Displays

Italy is a territory characterized by complex orography. Its main mountain chains are the Alps, which identify the northern Italian border, and the Apennines, which cross the entire Italian peninsula ranging from north-west to south-east. The major Apennines peaks reach almost 3000 meters and are located in central Italy, in the Abruzzo region. The near Mediterranean sea is an important source of moisture, which permits to this region to experience a substantial snow cover during winter. Thanks to the orientation of the Apennines chain and the height of its peaks the Abruzzo region is characterized by different climate types. This affects the precipitation patterns and the snowpack evolution, resulting in high regional variability of the snow cover. The goal of this study is to investigate the snow cover evolution in the Abruzzo region, using and comparing different snowpack models. To this end we have used the Weather Research and Forecasting (WRF) model to drive the Noah Land Surface Model (LSM) and the sophisticated three-dimensional snow cover model Alpine3D to simulate the snow cover evolution at regional scale. Noah LSM is already on-line coupled with WRF, but this is not the case for Alpine3D. Thus we have modified and used the interfacing library MeteoIO to force Alpine3D with the meteorological data simulated with WRF, off-line coupling the two models. We have validated the WRF simulation using a dense network of automatic weather stations (AWS), obtaining good agreement between simulated and observed data. We have found that the snow depth simulated with Noah LSM presents a negative bias, caused by the inability of the model to reproduce correctly the snow densification rate. Instead, Alpine3D is capable to better reproduce the observed densification rate, thanks to its more detailed description of the snow metamorphism processes. However, the snow depth simulated with Alpine3D presents a negative bias, caused by an underestimation of the new snow depth, which has a negative impact on the entire simulation.

How to cite: Raparelli, E., Tuccella, P., Ferretti, R., and Marzano, F. S.: Snowpack modelling in central Italy: analysis and comparison of high-resolution WRF-driven Noah LSM and Alpine3D simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19408, https://doi.org/10.5194/egusphere-egu2020-19408, 2020.

Conventional wisdom holds that confidence in future projections of snow cover extent and snow mass requires an understanding of the expected changes in future snow characteristics as a function of modelled snow processes. We will highlight contrasting results which suggest differing importance in the role of sub-grid scale processes on simulations of seasonal snow.

The first study is an evaluation of simulated snow cover extent projections from models participating in the 6th phase of the World Climate Research Programme Coupled Model Inter-comparison Project (CMIP-6). We demonstrate a single linear relationship between projected spring snow extent and global surface air temperature (GSAT) changes, which is valid across all future climate scenarios. This finding suggests that Northern Hemisphere spring snow extent will decrease by about 8% relative to the 1995-2014 level per °C of GSAT increase. The sensitivity of snow to temperature forcing largely explains the absence of any climate change pathway dependency, similar to other fast response components of the cryosphere such as sea ice and near surface permafrost.

The second study makes use of an ensemble of land surface models, downscaled to 5 km resolution across North America over the 2009-2017 period. In this case, uncertainty in total North American snow mass is dominated by differences among land surface model configurations. While the largest absolute spread in snow mass is found in mountainous regions, heavily vegetated boreal regions have the largest fractional spread compared to climatological values. In particular, differences in rain-snow partitioning and sublimation rates control the largest portions of the total uncertainty. These results suggest that projections of future snow mass depend specifically on how such processes are modelled and parameterized.

How to cite: Mudryk, L., Krinner, G., Derksen, C., Santolaria-Otin, M., Menegoz, M., Brutel-Vuilmet, C., Vuyovich, C., Kumar, S., and Kim, R. S.: The importance of modelled processes in the evolution of snow cover versus snow mass, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20859, https://doi.org/10.5194/egusphere-egu2020-20859, 2020.

Mountain snow regimes will be significantly altered by climate warming, resulting in shallower snowpacks whose duration is also reduced. The sensitivity of snowpacks to a unit of air temperature warming depends strongly on climate; in addition, for a given climate, the sensitivity also depends on the details of energy balance partitioning. A synthesis of these factors remains challenging. Here we evaluate to what extent a naïve theory of snowpack response to warming can reproduce the sensitivity which is calculated by a detailed physically based model of the snowpack (Snobal), applied to a diverse global set of mountain locations. Our hypothesis is that the naïve theory will adequately predict the range of snow sensitivity values across diverse climates, but not the additional impacts of inter-site differences in energy partitioning for a given climate. The potential benefits of the naïve theory are that it enables a significant reduction of the uncertainty of snowpack sensitivity, and an improved conceptual understanding of the impacts of climate parameters (e.g. the seasonality and fluctuations of temperature and precipitation) on snowpack accumulation and melt-sensitivity under warming climates.

