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

<p>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 °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 °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 (<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 °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 <em>Sphagnum</em> sp. to <em>Cyperaceae</em>. 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 (>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.</p><p> </p><p>References</p><p>Kjellman, S.E. et al., 2018: Holocene development of subarctic permafrost peatlands in Finnmark, northern Norway. <em>The Holocene</em> 28, 1855–1869, doi:10.1177/0959683618798126.</p><p>Sannel, A.B.K. et al., 2018: Holocene development and permafrost history in sub-arctic peatlands in Tavvavuoma, northern Sweden. <em>Boreas</em> 47, 454–468, doi:10.1111/bor.12276.</p>

scholarly journals Evaluation of Remotely Sensed Snow Water Equivalent and Snow Cover Extent over the Contiguous United States

2018 ◽  
Vol 19 (11) ◽  
pp. 1777-1791 ◽  
Author(s):  
Nicholas Dawson ◽  
Patrick Broxton ◽  
Xubin Zeng
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Snow Cover Extent ◽  
Snow Water ◽  
The Mean

Abstract Global snow water equivalent (SWE) products derived at least in part from satellite remote sensing are widely used in weather, climate, and hydrometeorological studies. Here we evaluate three such products using our recently developed daily 4-km SWE dataset available from October 1981 to September 2017 over the conterminous United States. This SWE dataset is based on gridded precipitation and temperature data and thousands of in situ measurements of SWE and snow depth. It has a 0.98 correlation and 30% relative mean absolute deviation with Airborne Snow Observatory data and effectively bridges the gap between small-scale lidar surveys and large-scale remotely sensed data. We find that SWE products using remote sensing data have large differences (e.g., the mean absolute difference from our SWE data ranges from 45.8% to 59.3% of the mean SWE in our data), especially in forested areas (where this percentage increases up to 73.5%). Furthermore, they consistently underestimate average maximum SWE values and produce worse SWE (including spurious jumps) during snowmelt. Three additional higher-resolution satellite snow cover extent (SCE) products are used to compare the SCE values derived from these SWE products. There is an overall close agreement between these satellite SCE products and SCE generated from our SWE data, providing confidence in our consistent SWE, snow depth, and SCE products based on gridded climate and station data. This agreement is also stronger than that between satellite SCE and those derived from the three satellite SWE products, further confirming the deficiencies of the SWE products that utilize remote sensing data.

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 Validation of the operational MSG-SEVIRI snow cover product over Austria

2013 ◽  
Vol 10 (10) ◽  
pp. 12153-12185
Author(s):  
S. Surer ◽  
J. Parajka ◽  
Z. Akyurek
Keyword(s):  
Snow Cover ◽  
Spatial Resolution ◽  
Snow Depth ◽  
Second Generation ◽  
Mapping Accuracy ◽  
Cloud Coverage ◽  
Temporal Sampling ◽  
Elevation Difference ◽  
The Mean ◽  
Alpine Valleys

Abstract. The objective of this study is to evaluate the mapping accuracy of the MSG-SEVIRI operational snow cover product over Austria. The SEVIRI instrument is on board of the geostationary Meteosat Second Generation (MSG) satellite. The snow cover product provides 32 images per day with a relatively low spatial resolution of 5 km over Austria. The mapping accuracy is examined at 178 stations with daily snow depth observations and compared with the daily MODIS combined (Terra + Aqua) snow cover product in the period April 2008–June 2012. The results show that the 15 min temporal sampling allows a significant reduction of clouds in the snow cover product. The mean annual cloud coverage is less than 30% in Austria, as compared to 52% for the combined MODIS product. The mapping accuracy for cloud-free days is 89% as compared to 94% for MODIS. The largest mapping errors are found in regions with large topographical variability. The errors are noticeably larger at stations with elevations that differ much from those of the mean MSG-SEVIRI pixel elevations. The median of mapping accuracy for stations with absolute elevation difference less than 50 m and more than 500 m is 98.9% and 78.2%, respectively. A comparison between the MSG-SEVIRI and MODIS products indicates an 83% overall agreement. The largest disagreements are found in alpine valleys and flatland areas in the spring and winter months, respectively.

scholarly journals Simulation of Snowmelt in a Subarctic Spruce Woodland: Scale Considerations

2000 ◽  
Vol 31 (4-5) ◽  
pp. 301-316 ◽  
Author(s):  
Ming-ko Woo ◽  
Mark A. Giesbrecht
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Depth Distribution ◽  
Information Loss ◽  
Grid Size ◽  
Absolute Value ◽  
The Mean ◽  
The Difference ◽  
A Site ◽  
Cover Pattern

Subarctic woodlands comprise stands of spruce trees with varying degrees of openness, giving rise to large contrasts in melt rates within the forest. The spatial variability of the changing snow depth during a melt season was investigated at three scales (2,4 and 16 m), using an example from a site in Yukon, Canada, where the computation of snowmelt takes into account the differential rates within the woodland. During the melt period, the mean daily snow depth decreases but the variability increases as continued ablation leads to greater unevenness of the snow cover. At the three scales of representation, increasing the grid size results in a reduction in the standard deviation and the skewness of depth distribution. The blurring of snow cover pattern at the larger scales is due to a loss in information, considered as the absolute value of the difference in snow depth calculated at two scales for the same location. This loss increases as the snow depth becomes more variable during the melt season. Knowledge of the scale-induced information loss is relevant to the modelling of snowmelt that exhibits large spatial variations.

