger positive albedo bias at OAS by some models suggests that snow masking by leafless trees requires attention. Average albedo bias from these off-line simulations, which lack atmospheric feedbacks, is not strongly related to bias in snowpack properties or the treatment or lack thereof of intercepted snow.

About half the models simulated snow water equivalent (SWE) with a RMSE smaller than the standard deviation of the observations. Snow depth was simulated slightly worse and only three models met this standard with respect to snowpack density. SWE was underestimated by just over half the models but the density of these sheltered snowpacks was overestimated by most models, resulting in snowpack depth being underestimated by an average 0.1 m. Models with multiple simplified surface parameterizations tend to show the greatest underestimation of SWE and depth and overestimation of density.

Biases in above-canopy radiative, snow surface and bulk snowpack temperatures are not consistent with respect to size and sign; many models show a combination of positive and negative biases. Radiative and snowpack surface temperatures are associated with trends in turbulent heat fluxes. Models with multiple simplified surface parameterizations (e.g. large or fixed density or thermal conductivity values, a composite snowpack, no organic soil) show more negative soil temperature biases and appear to be associated with a colder snowpack, but unfortunately, bulk snowpack temperature was not reported for many such models. Negative SWE and depth biases are associated with colder winter soil temperatures and shorter snow seasons. Most models simulate snow thermal conductivity with one of many relationships with density. Soil temperature bias is highly sensitive to the choice of snow thermal conductivity parameterization.

Models with many snow layers tend to show smaller errors in snowpack properties and are less likely to show cold biases in the snowpack and soil compared with composite or single layer models. However, as found in previous SnowMIPs, some single-layer models occupy the same bias range as multi-layer models. Models employing a multi-layer snowpack tend not to employ multiple “simplified parameterizations” as described above whereas the models with a single snow layer employ surface parameterizations with a range of sophistication.

How to cite: Bartlett, P., Wang, L., Derksen, C., Essery, R., Menard, C., and Krinner, G. and the ESM-SnowMIP Site Level Modelling Groups: Factors Influencing Snow Model Performance in Boreal Forests - Results from the ESM-SnowMIP Forest Site Simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-949, https://doi.org/10.5194/egusphere-egu21-949, 2021.

Terrestrial laser scanners (TLSs) are increasingly used to monitor glaciers. Recent developments enabled longer ranged and more frequent measurements. The quality of these high resolution topography data, especially in high mountain environments, has not been assessed in detail up to now.  An automated and permanent long-range TLS system is installed at Hintereisferner glacier (Ötztal, Austria) with the aim to detect changing snow surface patterns due to wind drift.

The scanner is controlled from Innsbruck and data is transferred daily. The system covers 66.5% of the glacier area and scans can be conducted on demand in very high frequency (e.g. daily or hourly measurements). The measurement distances range between 660 and 4600 m and with an angular step width of 0.01° (vertical and horizontal), this leads to a point spacing of 35 cm at a distance of 2 km and a resulting point cloud of approx. 43 million points. The point cloud is converted into grids with a 1 meter resolution.

Two main error sources of the system are indentified. First, the TLS used at Hintereisferner, a Riegl VZ-6000 is influenced by movements that cannot be corrected with the internal inclination sensors of the scanner. Small fluctuations in the roll and pitch of the scanner (ca. ±0.02°) result in deviations in decimetre range on the glacier. The movement of the scanner increases with increasing turbulent kinetic energy (TKE) measured with a nearby 3D sonic anemometer.

Second, atmospheric conditions at the glacier influence the laser beam way. The TLS operates by emitting light pulses and measuring the time of flight for the pulse to return. The pulse travel time changes depending on the atmospheric properties. The changes of pressure, temperature and humidity in the atmosphere differ from accumulation zone to glacier tongue and influence the pathway between TLS and the glacier surface, leading to an uncertainty in the scanning data in centimetre range.

