CR6.2 | Modelling and measuring snow processes across scales
EDI PICO
Modelling and measuring snow processes across scales
Convener: Henning Löwe | Co-conveners: Nora Helbig, Neige CalonneECSECS, Richard L.H. Essery, Vincent Vionnet
PICO
| Mon, 24 Apr, 08:30–10:15 (CEST)
 
PICO spot 3a
Mon, 08:30
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 regions. 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.

PICO: Mon, 24 Apr | PICO spot 3a

Chairpersons: Nora Helbig, Richard L.H. Essery
08:30–08:32
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PICO3a.1
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EGU23-755
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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 experiencing 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. This gap is even more pronounced in mediterranean mountainous regions, where the complex processes involved in snowpack growth and ablation, together with its high spatial variability, pose a challenge for the models. To overcome these challenges, a hyper-high resolution state-of-the-art chain model (SnowCast) has been developed and validated in Penalara Massif (Sierra de Guadarrama, Central Spain). It couples ERA5 atmospheric reanalysis (ECMWF) with the Intermediate Atmospheric Research model (ICAR, NCAR) and the Flexible Snow Model (FSM2, University of Edinburgh) along with own developed parametrizations and high resolution topographic forcing models. A multi-year simulation has been performed for this area and sensitivity tests have been performed with different resolutions and topographically induced air and soil forcings. Results after validation using intensive field work, automatic snowpack monitoring and satellite imagery look very promising. A temporal and spatial realistic representation of the snow cover will be presented along with an analysis of the performance of the model and a discussion about new processes to be implemented, additional validation techniques and future coupling with a hydrological model.

 

How to cite: González Cervera, Á. and Durán, L.: Multi-year Hyper-high Resolution Snow Cover Simulation in a Mountainous Region in Central Spain (Peñalara Massif)., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-755, https://doi.org/10.5194/egusphere-egu23-755, 2023.

08:32–08:34
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PICO3a.2
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EGU23-1064
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ECS
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On-site presentation
Benjamin Poschlod

Snow dynamics are affecting the climate system, water cycle, ecology, human society, and infrastructure. Furthermore, the representation of snow on the land surface within regional climate models is crucial for the mass and energy balance in the modelled climate. Simulated daily snow depths of two high-resolution regional climate models, the WRF at 1.5 km resolution and the COSMO-CLM (CCLM) at 3 km resolution both driven by ERA5 reanalysis data are evaluated with 83 station observations in southern Germany during 1987 – 2018. Furthermore, based on the atmospheric output of CCLM, the hydrometeorological snow model AMUNDSEN is run at the point scale of the climate stations. In addition, the ERA5-Land dataset (9 km) complements the comparison as state-of-the-art reanalysis land surface product driven by the same atmospheric conditions of ERA5. ERA5-Land shows considerable deviations of grid cell elevation and station elevation (mean absolute error: 93 m) and moderate biases in air temperature (-0.3 °C) and precipitation (+19.1 %). However, there is a strong positive bias in mean winter snow depth (+3.5 cm) and snow cover duration (+33.3 d). The WRF simulation features a cold bias of -1.2 °C and moderate bias in precipitation (-5.2 %) during winter. This results in a slight overestimation of snow depth (+0.4 cm) and snow cover duration (+6.6 d). The CCLM strongly underestimates snow depth (-2.5 cm) and snow cover duration (-19.8 d), albeit a very good reproduction of air temperature (+0.0°C) and precipitation (+9.7 %). AMUNDSEN reverses the underestimations of the CCLM to an overestimation of snow depth (+2.2 cm), however improving the reproduction of snow cover duration (+6.4 d). All models fail to skilfully predict white Christmas.

Extremes of snow dynamics such as annual maximum snow depths, maximum daily snow accumulation and melting are not well reproduced by ERA5L and CCLM. WRF and AMUNDSEN can improve the representation of extremes but still with considerable limitations.    

In conclusion, the simulation of snow depths with WRF and AMUNDSEN can benefit from the finer resolution of the topography in the high-resolution climate models compared to ERA5-Land. However, even though driven by the same large-scale atmospheric conditions of ERA5, the four snow depth simulations vary by a huge margin. The high spatial resolution of convection-permitting climate models shows potential in reproducing the winter climate in southern Germany. However, the uncertainties within the snow modelling prevent a further straightforward use for impact research. Hence, careful evaluation is needed before any impact-related interpretation of the simulations, also in the context of climate change research.

