Measurement and parameterization of aerodynamic roughness length variations at Haut Glacier d’Arolla, Switzerland

Brock, Benjamin; Willis, Ian; Sharp, Martin

doi:10.3189/172756506781828746

Cited by 223 publications

(361 citation statements)

References 73 publications

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“…Similar to Mölg et al (2012), the surface roughness length z 0 increases linearly from 0.24 mm for fresh snow (Gromke et al, 2011) to 4 mm for aged snow (Brock et al, 2006). If the surface is snow-free, we assume z 0 to be 1.7 mm (Cullen et al, 2007).…”

Section: Seb/mb Modelingmentioning

confidence: 99%

Evaluation of a Coupled Snow and Energy Balance Model for Zhadang Glacier, Tibetan Plateau, Using Glaciological Measurements and Time-Lapse Photography

Huintjes

Sauter

Schröter³

et al. 2015

Arctic, Antarctic, and Alpine Research

106

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Section: Seb/mb Modelingmentioning

confidence: 99%

Evaluation of a Coupled Snow and Energy Balance Model for Zhadang Glacier, Tibetan Plateau, Using Glaciological Measurements and Time-Lapse Photography

Huintjes

Sauter

Schröter³

et al. 2015

Arctic, Antarctic, and Alpine Research

106

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“…(1) through a simplified standard deviation approach (which will be referred to as the LettauMunro method), based on the variations in elevations and the number of continuous positive groups above the mean elevation (Munro, 1989;Rees and Arnold, 2006;Brock et al, 2006). Initially, the Lettau-Munro method was applied to measure z 0 for every row and column transect of the four DEMs; however, the resulting values of z 0 did not capture the variations between sites and may have been slightly underestimated (see results).…”

Section: Surface Roughness (Z 0 )mentioning

confidence: 99%

“…The thermal conductivity of debris in the Everest region has been found to range from 0.60 to 1.29 W m −1 K −1 (Conway and Rasmussen, 2000;Rounce and McKinney, 2014). The surface roughness, z 0 , is arguably the most difficult parameter to measure as it requires an eddy covariance instrument, horizontal wind speed measurements at multiple heights above the surface, or detailed microtopographic measurements (Brock et al, 2006). In the Everest region, Inoue and Yoshida (1980) estimated z 0 to be 0.0035 and 0.060 m for two sites, one consisting of small schist and bare ice and another comprising mainly large granite, respectively.…”

Section: Introductionmentioning

confidence: 99%

Debris-covered glacier energy balance model for Imja–Lhotse Shar Glacier in the Everest region of Nepal

2015

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Abstract. Debris thickness plays an important role in regulating ablation rates on debris-covered glaciers as well as controlling the likely size and location of supraglacial lakes. Despite its importance, lack of knowledge about debris properties and associated energy fluxes prevents the robust inclusion of the effects of a debris layer into most glacier surface energy balance models. This study combines fieldwork with a debris-covered glacier energy balance model to estimate debris temperatures and ablation rates on Imja-Lhotse Shar Glacier located in the Everest region of Nepal. The debris properties that significantly influence the energy balance model are the thermal conductivity, albedo, and surface roughness. Fieldwork was conducted to measure thermal conductivity and a method was developed using Structure from Motion to estimate surface roughness. Debris temperatures measured during the 2014 melt season were used to calibrate and validate a debris-covered glacier energy balance model by optimizing the albedo, thermal conductivity, and surface roughness at 10 debris-covered sites. Furthermore, three methods for estimating the latent heat flux were investigated. Model calibration and validation found the three methods had similar performance; however, comparison of modeled and measured ablation rates revealed that assuming the latent heat flux is zero may overestimate ablation. Results also suggest that where debris moisture is unknown, measurements of the relative humidity or precipitation may be used to estimate wet debris periods, i.e., when the latent heat flux is non-zero. The effect of temporal resolution on the model was also assessed and results showed that both 6 h data and daily average data slightly underestimate debris temperatures and ablation rates; thus these should only be used to estimate rough ablation rates when no other data are available.

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“…The left-hand terms in the equation can be constrained by AWS measurements and MODIS-derived surface albedo. The calculation of the turbulent heat fluxes SH and LH is based on near-surface gradients of meteorological variables, using the surface as the lower level for gradient calculation, and makes use of well-tested stability correction functions and common values for aerodynamic surface roughness length for momentum (1 × 10 −4 m for snow and 1 × 10 −3 m for ice, Brock et al, 2006). Calculation of SH, LH, SSH, and upward longwave radiation makes use of the unknown variable of surface temperate, for which the equation can be solved iteratively.…”

Section: Van As Et Al: Large Meltwater Discharge From the Greenlamentioning

confidence: 99%

Large surface meltwater discharge from the Kangerlussuaq sector of the Greenland ice sheet during the record-warm year 2010 explained by detailed energy balance observations

et al. 2012

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Abstract. This study uses data from six on-ice weather stations, calibrated MODIS-derived albedo and proglacial river gauging measurements to drive and validate an energy balance model. We aim to quantify the record-setting positive temperature anomaly in 2010 and its effect on mass balance and runoff from the Kangerlussuaq sector of the Greenland ice sheet. In 2010, the average temperature was 4.9 • C (2.7 standard deviations) above the 1974-2010 average in Kangerlussuaq. High temperatures were also observed over the ice sheet, with the magnitude of the positive anomaly increasing with altitude, particularly in August. Simultaneously, surface albedo was anomalously low in 2010, predominantly in the upper ablation zone. The low albedo was caused by high ablation, which in turn profited from high temperatures and low winter snowfall. Surface energy balance calculations show that the largest melt excess (∼170 %) occurred in the upper ablation zone (above 1000 m), where higher temperatures and lower albedo contributed equally to the melt anomaly. At lower elevations the melt excess can be attributed to high atmospheric temperatures alone. In total, we calculate that 6.6 ± 1.0 km 3 of surface meltwater ran off the ice sheet in the Kangerlussuaq catchment in 2010, exceeding the reference year 2009 (based on atmospheric temperature measurements) by ∼150 %. During future warm episodes we can expect a melt response of at least the same magnitude, unless a larger wintertime snow accumulation delays and moderates the melt-albedo feedback. Due to the hypsometry of the ice sheet, yielding an increasing surface area with elevation, meltwater runoff will be further amplified by increases in melt forcings such as atmospheric heat.

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Measurement and parameterization of aerodynamic roughness length variations at Haut Glacier d’Arolla, Switzerland

Cited by 223 publications

References 73 publications

Evaluation of a Coupled Snow and Energy Balance Model for Zhadang Glacier, Tibetan Plateau, Using Glaciological Measurements and Time-Lapse Photography

Evaluation of a Coupled Snow and Energy Balance Model for Zhadang Glacier, Tibetan Plateau, Using Glaciological Measurements and Time-Lapse Photography

Debris-covered glacier energy balance model for Imja–Lhotse Shar Glacier in the Everest region of Nepal

Large surface meltwater discharge from the Kangerlussuaq sector of the Greenland ice sheet during the record-warm year 2010 explained by detailed energy balance observations

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