The permanent and automated long-range TLS system promises high potential for the glaciological and environmental sciences, given the decimetre accuracy at a high spatiotemporal resolution. The first results of permanent TLS system at Hintereisferner show the ability to detect changing snow surface patterns and indicate the possibility of geodetic glacier mass balance acquisition.

How to cite: Voordendag, A., Klug, C., Prinz, R., Rutzinger, M., and Kaser, G. and the SCHISM Team: Uncertainty assessment of a permanent long-range terrestrial laser scanning system at an Alpine glacier, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4649, https://doi.org/10.5194/egusphere-egu21-4649, 2021.

            Snow deposition patterns in complex terrain are heavily dependent on the underlying topography. This topography affects precipitating clouds at the kilometer-scale and causes changes to the wind field at the sub-kilometer scale, resulting in altered advection of falling hydrometeors. Snow particles are particularly sensitive to changes in the near-surface flow field due to their low density. Atmospheric models which run at the kilometer scale cannot resolve the actual heterogeneity of the underlying terrain, resulting in precipitation maps which do not capture terrain-affected precipitation patterns. Thus, snow-atmosphere interactions such as preferential deposition are often not resolved in precipitation data used as input to snow models. To bridge this spatial gap and resolve snow-atmosphere interactions at the sub-kilometer scale, we couple an intermediate complexity atmospheric model (ICAR) to the COSMO NWP model. Applying this model to sub-kilometer terrain (horizontal resolution of 50 and 250 m) required changes to ICAR’s computational grid, atmospheric dynamics, and boundary layer flow. As a result, the near-surface flow now accounts for surface roughness and topographically induced speed up. This has been achieved by using terrain descriptors calculated once at initialization which consider a point’s exposure or sheltering relative to surrounding terrain. In particular, the use of a 3-dimensional Sx parameter allows us to simulate areas of stagnation and recirculation on the lee of terrain features. Our approach maintains the accurate large-scale precipitation patterns from COSMO but resolves the dynamics induced by terrain at the sub-kilometer scale without adding additional computational burden. We find that solid precipitation patterns at the ridge scale, such as preferential deposition of snow, are better resolved in the high-resolution version of ICAR than the current ICAR or COSMO models. This updated version of ICAR presents a new tool to dynamically downscale NWP output for snow models and enables future studies of snow-atmosphere interactions at domain scales of 100’s of kilometers.

How to cite: Reynolds, D., Kruyt, B., Gutmann, E., Jonas, T., Lehning, M., and Mott, R.: Improvements to an intermediate complexity atmospheric model for high-resolution downscaling in very complex terrain., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12662, https://doi.org/10.5194/egusphere-egu21-12662, 2021.

Snow on Arctic sea ice plays many, sometimes contrasting roles in Arctic climate feedbacks. During the sea ice growth season, the presence of snow on sea ice can enhance ice growth by increasing the sea ice albedo, or conversely, inhibit sea ice growth by insulating the ice from the cold atmosphere. Furthermore, estimates of snow depth on Arctic sea ice are also a key input for deriving sea ice thickness from altimetry measurements, such as satellite lidar altimetry measurements from ICESat-2. Due to the logistical challenges of making measurements in as remote a region as the Arctic, snow depth on Arctic sea ice is difficult to observationally constrain.

The NASA Eulerian Snow On Sea Ice Model (NESOSIM) can be used to provide snow depth and density estimates over Arctic sea ice with pan-Arctic coverage within a relatively simple framework. The latest version of NESOSIM, version 1.1, is a 2-layer model with simple representations of the processes of accumulation, wind packing, loss due to blowing snow, and redistribution due to sea ice motion. Relative to version 1.0, NESOSIM 1.1 features an extended model domain, and reanalysis snowfall input scaled to observed snowfall retrieved from CloudSat satellite radar reflectivity measurements.

