CR3.1

CR3 EDI
Modelling and measuring snow processes across scales 

Snow cover characteristics (e.g. spatial distribution, surface and internal physical properties) are continuously evolving over a wide range of scales due to meteorological conditions, such as precipitation, wind and radiation.
Most processes occurring in the snow cover depend on the vertical and horizontal distribution of its physical properties, which are primarily controlled by the microstructure of snow (e.g. density, specific surface area). In turn, snow metamorphism changes the microstructure, leading to feedback loops that affect the snow cover on coarser scales. This can have far-reaching implications for a wide range of applications, including snow hydrology, weather forecasting, climate modelling, and avalanche hazard forecasting or remote sensing of snow. The characterization of snow thus demands synergetic investigations of the hierarchy of processes across the scales ranging from explicit microstructure-based studies to sub-grid parameterizations for unresolved processes in large-scale phenomena (e.g. albedo, drifting snow).

This session is therefore devoted to modelling and measuring snow processes across scales. The aim is to gather researchers from various disciplines to share their expertise on snow processes in seasonal and perennial snowpacks. We invite contributions ranging from “small” scales, as encountered in microstructure studies, over “intermediate” scales typically relevant for 1D snowpack models, up to “coarse” scales, that typically emerge for spatially distributed modelling over mountainous or polar snow- and ice-covered terrain. Specifically, we welcome contributions reporting results from field, laboratory and numerical studies of the physical and chemical evolution of snowpacks, statistical or dynamic downscaling methods of atmospheric driving data, assimilation of in-situ and remotely sensed observations, representation of sub-grid processes in coarse-scale models, and evaluation of model performance and associated uncertainties.

Convener: Henning Löwe | Co-conveners: Nora Helbig, Neige CalonneECSECS, Richard L.H. Essery, Vincent Vionnet
Presentations
| Tue, 24 May, 15:10–18:30 (CEST)
 
Room 1.15/16

Presentations: Tue, 24 May | Room 1.15/16

Chairpersons: Nora Helbig, Vincent Vionnet, Henning Löwe
15:10–15:12
15:12–15:22
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EGU22-5717
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ECS
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solicited
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Highlight
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On-site presentation
Sanne Veldhuijsen, Willem Jan van de Berg, Max Brils, Peter Kuipers Munneke, and Michiel van den Broeke

Firn covers ~99% of the Antarctic ice sheet, providing pore space in which nearly all of the surface meltwater refreezes or is retained in liquid form. For now, this prevents most of the surface melt to contribute to sea level rise, however as atmospheric warming continues, changes in precipitation, temperature, melt and refreezing will cause the firn layer to evolve. Surface melt and densification rates are expected to increase, which will lead to firn air content depletion and increased firn saturation. Such conditions are extremely important for the Antarctic ice sheet: saturation of firn layers can lead to hydrofracturing induced ice shelf disintegration. On the other hand, snowfall is expected to increase as well, which will add additional pore space to the firn. Firn models can be used to simulate such firn processes and identify future firn conditions.

       In this study, we force the recently improved version of the IMAU Firn Densification model (IMAU-FDM v1.2A) with outputs of the regional atmospheric climate model RACMO2.3p2 at resolution of 27 km covering the period 1950-2100. RACMO2.3p2 is forced with CESM2, which includes the historical period 1950-2015 followed by two climate change scenarios for the period 2015-2100 (SSP126 and SSP585). The historical performance is evaluated by comparing the outputs to a run in which IMAU-FDM v1.2A was forced with RACMO2.3p2, which was forced with ERA-5, a climate model reanalysis nudged towards surface and satellite observations. After evaluation, we present how the firn layer will evolve over the coming century under these scenarios, and identify which areas will potentially become susceptible to ice shelf disintegration.

How to cite: Veldhuijsen, S., van de Berg, W. J., Brils, M., Kuipers Munneke, P., and van den Broeke, M.: Evolution of the Antarctic firn layer until 2100 under two climate change scenarios. , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5717, https://doi.org/10.5194/egusphere-egu22-5717, 2022.

