VARIATION OF ALBEDO OF FRESH SNOW

Abstract. Blowing snow impacts Antarctic ice sheet surface mass balance by snow redistribution and sublimation. However, numerical models poorly represent blowing snow processes, while direct observations are limited in space and time. Satellite retrieval of blowing snow is hindered by clouds and only the strongest events are considered. Here, we develop a blowing snow detection (BSD) algorithm for ground-based remote-sensing ceilometers in polar regions and apply it to ceilometers at Neumayer III and Princess Elisabeth (PE) stations, East Antarctica. The algorithm is able to detect (heavy) blowing snow layers reaching 30 m height. Results show that 78 % of the detected events are in agreement with visual observations at Neumayer III station. The BSD algorithm detects heavy blowing snow 36 % of the time at Neumayer (2011–2015) and 13 % at PE station (2010–2016). Blowing snow occurrence peaks during the austral winter and shows around 5 % interannual variability. The BSD algorithm is capable of detecting blowing snow both lifted from the ground and occurring during precipitation, which is an added value since results indicate that 92 % of the blowing snow is during synoptic events, often combined with precipitation. Analysis of atmospheric meteorological variables shows that blowing snow occurrence strongly depends on fresh snow availability in addition to wind speed. This finding challenges the commonly used parametrizations, where the threshold for snow particles to be lifted is a function of wind speed only. Blowing snow occurs predominantly during storms and overcast conditions, shortly after precipitation events, and can reach up to 1300 m a. g. l. in the case of heavy mixed events (precipitation and blowing snow together). These results suggest that synoptic conditions play an important role in generating blowing snow events and that fresh snow availability should be considered in determining the blowing snow onset.

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On the Use of Geophysical Parameters for the Top-of-Atmosphere Shortwave Clear-Sky Radiance-to-Flux Conversion in EarthCARE

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-18-0087.1 ◽

2019 ◽

Vol 36 (4) ◽

pp. 717-732 ◽

Cited By ~ 1

Author(s):

F. Tornow ◽

C. Domenech ◽

J. Fischer

Keyword(s):

Permutation Test ◽

Climate Modeling ◽

Radiant Energy ◽

Energy System ◽

Max Planck ◽

Distribution Models ◽

Dynamic Surface ◽

Clear Sky ◽

Fresh Snow ◽

Bidirectional Reflectance Distribution

AbstractWe have investigated whether differences across Clouds and the Earth’s Radiant Energy System (CERES) top-of-atmosphere (TOA) clear-sky angular distribution models, estimated separately over regional (1° × 1° longitude–latitude) and temporal (monthly) bins above land, can be explained by geophysical parameters from Max Planck Institute Aerosol Climatology, version 1 (MAC-v1), ECMWF twentieth-century reanalysis (ERA-20C), and a MODIS bidirectional reflectance distribution function (BRDF)/albedo/nadir BRDF-adjusted reflectance (NBAR) Climate Modeling Grid (CMG) gap-filled products (MCD43GF) climatology. Our research aimed to dissolve binning and to isolate inherent properties or indicators of such properties, which govern the TOA radiance-to-flux conversion in the absence of clouds. We collocated over seven million clear-sky footprints from CERES Single Scanner Footprint (SSF), edition 4, data with above geophysical auxiliary data. Looking at data per surface type and per scattering direction—as perceived by the broadband radiometer (BBR) on board Earth Clouds, Aerosol and Radiation Explorer (EarthCARE)—we identified optimal subsets of geophysical parameters using two different methods: random forest regression followed by a permutation test and multiple linear regression combined with the genetic algorithm. Using optimal subsets, we then trained artificial neural networks (ANNs). Flux error standard deviations on unseen test data were on average 2.7–4.0 W m−2, well below the 10 W m−2 flux accuracy threshold defined for the mission, with the exception of footprints containing fresh snow. Dynamic surface types (i.e., fresh snow and sea ice) required simpler ANN input sets to guarantee mission-worthy flux estimates, especially over footprints consisting of several surface types.

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Characteristics and sources of dissolved organic matter in a glacier in the northern Tibetan Plateau: differences between different snow categories

Annals of Glaciology ◽

10.1017/aog.2018.20 ◽

2018 ◽

Vol 59 (77) ◽

pp. 31-40 ◽

Cited By ~ 2

Author(s):

Lin Feng ◽

Yanqing An ◽

Jianzhong Xu ◽

Shichang Kang

Keyword(s):