In this work, we present a systematic calibration, and an accompanying estimate in the uncertainty of the free parameters in NESOSIM, targeting airborne snow radar measurements from Operation IceBridge. We further investigate uncertainties in snow depth and the resulting uncertainties in derived sea ice thickness from ICESat-2 altimetry measurements using NESOSIM snow depths.

How to cite: Cabaj, A., Kushner, P., and Petty, A.: Parameter calibration and uncertainty analysis for snow depths from the NASA Eulerian Snow On Sea Ice Model and derived sea ice thickness from ICESat-2, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13791, https://doi.org/10.5194/egusphere-egu21-13791, 2021.

Boreal forests occupy a large fraction of the continental surfaces and receive a lot of solid precipitation in winter. Evergreen canopies are often represented as a single and homogeneous layer in hydrological and weather forecasting models. However, in reality, boreal canopies are composed of a rather complex mosaic of trees unevenly spaced apart, with gaps of various sizes. Therefore, mass and energy inputs to the snowpack show remarkable variability at small scales resulting not only in strong spatial heterogeneity in snow depth (SD) and snow water equivalent (SWE), but also in the vertical temperature gradient in the snow column (). Unlike SD and SWE, has been little documented in discontinuous needleleaf forests, despite its impact on snow cover metamorphism and on a range of physical properties of snow such as density (), specific surface area (SSA) and effective thermal conductivity (k_eff). This work investigates the snowpack underneath the canopy and inside small forest gaps using continuous measurements of SD and k_eff and weekly snow pit surveys during winter 2018-19 in a juvenile balsam fir stand of eastern Canada (47°17’18’’N, 71°10’05’’W). This site receives an average of almost 1600 mm of precipitation annually, including 40 % falling as snow. Snow cover typically lasts over 6 months. Observations show that less snow accumulates in the subcanopy and therefore  is more pronounced than inside the gaps. Moreover,  and SSA are lower underneath the canopy where faceted crystals are observed. Large  in that environment results in a decreasing k_eff over time. Overall, kinetic grain growth takes place in the subcanopy whereas settlement and isothermal conditions prevail inside the gaps. This research provides accurate observations of the snowpack in forested environments needed for a better representation of SWE, heat fluxes and ground thermal regime in hydrological and meteorological models.

How to cite: Bouchard, B., F. Nadeau, D., and Domine, F.: A Detailed and in Situ Assessment of the Snowpack Physical Properties in a Discontinuous Humid Boreal Forest, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3539, https://doi.org/10.5194/egusphere-egu21-3539, 2021.

Snow depth estimates remain a large uncertainty for constraining the accuracy of sea ice thickness retrievals from polar altimetry. There have been several recent investigations into methods for estimating snow depth from airborne observations over sea ice; this poster outlines a comparison between two different methods applied to Operation IceBridge data from the Spring 2016 campaign. The first co-locates visible-band laser scanner data from the Airborne Topographic Mapper with Ku-band data from the CReSIS radar, using a fixed threshold first-maximum retracker algorithm for retracking radar waveforms and applying a calibration step to remove the vertical offset between sensors at leads. This method represents an airborne proxy for the newly-aligned ICESat-2 and CryoSat-2 orbits of the Cryo2Ice campaign. The second method uses the conventional CReSIS ultrawide-band frequency‐modulated continuous‐wave ‘snow radar’ system, that ranges between S- and C-band, applying the retracker algorithm described by Newman et al 2014. We evaluate properties of the estimated snow depth distribution, and alignment of air-snow and snow-ice interfaces, along the aircraft track and the scale of correlation between sensors.

How to cite: de Rijke-Thomas, C., Landy, J., King, J., and Tsamados, M.: A comparison between coincident laser and Ku radar versus S- to C-band 'snow radar' data for airborne retrievals of snow depth on sea ice, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15636, https://doi.org/10.5194/egusphere-egu21-15636, 2021.