scholarly journals Thermal erosion patterns of permafrost peat plateaus in northern Norway

2020 ◽  
Author(s):  
Léo C. P. Martin ◽  
Jan Nitzbon ◽  
Johanna Scheer ◽  
Kjetil S. Aas ◽  
Trond Eiken ◽  
...  
Keyword(s):  
Thermal Regime ◽  
Snow Depth ◽  
Subsurface Water ◽  
Small Scale ◽  
Surface Model ◽  
Permafrost Degradation ◽  
Modeling Framework ◽  
Northern Norway ◽  
Thermal Erosion ◽  
Peat Plateaus

Abstract. Subarctic peatlands underlain by permafrost contain significant amounts of organic carbon and our ability to quantify the evolution of such permafrost landscapes in numerical models is critical to provide robust predictions of the environmental and climatic changes to come. Yet, the accuracy of large-scale predictions is so far hampered by small-scale physical processes that create a high spatial variability of surface ground thermal regime and thus of permafrost degradation patterns. In this regard, a better understanding of the small-scale interplay between microtopography and lateral fluxes of heat, water and snow can be achieved by field monitoring and process-based numerical modeling. Here, we quantify the topographic changes of the Šuoššjávri peat plateau (Northern Norway) over a three-years period using repeated drone-based high-resolution photogrammetry. Our results show that edge degradation is the main process through which thermal erosion occurs and represents about 80 % of measured subsidence, while most of the inner plateau surface exhibits no detectable subsidence. Based on detailed investigation of eight zones of the plateau edge, we show that this edge degradation corresponds to a volumetric loss of 0.13 ± 0.07 m3 yr−1 m−1 (cubic meter per year and per meter of plateau circumference). Using the CryoGrid land surface model, we show that these degradation patterns can be reproduced in a modeling framework that implements lateral redistribution of snow, subsurface water and heat, as well as ground subsidence due to melting of excess ice. We reproduce prolonged climate-driven edge degradation that is consistent with field observations and present a sensitivity test of the plateau degradation on snow depth over the plateau. Small snow depth variations (from 0 to 30 cm) result in highly different degradation behavior, from stability to fast degradation. These results represent a new step in the modeling of climate-driven landscape development and permafrost degradation in highly heterogeneous landscapes such as peat plateaus. Our approach provides a physically based quantification of permafrost thaw with a new level of realism, notably, regarding feedback mechanisms between the dynamical topography and the lateral fluxes through which a small modification of the snow depth result in dramatic modifications of the permafrost degradation intensity. In this regard, these results also highlight the major control of snow pack characteristics on the ground thermal regime and the potential improvement that accurate snow representation and prediction could bring to projections of permafrost degradation.

scholarly journals Spatiotemporal dynamics of snow cover based on multi-source remote sensing data in China

2016 ◽  
Vol 10 (5) ◽  
pp. 2453-2463 ◽  
Author(s):  
Xiaodong Huang ◽  
Jie Deng ◽  
Xiaofang Ma ◽  
Yunlong Wang ◽  
Qisheng Feng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Depth ◽  
Winter Season ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
The Tibetan Plateau ◽  
Optical Remote Sensing ◽  
The North ◽  
The Mean

Abstract. By combining optical remote sensing snow cover products with passive microwave remote sensing snow depth (SD) data, we produced a MODIS (Moderate Resolution Imaging Spectroradiometer) cloudless binary snow cover product and a 500 m snow depth product. The temporal and spatial variations of snow cover from December 2000 to November 2014 in China were analyzed. The results indicate that, over the past 14 years, (1) the mean snow-covered area (SCA) in China was 11.3 % annually and 27 % in the winter season, with the mean SCA decreasing in summer and winter seasons, increasing in spring and fall seasons, and not much change annually; (2) the snow-covered days (SCDs) showed an increase in winter, spring, and fall, and annually, whereas they showed a decrease in summer; (3) the average SD decreased in winter, summer, and fall, while it increased in spring and annually; (4) the spatial distributions of SD and SCD were highly correlated seasonally and annually; and (5) the regional differences in the variation of snow cover in China were significant. Overall, the SCD and SD increased significantly in south and northeast China, and decreased significantly in the north of Xinjiang province. The SCD and SD increased on the southwest edge and in the southeast part of the Tibetan Plateau, whereas it decreased in the north and northwest regions.

scholarly journals Atmospheric Flow Development and Associated Changes in Turbulent Sensible Heat Flux over a Patchy Mountain Snow Cover

2015 ◽  
Vol 16 (3) ◽  
pp. 1315-1340 ◽  
Author(s):  
Rebecca Mott ◽  
Megan Daniels ◽  
Michael Lehning
Keyword(s):  
Boundary Layer ◽  
Heat Flux ◽  
Energy Balance ◽  
Snow Cover ◽  
Sensible Heat Flux ◽  
Ambient Air ◽  
Small Scale ◽  
Sensible Heat ◽  
Katabatic Winds ◽  
The Mean