15:22–15:28
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EGU22-560
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ECS
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On-site presentation
Alexander Kyriacou, Gianluca Boccarella, Pia Friend, and Klaus Helbing

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.

15:28–15:34
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EGU22-9092
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ECS
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Presentation form not yet defined
Romilly Harris Stuart, Amaëlle Landais, Patricia Martinerie, Marie Dumont, Mathieu Fructus, Anaïs Orsi, Quentin Libois, Laurent Arnaud, C. Max Stevens, Antoine Grisart, and Frédéric Prié

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.

15:34–15:40
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EGU22-4509
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ECS
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On-site presentation
Lisa Bouvet, Neige Calonne, Christian Geindreau, and Frédéric Flin

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.

15:40–15:46
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EGU22-1145
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ECS
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Virtual presentation
Kevin Fourteau, Pascal Hagenmuller, and Florent Domine

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 snowthe 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.

15:46–15:52
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EGU22-10534
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ECS
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Virtual presentation
Alex Cabaj, Paul Kushner, and Alek Petty

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.

15:52–15:58
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EGU22-6388
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ECS
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Presentation form not yet defined
Characteristics of the contemporary Greenland ice sheet firn layer (1958-2020)
(withdrawn)
Max Brils, Peter Kuipers Munneke, and Michiel van den Broeke
15:58–16:04
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EGU22-3839
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Presentation form not yet defined
Jakob Abermann, Kerstin Rasmussen, Kirsty Langley, Jorrit van der Schot, Tiago Silva, Michael Winkler, Harald Schellander, and Wolfgang Schöner

In this contribution we compile hitherto little or unused snow height data for Greenland. We present time-series of autonomously measured snow heights at around 10 locations in different parts of Greenland dating back to 1997. This data was largely measured and archived by Asiaq, Greenland Survey, for varying applications. We show the wide variability of snow heights and determine snow water equivalent using a recently developed model approach. The performance of the model to reproduce manually measured snow water equivalent is striking given the simplicity of input (solely snow depth) and the complexity of the different snow climates. We assess the hydrological significance of seasonal snow cover for very varying climatological conditions in Greenland and evaluate that the hysteresis between snow depth and snow water equivalent formation and depletion differs in shape and strength depending on the general climatological conditions.

In a further step we analyze the drivers of the observed variability relating snow height anomalies to climate oscillation indices (such as NAO, GBI). We hypothesize that the impact of climate oscillations on snow height anomalies is spatially variable in coastal Greenland. Furthermore, we assess to which extent the timing of spring onset determines snow depletion rates.

Finally, given the spatial heterogeneity of snow measurements, we assess the capability of a regional climate model to reproduce snow height and snow water equivalent and relate its performance to topography.

How to cite: Abermann, J., Rasmussen, K., Langley, K., van der Schot, J., Silva, T., Winkler, M., Schellander, H., and Schöner, W.: Snow height monitoring in coastal Greenland – datasets, scales and climatic drivers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3839, https://doi.org/10.5194/egusphere-egu22-3839, 2022.

16:04–16:10
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EGU22-2326
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Virtual presentation
Michael Winkler and Harald Schellander

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.

16:10–16:16
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EGU22-5111
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Virtual presentation
Harald Schellander, Michael Winkler, and Anna-Maria Tilg

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.

16:16–16:22
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EGU22-184
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ECS
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On-site presentation
Ondrej Hotovy and Michal Jenicek

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.

16:22–16:28
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EGU22-7626
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Virtual presentation
Carlo Marin, Francesca Cigna, Giovanni Cuozzo, Claudia Notarnicola, Simonetta Paloscia, Emanuele Santi, and Deodato Tapete

The seasonal snow is one of the largest water reservoirs in nature, storing water during winter, and gradually releasing it in spring during the melt. This guarantees freshwater supply for the lowlands even in the long term, making the mountains the “water towers” of the downstream regions. In fact, the delayed water release from the head watersheds to the forelands is essential for a large number of human activities such as irrigation, drinking water supply and hydropower production. On the other hand, snowmelt may cause natural disasters such as wet-snow avalanches, gliding or release of highly enriched accumulated contaminants able to cause severe impact on water quality.