The Rofental is a high Alpine environmental research basin in the Ötztal Alps (Austria, 1890 - 3770 m a.s.l.). The existing measurement network has recently been extended by new stations and sensors that focus on automated recordings of snow cover properties. Core of the network are three automatic weather stations (AWS) that incorporate 10 min. recordings of snow depth (SD), snow water equivalent (SWE), layered snow temperatures, snow surface temperature, snow density, as well as solid and liquid water content of the snowpack. One AWS is extended by a particular setup of two SD and SWE measurements at nearby wind-exposed and sheltered locations, complemented by an acoustic-based snow drift sensor to quantify wind-driven snow redistribution.

We here present analyses of the publicly available data that focus on snow drift events in an avalanche-prone winter season. The two nearby SWE measurements show differences of around 500% of measured peak SWE at a horizontal distance of only 25 m caused by wind-driven redistribution. In addition, the presented data is used to develop and validate the new open source, distributed snow cover model openAMUNDSEN. We evaluate different integrated energy balance and snow layer schemes and compare the data to results of the ESM-SnowMIP project.

How to cite: Warscher, M., Hanzer, F., Becker, C., and Strasser, U.: Monitoring snow processes in the Ötztal Alps (Austria) and development of an open source snow model framework, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9101, https://doi.org/10.5194/egusphere-egu21-9101, 2021.

The Factorial Snowpack Model (FSM, Essery, 2015) has been applied for the winters ranging from 2008 to 2021 to predict snow height in a location at 1800 m of altitude in Peñalara Massif (Sierra de Guadarrama, Central Spain). Data from an automatic meteorological station is used as input after a thorough validation and completion using different methods. Several configurations of the model have been tested and sensitivity runs regarding long-wave and short-wave radiative flux, air temperature, liquid and solid precipitation rate, surface pressure, relative humidity and wind velocity, have been performed. Comparison of predictions versus automatic and manual in-situ measurements show a coherent evolution of the snow height. A satisfactory degree of precision regarding the beginning and end of the snow cover has been found but also a high sensitivity to radiative flux, mainly long-wave, air temperature and total solid precipitation rates that need further research. Future work will be carried out testing other snowpack models, developing new parametrizations and performing predictions for the  whole basin considering side effects and other factors.

How to cite: González-Cervera, Á. and Durán, L.: Point simulation of snow cover in Peñalara Massif (Sierra de Guadarrama, Central Spain), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4896, https://doi.org/10.5194/egusphere-egu21-4896, 2021.

Vapor fluxes in snow are often inferred from the temperature field by assuming vapor concentrations in local thermodynamic equilibrium with the temperature. Here we give evidence that, at the pore scale, this picture is in clear contradiction with the observed evolution of the specific surface area (SSA) under temperature gradient metamorphism. To this end, we have calculated pore-scale temperature fields using the Finite Element Method on micro-tomography images. Subsequently, we utilized the exact volume-averaged evolution equation for the SSA to infer that the disagreement stems from the employed diffusion-limited growth law which manifests in local thermodynamic equilibrium of vapor and temperature. Via sensitivity studies we confirm that this conclusion is not affected by the involved image analysis and numerical procedures. We outline how and why attachment kinetics may resolve the observed contradiction.

How to cite: Karpova, A., Lehning, M., and Löwe, H.: On the necessity of including vapor kinetics to model the specific surface area evolution in snow, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12581, https://doi.org/10.5194/egusphere-egu21-12581, 2021.

Most classical firn densification models merely consider temperature and accumulation rate as variable input parameters. However, in locations with high horizontal strain rates, such as the shear margins of ice streams, a reduced firn thickness can be observed. This is explained by an enhancement of power-law creep due to the effect of strain softening, which is not yet captured by existing firn models. We present a model extension that corrects the densification rate, predicted by any classical, climate-forced firn model, for the effect of strain softening caused by horizontal strain rates. With the presented model firn densities measured along a cross-section of the North-East Greenland ice stream (NEGIS) are reproduced with good agreement, validating the accuracy of the developed model. The results further indicate the general importance of considering strain rates in firn densification modeling and pave the way for the development of a firn model that inherently uses temperature, accumulation rate and horizontal strain rates as forcing parameters.

