scholarly journals How much can we save? Impact of different emission scenarios on future snow cover in the Alps

2017 ◽  
Vol 11 (1) ◽  
pp. 517-529 ◽  
Author(s):  
Christoph Marty ◽  
Sebastian Schlögl ◽  
Mathias Bavay ◽  
Michael Lehning
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Swiss Alps ◽  
Small Scale ◽  
Emission Scenarios ◽  
Scenario Period ◽  
Temperature And Precipitation ◽  
Elevation Zone ◽  
Time Periods ◽  
Intervention Scenario

Abstract. This study focuses on an assessment of the future snow depth for two larger Alpine catchments. Automatic weather station data from two diverse regions in the Swiss Alps have been used as input for the Alpine3D surface process model to compute the snow cover at a 200 m horizontal resolution for the reference period (1999–2012). Future temperature and precipitation changes have been computed from 20 downscaled GCM-RCM chains for three different emission scenarios, including one intervention scenario (2 °C target) and for three future time periods (2020–2049, 2045–2074, 2070–2099). By applying simple daily change values to measured time series of temperature and precipitation, small-scale climate scenarios have been calculated for the median estimate and extreme changes. The projections reveal a decrease in snow depth for all elevations, time periods and emission scenarios. The non-intervention scenarios demonstrate a decrease of about 50 % even for elevations above 3000 m. The most affected elevation zone for climate change is located below 1200 m, where the simulations show almost no snow towards the end of the century. Depending on the emission scenario and elevation zone the winter season starts half a month to 1 month later and ends 1 to 3 months earlier in this last scenario period. The resulting snow cover changes may be roughly equivalent to an elevation shift of 500–800 or 700–1000 m for the two non-intervention emission scenarios. At the end of the century the number of snow days may be more than halved at an elevation of around 1500 m and only 0–2 snow days are predicted in the lowlands. The results for the intervention scenario reveal no differences for the first scenario period but clearly demonstrate a stabilization thereafter, comprising much lower snow cover reductions towards the end of the century (ca. 30 % instead of 70 %).

scholarly journals How much can we save? Impact of different emission scenarios on future snow cover in the Alps

2016 ◽  
Author(s):  
Christoph Marty ◽  
Sebastian Schlögl ◽  
Mathias Bavay ◽  
Lehning Michael
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Swiss Alps ◽  
Small Scale ◽  
Emission Scenarios ◽  
Scenario Period ◽  
Temperature And Precipitation ◽  
Elevation Zone ◽  
Time Periods ◽  
Intervention Scenario

Abstract. This study focuses on an assessment of the future snow depth for two larger Alpine catchments. Automatic weather station data from two diverse regions in the Swiss Alps have been used as input for the Alpine3D surface process model to compute the snow cover at 200 m horizontal resolution for the reference period (1999–2012). Future temperature and precipitation change have been computed from 20 downscaled GCM-RCM chains for three different emission scenarios, including one intervention scenario (2° C target) and for three future time periods (2020–2049, 2045–2074, 2070–2099). By applying simple daily change values to measured time series of temperature and precipitation series small-scale climate scenarios have been calculated for the ensemble mean and extreme changes. The projections reveal a decrease in snow depth for all elevations, time periods and emission scenarios. The non-interventions scenarios demonstrate a decrease of about 50 % even for the elevations above 3000 m. The most affected elevation zone for climate change is located below 1200 m, where the simulations show almost no snow towards the end of the century. Depending on the emission scenario and elevation zone the winter season starts half a month to one month later and ends one to three month earlier in this last scenario period. The resultant snow cover changes may roughly be equivalent to an elevation shift of 500–800 m or 700–1000 m for the two non-intervention emissions scenario. At the end of the century the number of snow days may be more than halved at an elevation of around 1500 m and is predicted to only 0–2 snow days in the lowlands. The results for the intervention scenario reveal no differences for the first scenario period, but clearly demonstrate much lower snow cover reductions towards the end of the century (ca. 30 % instead of 70 %).

scholarly journals Distributed snow and rock temperature modelling in steep rock walls using Alpine3D