In recent years, Synthetic Aperture Radar (SAR) has demonstrated capable to provide information about the melting process. In particular, with the launch of the European Commission (EC) Copernicus Programme Sentinel-1 mission, C-band SAR images are regularly acquired every 6 days and delivered free of charge. This opened the possibility to observe a phenomenological relationship between the snow melting process of high altitude snowpacks and the multi-temporal radar backscattering acquired by Sentinel-1. The identification of the temporal signature for each pixel of a Sentinel-1 time series allowed us to detect the onset of the three phases that made up the snowmelt i.e., moistening, ripening and runoff, with a good reliability. However, the mechanisms that drive the snowpack response at microwaves depend on frequency; therefore, different snowpack signatures are expected if using different frequency bands, as the X band available onboard the Italian Space Agency (ASI)’s COSMO-SkyMed (CSK) constellation.

In this work, we analyze a dense X-band time series acquired by the CSK over the Schnalstal catchment in Italy during the snowmelt season. This allows us to point out the similarities and the differences between the electromagnetic interactions using C- and X-band SAR during the snowmelt. Depending on the shorter wavelength, the X-band is more sensitive than C-band to small quantities of liquid water inside the snowpack. Therefore, X band shows an earlier response than C band to the moistening of the surface snow layer (especially for steep local incidence angles), and a more pronounced loss of interaction with deeper layers. X-band is also more sensitive to the increase in the superficial roughness with the consequence of possibly anticipating the runoff onset. However, by comparing the runoff time in the Schnalstal catchment during the melting season 2020-2021, a general agreement between C- and X-band is found even though the characteristic shape of the signature exhibits more variations at X-band than C-band.

This research is part of the 2019-2022 project ‘Development of algorithms for estimation and monitoring of hydrological parameters from satellite and drone’, funded by ASI under grant agreement n. 2018-37-HH.0.

How to cite: Marin, C., Cigna, F., Cuozzo, G., Notarnicola, C., Paloscia, S., Santi, E., and Tapete, D.: Snowmelt dynamics observed by dense X-band time series acquired by COSMO-SkyMed constellation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7626, https://doi.org/10.5194/egusphere-egu22-7626, 2022.

16:28–16:34
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EGU22-12019
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Virtual presentation
Carrie Vuyovich, Ana Barros, Dorothy Hall, Rhae Sung Kim, Eunsang Cho, Melissa Wrzesien, and Sujay Kumar

Global snow cover is an integral part of the Earth’s water and energy cycles, contributing life-giving water resources to billions of people around the world while helping to cool the planet by reflecting solar energy back to space.  Accurate measurements of snow at a regional scale are needed to improve runoff predictions to inform water supply and hydropower needs and to help predict conditions that are associated with floods, drought, and wildfires. Though we can measure the extent of snow cover globally, we cannot yet reliably measure the amount of water stored in a snowpack, or snow-water equivalent (SWE) from space at the resolution and accuracy needed to understand its role in the water cycle. In addition, it is uncertain how the extent and the volume of snow will be changed across the globe in a warmer climate. Here we will review the snow data needs to address our most pressing science questions and operational requirements. We will also present the results of a coverage analysis of SWE data from currently-available and upcoming sensors over the northern hemisphere to identify gaps in current capabilities.

How to cite: Vuyovich, C., Barros, A., Hall, D., Kim, R. S., Cho, E., Wrzesien, M., and Kumar, S.: Analysis of snow water equivalent data needs and capabilities, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12019, https://doi.org/10.5194/egusphere-egu22-12019, 2022.

16:34–16:40
Coffee break
Chairpersons: Neige Calonne, Richard L.H. Essery, Henning Löwe
17:00–17:02
17:02–17:12
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EGU22-3550
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ECS
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solicited
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Highlight
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Presentation form not yet defined
Jacopo Grazioli, Gionata Ghiggi, Anne-Claire Billault-Roux, and Alexis Berne

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.