How to cite: Poschlod, B.: Snow depth in convection-permitting regional climate model simulations over southern Germany - ready for impact-related research?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1064, https://doi.org/10.5194/egusphere-egu23-1064, 2023.

08:34–08:36
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PICO3a.3
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EGU23-5236
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ECS
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On-site presentation
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Annelies Voordendag, Brigitta Goger, Rainer Prinz, Tobias Sauter, and Georg Kaser

The representation of the snow cover dynamics (including accumulation, redistribution, and compaction), due to their impact on the snow-albedo feedback, poses the central deficiency in distributed mass balance models for most temperate and land-terminating glaciers. Data quantity and quality both from ground measurements and from remote sensor systems have not yet been sufficient to resemble these actual processes on a glacier scale until now. This limits the calibration and evaluation of distributed mass balance models.

Yet, we installed a permanently terrestrial laser scanning (TLS) system at the Hintereisferner glacier (Ötztal Alps, Austria), which provides a daily digital elevation model (DEM). These DEMs with an accuracy of about ±10 cm can serve as calibration and validation data of distributed glacier mass balance models.

We present a case study of snow cover dynamics between 6 and 9 February 2021. Snow fall of approximately 50 cm was registered and moderate wind speeds were measured on these days. Furthermore, wind-blown snow and small avalanches are visible on the webcam pictures. Three high-quality DEMs (e.g. no clouds) were taken on these days. The snow fall and snow redistribution thereafter can be reconstructed with the these DEMs and data from automatic weather stations on Hintereisferner and its surrounding slopes. To support the process analysis, we simulated the case study days with the Weather Research and Forecasting model (WRF) with a high-resolution setup of dx=48m. A recently implemented snow drift module allows to assess and understand wind-driven snow redistribution on the glacier.

This high-resolution set-up, both on the observational and modelling side, allows an improved understanding of snow distribution over glaciers and has the potential to be applied at other glaciers as well.

How to cite: Voordendag, A., Goger, B., Prinz, R., Sauter, T., and Kaser, G.: Modeling and measuring glacier-wide snow redistribution at Hintereisferner, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5236, https://doi.org/10.5194/egusphere-egu23-5236, 2023.

08:36–08:38
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PICO3a.4
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EGU23-10411
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ECS
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On-site presentation
Seika Tanji, Masaru Inatsu, and Tsubasa Okaze

We developed a snowdrift model to evaluate the snowdrift height around snow fences, which are often installed along roads in snowy, windy locations. The model consisted of the conventional computational fluid dynamics solver that used the lattice Boltzmann method and a module for calculating the snow particles’ motion and accumulation. The calculation domain was a half channel with a flat free-slip boundary on the top and a non-slip boundary on the bottom, and an inflow with artificially generated turbulence from one side to the outlet side was imposed. In addition to the reference experiment with no fence, experiments were set up with a two-dimensional and a three-dimensional fence normal to the dominant wind direction in the channel center. The estimated wind flow over the two-dimensional fence was characterized by a swirling eddy in the cross-section, whereas the wind flow in the three-dimensional fence experiment was horizontally diffluent with a dipole vortex pair on the leeward side of the fence. Almost all the snowdrifts formed on the windward side of the two-dimensional and three-dimensional fences and the outlines were reasonable for the observation results. The snowdrift around the three-dimensional fence also formed along the split streaks on the leeward side. Our results suggested that the fence should be as long as possible to avoid snowdrifts on roads.

How to cite: Tanji, S., Inatsu, M., and Okaze, T.: Development of a snowdrift model with the lattice Boltzmann method and comparison with the observation results, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10411, https://doi.org/10.5194/egusphere-egu23-10411, 2023.

08:38–08:40
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PICO3a.5
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EGU23-12384
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ECS
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On-site presentation
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Manuel Saigger, Thomas Mölg, Christina Schmid, and Tobias Sauter

We present a new framework to simulate snow drift in the Weather Research and Forecasting (WRF) model. Here, we show the basic structure of the module, first results of several test applications as well as our future plans with the model.

In past studies, wind-driven redistribution of snow has been shown to greatly influence the spatial structure of snow accumulation. Additionally, sublimation from blowing snow particles can - depending on the atmospheric conditions - act as an important process for mass loss of snow. Hence, to improve our understanding of snow accumulation an accurate representation of snow drift in our models is needed.