How to cite: Woods, R., López Moreno, J. I., Alonso-Gonzalez, E., Revuelto, J., Larsen, J., and Schaefli, B.: Testing a Naive Snow Theory Against a Physically Based Model: Sensitivity of Global Mountain Snow Regimes to Increased Air Temperatures, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5467, https://doi.org/10.5194/egusphere-egu2020-5467, 2020.

This contribution presents an overview of the last stable release of the Crocus detailed snowpack model in the SURFEX opensource modelling platform. It gathers numerous recent scientific and technical developments in a common code version. An explicit representation of the evolution of light absorbing particles mass in snow (e.g. black carbon, mineral dust) allows representing their impact on solar radiation absorption in the snowpack in the visible and near-infrared spectrum through the TARTES optical scheme, and the consequences on all snowpack properties. Crocus is now coupled to the MEB (Multiple Energy Balance) vegetation scheme and can therefore be applied on forested areas. A module of snow management including grooming and snow making can also be optionally activated to simulate the snowpack on ski slopes. Developments used in the French operational system in support of avalanche hazard forecasting were also fully integrated in SURFEX: the SYTRON module for snow erosion and accumulation by the wind and the expert system MEPRA which analyses the mechanical stability of the simulated snowpacks. Finally, an ensemble multiphysics version of the model (ESCROC) was also developed by implementing from 2 to 4 parameterizations from the literature for each physical process represented by an uncertain empirical parameterization. The different combinations enable the quantification of simulations uncertainty required in various applications: future projections of snow cover; sensitivity analyses of a given process ; data assimilation of snow observations. Crocus and ESCROC are included in the ESM-SnowMIP model intercomparison and exhibit a robust skill in various climates and environments. Several running-time optimizations were also implemented in the latest release. We present an overview of the current numerical cost with a comparison to more classical snow schemes used in NWP and climate model applications. The code is provided through a git repository and with a simple visualization software to help users to display snowpack internal properties at local scale. On-going works are focused on the implementation of new data assimilation algorithms well suited to the numerical specificities of this scheme. An externalized version for coupling with other surface schemes is currently under development.

How to cite: Lafaysse, M., Dumont, M., Nheili, R., Viallon-Galinier, L., Carmagnola, C., Cluzet, B., Fructus, M., Hagenmuller, P., Morin, S., Spandre, P., Tuzet, F., and Vionnet, V.: Latest scientific and technical evolutions in the Crocus snowpack model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10217, https://doi.org/10.5194/egusphere-egu2020-10217, 2020.

Snow management, i.e., snowmaking and grooming, is an integral part of modern ski resort operation. While the current snow cover distribution on the slopes is often well known thanks to the usage of advanced monitoring techniques, estimates about its future evolution are usually lacking. Management-enabled numerical snowpack models driven by meteorological forecasts can help to fill this gap. In the frame of the H2020 project PROSNOW such software tools are developed to be run on an operational basis with the aim to optimize snow management as well as the use of water and energy resources. As part of PROSNOW, model simulations for the ski resorts Seefeld and Obergurgl (both Austria) as well as Colfosco and San Vigilio (both Italy) are performed with the physically based snow model AMUNDSEN. In its particular snow management module, both socioeconomic and physical factors are considered, the former concerning the decision when, where and how much snow should be produced, and the latter considering the snowmaking conditions, i.e., how much snow can be produced in the current ambient conditions (in terms of temperature and humidity) and the given ski resort infrastructure (number and efficiency of snow guns, water availability, etc.).

In our contribution we show the implementation of snowmaking and grooming practices in the AMUNDSEN model, its adaptation to individual ski resorts, and how different potential snow management strategies are accounted for. Model results obtained using historical meteorological observations and hindcast simulations are validated against observations from numerous data sources such as Sentinel-2 snow cover maps, distributed snow depth measurements from groomers, temperature and humidity measurements from snow guns as well as water consumption recordings.