17:12–17:18
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EGU22-8516
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Presentation form not yet defined
Pascal Hagenmuller, Neige Calonne, Marie Dumont, Julien Brondex, Francois Tuzet, and Jacques Roulle

Due to very active metamorphism, snow on the ground exhibits a wide range of microstructural patterns. Indeed, snow is a very porous material and it exists on Earth close to its melting point, which promotes its structural evolution through vapor transport and melting-refreezing processes. State-of-the-art detailed snowpack models such as SURFEX/Crocus still represent this microstructure in a very rough way. This representation is based on manual observations from the 1990’s using magnification lenses, where the snow grain shape were classified into different types. The descriptors derived from this classification, such as sphericity or grain size, are not based on a sound physical background and cannot be measured, which limits any further improvement of the existing parameterizations. Nowadays, tomography has become a standard technique to capture the 3D snow microstructure at a micrometrical scale in laboratory conditions. Besides, homogenization methods can now numerically estimate several essential but difficult-to-measure snow properties such as thermal conductivity or mechanical viscosity from tomographic images and the ice and air properties. To overcome the limitations of existing snowpack models and to benefit from the wealth of data provided by tomography and numerical homogenization, a new generation of snow models with an explicit and objective representation of the snow microstructure is currently under development. To develop and evaluate these new models, characterization of the snow microstructure evolving in the field is required. The objective of the presented work is to develop a measurement and data processing protocol to be able to conduct these measurements. This represents a challenge because, to date, tomography was mainly limited to small volumes of snow mostly harvested in laboratory conditions. First, we installed a tomograph directly at our snow field site, Col de Porte, 1325 m a.s.l., french Alps. Second, we designed a specific snow cutter to sample snow cores without destroying their very fragile microstructure. Cutters equipped with a sharp hole saw and with an inner diameter of 44 mm and a height of 100 mm are sufficiently large to prevent sample failure and small enough to conduct partial tomography at a very high resolution. Last, we combined two types of tomographic scan in order to capture a high-order approximation of the snow microstructure while maintaining the scanning time short enough. In particular, on each snow core, we scanned a sub-volume (15 mm diameter, 15 mm height, 48 min scan duration) at an effective resolution of 10 microns and the whole sample column (25 mm diameter, 100 mm height, 20 min scan duration) at a resolution of 42 microns. Based on a modified two-point correlation function which applies directly to the greyscale tomographic images and the combination of the two scans, we were able to recover physically-based proxies of the snow microstructure of the full core in a reasonable measurement duration. This includes density, specific surface area and mean curvature.

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.

17:18–17:24
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EGU22-9316
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ECS
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On-site presentation
Antoine Bernard, Maurine Montagnat, Guillaume Chambon, and Pascal Hagenmuller

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.

17:24–17:30
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EGU22-12623
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ECS
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On-site presentation
Daniela Brito Melo, Alec Petersen, Filippo Coletti, Benjamin Walter, Matthias Jaggi, and Michael Lehning

Drifting snow is a multi-scale process. It is composed of particles rolling and sliding along the surface, particles in saltation following short ballistic trajectories in the first 10 cm above the surface and particles in suspension at higher regions of the atmosphere. Drifting snow is currently represented in some regional and mesoscale atmospheric models by taking into account its effect on snow height, snow sublimation and snow densification. Snow saltation is a sub-grid process in these models and is therefore parameterized. However, the current parameterizations are based on limited field and wind tunnel measurements and do not take into account the effect of the bed characteristics, as grain size, inter-particle cohesion and snow density, on the saltation dynamics.

