CR3.1

We investigate the use of Parabolic Equation (PE) simulations in modelling the propagation of radio waves in inhomogeneous ice environments, such as the firn layer of terrestrial glaciers in the Alps and Antarctic. In particular PEs allow for an accurate and efficient means to simulate pulsed radar on a multi-km scale for depth-dependent permittivity profiles, and inhomogeneities that are the targets of radar scans.

PEs are an approximate solution to Maxwell's equations which are valid within a cone that is perpendicular to the source, which defines the 'paraxial direction'. For monochromatic (single-frequency) radio emission, the electric field can be solved using a numerical step-wise solver, where the next range increment can be solved from the previous step. The emission profile of the source is used to define the starting condition. To solve in the time-domain for pulses, the pulse is decomposed into its Fourier spectrum, and the electric field throughout the geometry is solved for each frequency. By sampling the frequency dependent field amplitude at a given range and depth in the geometry, one can reconstruct the pulse and measure the time of flight. We implement a two-stage PE solver which first models propagation in the forwards direction from a transmitter, and then solves in the 'backwards' direction in order to calculate reflected signals. 

We present a Python based PE solver which simulates emission from a high frequency (300 MHz to 2000 MHz) radar transmitter into ice on a multi km-scale, using depth dependent permittivity profiles and a list of objects, such as boulders, crevasses and aquifers, which cause scattering. We find that we can accurately solve pulsed radar emission, and test target reconstruction techniques. We compare radar images for targets with constant permittivity and varying permittivity. We apply our simulation method to the firn layer of numerous real-world glaciers, with their permittivity estimated from density profiles, and observed birefringence and wave-guide-like behaviour between bands of solid ice, caused by melting and refreezing of the firn. 

Additionally we apply PE simulations to assess the viability of a future melting probe mission on Saturn's ice moon Enceladus, in which the melting probe would seek a near surface water pocket, which it localized with a combined orbital and surface radar scan.

How to cite: Kyriacou, A., Boccarella, G., Friend, P., and Helbing, K.: Simulating radio propagation in ice with Parabolic Equations: applications to terrestrial glaciers and ice moons, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-560, https://doi.org/10.5194/egusphere-egu22-560, 2022.

Processes controlling pore closure in deep polar firn are broadly understood, yet defining the physical mechanisms remains ambiguous. Firn densification models predict pore close-off depths which are subsequently used in firn air models to predict the gas-ages of entrapped air bubbles. However, current firn models require observational tuning which causes variable model performance for sites with different characteristics. Furthermore, layering in the deep firn, which is not simulated in many firn densification models, is expected to cause a large distribution of pore lock-in depths. Observations from numerous firn cores have identified neighbouring layers with different physical properties, such as density, grain size and impurities, which experience pore-closure at different depths. These properties are strongly influenced by 1) snow metamorphism due to temperature gradients within the snowpack, and 2) accumulation rate. The relative influence of each of these properties on pore closure remains in question.

Based on current understanding, we propose to quantify the changes in density and snow microstructural properties near the surface as a result of the interplay between accumulation rate and insolation using the Crocus snowpack model. To support the modelling effort, we have compiled δO₂/N₂ records - a proxy for local summer solstice insolation - from several polar ice cores. The relationship between insolation and δO₂/N₂ is understood to be linked to near-surface snow metamorphism, which largely determines the properties of deep-firn layers, and thus, the pore-closure process. By first identifying how insolation and accumulation rate influence the near-surface snow properties, we aim to implement this effect into firn models to develop our understanding of the physical mechanisms controlling pore-closure and the associated elemental fractionation.

How to cite: Harris Stuart, R., Landais, A., Martinerie, P., Dumont, M., Fructus, M., Orsi, A., Libois, Q., Arnaud, L., Stevens, C. M., Grisart, A., and Prié, F.: Investigating the influence of local insolation on near-surface snow grain properties to constrain the mechanisms of pore close-off and associated elemental fractionation in polar firn, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9092, https://doi.org/10.5194/egusphere-egu22-9092, 2022.

