Statistical understanding for snow cover effects on near-surface ground temperature at the margin of maritime Antarctica, King George Island

Geoderma ◽  
2022 ◽  
Vol 410 ◽  
pp. 115661
Author(s):  
Hyoun Soo Lim ◽  
Hyun-Cheol Kim ◽  
Ok-Sun Kim ◽  
Hyejung Jung ◽  
Jeonghoon Lee ◽  
...  
Keyword(s):  
Snow Cover ◽  
Ground Temperature ◽  
King George Island ◽  
Maritime Antarctica ◽  
Near Surface

scholarly journals Sources and composition of chemical pollution in Maritime Antarctica (King George Island), part 2: Organic and inorganic chemicals in snow cover at the Warszawa Icefield

2021 ◽  
pp. 149054
Author(s):  
Danuta Szumińska ◽  
Joanna Potapowicz ◽  
Małgorzata Szopińska ◽  
Sebastian Czapiewski ◽  
Ulrike Falk ◽  
...  
Keyword(s):  
Snow Cover ◽  
Chemical Pollution ◽  
King George Island ◽  
Maritime Antarctica

scholarly journals Small-scale variation of snow in a regional permafrost model

2016 ◽  
Vol 10 (3) ◽  
pp. 1201-1215 ◽  
Author(s):  
Kjersti Gisnås ◽  
Sebastian Westermann ◽  
Thomas Vikhamar Schuler ◽  
Kjetil Melvold ◽  
Bernd Etzelmüller
Keyword(s):  
Snow Cover ◽  
Ground Surface ◽  
Grid Cell ◽  
Ground Temperature ◽  
Fine Tuning ◽  
Small Scale ◽  
Near Surface ◽  
Ground Temperatures ◽  
Surface Temperatures ◽  
Scale Variation

Abstract. The strong winds prevalent in high altitude and arctic environments heavily redistribute the snow cover, causing a small-scale pattern of highly variable snow depths. This has profound implications for the ground thermal regime, resulting in highly variable near-surface ground temperatures on the metre scale. Due to asymmetric snow distributions combined with the nonlinear insulating effect of snow, the spatial average ground temperature in a 1 km2 area cannot be determined based on the average snow cover for that area. Land surface or permafrost models employing a coarsely classified average snow depth will therefore not yield a realistic representation of ground temperatures. In this study we employ statistically derived snow distributions within 1 km2 grid cells as input to a regional permafrost model in order to represent sub-grid variability of ground temperatures. This improves the representation of both the average and the total range of ground temperatures. The model reproduces observed sub-grid ground temperature variations of up to 6 °C, and 98 % of borehole observations match the modelled temperature range. The mean modelled temperature of the grid cell reproduces the observations with an accuracy of 1.5 °C or better. The observed sub-grid variations in ground surface temperatures from two field sites are very well reproduced, with estimated fractions of sub-zero mean annual ground surface temperatures within ±10 %. We also find that snow distributions within areas of 1 km2 in Norwegian mountain environments are closer to a gamma than to a lognormal theoretical distribution. The modelled permafrost distribution seems to be more sensitive to the choice of distribution function than to the fine-tuning of the coefficient of variation. When incorporating the small-scale variation of snow, the modelled total permafrost area of mainland Norway is nearly twice as large compared to the area obtained with grid-cell average snow depths without a sub-grid approach.

scholarly journals Small-scale variation of snow in a regional permafrost model

2015 ◽  
Vol 9 (6) ◽  
pp. 6661-6696
Author(s):  
K. Gisnås ◽  
S. Westermann ◽  
T. V. Schuler ◽  
K. Melvold ◽  
B. Etzelmüller
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Ground Temperature ◽  
Small Scale ◽  
Spatial Average ◽  
Near Surface ◽  
Ground Temperatures ◽  
Grid Approach ◽  
Realistic Representation ◽  
Strong Winds

Abstract. The strong winds prevalent in high altitude and arctic environments heavily redistribute the snow cover, causing a small-scale pattern of highly variable snow depths. This has profound implications for the ground thermal regime, resulting in highly variable near-surface ground temperatures on the meter scale. Asymmetric snow distributions combined with the non-linear insulating effect of snow also mean that the spatial average ground temperature in a 1 km2 area can not necessarily be determined based on the average snow cover for that area. Land surface or permafrost models employing a coarsely classified average snow depth will therefore not yield a realistic representation of ground temperatures. In this study we employ statistically derived snow distributions within 1 km2 grid cells as input to a regional permafrost model in order to represent sub-grid variability of ground temperatures. This is shown to improve the representation of both the average and the total range of ground temperatures: the model results show that we reproduce observed sub-grid ground temperature variations of up to 6 °C, with 98 % of borehole observations within the modelled temperature range. Based on this more faithful representation of ground temperatures, we find the total permafrost area of mainland Norway to be nearly twice as large as what is modelled without a sub-grid approach.