Our new module calculates snow drift inside WRF which allows for a more direct coupling to the fields of wind and turbulence. Additionally, with our approach drifting snow sublimation can also feed back into the model’s fields of temperature and moisture and consequently the wind field.

The model has been tested extensively both in idealized and realistic settings in the Alps and provided physically reasonable results consistent with our basic understanding of drifting snow.

With the model we intend to get a deeper understanding on the role of snow drift for glacier mass balance. Our future plans with the model are twofold. For specific case studies high-resolution simulations of drifting snow events will be carried out. Apart from that we plan to expand the evaluation to a (multi-)seasonal perspective. In order to keep a high degree of complexity while staying computationally feasible (also with regard to climatological time scales), we plan to train a deep-learning model on the WRF-simulated fields of snow drift. We intend to use this trained model to reconstruct high-resolution seasonal snow accumulation including drifting snow.

How to cite: Saigger, M., Mölg, T., Schmid, C., and Sauter, T.: Simulating snow drift in WRF – First results and future plans of a novel module, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12384, https://doi.org/10.5194/egusphere-egu23-12384, 2023.

08:40–08:42
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PICO3a.6
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EGU23-9017
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On-site presentation
Luis Durán and Álvaro González-Cervera

The snowpack is a fundamental element of the cryosphere and understanding its dynamics is crucial for regions where runoff  is the main source of freshwater. Snowpack variations in very short periods of time can have important security and logistical consequences. On the other hand, snowpack height measurements are complex due to its high spatial variability. The thermodynamic and physical processes that the snowpack undergoes are complex and are dominated by meteorological forcings which are also complex, specially in mountain regions. The most important forcing in terms of snowpack height variation is precipitation. It is well known how precipitation in the form of rain decreases the height of the snowpack almost immediately, while precipitation in the form of snow,  increases its height. The problem is that precipitation occurs with a variety of populations of phases, so this mixed precipitation makes the conversion between precipitation and snow height increase not straightforward.  Disdrometers are instruments capable of determining the size and speed at which precipitation falls very precisely. The population of different sizes and terminal velocities is known as the spectrogram. This map of velocities and sizes makes it possible to estimate the phase since their terminal fall velocities of rain and snow are very different. These instruments are very useful to determine the intensity of mixed precipitation and are widely spreaded in airports, highways and mountain areas. In this work we analyse the possibility of developing a relatively simple algorithm that from the size and velocity distributions detected by a disdrometer we could predict the variation of the snowpack in the next few hours. Several techniques have been tested in this work, some of them simple correlations. But the method that really outstanded was the one based on a reduction of the dimensions of the spectrograms applying a principal component analysis which is then used to search analogue situations. Although the available data is still very small, the results encourage to refine this technique when more data will be available in the next winters.

How to cite: Durán, L. and González-Cervera, Á.: Linking Precipitation Size and Velocity Distribution with Snowpack Height Changes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9017, https://doi.org/10.5194/egusphere-egu23-9017, 2023.

08:42–08:44
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PICO3a.7
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EGU23-11393
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On-site presentation
Present and future evolution of the winter snow cover in the French Vosges massif with the help of the regional climate MAR model
(withdrawn)
Xavier Fettweis, Nicolas Ghilain, Bruno Ambroise, Pierre-Marie David, Patrice Paul, and Cyril Wuest
08:44–08:46
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PICO3a.8
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EGU23-10566
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On-site presentation
Agnes Richards, Felix Ouellet, Erika Boisvert-Vigneault, and Alexandre Langlois

The climate is changing rapidly in the Canadian Arctic and in southern regions in Canada such as the Great Lakes. We adapted the SNOWPACK model from traditional avalanche applications for the Canadian Artic and for two southern regions with moderate (Bay of Quinte, Ontario) to low snowfall (Wigle Creek, Ontario). We developed innovative tools to process large meteorological forcing data and to spatialize output. We also developed a downscaling tool (Outil de Spatialisation de SNOWPACK pour l’Arctique - OSSA) using changes in slope, which refined the spatial resolution of simulations by 45-fold. Our simulations in the Arctic demonstrated that icing events tripled across the Canadian Arctic Archipelago between 1979-2011.  SNOWPACK simulations (1970s to 2020) for the Bay of Quinte focused on changes in snow parameters such as Snow Water Equivalent (SWE), which drives snow melt and flooding. Other parameters such as snow density will also be discussed. Simulations show a substantial change in SWE, especially after 2000. In the region with low snowfall (Wigle Creek), simulations of snow on and off will be presented.