How to cite: Hanzer, F., Günther, D., Strasser, U., Premier, V., Callegari, M., Marin, C., and Notarnicola, C.: Process-based simulation of snow cover evolution in ski resorts using the AMUNDSEN model: results and validation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17783, https://doi.org/10.5194/egusphere-egu2020-17783, 2020.

The snowpack continuously evolves due to metamorphic and mechanical processes. Understanding and quantifying these processes and in particular their complex interplay and impact on the snowpack's strength is challenging, yet of large interest to the snowpack modeling community. Due to the layer representation and the absence of an explicit numerical solution of the mass continuity equation in common snowpack models, competing effects of mechanical settling and phase changes (e.g. due to vapor transport) can hardly be assessed faithfully. Towards a remedy, we investigate the potential of a numerical scheme that treats the vapor recrystallization term on a moving mesh as imposed by the settling term of the continuity equation.
First, we introduce a continuum mechanical snowpack model that explicitly accounts for both water vapor transport induced by temperature gradients, and settling processes. Next, we describe a computational approach to solve the coupled snowpack model. Water vapor transport as a result of temperature and condensation rate evolution are solved by means of a finite difference scheme. Accounting for settling processes requires to solve an additional ice volume balance, which is done based on the method of characteristics. Its advantage is that it can exactly account for the moving upper free surface of the snowpack. Unstructured meshes enable us to track (potentially densifying) snow layers at high spatial resolution. A closure for the settling velocity is formulated in terms of stresses from the overburdened snow mass and snow viscosity. The proposed continuum-mechanical snowpack model enables us for the first time to investigate the coupled interplay and relative importance of water vapor transport and snowpack settling on time scales from minutes to several hours. 
In a series of numerical examples, we present simulation results for varying snow heights (0.02 - 1 m), snow densities (100 - 917 kgm^-3) and temperature gradients (20, 100, 1000 Km^-1) to assess the effect of simultaneous snowpack settling and water vapor transport. The proposed model allows us to observe the downward propagation of a snow layer interface coupled to water vapor deposition and sublimation, at high spatial resolution. One of our major findings is that while settling might strongly increase ice densities, it also has an additional (weaker) impact on the condensation rate. Finally, we will discuss to which extent the proposed novel computational approach could be used to study and quantify the interplay of coupled mechanical and metamorphic processes in future community snowpack models, for instance while identifying regimes that require to account for process coupling.

How to cite: Simson, A., Kowalski, J., and Löwe, H.: Numerical solution of the mass continuity equation for snowpack modeling on moving meshes: Coupling between mechanical settling and water vapor transport, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10629, https://doi.org/10.5194/egusphere-egu2020-10629, 2020.

The necessity of characterizing snow through objective, physically-motivated parameters has led to new model formulations and new measurement techniques. Consequently, essential structural parameters such as density and specific surface area (for basic characterization) or mechanical parameters such as the critical crack length (for avalanche stability characterization) gradually replace the semi-empirical indices acquired from traditional stratigraphy. These advances come along with new demands and potentials for validation. To this end, we conducted the RHOSSA field campaign, in resemblance of density (ρ) and specific surface area (SSA), at the Weissfluhjoch research site in the Swiss Alps to provide a multi-instrument, multi-resolution dataset of density, SSA, and critical crack length over the complete winter season 2015-2016. In this paper, we present the design of the campaign and a basic analysis of the measurements alongside with predictions from the model SNOWPACK. To bridge between traditional and new methods, the campaign comprises traditional profiles, density cutter, IceCube, SnowMicroPen (SMP), micro-computed-tomography, propagation saw tests, and compression tests. To bridge between different temporal resolutions, the traditional weekly to bi-weekly snow pits were complemented by daily SMP measurements. From the latter, we derived a re-calibration of the statistical retrieval of density and SSA for SMP version 4 that yields an unprecedented, spatio-temporal picture of the seasonal evolution of density and SSA in a snowpack. Finally, we provide an inter-comparison of measured and modeled estimates of density and SSA for 4 characteristic layers over the entire season to demonstrate the potential of high temporal resolution monitoring for snowpack model validation.

How to cite: Calonne, N., Richter, B., Löwe, H., Cetti, C., Ter Schure, J., Van Herwiijnen, A., Fierz, C., Jaggi, M., and Schneebeli, M.: The RHOSSA campaign: Multi-resolution monitoring of the seasonal evolution of the structure and mechanical stability of an alpine snowpack, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21893, https://doi.org/10.5194/egusphere-egu2020-21893, 2020.