A new experiment has been conducted to characterize experimentally heat and mass transport as well as metamorphism evolution during temperature gradient conditions. To be able to appreciate fine scale as well as larger scale processes, the experimentation lasted 3 weeks with a strong gradient of 100 K m^-1 and a mean temperature of -9.5°C. The studied snow layer was 12 cm thick and had a horizontal surface of 0.5 m². Temporal monitoring of the snow was made through 17 micro-tomographies at specific spots with a resolution of 8 μm and 9 micro-tomographies on full vertical profiles at 21 μm. The layer was also instrumented using 10 temperature and humidity sensors and 7 precise temperature sensors recording at different locations during the whole process. This experiment showed precisely the development of facets and depth hoar in the snow matrix, and leads to interesting results concerning the evolution of heat and mass fields during a strong temperature gradient. The results of this experiment are finally compared to numerical results predicted by coupled heat and mass transport models such as the one of Calonne et al., 2015.

How to cite: Bouvet, L., Calonne, N., Geindreau, C., and Flin, F.: Experimental and numerical study of heat and mass transport in snow in case of strong temperature gradient conditions., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4509, https://doi.org/10.5194/egusphere-egu22-4509, 2022.

Thanks to its ease of use, the heated needle probe method is broadly employed to measure snow thermal conductivity, both in the field and in laboratory. However, recent studies have highlighted that when compared to other measurement techniques, the needle probe shows a systematic underestimation bias. Here, we examine the theory at the base of the needle probe method and show that for a light and insulating material such as snow, the standard measurement protocol using heating times around 100 s leads to underestimations, as observed. Moreover, the damage done to the snow microstructure when manually inserting the probe leads to a further underestimation, that can exceed 50%. Nonetheless, needle probes remain the only easily deployed technique to measure snow thermal conductivity in remote areas. We thus propose a new measurement protocol to correct this underestimation and to obtain reasonably reliable values of snow thermal conductivity.

How to cite: Fourteau, K., Hagenmuller, P., and Domine, F.: Why does the heated needle probe method underestimate snow thermal conductivity?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1145, https://doi.org/10.5194/egusphere-egu22-1145, 2022.

Snow on Arctic sea ice plays many roles in Arctic climate feedbacks; in particular, through its impact on sea ice. Snow can have many, sometimes contrasting effects on sea ice thickness and extent. For example, during the ice growth season, snow can inhibit ice growth by insulating the ice from the cold atmosphere. Conversely, snow can allow sea ice to persist longer during the melt season, due to its high albedo. Furthermore, estimates of snow depth on Arctic sea ice are a key input for deriving sea ice thickness from satellite lidar altimetry measurements, such as those 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 quantify.

To provide widespread estimates of the depth and density of snow on Arctic sea ice, models such as the NASA Eulerian Snow On Sea Ice Model (NESOSIM) can be used. The latest version of NESOSIM, version 1.1, is a 2-layer three-dimensional model with simple representations of snow accumulation, wind packing, loss due to blowing snow, and redistribution due to sea ice motion. Relative to version 1.0, among other changes, NESOSIM 1.1 features an extended model domain and reanalysis snowfall input from ERA5 scaled to observed snowfall derived from CloudSat satellite radar measurements.

The free parameters in NESOSIM, which dictate the strength of the wind packing (densification) and blowing snow loss processes, cannot be directly constrained to observations. We present an indirect calibration of these free parameters, by calibrating NESOSIM output to observations from airborne snow depth observations from Operation IceBridge and in situ CRREL-Dartmouth snow buoy measurements, as well as historical Soviet drifting station density measurements, using a Metropolis Markov Chain Monte Carlo (MCMC) approach. This approach produces estimates of the free parameters and their uncertainty distributions, from which model snow depth and density uncertainties can be estimated. We find that introducing stricter observational constraints in the calibration produces narrower snow depth uncertainty distributions from NESOSIM. We then examine the impact of these uncertainties on sea ice thickness derived using NESOSIM output and freeboard measurements from ICESat-2.

How to cite: Cabaj, A., Kushner, P., and Petty, A.: Observationally calibrating snow-on-sea-ice model free parameters and estimating uncertainties using a Markov Chain Monte Carlo method, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10534, https://doi.org/10.5194/egusphere-egu22-10534, 2022.

Snow water equivalent (SWE) is probably the most important snowpack property, but due to various reasons it has been measured by far less frequently and continuously than, e.g., snow depth (HS). Recent modelling efforts led to ΔSNOW, a semi-empirical approach to derive daily SWE exclusively from consecutive HS series.