We will also illustrate how we advanced SNOWPACK model validation standards though a multi-pronged approach: 1) remote sensing data to validate snow spatial extent, 2) field measurements with sensors to quantify soil temperature feedback, 3) traditional snow pits to validate SWE, and 4) drones to measure snow height and SWE. Finally, we show that validation standards should be adapted to each region based on snowfall and snowmelt. 

How to cite: Richards, A., Ouellet, F., Boisvert-Vigneault, E., and Langlois, A.: Advancing snow modelling across Canada from the Arctic to southern regions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10566, https://doi.org/10.5194/egusphere-egu23-10566, 2023.

08:46–08:48
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PICO3a.9
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EGU23-10501
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ECS
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On-site presentation
Ian McDowell, Kaitlin Keegan, McKenzie Skiles, Christopher Donahue, Erich Osterberg, Robert Hawley, and Hans-Peter Marshall

The physical structure of the firn column directly influences the transport and storage of infiltrating water generated by surface melt in ice sheet accumulation zones. Firn density is relatively easy to measure in field or laboratory settings and provides porosity-based estimates of the meltwater storage capacity but does not describe meltwater movement through open pore space. Pore structure controls meltwater flow and is better characterized by microstructural parameters, such as grain size. Firn grain size is therefore a state variable that needs to be accurately modeled or measured to quantify meltwater transport and storage in the firn column. Manually or digitally measuring grain size from firn samples can be tedious, time consuming, and subjective. Here, we characterize firn structure from 14 firn cores spanning approximately 1000 km across western Greenland’s percolation zone. We scanned the top 10 m of each core with a near infrared hyperspectral imager (NIR-HSI; 900-1700 nm) mounted on a linear translation stage. Leveraging the relationship between ice grain size and near infrared absorption, we invert measured reflectance to retrieve an effective grain radius, resulting in a high-resolution (~ 0.4 mm) grain size map of the firn core. We compare the retrievals against traditional grain size measurements from 7 of the cores. Additionally, the hyperspectral firn core grain size maps allow for quickly retrieving vertical ice layer distributions within the firn column and identifying regions that have been previously wetted that are not readily apparent by visual inspection. We use our unique dataset to examine correlations between grain size, infiltration ice content, and measured firn density to determine whether microstructural information can be extracted from firn density measurements. While cores provide a snapshot of firn conditions at the time of collection, we show that hyperspectral imaging of firn cores can reveal a detailed hydrologic history of the firn column and provide validation data for modeling future meltwater percolation.

How to cite: McDowell, I., Keegan, K., Skiles, M., Donahue, C., Osterberg, E., Hawley, R., and Marshall, H.-P.: Unique insights into firn structure across western Greenland’s percolation zone from hyperspectral images of shallow firn cores, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10501, https://doi.org/10.5194/egusphere-egu23-10501, 2023.

08:48–08:50
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PICO3a.10
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EGU23-12163
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On-site presentation
Florent Domine, Kévin Fourteau, and Philippe Choler

Shrubs covered by snow enhance ground cooling in winter because branches act as thermal bridges between the cold atmosphere and the warmer ground. This process is particularly active in the Arctic, because frozen wood has a thermal conductivity 50 times larger than Arctic depth hoar. Since shrubs are widespread in the Arctic, thermal bridging must be incorporated in snow models for proper simulations of the ground thermal regime, of the temperature gradient in the snowpack, and of snow metamorphism. In alpine regions, the thermal contrast between wood and snow is less than 10 because unfrozen wood has a lower thermal conductivity than frozen wood and because alpine snow is more conductive than Arctic depth hoar. The thermal impact of mountain shrubs may therefore be considered negligible. Measurements of ground temperature and liquid water content at an Alpine site (Lautaret pass, 2050 m, French Alps) with 2 m tall alders next to mountain grasslands surprisingly show that alders do impact noticeably the ground thermal regime. Under grasslands, the ground remains at 0°C and very little ground water freezes. Under alders, most ground water freezes and the temperature drops below -1°C. We perform finite elements simulations to assess the capacity of the alders to act as thermal bridges though two phenomena: the thickness of the alder branches that compensate the lower wood/snow thermal contrast, and protruding branches acting as radiators releasing heat into the atmosphere. We conclude that shrubs covered by snow affect the ground and the snowpack thermal regime even in alpine regions. The impact of this process on carbon cycling in mountains deserve further investigations.