2017 ◽  
Vol 11 (1) ◽  
pp. 585-607 ◽  
Author(s):  
Anna Haberkorn ◽  
Nander Wever ◽  
Martin Hoelzle ◽  
Marcia Phillips ◽  
Robert Kenner ◽  
...  
Keyword(s):  
Energy Balance ◽  
Snow Cover ◽  
Snow Depth ◽  
Spatial Scales ◽  
Swiss Alps ◽  
Balance Model ◽  
Near Surface ◽  
Surface Rock ◽  
Depth Data ◽  
Rock Temperature

Abstract. In this study we modelled the influence of the spatially and temporally heterogeneous snow cover on the surface energy balance and thus on rock temperatures in two rugged, steep rock walls on the Gemsstock ridge in the central Swiss Alps. The heterogeneous snow depth distribution in the rock walls was introduced to the distributed, process-based energy balance model Alpine3D with a precipitation scaling method based on snow depth data measured by terrestrial laser scanning. The influence of the snow cover on rock temperatures was investigated by comparing a snow-covered model scenario (precipitation input provided by precipitation scaling) with a snow-free (zero precipitation input) one. Model uncertainties are discussed and evaluated at both the point and spatial scales against 22 near-surface rock temperature measurements and high-resolution snow depth data from winter terrestrial laser scans.In the rough rock walls, the heterogeneously distributed snow cover was moderately well reproduced by Alpine3D with mean absolute errors ranging between 0.31 and 0.81 m. However, snow cover duration was reproduced well and, consequently, near-surface rock temperatures were modelled convincingly. Uncertainties in rock temperature modelling were found to be around 1.6 °C. Errors in snow cover modelling and hence in rock temperature simulations are explained by inadequate snow settlement due to linear precipitation scaling, missing lateral heat fluxes in the rock, and by errors caused by interpolation of shortwave radiation, wind and air temperature into the rock walls.Mean annual near-surface rock temperature increases were both measured and modelled in the steep rock walls as a consequence of a thick, long-lasting snow cover. Rock temperatures were 1.3–2.5 °C higher in the shaded and sunny rock walls, while comparing snow-covered to snow-free simulations. This helps to assess the potential error made in ground temperature modelling when neglecting snow in steep bedrock.

Local simulations of snow redistribution by wind with an intermediate-complexity snow cover model driven by different wind downscaling methods

2021 ◽  
Author(s):  
Louis Quéno ◽  
Paul Morin ◽  
Rebecca Mott ◽  
Tobias Jonas
Keyword(s):  
Snow Cover ◽  
Transport Processes ◽  
Swiss Alps ◽  
Small Scale ◽  
Mountain Range ◽  
Model Framework ◽  
Intermediate Complexity ◽  
Operational Modelling ◽  
Snow Transport ◽  
Large Domains

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

Generation of a high-resolution snow cover dataset from Sentinel-2 images for snow model calibration 

2021 ◽  
Author(s):  
Florentin Hofmeister ◽  
Leonardo F. Arias-Rodriguez ◽  
Marco Borga ◽  
Valentina Premier ◽  
Carlo Marin ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Model Calibration ◽  
Data Availability ◽  
Cloud Detection ◽  
Snow Storage ◽  
Elevation Zone ◽  
Snow Model ◽  
Sentinel 2

<p>Modeling the runoff generation of high elevation Alpine catchments requires fundamental knowledge of the snow storage and the spatial distribution of snow cover. Since in-situ snow observations are often very scarce and represent only a point information, spatial snow information from satellite data is used since decades. However, the accuracy of snow cover maps through remote sensing products depends strongly on the cloudiness. In order to generate a spatial and temporal highly resolved dataset of snow cover maps, we applied the pixel identification processor (IdePix available in SNAP v7.0) to retrieve diverse cloud layers from Sentinel-2 Level-1C products. This makes it possible to use also high-clouded images for the snow detection, which increases significantly the data availability for the later performed snow model calibration. Cloudy areas, for which snow detection by the NDSI calculation is not possible, are set to no data. Sentinel-2 images that do not have cloud information require an extra correction based on the assumption that the snow cover has a pronounced elevation gradient. The entire NDSI dataset is subdivided into 200 m elevation zones and statistically analyzed. Thereby, the cloud-influenced images clearly stand out as outliers in the elevation zones >3000 m. If an elevation zone is detected as an outlier, the corresponding elevation zone is set to no data as well. After the comprehensive cloud detection, a pixel wise comparison with in-situ snow depth observation of four different sites allows us a first validation of the snow detection quality. In a second step, the generated snow maps are compared with the snow and cloud detection algorithm developed by Eurac Research. The final snow cover maps are used together with the in-situ snow depth observations to calibrate two different snowmelt approaches of the hydrological model WaSiM - the T-index and the energy balance-based approach (including gravitational snow redistribution) - over a mountainous basin in the Eastern Italian Alps.</p>

scholarly journals Evaluating Snow in EURO-CORDEX Regional Climate Models with Observations for the European Alps: Biases and Their Relationship to Orography, Temperature, and Precipitation Mismatches