In order to improve the current saltation models, we conducted wind tunnel experiments using natural snow at the WSL Institute for Snow and Avalanche Research SLF to measure the kinematics and shape of particles in saltation. The wind tunnel is located at 1670 m above sea level, has a cross section area of 1x1 m2 and a total length of 14 m. Naturally deposited snow is collected in trays after each snowfall and transported to the tunnel without disturbing the snowpack. We used a high speed camera, aquiring images at 5 kHz with backlighting provided by an LED to capture images of saltating snowflakes. We measured wind speed with an array of pitot tubes positioned 2-10 cm above the snowbed. We additionally measured the density and hardness of the snow cover before the experiments using a box density cutter and a Snow Micro Pen (SMP), respectively. We process the images with a 2D Particle Tracking Velocimetry (PTV) algorithm allowing us to obtain Eulerian and Lagrangian statistics of the kinematic quantities as well as estimates of the snowflake characteristics like size, aspect ratio and orientation. In addition, by assuming a constant particle density, we derive particle mass flux profiles.

The results show that the particle size distribution in saltation can indeed be characterized by a lognormal or a gamma distribution. From the analysis of the particle streamwise velocity profiles, it is clear that the assumption of a constant particle speed inside the saltation layer (common in simple saltation models) might not be a good approximation even for low friction velocities. We will present in how far we can assess the influence of the snow properties on mass flux and saltation dynamics as a basis to validate recent model results on the influence of inter-particle cohesion for example. Moreover, this data set will contribute to the development of new parameterizations for snow saltation mass flux and streamwise velocity that would take into account the effect of snow density and hardness.

How to cite: Melo, D. B., Petersen, A., Coletti, F., Walter, B., Jaggi, M., and Lehning, M.: High-speed imaging of snow saltation: wind tunnel experiments using natural snow, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12623, https://doi.org/10.5194/egusphere-egu22-12623, 2022.

17:30–17:36
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EGU22-2736
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ECS
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On-site presentation
Marine Poizat, Ghislain Picard, Laurent Arnaud, and Charles Amory

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.

17:36–17:42
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EGU22-9954
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ECS
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On-site presentation
Ange Haddjeri, Matthieu Baron, Matthieu Lafaysse, Rafife Nheili, Louis Le Toumelin, Simon Gascoin, and Marie Dumont

SnowPappus, a new wind snow transport model coupled with Crocus snowpack model, was developed to quantify and model snow drift at a hectometric scale. To reproduce the high spatial variability of the snow cover in Alpine regions, we represent the impact of wind and topography on the simulated snowpack properties. The accurate simulation of the impact of wind is key to anticipate natural hazard related to the snow cover and improve  hydrological predictions. Simulations were performed on a 902 km² French Alps region at 250 m resolution. Here we present the comparison of our snow drift model SnowPappus with in situ measurements and Sentinel 2 snow products, using various sublimation parametrizations and different numerical schemes to represent the horizontal divergence of blowing snow fluxes responsible for accumulation/erosion patterns.

How to cite: Haddjeri, A., Baron, M., Lafaysse, M., Nheili, R., Le Toumelin, L., Gascoin, S., and Dumont, M.: Sensitivity of a new hectometric scale snow transport scheme to various parameterizations for sublimation and accumulation/erosion, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9954, https://doi.org/10.5194/egusphere-egu22-9954, 2022.

17:42–17:48
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EGU22-5179
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ECS
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On-site presentation
Álvaro González Cervera and Luis Durán

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.

17:48–17:54
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EGU22-615
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ECS
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On-site presentation
Mickaël Lalande, Martin Ménégoz, Gerhard Krinner, and Catherine Ottlé

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.

17:54–18:00
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EGU22-10881
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Presentation form not yet defined
Seon Ki Park, Sujeong Lim, and Claudio Cassardo

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.

18:00–18:06
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EGU22-11474
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ECS
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Virtual presentation
Alexey Chernenkov, Evgeny Volodin, and Sergey Kostrykin

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.

18:06–18:12
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EGU22-6092
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ECS
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Virtual presentation
Yeliz A. Yılmaz, Kristoffer Aalstad, Simon Filhol, Simon Gascoin, Norbert Pirk, Janneke Remmers, Frode Stordal, and Lena M. Tallaksen

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.