The parametrization in numerical models of the behavior of water in snow is either oversimplified - the bucket method – or hugely complicated – the Richardson equation. The latter faithfully resembles the general behavior of water in snow, when a dual domain approach, representing slow matrix and fast preferential flow, is taken. However, this type of models are unsuitable for application in climate models due to their high computation costs.

Therefore, an implicit Richardson equation model is developed, which is able to run on time steps of several minutes, typical for climate models, and snow layer thickness down to a few centimeters. In order to reach to a differentiable governing equation, required for iterative implicit time stepping, with as few as possible discontinuities in the derivatives, favorable for convergence, modifications are made in the governing equations when the water content approaches the irreducible water content or water almost fills the available pore space. Here, we show the first results of this model, with a focus on the impact of parameterization choices on the modelled water flow, refreezing profile and melt water buffering capacity.

How to cite: van de Berg, W. J.: An implicit physical water percolation model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11824, https://doi.org/10.5194/egusphere-egu2020-11824, 2020.

The Snow Water Equivalent (SWE), combining the information of snow depth and snow density is a necessary variable for snow-hydrological studies and applications, as well as, for ecological function or avalanche forecasting. Direct automatics measurements of SWE requires an easy access to the monitoring site while manual measurements are costly and challenging. On the other hands, physically based models for snow density estimates require local meteorological data limiting their application in complex topography such as mountains areas. For this reason, different empirical regressions methods for the characterization of SWE and associated variability have been proposed for regional studies. In this study, we report our experience based on simple regression models able to characterize the new snow density and the snow bulk density at the scale of the entire Italian Alps, taking advantage of a decade of distributed observations. 12112 snowfall observations (2005-2015) gathered at 122 stations, ranging from 650 m to 2858 m a.s.l., have been analyzed to characterize the new snow density. 6078 snowpack depth and bulk density measurements (2009-2018) from 150 sites, ranging from 640 m to 3400 m a.s.l., have been collected to investigate the snow bulk density.

The mean air temperature of the 24 hours preceding the snowfall event, as a proxy of the transformation of freshly-fallen snow, has been found to be the best predictor of the new snow density, within 30% of uncertainty over the whole Italian Alps. While monthly regression allows considering part of the snow state variability through seasonality, the analysis of the associated residues suggests that, in the lack of local wind field information, the adoption of a local approach is not able to substantially increase the predictive capabilities of the model. The snow bulk density variability mainly responds to seasonality and can be estimated adopting the day of the year, as a proxy of the combined effect of compaction through seasonal snow accumulation and partial melting during the late season. Such approach enables a continuous (along the season) description of the SWE variation within 15% of uncertainty, similar to the within-site variability, presenting even better performances during the late season through the introduction of non-linearity. Differently from new snow density, regionalization performed considering separately those regions close to the sea improves the overall performances.

Although more performing models have already been proposed, the variables necessary to feed the proposed regressions (i.e. mean air temperature for new snow density and the day of the year for the bulk snow density) are easy to be acquired, making the proposed models valuable tools either in case of low instrumented watersheds or for past reconstruction. Finally, the low number of parameters to be calibrated makes the proposed regressions easy to be tested in other regions.  

How to cite: Guyennon, N., Salerno, F., Valt, M., Petrangeli, A. B., Salvatori, R. M., and Romano, E.: Simple estimations of new and bulk snow density in the Italian Alps: Lessons from a decade of distributed observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18125, https://doi.org/10.5194/egusphere-egu2020-18125, 2020.

The mechanics of snow friction are central to competitive skiing, safe winter driving, avalanche dynamics, and efficient Polar sleds. For nearly 80 years, prevailing theory has postulated self-lubrication: dry-contact sliding warms snow-grains to the melting point, and further sliding produces melt-water that lubricates the interface. We recently published micro-scale interface observations that contradicted this explanation: contacting snow grains abraded and did not melt under a polyethylene slider, despite low friction values. We obtained coordinated infrared, visible-light, and scanning-electron micrographs that confirm that the evolving shapes observed during our tribometer tests are contacting snow grains polished by abrasion, and that the wear particles can sinter together and fill the adjacent pore spaces. Furthermore, dry-contact abrasive wear reasonably predicts the evolution of snow-slider contact area, and sliding-heat-source theory confirms that contact temperatures would not reach 0°C during our tribometer tests. Importantly, published measurements of interface temperatures also indicate that melting did not occur during field tests on sleds and skis. We postulate that abraded ice crystals form a dry-lubricant layer that makes contacting snow-grains slippery and are currently undertaking additional observations and theoretical analyses to assess this hypothesis.