Abstract In this study, the small-scale boundary layer dynamics and the energy balance over a fractional snow cover are numerically investigated. The atmospheric boundary layer flows over a patchy snow cover were calculated with an atmospheric model (Advanced Regional Prediction System) on a very high spatial resolution of 5 m. The numerical results revealed that the development of local flow patterns and the relative importance of boundary layer processes depend on the snow patch size distribution and the synoptic wind forcing. Energy balance calculations for quiescent wind situations demonstrated that well-developed katabatic winds exerted a major control on the energy balance over the patchy snow cover, leading to a maximum in the mean downward sensible heat flux over snow for high snow-cover fractions. This implies that if katabatic winds develop, total melt of snow patches may decrease for low snow-cover fractions despite an increasing ambient air temperature, which would not be predicted by most hydrological models. In contrast, stronger synoptic winds increased the effect of heat advection on the catchment’s melt behavior by enhancing the mean sensible heat flux over snow for lower snow-cover fractions. A sensitivity analysis to grid resolution suggested that the grid size is a critical factor for modeling the energy balance of a patchy snow cover. The comparison of simulation results from coarse (50 m) and fine (5 m) horizontal resolutions revealed a difference in the spatially averaged turbulent heat flux over snow of 40%–70% for synoptic cases and 95% for quiescent cases.

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 Warming permafrost and active layer variability at Cime Bianche, Western Alps

2014 ◽  
Vol 8 (4) ◽  
pp. 4033-4074
Author(s):  
P. Pogliotti ◽  
M. Guglielmin ◽  
E. Cremonese ◽  
U. Morra di Cella ◽  
G. Filippa ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Snow Cover ◽  
Layer Thickness ◽  
Active Layer ◽  
Ground Surface ◽  
Small Scale ◽  
Active Layer Thickness ◽  
Spatial And Temporal Variability ◽  
Monitoring Site ◽  
Surface Temperatures

Abstract. The objective of this paper is to provide a first synthesis on the state and recent evolution of permafrost at the monitoring site of Cime Bianche (3100 m a.s.l.). The analysis is based on seven years of ground temperatures observations in two boreholes and seven surface points. The analysis aims to quantify the spatial and temporal variability of ground surface temperatures in relation to snow cover, the small scale spatial variability of the active layer thickness and the warming trends on deep permafrost temperatures. Results show that the heterogeneity of snow cover thickness, both in space and time, is the main factor controlling ground surface temperatures and leads to a mean range of spatial variability (2.5±0.15°C) which far exceeds the mean range of observed inter-annual variability (1.6±0.12°C). The active layer thickness measured in two boreholes 30 m apart, shows a mean difference of 2.03±0.15 m with the active layer of one borehole consistently lower. As revealed by temperature analysis and geophysical soundings, such a difference is mainly driven by the ice/water content in the sub-surface and not by the snow cover regimes. The analysis of deep temperature time series reveals that permafrost is warming. The detected linear trends are statistically significant starting from depth below 8 m, span the range 0.1–0.01°C year−1 and decrease exponentially with depth. Our findings are discussed in the context of the existing literature.

scholarly journals Linking tundra vegetation, snow, soil temperature, and permafrost

2020 ◽  
Author(s):  
Inge Grünberg ◽  
Evan J. Wilcox ◽  
Simon Zwieback ◽  
Philip Marsh ◽  
Julia Boike
Keyword(s):  
Snow Cover ◽  
Layer Thickness ◽  
Active Layer ◽  
Large Degree ◽  
Small Scale ◽  
Active Layer Thickness ◽  
Snow Melt ◽  
Vegetation Types ◽  
Soil Temperatures ◽  
Temporal And Spatial Characteristics

Abstract. Soil temperatures in permafrost regions are highly heterogeneous on small scales, in part due to variable snow and vegetation cover. Moreover, the temperature distribution that results from the interplay of complex biophysical processes remains poorly constrained. Sixty-eight temperature loggers were installed to record the distribution of topsoil temperatures at the Trail Valley Creek study site in the Northwestern Canadian Arctic. The measurements were distributed across six different vegetation types characteristic for this landscape. Two years of topsoil temperature data were analysed statistically to identify temporal and spatial characteristics and their relationship to vegetation, snow cover and active layer thickness. The mean annual topsoil temperature varied between −3.7 °C and 0.1 °C within a 1.2 km distance, with an approximate average across the landscape of −2.3 °C in 2017 and −1.7 °C in 2018. The observed variation can, to a large degree, be explained by variation in snow cover. Differences in height between vegetation types cause spatially variable snow depth during winter, leading to spatially variable snow melt timing in spring, causing pronounced differences in topsoil mean temperature and temperature variability during those time periods. Summer topsoil temperatures were quite similar below most vegetation types, and not consistently related to active layer thickness at the end of August. The small-scale pattern of vegetation and its influence on snow cover height and snow melt governs the annual topsoil temperature in this permafrost-underlain landscape.

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