18:12–18:18
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EGU22-2460
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ECS
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Virtual presentation
Eleftheria Tetoula Tsonga, Gianluca Palermo, Edoardo Raparelli, Paolo Tuccella, Maria Paola Manzi, and Frank Marzano

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.

18:18–18:24
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EGU22-3463
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ECS
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On-site presentation
Alvaro Robledano, Ghislain Picard, Marie Dumont, Laurent Arnaud, and Frédéric Flin

Snow plays a crucial role in the climate system, as its high albedo is unique among the Earth’s surface materials. Several microstructural properties such as the snow specific surface area strongly modulate the optical properties of snow. Light penetration and scattering by ice particles are also impacted by other microstructural parameters, such as the grain shape. Nevertheless, most radiative transfer models still treat snow as a medium composed of idealized and simplified geometries, which limits the understanding of how the snow microstructure impacts the snow optical properties. Assuming geometric optics and weak ice absorption, only two parameters are needed to describe the snow grain shape in the diffusion regime. These are the absorption enhancement parameter B and the geometric asymmetry factor gG. Here we aim to understand the relationship between the snow microstructure properties and the shape parameters, B and gG.

 

To do so, we combine ray-tracing Monte Carlo methods with 3D images of the actual microstructure of snow, obtained with X-ray imaging and computed microtomography (µCT). The existing Rough Surface Ray-Tracer (RSRT) model, originally designed to simulate snow albedo over rough surfaces, has been adapted to trace light propagation in microstructure 3D images. This approach allows getting rid of the simplified representation of snow in radiative transfer models, and benefits from the accurate ray-tracing calculations. We present here our initial findings and results, which compare well with the results of the advanced radiative transfer theories that relate snow optical properties to the chord length distribution in snow microstructure.

How to cite: Robledano, A., Picard, G., Dumont, M., Arnaud, L., and Flin, F.: Linking snow optical properties to snow microstructure, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3463, https://doi.org/10.5194/egusphere-egu22-3463, 2022.

18:24–18:30
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EGU22-5541
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On-site presentation
didier voisin, Celine Voiron, Hervé Denis, Sophie Darfeuil, Patrick Ginot, Marie Dumont, Marion Reveillet, and Simon Gascoin

Light absorbing particles (LAPs) from various origins get deposited to the snow where they greatly influence its physical evolution, and most particularly its melt by changing the energy absorbed from solar radiation. Black Carbon and Dust are the most important such particles. How those particles change snow physics depends on their optical properties, which are a function of their chemical composition and size distribution.

In European mountain ranges, part of the deposited dust comes as sporadic Saharan dust outbreaks, which cause important dust layers in the snowpack. One such event was the object of a citizen science collaborative sampling campaign, which resulted in 150 samples collected over the Pyrenees, the Jura, and the French and Swiss Alps.

Dust in these samples was filtered out and weighted, in order to get total deposition fluxes. Size distributions were measured between 4 and 60 µm. This size fraction represents less than 10% of the total mass measured in those samples. Assuming lognormal distributions to extend the measured size distributions beyond 60 µm only explained a fraction of the missing mass.

The relative importance of the particles not measured between 4 and 60 µm for the optical properties of the snowpack depend strongly on their size. A rough estimate of the importance of the missing fraction was attempted by assuming that the overall optical effect scales with the surface area of the particles. Depending on the assumed diameter of particles in this fraction, the missing mass (~90% of the total mass) overall optical impact is estimated between 80 and 40% of the estimated total optical impact of the dust present in the snow.

A limited set of samples was used to assess the size of this missing fraction, using different methods. This preliminary assessment suggests the potential importance of grain aggregation in natural snow and the importance of unbiased size distribution measurements for dust in snow.

How to cite: voisin, D., Voiron, C., Denis, H., Darfeuil, S., Ginot, P., Dumont, M., Reveillet, M., and Gascoin, S.: Dust particles size distributions in snow and its importance for optical properties of snow, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5541, https://doi.org/10.5194/egusphere-egu22-5541, 2022.