How to cite: Oraschewski, F. and Grinsted, A.: How to model enhanced firn densification due to strain softening, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9427, https://doi.org/10.5194/egusphere-egu21-9427, 2021.

In mountainous terrain, the spatial and temporal variability of the snow cover is driven by the interaction of meteorological processes with the underlying topography. Typically, terrain-precipitation-wind interactions predominantly shape the spatial snow depth distribution during the accumulation season through drifting snow and preferential deposition of snowfall. While a suspension model forced with fine-scale three-dimensional wind fields can generate spatial preferential deposition patterns, fine-scale three-dimensional wind fields or the necessary computational demands cannot be met by most model applications over larger areas.

We present an efficient statistical snowfall downscaling scheme over complex topography reproducing preferred fine-scale snowfall deposition patterns. Towards this we generated several thousands of spatial new snow distributions on artificial topographies by modeling preferential deposition with a suspension model and pre-computed Advanced Regional Prediction System (ARPS) wind fields. To systematically analyze spatial preferential deposition patterns, we chose artificial topographies covering a broad range of real terrain characteristics as well as controlled conditions for the model runs. We developed two statistical downscaling schemes using several millions of distributed fine-scale snowfall values. With one parameterization, we scale coarse-scale snowfall with fine-scale surface vertical wind components and topographic parameters. If fine-scale vertical wind components are not available, a second parameterization can be used to scale coarse-scale snowfall with coarse-scale wind direction and fine-scale topographic parameters. The spatial patterns of preferential snowfall deposition were well reproduced by the parameterizations, indicating that the downscaling scheme can be used for various model applications such as hydrological, avalanche, weather, and climate forecasts or hazard mapping.

How to cite: Helbig, N., Mott, R., Bühler, Y., Lehning, M., and Bartelt, P.: A snowfall downscaling scheme for mountainous terrain, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13227, https://doi.org/10.5194/egusphere-egu21-13227, 2021.

The European Space Agency Snow CCI+ project provides global homogenized long time series of daily snow extent and snow water equivalent (SWE). The Snow CCI SWE product is built on the Finish Meteorological Institute's GlobSnow algorithm, which combines passive microwave data with in situ snow depth information to estimate SWE. The CCI SWE product improves upon previous versions of GlobSnow through targeted changes to the spatial resolution, ancillary data, and snow density parameterization.

Previous GlobSnow SWE products used a constant snow density of 0.24 kg m^-3 to convert snow depth to SWE. The CCI SWE product applies spatially and temporally varying density fields, derived by krigging in situ snow density information from historical snow transects to correct biases in estimated SWE. Grid spacing was improved from 25 km to 12.5 km by applying an enhanced spatial resolution microwave brightness temperature dataset. We assess step-wise how each of these targeted changes acts to improve or worsen the product by evaluating with snow transect measurements and comparing hemispheric snow mass and trend differences.

Together, when compared to GlobSnow v3, these changes improved RMSE by ~5 cm and correlation by ~0.1 against a suite of snow transect measurements from Canada, Finland, and Russia. Although the hemispheric snow mass anomalies of CCI SWE and GlobSnow v3 are similar, there are sizeable differences in the climatological SWE, most notably a one month delay in the timing of peak SWE and lower SWE during the accumulation season. These shifts were expected because the variable snow density is lower than the former fixed value of 0.24 kg m^-3 early in the snow season, but then increases over the course of the snow season. We also examine intermediate products to determine the relative improvements attributable solely to the increased spatial resolution versus changes due to the snow density parameterizations. Such systematic evaluations are critical to directing future product development.