How to cite: Domine, F., Fourteau, K., and Choler, P.: Shrubs are widespread in Snowpacks and Affect Ground Temperature. Models Must Include this Process, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12163, https://doi.org/10.5194/egusphere-egu23-12163, 2023.

08:50–08:52
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PICO3a.11
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EGU23-8118
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ECS
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On-site presentation
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Dominik Míka and Michal Jeníček

Spatial and seasonal distribution of runoff in mountain catchments is largely influenced by snow. Therefore, snow storage is an important component of every hydrological model simulating runoff from mountainous catchments. Besides snow cover on the ground, snow storage includes also snow interception, a certain amount of snow captured by vegetation on its canopies. Generally, snow interception is an important part of the hydrological balance in forested catchments. This study builds on results of a snow interception model applied for six consecutive winter seasons 2016-2022 in the Vydra catchment, an experimental research catchment of the Charles University located in the Bohemian Forest, Czechia. The model was validated against measured snow depth and snow water equivalent data in the forest (dominant species Picea abies) and in adjacent open area. Field research has been carried out at the research site to describe the canopy structure of the spruce forest using hemispherical images. The vegetation characteristics were essential for modelling of the snow interception. The mean Leaf Area Index (LAI) calculated from the hemispherical images at the study plot reached 2.34 with the respective canopy closure equal to 86.16%. The LAI values ranged from 2.03 to 2.72 representing the range of canopy closure from 83.6 to 90.2%. These values were further used for calculation of seasonal cumulative snow interception at the study plot for the selected period. The snow interception reached from 68.2 to 105.3 mm in individual years which represent from 31 to 49% of the total seasonal snowfall. The snow interception efficiency differed in every winter season, reflecting the varying weather conditions during seasons and different extremity of snowfall events. Overall, the results of snow interception model is promising and will be further use to improve runoff simulation in experimental catchment.

How to cite: Míka, D. and Jeníček, M.: Modelling snow interception in a spruce forest in varying climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8118, https://doi.org/10.5194/egusphere-egu23-8118, 2023.

08:52–08:54
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PICO3a.12
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EGU23-13880
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ECS
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On-site presentation
Jari-Pekka Nousu, Giulia Mazzotti, Pertti Ala-aho, Hannu Marttila, Samuli Launiainen, Bertrand Cluzet, Mika Aurela, Pasi Kolari, Annalea Lohila, and Matthieu Lafaysse

The snow cover has a major influence on the wintertime surface energy budget. Accurate simulation of the snowpack energy fluxes is difficult due to limitations in the parameterization of turbulent fluxes under stable conditions and landscape properties (e.g. canopy and topography) that complicate the radiation budget. In fact, description of turbulent fluxes is subject to major uncertainties in snow modelling, and simulating snow in forests is critical for hydrological and climate modelling. Yet, detailed studies that evaluate the models with surface energy flux observations at high latitudes are rare. In this study, we evaluate components of the SURFEX land surface model on four eddy covariance sites in Finland. These sites cover two different climate and snow conditions, the southern and northern subarctic zone, and two different boreal landscape types, peatland and forest. On the peatland sites, we evaluate the sensitivity of simulated surface energy fluxes and snow conditions to different process parameterizations (e.g. snow processes and turbulent exchange) implemented in the detailed snowpack model Crocus. On the forest sites, we examine alternative approaches to represent the energy and mass budgets of the soil and vegetation with the ISBA and MEB models, and assess their performance in simulating energy fluxes, snow conditions and soil thermal regimes. We show that the turbulent fluxes under stable conditions simulated by the default stability correction function do not match the observed values, and thus, it is necessary to increase the simulated turbulent exchange under stable conditions. Moreover, we demonstrate that explicit vegetation is required to concurrently simulate accurate surface heat fluxes and snow/soil conditions in forests. Our results have larger implications for choosing suitable model parameterizations and structures depending on the use case of interest.

How to cite: Nousu, J.-P., Mazzotti, G., Ala-aho, P., Marttila, H., Launiainen, S., Cluzet, B., Aurela, M., Kolari, P., Lohila, A., and Lafaysse, M.: Snowpack, soil and forest energy budget and flux partitioning in boreal ecosystems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13880, https://doi.org/10.5194/egusphere-egu23-13880, 2023.