The ΔSNOW model - freely available as part of the R-package “nixmass” - builds on basic snow physics and needs seven parameters. If available, those should be fitted using SWE measured at the respective HS observation site(s), otherwise a standard set of parameters is provided, which was calibrated with data from the Alps. In the current model version, the parameters are kept unchanged over time. New-snow density and the maximum density model layers can reach are among the parameters. For natural snow, those vary significantly from day to day and during the winter season, also site specifics like, e.g., altitude influence them.

With this contribution the restriction of fixed density parameters in the ΔSNOW model is probed. Improvements might be achieved if new-snow density was made dependent on the amount of freshly fallen snow, rather than on altitude, date or region. The model-intrinsic maximum snow density could probably be improved if it was increased by the age of the respective snow layers as well as the overburden mass of snow. All validation experiments were performed with SWE and HS data from the Alps and from Germany. The latter comprising a huge high quality data set not only from mountainous regions but also from lowlands and maritime regions. The ΔSNOW model`s ability to simulate daily SWE outperforms other models of comparable complexity also in these areas, even more with adjusted density parameters.

How to cite: Winkler, M. and Schellander, H.: Snow water equivalents from snow depths: improvements of the DeltaSNOW model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2326, https://doi.org/10.5194/egusphere-egu22-2326, 2022.

The spatial and temporal snow mass variation is clearly indicated as knowledge gap by the “IPCC Special Report on the Ocean and Cryosphere in a Changing Climate”, thus impeding current efforts to quantify historic and future trends. This is especially true for the Alps, which are very rich in snow depth (HS) records (both in number and length), but notoriously lack the same for snow water equivalent (SWE) observations.

The ∆SNOW model - freely available as part of the R-package “nixmass” - improved by temporally varying density parameters (cf. EGU22-2326), is used to estimate SWE at more than 2000 stations with continuous, daily HS records in and around the Alps from flatlands to very high Alpine regions with 130 stations above 2000 m.

In this contribution first results of a trend analysis of historical SWE observations of this very large number of stations is presented. The very high station density and large elevation range covered opens the opportunity for a climatologically detailed and elevation dependent analysis of historic trends of seasonal mean and peak SWE at an unprecedented local scale.

How to cite: Schellander, H., Winkler, M., and Tilg, A.-M.: Local trends of snow water equivalents in the Alps, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5111, https://doi.org/10.5194/egusphere-egu22-5111, 2022.

Seasonal snowpack significantly influences the catchment runoff and thus represents an important input for the hydrological cycle. A shift from snowfall to rain is expected in the future due to climate changes, as well as changes in the precipitation distribution and intensity. As a result, changes in the frequency and extremity of rain-on-snow events, which are considered to be one of the main causes of floods in winter and spring, may occur.

The objective of this study is 1) to evaluate the frequency, extremity, and trends in occurrence of rain-on-snow events in the past based on existing measurements, and 2) to simulate and evaluate the effect of predicted increase in air temperature on the occurrence of rain-on-snow events in the future. We selected several near-natural mountain catchments in Czechia and Switzerland with significant snow influence on runoff and with available long-time series 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. Each study catchment was divided into several elevation zones by 100 m, for which all data at a daily resolution were distributed by the model. Rain-on-snow events definition by threshold values for air temperature, rain intensity and snow depth allowed us to analyze inter-annual variations and trends in rain-on-snow events during the study period 1965-2019 and in the future.

The results show that a change of rain-on-snow events related to increasing air temperature differs among individual study catchments and individual elevation zones during winter season. Since both air temperature and elevation seem to be an important rain-on-snow drivers, there is an increasing rain-on-snow events occurrence 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.: Changes in the frequency and extremity of rain-on-snow events in the warming climate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-184, https://doi.org/10.5194/egusphere-egu22-184, 2022.

Snowfall information at the scale of individual particles is rare, difficult to gather, but fundamental for a better understanding of solid precipitation microphysics.

We present a dataset, MASCDB, (and a dedicated python software) of in-situ measurements of snow particles in free fall collected by a multi-angle snowflake camera. The dataset, includes gray-scale (255 shades) images of snowflakes, co-located surface environmental measurements, a large number of geometrical and textural snowflake descriptors as well as the output of previously published retrieval algorithms. Noteworthy examples include: hydrometeor classification, riming degree estimation, identification of melting particles, discrimination of wind-blown snow, as well as estimates of snow particle mass and volume. The measurements were collected in various locations of the Alps, Antarctica and Korea for a total of 2'555'091 snowflake images (or 851'697 image triplets). MASCDB aims to accelerate reproducible research on precipitation microphysics and to address longstanding scientific challenges on snowflake research. Given the large amount of snowflake images and associated descriptors, MASCDB can be exploited by the computer vision community for the training and benchmarking of image processing systems. MASCDB can be accessed on Zenodo (DOI: https://doi.org/10.5281/zenodo.5578920), while the pymascdb package at https://github.com/ltelab/pymascdb.