How to cite: Mortimer, C., Mudryk, L., Derksen, C., Luojus, K., Venalainen, P., and Moisander, M.: Improved Northern Hemisphere Snow Water Equivalent product from passive microwave remote sensing and in situ data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1325, https://doi.org/10.5194/egusphere-egu21-1325, 2021.

Effective elastic properties of snow, firn, and porous ice are key for
various applications and influenced by ice volume fraction and
different types of anisotropy. The geometrical anisotropy of the ice-matrix created by temperature gradient metamorphism in low-density
snow and firn and the crystallographic anisotropy commonly created
upon deformation in high-density, porous ice. Towards a quantitative-distinction of the impact of the different anisotropies on elasticity,
we derived a parametrization for the effective elasticity tensor over
the entire range of volume fractions as a function of density and
geometrical anisotropy. We employed FEM simulations on 395 X-ray
tomography microstructures of Lab, Alpine, Arctic, and Antarctic
samples. We employed an empirical two-parameter modification of the
anisotropic Hashin Shtrikman bounds to obtain a closed-form
parametrization accounting for density, anisotropy, and the correct
limiting behavior for bubbly ice. We compare our prediction to
previous parametrizations derived in limited density regimes and we
utilize the Thomson parameter to compare the geometrical-elastic
anisotropy to the crystallographic-elastic anisotropy of
monocrystalline ice. Our results suggest that a coupled treatment of
geometrical and crystallographic effects would be beneficial for a
careful interpretation of acoustic measurements in deep firn.

How to cite: sundu, K. and Loewe, H.: A microstructure-based parameterization of the effectivetransverse isotropic elasticity tensor of snow, firn, and porous ice, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2129, https://doi.org/10.5194/egusphere-egu21-2129, 2021.

In this submission we report on observations of the coherent backscatter opposition effect (CBOE) in seasonal snow layers using bistatic radar, and the possible pathways towards estimation of snow properties from these radar observations.

Bistatic radar refers to a configuration where the transmitter and the receiver are not in the same location. From the point of view of the observed target, there thus exists a non-zero angular separation between  directions towards the transmitter and towards the receiver, referred to as the bistatic angle. The coherent backscatter opposition effect (CBOE) is a phenomenon that causes increased backscatter of coherent radiation at small bistatic angles (less than 1 degree) in refractive but non-absorbing disordered media (e.g. snow). It has been previously investigated to characterize surfaces of various water-ice covered Solar System bodies [1], however it has received comparatively little attention in Earth-focused observations, despite the well-known occurrence of significant volume scattering within snow and ice.

Scattering models of CBOE relate the shape of the intensity peak (width, height) to specific parameters of the random medium (grain size, mean free path, reflectivity) [2]. Measurements of the CBOE peak profile are thus a possible pathway towards improving the accuracy of estimates of these parameters, and those closely connected to them, such as the snow water equivalent (SWE).

We report on two separate observations of the CBOE-intensity peak in snow. We carried out ground-based observations using an experimental bistatic Ku-band radar system KAPRI [3], to observe the effect in a winter snow layer on top of the peak Rinerhorn in Davos, Switzerland. We also report on observations of backscatter enhancement in the accumulation zone of Aletsch glacier, using the spaceborne bistatic X-band synthetic aperture radar system TanDEM-X. Applying the aforementioned scattering models to the observations, we can estimate the mean free path of the scattered signal within the snow layer to be 10 cm at Ku-band, and 17 cm at X-band.

We believe that further study of CBOE in the context of Earth-focused observations of snow and ice opens new opportunities for development of quantitative models aiming to derive snow properties from bistatic radar observations.

REFERENCES

[1] Black et al. 2001: Icy Galilean Satellites: Modeling Radar Reflectivities as a Coherent Backscatter Effect. Icarus, 151(2), 167–180.
[2] Hapke et al. 1998: The Opposition Effect of the Moon: Coherent Backscatter and Shadow Hiding. Icarus, 133(1), 89–97.
[3] Baffelli et al. 2017: Polarimetric Calibration of the Ku-Band Advanced Polarimetric Radar Interferometer. IEEE Transactions on Geoscience and Remote Sensing, 56(4), 2295–2311.