How to cite: Lever, J., Taylor, S., Hoch, G., and Asenath-Smith, E.: Why is Snow Slippery? The Role of Abrasion in Snow Kinetic Friction, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1468, https://doi.org/10.5194/egusphere-egu2020-1468, 2020.

The seasonal melt of mountain snow-cover provides a vital source of freshwater for downstream systems, sustaining multiple productive uses, population needs, and unique ecosystems. In the semi-arid Andes Cordillera, the snowpack acts as a natural water reservoir, releasing spring snowmelt runoff that accounts for more than 60 % of the total annual streamflow. Hence, understanding and characterizing the spatial variability of snow over this large domain is critical for accurate hydrological predictions. We examine the probability density functions and the geostatistical structure of snow depth through variogram analysis, using terrestrial lidar scans acquired during two seasons (2018 and 2019). First, we compare the spatial patterns of snow depth near maximum accumulation at three experimental sites: (i) the Tascadero catchment (-31.26°N, 3270-3790 m a.s.l.), (ii) the Las Bayas experimental catchment (-33.31°N, 3218-4022 m a.s.l.); and (iii) the Valle Hermoso catchment (-36.91°N, 1449-2563 m a.s.l.). Second, we analyze the inter- and intra-annual variability of snow depth patterns in the Las Bayas catchment, where seven scans were acquired during seasons 2018 and 2019. 

The comparison across sites reveals snow depth fractal behavior until a first omnidirectional scale break in the range 15-22 m for unvegetated areas, and a short-range fractal dimension spanning 2.5-2.65. In the woodland of Valle Hermoso, a much shorter (5 m) scale break and a larger short-range fractal dimension (2.73) are found. Secondary scale ranges and breaks spanning 62-125 m are found in all sites but Tascadero, where snow depth follows a bimodal distribution across the domain. In the Las Bayas domain, inter-annual consistency is found in snow scaling patterns, with two distinct regions separated by a short scale break ~6 m early in the winter, increasing to larger break lengths (15-18 m) in July and August. These results help to inform about the appropriate spatial configuration for snowpack modeling across the Andes. Efforts to better understand the modulation of topography (slope and wind exposure) and vegetation on snow depth distribution patterns, as well as impacts of dominant wind directions on anisotropies in fractal parameters, are ongoing.

How to cite: Mendoza, P., Shaw, T., Pinto, F., Lagos, M., Revuelto, J., Musselman, K., MacDonell, S., and McPhee, J.: Scaling behavior of lidar-derived snow depth across the semi-arid Chilean Andes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11313, https://doi.org/10.5194/egusphere-egu2020-11313, 2020.

Whenever there is snow on the ground, there will be large spatial variability in snow depth. The spatial distribution of snow is significantly influenced by topography due to wind, precipitation, shortwave and longwave radiation, and even snow avalanches relocate the accumulated snow. Fractional snow-covered area (fSCA) is an important model parameter characterizing the fraction of the ground surface that is covered by snow and is crucial for various model applications such as weather forecasts, climate simulations and hydrological modeling.

We recently suggested an empirical fSCA parameterization based on two spatial snow depth data sets acquired at peak of winter in Switzerland and Spain, which yielded best performance for spatial scales larger than 1000 m. However, this parameterization was not validated on independent snow depth data. To evaluate and improve our fSCA parameterization, in particular with regards to other spatial scales and snow climates (or geographic regions), we used spatial snow depth data sets form a wide range of mountain ranges in USA, Switzerland and France acquired by 5 different measuring methods. Pooling all snow depth data sets suggests that a scale-dependent parameter should be introduced to improve the fSCA parameterization, in particular for sub-kilometer spatial scales. Extending our empirical fSCA parameterization to a broader range of scales and snow climates is an important step towards accounting for spatio-temporal variability in snow depth in multiple snow model applications.