Atmosphere ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 46
Author(s):  
Michael Matiu ◽  
Marcello Petitta ◽  
Claudia Notarnicola ◽  
Marc Zebisch
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Horizontal Resolution ◽  
Winter Period ◽  
European Alps ◽  
Temperature And Precipitation

Climate models are important tools to assess current and future climate. While they have been extensively used for studying temperature and precipitation, only recently regional climate models (RCMs) arrived at horizontal resolutions that allow studies of snow in complex mountain terrain. Here, we present an evaluation of the snow variables in the World Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) RCMs with gridded observations of snow cover (from MODIS remote sensing) and temperature and precipitation (E-OBS), as well as with point (station) observations of snow depth and temperature for the European Alps. Large scale snow cover dynamics were reproduced well with some over- and under-estimations depending on month and RCM. The orography, temperature, and precipitation mismatches could on average explain 31% of the variability in snow cover bias across grid-cells, and even more than 50% in the winter period November–April. Biases in average monthly snow depth were remarkably low for reanalysis driven RCMs (<approx. 30 cm), and large for the GCM driven ones (up to 200 cm), when averaged over all stations within 400 m of altitude difference with RCM orography. Some RCMs indicated low snow cover biases and at the same time high snow depth biases, and vice versa. In summary, RCMs showed good skills in reproducing alpine snow cover conditions with regard to their limited horizontal resolution. Detected shortcomings in the models depended on the considered snow variable, season and individual RCM.

Ground thermal variability and landscape dynamics in a northern Swedish permafrost peatland

2020 ◽  
Author(s):  
A. Britta K. Sannel
Keyword(s):  
Snow Cover ◽  
Active Layer ◽  
Snow Depth ◽  
Small Scale ◽  
Northern Sweden ◽  
Thermokarst Lake ◽  
The Mean ◽  
Landscape Units ◽  
Topographic Variability ◽  
Peat Plateaus

&lt;p&gt;Permafrost peatlands cover extensive areas in subarctic regions, and store large amounts of soil organic carbon that can be remobilized as active layer deepening and thermokarst formation is expected to increase in a future warmer climate. In northern Fennoscandia peatland initiation started soon after the last deglaciation, and throughout most of the Holocene the peatlands were permafrost-free fens. Colder conditions during the Little Ice Age resulted in epigenetic permafrost aggradation (Kjellman et al., 2018; Sannel et al., 2018). Today, these ecosystems are characterized by a complex mosaic of different landscape units including elevated peat plateaus and palsas uplifted above the surrounding wetlands by frost heave, and collapse features such as fens and thermokarst lakes formed as a result of ground-ice melt. This small-scale topographic variability makes the local hydrology, and possibly also the ground thermal regime very variable. In a peat plateau complex in Tavvavuoma, northern Sweden, ground temperatures and snow depth have been monitored within six different landscape units; on a peat plateau, in a depression within a peat plateau, along a peat plateau edge (close to a thermokarst lake), at a thermokarst lake shoreline, in lake sediments and in a fen. A thermal snapshot from 2007/08 shows that permafrost is present in all three peat plateau landscape units, and the mean annual ground temperature (MAGT) at 2 m depth is around -0.3 &amp;#176;C. In the three low-lying and saturated landscape units taliks are present and the MAGT at 1 m depth is 1.0-2.7 &amp;#176;C. Small-scale topographic variability is a key parameter for ground thermal patterns in this landscape affecting both local snow depth and soil moisture. Wind redistribution of snow creates a distinctive pattern with thin snow cover on elevated landforms and thicker cover in low-lying landscape units. Permafrost is present in peat plateaus where the mean December-April snow cover is shallow (&lt;20 cm). In a small depression on the peat plateau permafrost exists despite a 60-80 cm mean December-April snow cover, but here the maximum annual ground temperature at 0.5 m depth is 8-9 &amp;#176;C warmer than in the surrounding peat plateau and the active layer is deeper (100-150 cm compared to 50-55 cm). In recent years, 2006-2019, the depression has experienced continued ground subsidence as a result of permafrost thaw, and the dominant vegetation has shifted from &lt;em&gt;Sphagnum&lt;/em&gt; sp. to &lt;em&gt;Cyperaceae&lt;/em&gt;. This transition could be the initial stage in collapse fen or thermokarst pond formation. In the same time period extensive block erosion and shoreline retreat has occurred along sections of the peat plateau edge where the mean December-April snow cover is deep (&gt;80 cm). In a future warmer climate, permafrost thaw will have a continued impact on landscape changes, shifts in hydrology, vegetation and carbon exchange in this dynamic and climate-sensitive environment.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;References&lt;/p&gt;&lt;p&gt;Kjellman, S.E. et al., 2018: Holocene development of subarctic permafrost peatlands in Finnmark, northern Norway. &lt;em&gt;The Holocene&lt;/em&gt; 28, 1855&amp;#8211;1869, doi:10.1177/0959683618798126.&lt;/p&gt;&lt;p&gt;Sannel, A.B.K. et al., 2018: Holocene development and permafrost history in sub-arctic peatlands in Tavvavuoma, northern Sweden. &lt;em&gt;Boreas&lt;/em&gt; 47, 454&amp;#8211;468, doi:10.1111/bor.12276.&lt;/p&gt;