How to cite: Stefko, M., Leinss, S., and Hajnsek, I.: Bistatic radar observations of the coherent backscatter opposition effect in dry snow, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4563, https://doi.org/10.5194/egusphere-egu21-4563, 2021.

Seasonal snow contributes significantly to the annual runoff in the Himalaya and both timing and volume are important for downstream users.  In  polar regions, meltwater refreezing within snowpacks has been well-studied. While the conditions in the Himalaya are considered favorable for refreezing, little is known about refreezing in this region, hindering a complete understanding of seasonal snowmelt dynamics. In this study, we simulated refreezing with the seNorge (v2.0) snow model for the Langtang catchment in the Nepalese Himalaya covering a 5-year period. Thereby, we aim to improve our understanding about how refreezing varies in space and time and to provide a framework for future snow modeling studies. The first part of this study focuses extensively on developing meteorological forcing data, which were derived from an unique elaborate network of meteorological stations and high-resolution meteorological simulations. The snow model was validated against in-situ snow observations and snow cover satellite data. In the second part of this study, we analyze the spatial and temporal refreezing patterns, and attempt to identify possible driving factors. The results show that the annual average refreezing amounts to 122 mm (21% of the total melt). We found that the magnitude of refreezing varies strongly in space depending on elevation and aspect. In addition, there is a strong seasonal altitudinal variability related to air temperature and snow depth, with most refreezing during the early melt season. We also found a substantial intra-annual variability, which mainly results from fluctuations of snowfall, highlighting the importance of using multi-year time series in refreezing assessments. Daily refreezing simulations decreased by 84% (to an average 19 mm year^-1) compared to hourly simulations, emphasizing the importance of using sub-daily time steps to capture diurnal melt-refreeze cycles. Climate sensitivity experiments revealed that refreezing is highly sensitive to future changes in air temperature, as a temperature increase of 2°C leads to a refreezing decrease of 35%. We conclude that including refreezing with a sub-daily temporal resolution is highly relevant for understanding snow dynamics in the current and future climate of the Himalaya.

How to cite: Veldhuijsen, S., De Kok, R., Stigter, E., Steiner, J., Saloranta, T., and Immerzeel, W.: Spatial and temporal patterns of snowmelt refreezing in a Himalayan catchment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-387, https://doi.org/10.5194/egusphere-egu21-387, 2021.

In mountainous terrain, wind-driven transport of deposited snow affects the overall distribution of snow, and can have a significant effect on snowmelt patterns even at coarser resolution.  In an operational modelling perspective, a compromise must be found to represent this complex small-scale process with enough accuracy while mitigating the computational costs of snow cover simulations over large domains. To achieve this compromise, we implemented the SNOWTRAN-3D snow transport module within the FSM intermediate complexity snow cover model. We included a new layering scheme and a historical variable of past snow wetting, but without resolving the snow microstructure. Simulations are run and evaluated over a small mountain range in the Swiss Alps at 25 to 100 m resolution. Being implemented in the model framework of the SLF operational snow hydrology service (OSHD), simulations further benefit from snow data assimilation techniques to provide improved estimates of solid precipitation fields. As complex wind patterns in mountains are the key processes driving snow transport, we tested statistical and dynamical methods to downscale 1 km resolution COSMO winds to better reflect topographically-induced flow patterns. These simulations are a first step working towards the integration of wind transport processes over large domains in an intermediate-complexity and -resolution operational modelling framework.

How to cite: Quéno, L., Morin, P., Mott, R., and Jonas, T.: Local simulations of snow redistribution by wind with an intermediate-complexity snow cover model driven by different wind downscaling methods, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14629, https://doi.org/10.5194/egusphere-egu21-14629, 2021.