How to cite: Grazioli, J., Ghiggi, G., Billault-Roux, A.-C., and Berne, A.: MASCDB: a database of images, descriptors and microphysical properties of individual snowflakes in free fall, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3550, https://doi.org/10.5194/egusphere-egu22-3550, 2022.

How to cite: Hagenmuller, P., Calonne, N., Dumont, M., Brondex, J., Tuzet, F., and Roulle, J.: Multiscale tomography of the seasonal evolution of the snow microstructure, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8516, https://doi.org/10.5194/egusphere-egu22-8516, 2022.

Once fallen on the ground, snowflakes evolve quickly and form the snowpack. Under its own weight, the snowpack slowly deforms and settles. On a steep slope, some layers may rapidly deform and fail, which yield to the release of an avalanche. At the macroscopic scale, snow mechanical behavior is highly strain-rate dependent: ductile at low strain rates and brittle at high strain rates. The ductile-to-brittle transition has recently been shown to occur in two stages, with an intermediate regime of intermittent brittle failures, assumed to result from a competition between different time scales. At the micro-scale, this mechanical behavior is controlled by the microstructure and the visco-plastic and sintering properties of the ice skeleton. In this work, we investigate snow brittle-to-ductile transition by conducting displacement-controlled compression tests monitored with X-ray micro-computed tomogaphy.

We specifically designed a loading apparatus to perform displacement-controlled compression tests in cold environment and the constrained space of the tomographic cabin, so that microstructure evolution could be followed by regular scans. Samples (14 mm in diameter, 14 mm in height) were prepared from natural snow, sieved directly into samples holders, in batch of 10 samples and sintered for 72h at -20°C then stored at -50°C to prevent further microstructure evolution. The sample were taken out and placed in the compression device 30min before the first scan. We explored strain rates from 10^-6 s^-1 to 10^-2 s^-1 by vertically compressing 30 samples, up to a peak stress of 250 kPa and at a constant temperature of -18.5 °C. At high strain rates, only the initial and final 3D microstructures were scanned and simple radiographs were acquired during loading at a rate of 5 frames per second. At low strain rates, the 3D microstructure was regularly scanned during the loading. The obtained time series comprises one of the most-resolved (8.5 µm, 1h) and complete dataset on snow microstructure evolution near the ductile-to-brittle transition to date.

Results indicate a clear dependency of snow mechanical response on the strain rate. At strain rates larger than about 10^-3 s^-1, snow samples display heterogeneous deformations with the formation of compaction bands, while the stress-strain curve shows a serrated behavior. To relate this macroscopic behavior to micro-structural evolution, quantitative investigation of local density and specific surface area changes, as well as of bond network evolution, will be presented. These results should help identifying the micro-scale mechanisms at play during deformation of snow through both ductile and brittle range.

How to cite: Bernard, A., Montagnat, M., Chambon, G., and Hagenmuller, P.: Investigation of the ductile-to-brittle transition in snow with compression tests and tomography monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9316, https://doi.org/10.5194/egusphere-egu22-9316, 2022.

Wind drives the formation, shape and dynamics of aeolian snow dunes. Depending on the wind regime, different bedforms are formed such as as erosional shape (sastrugi) or linear dunes, which are straight or slightly sinuous dunes. Constraining the relationship between snow dunes and wind regimes, notably orientation, is essential for a better understanding of snow redistribution and therefore local surface mass balance. Snow dunes are widely spread in the windy polar regions and have an influence on the surface energy balance. However, relative to their sand analogues there have been few investigations relating snow bedforms orientation to wind direction. In Antarctica where snow bedforms are widely spread, wind direction has been inferred from sastrugi direction, but the relationship between the orientation of dunes and wind regime remains unclear.

In this study, we present a large-scale investigation of linear dune orientation in East Antarctica related to wind direction. We used optical Sentinel-2 images to identify linear dune fields location during summer with a 10-m resolution and retrieved their orientation. Inferring wind direction and speed from ERA5 reanalysis, at 0.25° resolution, we demonstrate that linear snow dunes are found even in areas with weak mean annual wind speed, providing some insights about the conditions of their formation. In addition, the comparison between wind direction statistics (prevailing direction and constancy) and dune orientations provides new insight into the relationship between linear snow dunes and the local wind regimes.

