Challenges of Quantifying Meltwater Retention in Snow and Firn: An Expert Elicitation

As, Dirk van; Box, Jason E.; Fausto, Robert S.

doi:10.3389/feart.2016.00101

Cited by 16 publications

(20 citation statements)

References 18 publications

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“…Our parameterization could benefit from acquiring additional measurements from elevations between 1,000 and 1,750 m above sea level, i.e., in the lower percolation area of the ice sheet (Benson, 1962). The lower percolation area is considered crucial for properly determining the surface mass budget, as firn properties influencing meltwater retention capacity vary substantially across the ice sheet (Van As et al, 2016a;Langen et al, 2017).…”

Section: Modeling Implications and Limitationsmentioning

confidence: 99%

A Snow Density Dataset for Improving Surface Boundary Conditions in Greenland Ice Sheet Firn Modeling

et al. 2018

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The surface snow density of glaciers and ice sheets is of fundamental importance in converting volume to mass in both altimetry and surface mass balance studies, yet it is often poorly constrained. Site-specific surface snow densities are typically derived from empirical relations based on temperature and wind speed. These parameterizations commonly calculate the average density of the top meter of snow, thereby systematically overestimating snow density at the actual surface. Therefore, constraining surface snow density to the top 0.1 m can improve boundary conditions in high-resolution firn-evolution modeling. We have compiled an extensive dataset of 200 point measurements of surface snow density from firn cores and snow pits on the Greenland ice sheet. We find that surface snow density within 0.1 m of the surface has an average value of 315 kg m −3 with a standard deviation of 44 kg m −3 , and has an insignificant annual air temperature dependency. We demonstrate that two widely-used surface snow density parameterizations dependent on temperature systematically overestimate surface snow density over the Greenland ice sheet by 17-19%, and that using a constant density of 315 kg m −3 may give superior results when applied in surface mass budget modeling.

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Section: Modeling Implications and Limitationsmentioning

confidence: 99%

A Snow Density Dataset for Improving Surface Boundary Conditions in Greenland Ice Sheet Firn Modeling

et al. 2018

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“…Firn is the intermediate product created when snow is converted to glacier ice by a combination of compaction and/or melt–freeze processes. Typically, the objectives of firn monitoring are to measure annual accumulation and densification, identify ice lenses and layers, measure temperature and track percolation of water and estimate the volume of meltwater stored in the firnpack (van As and others, 2016). These parameters are critical for the assessment of glacier mass balance (Sørensen and others, 2011; Simonsen and others, 2013; Munneke and others, 2015; Schaller and others, 2016) and accurate dating of ice cores, by estimating annual layer thickness (Vallelonga and others, 2014).…”

Section: Introductionmentioning

confidence: 99%

Prototype wireless sensors for monitoring subsurface processes in snow and firn

et al. 2018

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The detection and monitoring of meltwater within firn presents a significant monitoring challenge. We explore the potential of small wireless sensors (ETracer+, ET+) to measure temperature, pressure, electrical conductivity and thus the presence or absence of meltwater within firn, through tests in the dry snow zone at the East Greenland Ice Core Project site. The tested sensor platforms are small, robust and low cost, and communicate data via a VHF radio link to surface receivers. The sensors were deployed in low-temperature firn at the centre and shear margins of an ice stream for 4 weeks, and a ‘bucket experiment’ was used to test the detection of water within otherwise dry firn. The tests showed the ET+ could log subsurface temperatures and transmit the recorded data through up to 150 m dry firn. Two VHF receivers were tested: an autonomous phase-sensitive radio-echo sounder (ApRES) and a WinRadio. The ApRES can combine high-resolution imaging of the firn layers (by radio-echo sounding) with in situ measurements from the sensors, to build up a high spatial and temporal resolution picture of the subsurface. These results indicate that wireless sensors have great potential for long-term monitoring of firn processes.

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“…Field investigations suggest ice lenses are widespread in the percolation zone and have thickened in recent decades [20,158]. The vertical and horizontal distribution of meltwater percolation and refreezing is difficult to model and may not be accurately represented by regional climate models [158,159]. Although these features present challenges for dH/dt detection, they also provide unique opportunities for characterizing snow and firn processes, including detection of extreme melt events, ice lens formation, and snow accumulation rates following ice lens formation [115].…”

Section: Current Challenges and Future Opportunitiesmentioning

confidence: 99%

Satellite Remote Sensing of the Greenland Ice Sheet Ablation Zone: A Review

Cooper

Smith

2019

Remote Sensing

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The Greenland Ice Sheet is now the largest land ice contributor to global sea level rise, largely driven by increased surface meltwater runoff from the ablation zone, i.e., areas of the ice sheet where annual mass losses exceed gains. This small but critically important area of the ice sheet has expanded in size by~50% since the early 1960s, and satellite remote sensing is a powerful tool for monitoring the physical processes that influence its surface mass balance. This review synthesizes key remote sensing methods and scientific findings from satellite remote sensing of the Greenland Ice Sheet ablation zone, covering progress in (1) radar altimetry, (2) laser (lidar) altimetry, (3) gravimetry, (4) multispectral optical imagery, and (5) microwave and thermal imagery. Physical characteristics and quantities examined include surface elevation change, gravimetric mass balance, reflectance, albedo, and mapping of surface melt extent and glaciological facies and zones. The review concludes that future progress will benefit most from methods that combine multi-sensor, multi-wavelength, and cross-platform datasets designed to discriminate the widely varying surface processes in the ablation zone. Specific examples include fusing laser altimetry, radar altimetry, and optical stereophotogrammetry to enhance spatial measurement density, cross-validate surface elevation change, and diagnose radar elevation bias; employing dual-frequency radar, microwave scatterometry, or combining radar and laser altimetry to map seasonal snow depth; fusing optical imagery, radar imagery, and microwave scatterometry to discriminate between snow, liquid water, refrozen meltwater, and bare ice near the equilibrium line altitude; combining optical reflectance with laser altimetry to map supraglacial lake, stream, and crevasse bathymetry; and monitoring the inland migration of snowlines, surface melt extent, and supraglacial hydrologic features.

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Challenges of Quantifying Meltwater Retention in Snow and Firn: An Expert Elicitation

Cited by 16 publications

References 18 publications

A Snow Density Dataset for Improving Surface Boundary Conditions in Greenland Ice Sheet Firn Modeling

A Snow Density Dataset for Improving Surface Boundary Conditions in Greenland Ice Sheet Firn Modeling

Prototype wireless sensors for monitoring subsurface processes in snow and firn

Satellite Remote Sensing of the Greenland Ice Sheet Ablation Zone: A Review

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