scholarly journals Variations and changes in snow depth at meteorological stations Barentsburg and Hornsund (Spitsbergen)

2017 ◽  
Vol 58 (75pt1) ◽  
pp. 11-20 ◽  
Author(s):  
Marzena Osuch ◽  
Tomasz Wawrzyniak
Keyword(s):  
Snow Cover ◽  
Interannual Variability ◽  
Trend Analysis ◽  
Air Temperature ◽  
Snow Depth ◽  
Moving Average ◽  
Onset Date ◽  
The Novel ◽  
Temperature And Precipitation ◽  
Snow Cover Duration

ABSTRACTIn this study, seasonality and interannual variability of snow depth at two stations (Hornsund and Barentsburg) located in western Spitsbergen are investigated. For this purpose, the novel Moving Average over Shifting Horizon method combined with trend estimation was used. The Hornsund and Barentsburg stations exhibit similar snow depth trends during early autumn and late spring when statistically significant decreases were estimated at both stations (for August 1984–July 2016). In the remaining period, there are differences in outcomes between stations. The results for Barentsburg from October till the end of May are characterised by the lack of a trend while at Hornsund decreases of snow depth were estimated. The largest changes occur in May when the snow depth was at its maximum. Differences in the estimated tendencies were explained with the help of a trend analysis for air temperature and precipitation. An analysis of maximum snow depth, snow onset date, snow disappearance date and snow-cover duration is included. The results of the assessment depend on the location, with a lack of statistically significant changes in Barentsburg, and later snow onset date, shorter duration and decrease of maximum depth in Hornsund.

Twentieth-century variability in snow- cover conditions and approaches to detecting and monitoring changes: status and prospects

1995 ◽  
Vol 19 (4) ◽  
pp. 520-532 ◽  
Author(s):  
R.G. Barry ◽  
J.-M. Fallot ◽  
R.L. Armstrong
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Regional Scale ◽  
Practical Significance ◽  
Late Nineteenth Century ◽  
Temperature And Precipitation ◽  
Snow Cover Extent ◽  
Microwave Signatures ◽  
Relationship Of ◽  
Former Ussr

The extent, and variability of seasonal snow cover are important parameters in the climate system. Changes in snow cover may provide an indicator of global climatic trends and are of considerable practical significance. The question of the most suitable indices of changes in snow cover conditions, in terms of their use for change detection and for monitoring applications, is discussed. The use of passive microwave-derived estimates of snow cover extent and water equivalent for continental and regional-scale mapping is illustrated. Problems in interpreting the microwave signatures, as well as difficulties in comparing such data to ground observations, are also noted. Up to now analyses have focused primarily on trends in Northern Hemisphere snow extent based on monthly averages using the NOAA weekly snow charts 1972-present, or on station data spanning 50-100 years. However, the latter are generally less readily available, or accessible. An overview is provided of current information on recent hemispheric trends and, for the former USSR, the relationship of changes in snow depth, to variations in temperature and precipitation since the late nineteenth century are described, based on newly available station records. Interpretation of these changes and comparisons with other records are presented. Model projections of changes in snow cover conditions and associated snowmelt runoff that may occur as a result of greenhouse gas-induced warming are discussed for several mountain regions. Long-term station records of snow depth variability provided a valuable context within which such modeling results can be examined.