scholarly journals Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland

2021 ◽  
Vol 13 (11) ◽  
pp. 2045
Author(s):  
Anaí Caparó Bellido ◽  
Bradley C. Rundquist
Keyword(s):  
Snow Cover ◽  
Near Infrared ◽  
Accuracy Assessment ◽  
Important Variable ◽  
Field Station ◽  
Near Surface ◽  
Fractional Snow Cover ◽  
Rgb Images ◽  
Phenological Metrics

Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology. This research explores the utility of the PhenoCam Network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve the satellite-based determination of phenological metrics. The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. We developed a semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research employing RGB images only, our use of the monochrome RGB + NIR (near-infrared) reduced pixel misclassification and increased accuracy. The results had an average RMSE of less than 8% FSC compared to visual estimates. Our pixel-based accuracy assessment showed that the overall accuracy of the images selected for validation was 92%. This is a promising outcome, although not every PhenoCam Network system has NIR capability.

Seashore sediment and water chemistry at the Admiralty Bay (King George Island, Maritime Antarctica) – Geochemical analysis and correlations between the concentrations of chemical species

2020 ◽  
Vol 152 ◽  
pp. 110888
Author(s):  
Joanna Potapowicz ◽  
Danuta Szumińska ◽  
Małgorzata Szopińska ◽  
Robert Józef Bialik ◽  
Katarzyna Machowiak ◽  
...  
Keyword(s):  
Water Chemistry ◽  
Chemical Species ◽  
Geochemical Analysis ◽  
King George Island ◽  
Maritime Antarctica ◽  
Admiralty Bay

scholarly journals Seasonal Influence of Insolation on Fine-Resolved Air Temperature Variation and Snowmelt

2014 ◽  
Vol 53 (2) ◽  
pp. 323-332 ◽  
Author(s):  
Nikki Vercauteren ◽  
Steve W. Lyon ◽  
Georgia Destouni
Keyword(s):  
Solar Radiation ◽  
Snow Cover ◽  
Eastern Coast ◽  
Seasonal Influence ◽  
Near Surface ◽  
Average Temperature ◽  
Monthly Average ◽  
Air Temperatures ◽  
Low Performance ◽  
Snow Cover Duration

AbstractThis study uses GIS-based modeling of incoming solar radiation to quantify fine-resolved spatiotemporal responses of year-round monthly average temperature within a field study area located on the eastern coast of Sweden. A network of temperature sensors measures surface and near-surface air temperatures during a year from June 2011 to June 2012. Strong relationships between solar radiation and temperature exhibited during the growing season (supporting previous work) break down in snow cover and snowmelt periods. Surface temperature measurements are here used to estimate snow cover duration, relating the timing of snowmelt to low performance of an existing linear model developed for the investigated site. This study demonstrates that linearity between insolation and temperature 1) may only be valid for solar radiation levels above a certain threshold and 2) is affected by the consumption of incoming radiation during snowmelt.

Climate change in the High Mountain Asia simulated with CMIP6 models

2021 ◽  
Author(s):  
Mickaël Lalande ◽  
Martin Ménégoz ◽  
Gerhard Krinner
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Climate Models ◽  
Global Climate Models ◽  
Cold Bias ◽  
High Mountain ◽  
The Tibetan Plateau ◽  
Near Surface ◽  
Model Biases ◽  
Snow Cover Extent

<p>The High Mountains of Asia (HMA) region and the Tibetan Plateau (TP), with an average altitude of 4000 m, are hosting the third largest reservoir of glaciers and snow after the two polar ice caps, and are at the origin of strong orographic precipitation. Climate studies over HMA are related to serious challenges concerning the exposure of human infrastructures to natural hazards and the water resources for agriculture, drinking water, and hydroelectricity to whom several hundred million inhabitants of the Indian subcontinent are depending. However, climate variables such as temperature, precipitation, and snow cover are poorly described by global climate models because their coarse resolution is not adapted to the rugged topography of this region. Since the first CMIP exercises, a cold model bias has been identified in this region, however, its attribution is not obvious and may be different from one model to another. Our study focuses on a multi-model comparison of the CMIP6 simulations used to investigate the climate variability in this area to answer the next questions: (1) are the biases in HMA reduced in the new generation of climate models? (2) Do the model biases impact the simulated climate trends? (3) What are the links between the model biases in temperature, precipitation, and snow cover extent? (4) Which climate trajectories can be projected in this area until 2100? An analysis of 27 models over 1979-2014 still show a cold bias in near-surface air temperature over the HMA and TP reaching an annual value of -2.0 °C (± 3.2 °C), associated with an over-extended relative snow cover extent of 53 % (± 62 %), and a relative excess of precipitation of 139 % (± 38 %), knowing that the precipitation biases are uncertain because of the undercatch of solid precipitation in observations. Model biases and trends do not show any clear links, suggesting that biased models should not be excluded in trend and projections analysis, although non-linear effects related to lagged snow cover feedbacks could be expected. On average over 2081-2100 with respect to 1995-2014, for the scenarios SSP126, SSP245, SSP370, and SSP585, the 9 available models shows respectively an increase in annual temperature of 1.9 °C (± 0.5 °C), 3.4 °C (± 0.7 °C), 5.2 °C (± 1.2 °C), and 6.6 °C (± 1.5 °C); a relative decrease in the snow cover extent of 10 % (± 4.1 %), 19 % (± 5 %), 29 % (± 8 %), and 35 % (± 9 %); and an increase in total precipitation of 9 % (± 5 %), 13 % (± 7 %), 19 % (± 11 %), and 27 % (± 13 %). Further analyses will be considered to investigate potential links between the biases at the surface and those at higher tropospheric levels as well as with the topography. The models based on high resolution do not perform better than the coarse-gridded ones, suggesting that the race to high resolution should be considered as a second priority after the developments of more realistic physical parameterizations.</p>