How to cite: Poizat, M., Picard, G., Arnaud, L., and Amory, C.: Snow dunes orientation in East Antarctica, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2736, https://doi.org/10.5194/egusphere-egu22-2736, 2022.

The snowpack over mountains represents an important source of water both in these areas and in adjacent lowlands. It also has a large impact on their economy since it affects tourism, communications, logistics and risks associated with its recreational use. Snow cover in mid elevations is experimenting a significant decrease as a consequence of climate change (IPCC-2021) and it is becoming an important issue in the water management agenda. Despite its importance there is a lack of understanding of its dynamics, due to the scarcity of properly distributed temporally and spatially mountain snowpack observations and the availability of specific simulation tools. With the aim of overcoming this scarcity, we present a new processing chain that couples ERA5 atmospheric reanalysis (ECMWF) with the Intermediate Atmospheric Research model (ICAR, from NCAR) and the Flexible Snow Model (FSM2, University of Edinburgh) in order to assess the snowpack in a small area in Penalara Massif, a mountain region in Central Spain. The 2021-2022 winter season have been simulated with a resolution of 2 m for the snowpack output. Several sensitivity experiments have been conducted in order to assess the impact of the uncertainties on the input forcing data. Also, automatic and manual meteorological observations have been used to validate the model and draw future lines of improvement. First results are very promising. This system has been able to explain the main features of the dynamics of the snowpack and future improvements are foreseen like the impact of snow redistribution of fresh snow due to wind drift and a better knowledge of the transformation of the snow pack and melting process.

How to cite: González Cervera, Á. and Durán, L.: Quasi-dynamically downscaled snowpack simulation in Penalara Massif (Sierra de Guadarrama, Central Spain), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5179, https://doi.org/10.5194/egusphere-egu22-5179, 2022.

Snow cover strongly modulates the energy fluxes between the atmosphere and the Earth's surface. Indeed, snow has generally a much higher albedo compared to other surfaces and therefore reduces the amount of solar radiation absorbed by the surface. Moreover, because of its low conductivity, snow isolates the ground from the atmosphere, impacting soil surface temperatures and energy balance (Zhang 2005). In general circulation models (GCMs) the snow cover fraction (SCF) is usually a diagnostic variable derived from other snow quantities, as for instance, the snow water equivalent (SWE) or the snow depth (SD). The relationship between SWE and SCF varies from simple linear relationships to more advanced parameterizations taking into account the snow density allowing to represent the hysteresis effect between the accumulation phase and the more disparate melting phase (e.g., Niu and Yang 2007). Swenson and Lawrence (2012) highlighted strong differences of snow cover extents between plains and mountainous areas, which may be explained by the persistence of snow on the summits whereas a faster melting occurs in the valleys. However, the dependency of SCF on the topography is considered only in a reduced number of GCMs, whereas mountainous areas represent nearly 1/5 of the world's surface area (Huddlestone et al., 2003). In this study, we designed three new snow parameterizations that include the impact of the sub-grid topography on the SCF in the ORCHIDEE land surface model (LSM) coupled to the LMDZ atmospheric model (part of the French GCM of IPSL). This model shows a strong cold bias and an excess of SCF over the High Mountains of Asia (HMA)  (Lalande et al., 2021). The new SCF parameterizations are based on the following existing ones: Swenson and Lawrence (2012; hereafter SL12), Roesch et al. (2001; hereafter R01), and a modified version of Niu and Yang (2007; hereafter NY07). These new parameterizations were calibrated over HMA using a high-resolution snow reanalysis (Liu et al., 2021), and compared to a deep learning model trained on the reanalysis dataset. The calibrated parameterizations SL12, R01, and the modified version of NY07 were then tested in coupled ORCHIDEE/LMDZ simulations. Preliminary results show improvements in simulated snow cover in HMA but slight deterioration in other areas. They suggest also that calibration should be extended to other snow-covered areas and should include other parameters such as the type of vegetation in particular.

How to cite: Lalande, M., Ménégoz, M., Krinner, G., and Ottlé, C.: Adaptation of a snow cover scheme for complex topography areas: regional calibration over High Mountain Asia and application in global models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-615, https://doi.org/10.5194/egusphere-egu22-615, 2022.