08:54–08:56
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PICO3a.13
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EGU23-12620
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ECS
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On-site presentation
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Adrià Fontrodona-Bach, Bettina Schaefli, Ross Woods, Ryan Teuling, and Josh Larsen

Ground-based observation datasets of Snow Water Equivalent (SWE) are scarce. In contrast, numerous long-term and good quality ground observations of snow depth are available. Furthermore, an increasing number of models can accurately convert snow depth to SWE. We present a novel dataset of SWE time series over the Northern Hemisphere based on in-situ observations of snow depth. We convert snow depth to SWE using the DeltaSNOW model and we present a method to generalise the conversion model for global use. We calibrate the model over a wide range of climates with the SNOTEL dataset and we regionalise the model parameters based on climate variables. We evaluate this approach on independent datasets such as the Canadian SWE dataset and other European SWE datasets. The key strengths of the modelling approach and the SWE dataset are the excellent performance of peak SWE and timing of snowmelt season onset. The final SWE dataset contains 11,003 stations with daily SWE and snow density time series distributed across the Northern Hemisphere, including mountain regions, at the point scale, and spanning the period 1950-2022. The dataset is available and free to access. It can be used for a variety of applications including validation of remote sensing of snow, hydrological modelling, water resources assessment and climate change impact analyses.

How to cite: Fontrodona-Bach, A., Schaefli, B., Woods, R., Teuling, R., and Larsen, J.: NH-SWE: A new Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series (1950-2022), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12620, https://doi.org/10.5194/egusphere-egu23-12620, 2023.

08:56–08:58
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PICO3a.14
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EGU23-14676
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On-site presentation
The Davos Environmental DataSet: applications to numerical modeling
(withdrawn)
Mathias Bavay, Dionis Anderegg, Charles Fierz, Joel Fiddes, Esther Frei, Julian Gröbner, Alec van Herwijnen, Franz Herzog, Markus Hubert, Tobias Jonas, Michael Lehning, David Liechti, Hanspeter Lötscher, Christoph Marty, Marcia Phillips, and Rudolf Weber
08:58–09:00
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PICO3a.15
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EGU23-10617
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ECS
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Virtual presentation
Hao Yin and Jie Su

Snow plays a vital role in the coupled ocean-ice-atmosphere system with its unique thermodynamic properties. Snow density is set to be a constant (about 320 kg·m-3) in most present sea ice models, ignoring the seasonal evolution of density and relevant thermodynamic regimes. We introduced the layered snow density evolution scheme into the Los Alamos Sea Ice Model (CICE), making it possible to access the diagnostic time-varying snow density. Forcing by ERA5, the modeled results of both CICE and its one-dimension submodule Icepack were compared with buoy observations (Ice Mass Balance buoys, IMB), remote sensing (The advanced Microwave Scanning Radiometer 2, AMSR2), as well as model results from SnowModel-LG, one of the popular snow models with the most sophisticated physical processes.The monthly average snow density absolute bias between the results of the improved CICE and SnowModel-LG, is about 30±13 kg·m-3 in most months except Jul. and Aug. Relatively fresh snow density is found in SnowModel-LG results because most of the winter snow has melted in these two months, while old snow still remains in CICE. This causes a 100~200 kg·m-3 differences of snow density in the two results in this period. The annual mean (1990~2018) contribution of strain compaction, fresh snowfall, and wind compaction on the density evolution is about 1:-16:17.5, respectively, with the effects of the latter two compensating each other and out to a value of the same magnitude as the first component. Verification of the 1D results with 42 IMBs observations showed great agreements among snow depth (Hs), ice thickness (Hi), and snow/ice temperature (Ts/Ti) in the standard Icepack run with constant snow density. Several improvements were found in the new simulations of Hs (reduce 30% of 3 cm overestimation), Hi (reduce 34% of 0.04 m overestimation), and Ts/Ti (increase 50% of 1.4°C underestimation for snow and 10% of 0.7°C underestimation for ice, respectively) with the layered snow scheme in the winter seasons. In 2D CICE model, the implement of new snow parameterization improved the simulation of Hs in the Central Arctic (CA, north of 80°N) obviously. The overestimated 5 cm Hs under the standard CICE run can be reduced about 10% in the new experiment relative to the AMSR2 retrieval snow depth data in winter (Nov.-Apr.) from 2013 to 2018.

How to cite: Yin, H. and Su, J.: Impacts of a layer snow density evolution scheme on the Arctic snow simulation based on the CICE sea-ice model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10617, https://doi.org/10.5194/egusphere-egu23-10617, 2023.

09:00–10:15