How to cite: Helbig, N., Bühler, Y., Eberhard, L., Deschamps-Berger, C., Gascoin, S., Dumont, M., Deems, J., and Jonas, T.: Towards a scale-independent fractional snow-covered area parameterization for complex terrain, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14575, https://doi.org/10.5194/egusphere-egu2020-14575, 2020.

Wind-blown snow does not lie flat. It self-organizes into dunes, waves, ripples, and anvil-shaped sastrugi. These features, called snow bedforms, are high-speed analogues of sand features barchans, ripples, and yardangs. Snow bedforms appear within hours or days after a blizzard, and may migrate as fast as several meters per hour. They are widespread, and affect the albedo and thermal properties of snow across the polar regions, but thus far they have attracted little attention within aeolian geomorphology.

For the past three winters, I have documented the growth of snow bedforms in Colorado Front Range. I present time-lapse footage showing the movement of snow dunes, ripples and sastrugi (see tinyurl.com/bedform-videos). These observations show that (1) snow is only flat when winds are slower than 6.4 m/s (2) snow dunes adjust minute-by-minute to changes in wind speed, (3) the most widespread bedform, sastrugi, evolve by migrating and eroding downwind, and (4) snow waves and dunes deposit layers of cohesive snow in their wakes, and thus aid snow deposition in windy conditions. These observations provide the basis for new conceptual models of bedform evolution based on the rates of snowfall, aeolian transport, erosion, and snow sintering across the snowscape.

How to cite: Kochanski, K., Anderson, R., and Tucker, G.: The growth of snow bedforms, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5918, https://doi.org/10.5194/egusphere-egu2020-5918, 2020.

Surface mass balance (SMB) represents a large uncertainty in characterizing Antarctic Ice Sheet (AIS) mass balance. Atmospheric reanalysis products, which are commonly used for AIS SMB studies, do not include small-scale snow redistribution processes even though these can be of the same order of magnitude as snow accumulation in many parts of the AIS. Therefore, a proper representation of these processes is critical to interpret local SMB and firn observations, such as from ICESat-2 repeat altimetry. In this study, we use a detailed, multi-layer snow model (SNOWPACK) forced by a global atmospheric reanalysis (MERRA-2). Firstly, we show that a new accumulation scheme, designed to better represent wind-driven snow compaction in SNOWPACK, substantially reduces simulated biases in near-surface snow density at 131 locations across the AIS. Next, we employ a distributed version of SNOWPACK to two regions on the AIS, and compare the simulation output to airborne radar and in-situ observations of SMB. Our results demonstrate that SNOWPACK can capture the timing of blowing snow events, snow erosion events, as well as observed kilometer-scale spatial SMB variability. This study illustrates the importance of using high-resolution SMB models when converting surface height (volume) observations to mass changes.

How to cite: Lenaerts, J., Keenan, E., Wever, N., Dattler, M., Reijmer, C., and Medley, B.: Detailed simulations of snow properties and accumulation across the Antarctic Ice Sheet , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10368, https://doi.org/10.5194/egusphere-egu2020-10368, 2020.

Understanding the deposition history and signal formation in ice cores from polar ice sheets is fundamental for the interpretation of paleoclimate reconstruction based on climate proxies. Polar surface snow responds to environmental changes on a seasonal time scale by snow metamorphism, displayed in the snow microstructure and archived in the snowpack. However, the seasonality of snow metamorphism and its link to the deposited signal in isotopes and impurity load is poorly known.

Here, we apply core-scale microfocus X-ray computer tomography to continuously measure snow microstructure of four snow cores from Greenlandic (Renland ice cap-drill site (2m), EASTGRIP drill site (5m)) and Antarctic sites (EDML-drill site (3m), COFI7/Plateau station (4m)) covering a wide range of annual temperatures from -18°C down to -56°C. In our multi-parameter approach we compare the derived microstructural properties on the mm- to cm-scale to discretely measured trace components and stable water isotopes, commonly used as climate proxies. We will show how ice and pore intercepts, the geometrical anisotropy, specific surface area, crusts anomalies and small-scale density distributions are represented under different climate conditions. Their profiles will be discussed in the context of snow metamorphism and deposition history using trace components and isotopes as additional constraints on timing.

How to cite: Freitag, J., Hörhold, M., Weinhart, A., Kipfstuhl, S., and Laepple, T.: The genesis of a climate archive: snow pack studies at four polar sites, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19249, https://doi.org/10.5194/egusphere-egu2020-19249, 2020.