Snow processes in the land surface models (LSMs) include the snow cover fraction, snow albedo, and snow depth — all interacting with the atmospheric conditions. Most LSMs include parameters based on empirical relations, resulting in uncertainties in model solutions. In addition, such parameters often reflect only the local characteristics where the empirical relations are made. Therefore, the empirical parameters need to be optimized when they are applied to different regions. This study seeks the optimal snow-related parameters over South Korea where heavy snowfall events occur in the winter. The optimization is conducted using a micro-genetic algorithm (micro-GA) and the in situ and satellite observations for the snow depth, snow cover fraction, and snow albedo. The micro-GA is one of the evolutionary algorithms to search for the best potential solution based on natural selection and the survival of fitness. To represent the regional empirical parameters using the single-column model (e.g., Noah LSM), we selected the representative stations over South Korea to cover various vegetation types. Next, we identify which snow-related parameters can be optimized and suggest the optimal parameters using the micro-GA over South Korea. As a result, the Noah LSM simulations, using the optimized parameters, reduced the biases by 45.1% and 32.6 % for the snow depth and snow albedo, respectively, and the root mean square errors by 17.0 %, 8.2 %, and 5.6 % for snow depth, snow cover fraction, and snow albedo, respectively.

How to cite: Park, S. K., Lim, S., and Cassardo, C.: Optimal Estimation of Snow Related Parameters in Noah Land Surface Model Using an Evolutionary Algorithm, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10881, https://doi.org/10.5194/egusphere-egu22-10881, 2022.

Snow cover has a great influence on the energy balance on the surface, in particular ability to reflect solar radiation. As well as radiation, when modeling the climate, it is important to correctly describe the water cycle. During the transitional seasons, when the temperature fluctuates around zero degrees Celsius, some of the melt water can be retained in the snow layer and refreeze. In addition, over time after a snowfall the snow becomes denser. Aged, wet, refrozen snows have different optical properties than new-fallen snow, in particular a lower albedo. Moreover, atmospheric aerosols falling on a snow-covered surface leads to its pollution and, as a consequence, reduces its reflectivity. Aerosols such as mineral dust and black carbon have the greatest effect on the albedo and radiation balance.

In the climatу model INMCM, some physical features of snow cover have been implemented. The porous structure of the snow is taken into account and the calculation of the water content of the snow layer is realized. During snowmelt, the ratio of the water is retained in the pores, and does not go immediately to the upper boundary of the soil. The possibility of refreezing of melt water contained in the snow layer has also been implemented. The change in snow density over time is taken into account. At the same time, it is assumed that the snow layer consists of a mixture of usual and refrozen snow, as well as melt water contained in snow pores. The influence of the composition of the snow layer and its density on the reflectivity is taken into account. The effect on the albedo of impurities contained in snow is also taken into account (for example, black carbon). Computational experiments were carried out with the INMCM model to assess the sensitivity to the realized physical processes.

This work is supported by the Russian Science Foundation, project No. 20-17-00190.

How to cite: Chernenkov, A., Volodin, E., and Kostrykin, S.: Physics of snow cover in the climate model INMCM, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11474, https://doi.org/10.5194/egusphere-egu22-11474, 2022.

The snow cover is an essential part of the climate system in cold regions through its effects on the terrestrial water, energy, and carbon balance. Due to the high spatiotemporal variability of snow, it is challenging to resolve snow cover dynamics in models. Thus, our ability to improve the representation of these dynamics in Earth System Models (ESMs) leans heavily on the accuracy and representativeness of the observational data sets used for model evaluation.

The big picture provided by the long-term climate data record from satellites helps us to monitor changes in land cover over large areas. At the same time, rapidly developing drone and terrestrial imaging technology provides higher resolution information over specific areas. This complimentary information from spaceborne, airborne, and terrestrial Earth observations is invaluable for better understanding the complex processes in the climate system. In our work, we are currently exploiting estimates of snow-covered area from different optical sensors onboard polar orbiting satellites that are imaging the Nordic region. Drone and terrestrial images are being explored as a source of validation and calibration data for the satellite products. 