scholarly journals Influence of Initial Snowpack Properties on Runoff Formation during Rain-on-Snow Events

2016 ◽  
Vol 17 (6) ◽  
pp. 1801-1815 ◽  
Author(s):  
Sebastian Würzer ◽  
Tobias Jonas ◽  
Nander Wever ◽  
Michael Lehning
Keyword(s):  
Energy Balance ◽  
Snow Cover ◽  
Liquid Water ◽  
Snow Depth ◽  
Liquid Water Content ◽  
Swiss Alps ◽  
Runoff Generation ◽  
Runoff Formation ◽  
Rain Events ◽  
Snowpack Properties

Abstract Rain-on-snow (ROS) events have caused severe floods in mountainous areas in the recent past. Because of the complex interactions of physical processes, it is still difficult to accurately predict the effect of snow cover on runoff formation for an upcoming ROS event. In this study, a detailed physics-based energy balance snow cover model (SNOWPACK) was used to assess snow cover processes during more than 1000 historical ROS events at 116 locations in the Swiss Alps. The simulations of the mass and energy balance, liquid water flow, and the temporal evolution of structural properties of the snowpack were used to analyze runoff formation characteristics during ROS events. Initial liquid water content and snow depth at the onset of rainfall were found to influence the temporal dynamics, intensities, and cumulative amount of runoff. The meteorological forcing is modulated by processes within the snowpack, leading to an attenuation of runoff intensities for intense and short rain events and an amplifying effect for longer rain events. The timing of runoff generation relative to the rainfall seems to be strongly dependent on initial liquid water content, snow depth, and rainfall intensities. As these snowpack and meteorological conditions usually exhibit a strong seasonality, cumulative runoff generation during ROS also varies seasonally. ROS events with intensified snowpack runoff were found to be most common during late snowmelt season, with several such events also occurring in late autumn. These results demonstrate the strong influence of initial snowpack properties on runoff formation during ROS events in the Swiss Alps.

scholarly journals Past and future snowmelt trends in the Swiss Alps: the role of temperature and snowpack

2021 ◽  
Vol 165 (3-4) ◽  
Author(s):  
Maria Vorkauf ◽  
Christoph Marty ◽  
Ansgar Kahmen ◽  
Erika Hiltbrunner
Keyword(s):  
Snow Depth ◽  
Growing Season ◽  
High Elevation ◽  
Alpine Plants ◽  
Swiss Alps ◽  
Climate Change Scenarios ◽  
Freezing Damage ◽  
Strong Shift ◽  
Air Temperatures ◽  
Early Snowmelt

AbstractThe start of the growing season for alpine plants is primarily determined by the date of snowmelt. We analysed time series of snow depth at 23 manually operated and 15 automatic (IMIS) stations between 1055 and 2555 m asl in the Swiss Central Alps. Between 1958 and 2019, snowmelt dates occurred 2.8 ± 1.3 days earlier in the year per decade, with a strong shift towards earlier snowmelt dates during the late 1980s and early 1990s, but non-significant trends thereafter. Snowmelt dates at high-elevation automatic stations strongly correlated with snowmelt dates at lower-elevation manual stations. At all elevations, snowmelt dates strongly depended on spring air temperatures. More specifically, 44% of the variance in snowmelt dates was explained by the first day when a three-week running mean of daily air temperatures passed a 5 °C threshold. The mean winter snow depth accounted for 30% of the variance. We adopted the effects of air temperature and snowpack height to Swiss climate change scenarios to explore likely snowmelt trends throughout the twenty-first century. Under a high-emission scenario (RCP8.5), we simulated snowmelt dates to advance by 6 days per decade by the end of the century. By then, snowmelt dates could occur one month earlier than during the reference periods (1990–2019 and 2000–2019). Such early snowmelt may extend the alpine growing season by one third of its current duration while exposing alpine plants to shorter daylengths and adding a higher risk of freezing damage.

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