Experiments on wind-driven heat exchange processes over melting snow

2021 ◽  
Author(s):  
Michael Haugeneder ◽  
Tobias Jonas ◽  
Dylan Reynolds ◽  
Michael Lehning ◽  
Rebecca Mott
Keyword(s):  
Snow Cover ◽  
Surface Wind ◽  
Infrared Camera ◽  
Snow Accumulation ◽  
Internal Boundary ◽  
Small Scale ◽  
Snow Melt ◽  
Two Dimensional ◽  
Atmospheric Conditions ◽  
Near Surface

<p>Snowmelt runoff predictions in alpine catchments are challenging because of the high spatial variability of t<span>he snow cover driven by </span>various snow accumulation and ablation processes. In spring, the coexistence of bare and snow-covered ground engages a number of processes such as the enhanced lateral advection of heat over partial snow cover, the development of internal boundary layers, and atmospheric decoupling effects due to increasing stability at the snow cover. The interdependency of atmospheric conditions, topographic settings and snow coverage remains a challenge to accurately account for these processes in snow melt models.<br>In this experimental study, we used an Infrared Camera (VarioCam) pointing at thin synthetic projection screens with negligible heat capacity. Using the surface temperature of the screen as a proxy for the air temperature, we obtained a two-dimensional instantaneous measurement. Screens were installed across the transition between snow-free and snow-covered areas. With IR-measurements taken at 10Hz, we capture<span> the dynamics of turbulent temperature fluctuations</span><span> </span>over the patchy snow cover at high spatial and temporal resolution. From this data we were able to obtain high-frequency, two-dimensional windfield estimations adjacent to the surface.</p><p>Preliminary results show the formation of a stable internal boundary layer (SIBL), which was temporally highly variable. Our data suggest that the SIBL height is very shallow and strongly sensitive to the mean near-surface wind speed. Only strong gusts were capable of penetrating through this SIBL leading to an enhanced energy input to the snow surface.</p><p>With these type of results from our experiments and further measurements this spring we aim to better understand small scale energy transfer processes over patch snow cover and it’s dependency on the atmospheric conditions, enabling to improve parameterizations of these processes in coarser-resolution snow melt models.</p>

scholarly journals Mapping the Distribution of Buried Glacier Ice – An Example From Lago Delle Locce, Monte Rosa, Italian Alps

1986 ◽  
Vol 8 ◽  
pp. 78-81 ◽  
Author(s):  
W. Haeberli ◽  
F. Epifani
Keyword(s):  
Snow Cover ◽  
Seismic Refraction ◽  
Italian Alps ◽  
Frozen Ground ◽  
Near Surface ◽  
Winter Snow ◽  
Glacier Ice ◽  
Geoelectrical Resistivity ◽  
Monte Rosa

Techniques for mapping the distribution of buried glacier ice are discussed and the results, from a study carried out within the framework of flood protection work in the Italian Alps, are presented. Bottom temperatures of the winter snow cover (BTS) primarily indicate the heat flow conditions in the underlying ground and mainly depend on the presence or absence of an ice layer beneath the surface. Determination of BTS values is therefore an inexpensive method for quickly mapping the near-surface underground ice in areas where there is 1 m or more of winter snow cover. At greater depths, and/or when more detail is required, geoelectrical resistivity soundings and seismic refraction soundings are most commonly used to investigate underground ice. A combination of the two sounding techniques allows the vertical extent and the main characteristics (frozen ground, dead glacier ice) to be determined in at least a semi-quantitative way. Complications mainly arise from irregularity in the horizontal extension of the studied underground ice bodies, and they may have to be overcome by expensive core drillings and borehole measurements. Widespread occurrence of buried glacier ice was observed in morainic deposits, surrounding an ice-dammed lake near Macugnaga, Italy.

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