Having representative snow cover maps enables us to better evaluate the terrestrial component of the Norwegian Earth System Model (NorESM), namely the Community Land Model (CLM5). With a focus on snow processes, we are conducting an analysis using satellite-based estimates of snow-covered area (MODIS, Sentinel-2, and Landsat 8), snow simulations from CLM5, snow variables from several climate reanalyses (ERA5, ERA5-Land, and MERRA-2), and in-situ data from eddy covariance stations (LATICE flux sites). Two offline CLM5 simulations are conducted with different atmospheric forcing, namely the default data set (GSWP3) and ERA5. We are investigating trends in the snow cover phenology, which we characterize using snow cover duration, first and last days of the snow cover, and consecutive snow cover days for each snow season over the last two decades. This work illuminates a path to integrate Earth observations with Earth system modeling in cold environments to both identify and constrain sources of uncertainty.

Acknowledgement: This ongoing study is supported by the LATICE (Land-ATmosphere Interactions in Cold Environments) strategic research initiative funded by the University of Oslo, and the project  EMERALD (294948) funded by the Research Council of Norway.

How to cite: Yılmaz, Y. A., Aalstad, K., Filhol, S., Gascoin, S., Pirk, N., Remmers, J., Stordal, F., and Tallaksen, L. M.: Evaluating modeled snow cover dynamics over Fennoscandia using Earth observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6092, https://doi.org/10.5194/egusphere-egu22-6092, 2022.

Seasonal snow cover is the largest cryospheric component of in areal extent, covering more than 50 million square kilometers of the Earth surface (more than 31% of its land area) every year. Snow cover area (SCA) and its local properties, in terms of snowpack height and snowpack density, are the main parameters characterizing the snow accumulation in mountainous regions. Such parameters result in particular importance in meteorology, hydrology, and climate monitoring applications. Anyway, in the general case, the considerable geographical extension of snow layers and their typical spatial heterogeneity makes it impractical to monitor the above three parameters regularly (i.e., with a high spatial and temporal resolution) by means of direct or indirect in situ measurements, suggesting the exploitation of satellite technologies for the provision of such data. Snow cover patterns are governed by the effects of topography, land cover, wind redistribution, solar irradiance, and air temperature. On the other hand, in the last few decades, a general back-scaling of snow observation networks occurred worldwide. Based on the above considerations, space-borne SAR sensors are particularly suitable for the analysis of snow deposits, providing data with resolutions up to some meters, with global coverage and a few days revisit time.

In this study, we introduce a satellite-based technique for mapping snow cover fraction balancing the requirements between spatial and temporal resolution, and using data from the European Sentinel constellation. The available current fractional snow cover (FSC) products, provided by Sentinel-2 MSI (Multispectral imager) cloud-gap-filled (CGF) products and Terra MODIS (Moderate-resolution Infrared Spectroradiometer) snow cover products, may suffer either of relatively poor spatial resolution and/or temporal resolution (e.g., FSC at 25-m spatial resolution every 5 days from Sentinel-2 MSI products or 500-m spatial resolution every day from Terra MODIS). For this purpose, we explore the use of the Sentinel-3 optical sensors, OLCI (Ocean Land Color Imager), and SLSTR (Sea-Land Surface Temperature Radiometer), showing a 300-m and 500-m spatial resolution with 2-3 and 1-2 days temporal resolution.

Using as a reference the Sentinel-2 FSC product and employing a DEM (Digital Elevation Model) at 90 m spatial resolution, a machine learning Snow-Cover-Area Random Forest (SCARF) approach has been developed. The proposed algorithm takes, as inputs, both DEM as well as OLCI and SLSTR data, linearly up-sampled at 90-m, and can provide as output FSC product at 90-m spatial resolution every 1-2 days. Input data are derived from NASA SRTM 3-arc-second DEM, OLCI multi-band reflectances, and SLSTR multi-band reflectance and brightness temperatures at nadir and oblique view. After creating 2 datasets (nadir and oblique), we have introduced a distinction between the complete dataset and a subset leaving only the pixel with an elevation higher than 1000 m. As a classification method, we used an RF gradient-boosting classifier (called XGBoostClassifier). In this work, we will illustrate the results of the proposed SCARF algorithm using area-of-interest the Italian Central Apennines and period-of-interest winter 2019-20. Statistical performances, potential developments, and critical issues of the SCARF algorithm will also be discussed.

How to cite: Tetoula Tsonga, E., Palermo, G., Raparelli, E., Tuccella, P., Manzi, M. P., and Marzano, F.: Retrieving fractional snow cover in Central Apennines from Sentinel 2 and 3 visible-infrared spectroradiometer data and random forest learning techniques, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2460, https://doi.org/10.5194/egusphere-egu22-2460, 2022.