Organic Matter ◽

Tibetan Plateau ◽

Dissolved Organic Matter ◽

Melting Rate ◽

Ion Cyclotron Resonance ◽

The Tibetan Plateau ◽

Mountain Glaciers ◽

Northern Tibetan Plateau ◽

Fresh Snow ◽

Northern Edge

AbstractDissolved organic matter (DOM) in mountain glaciers is an important source of carbon for downstream aquatic systems, and its impact is expected to increase due to the increased melting rate of glaciers. We present a comprehensive study of Laohugou glacier no. 12 (LHG) at the northern edge of the Tibetan Plateau to characterize the DOM composition and sources by analyzing surface fresh snow, granular ice samples, and snow pit samples which covered a whole year cycle of 2014/15. Excitation–emission matrix fluorescence spectroscopy analysis of the DOM with parallel factor analysis (EEM-PARAFAC) identified four components, including a microbially humic-like component (C1), two protein-like components (C2 and C3) and a terrestrial humic-like component (C4). The use of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) showed that DOM from all these samples was dominated by CHO and CHON molecular formulas, mainly corresponding to lipids and aliphatic/proteins compounds, reflecting the presence of significant amounts of microbially derived and/or deposited biogenic DOM. The molecular compositions of DOM showed more CHON compounds in granular ice than in fresh snow, likely suggesting newly formed DOM from microbes during snowmelting.

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Specifying the light-absorbing properties of aerosol particles in fresh snow samples, collected at the Environmental Research Station Schneefernerhaus (UFS), Zugspitze

Atmospheric Chemistry and Physics ◽

10.5194/acp-19-10829-2019 ◽

2019 ◽

Vol 19 (16) ◽

pp. 10829-10844 ◽

Cited By ~ 1

Author(s):

Martin Schnaiter ◽

Claudia Linke ◽

Inas Ibrahim ◽

Alexei Kiselev ◽

Fritz Waitz ◽

...

Keyword(s):

Cross Section ◽

Absorption Cross Section ◽

Aerosol Particles ◽

Particle Mass ◽

Research Station ◽

Absorption Cross ◽

Specific Absorption ◽

Fresh Snow ◽

Snow Mass ◽

Snow Samples

Abstract. Atmospheric aerosol particles like mineral dust, volcanic ash and combustion particles can reduce Earth's snow and ice albedo considerably even by very small amounts of deposited particle mass. In this study, a new laboratory method is applied to measure the spectral light absorption coefficient of airborne particles that are released from fresh snow samples by an efficient nebulizing system. Three-wavelength photoacoustic absorption spectroscopy is combined with refractory black carbon (BC) mass analysis to determine the snow mass-specific and BC mass-specific absorption cross sections. Fullerene soot in water suspensions are used for the characterization of the method and for the determination of the mass-specific absorption cross section of this BC reference material. The analysis of 31 snow samples collected after fresh snowfall events at a high-altitude Alpine research station reveals a significant discrepancy between the measured snow mass-specific absorption cross section and the cross section that is expected from the BC mass data, indicating that non-BC light-absorbing particles are present in the snow. Mineral dust and brown carbon (BrC) are identified as possible candidates for the non-BC particle mass based on the wavelength dependence of the measured absorption. For one sample this result is confirmed by environmental scanning electron microscopy and by single-particle fluorescence measurements, which both indicate a high fraction of biogenic and organic particle mass in the sample.

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Polycyclic aromatic hydrocarbons in fresh snow in the city of Harbin in northeast China

Atmospheric Environment ◽

10.1016/j.atmosenv.2019.116915 ◽

2019 ◽

Vol 215 ◽

pp. 116915 ◽

Cited By ~ 3

Author(s):

Song Cui ◽

Zihan Song ◽

Leiming Zhang ◽

Zulin Zhang ◽

Rupert Hough ◽

...

Keyword(s):

Polycyclic Aromatic Hydrocarbons ◽

Aromatic Hydrocarbons ◽

Northeast China ◽

Fresh Snow ◽

Polycyclic Aromatic ◽

The City

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Chemical composition of fresh snow on Xixabangma peak, central Himalaya, during the summer monsoon season

Journal of Glaciology ◽

10.3189/172756502781831467 ◽

2002 ◽

Vol 48 (161) ◽

pp. 337-339 ◽

Cited By ~ 21

Author(s):

Shichang Kang ◽

Dahe Qin ◽

Paul A. Mayewski ◽

Sharon B. Sneed ◽

Tandong Yao

Keyword(s):

Chemical Composition ◽

Summer Monsoon ◽

Monsoon Season ◽

Central Himalaya ◽

Summer Monsoon Season ◽

Fresh Snow

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The influence of forest stand characteristics on snow interception in the coastal forests of British Columbia

Canadian Journal of Forest Research ◽

10.1139/x88-082 ◽

1988 ◽

Vol 18 (5) ◽

pp. 566-573 ◽

Cited By ~ 20

Author(s):

R. Scott McNay ◽

Leslie D. Peterson ◽

J. Brian Nyberg

Keyword(s):

Snow Depth ◽

Forest Canopy ◽

Basal Area ◽

Related Phenomenon ◽

Linear Regression Models ◽

Coastal Forests ◽

Winter Habitat ◽

Independent Variables ◽

Fresh Snow ◽

Stand Characteristics

The capability of forest stands to intercept snow is an important factor in determining management prescriptions for such hydrologically related phenomenon as avalanches, floods, and water supply as well as suitability for ungulate winter habitat. This study tested the hypothesis that snow interception can be predicted as a function of various stand characteristics and storm sizes. The dependent variable was fresh snow depth under the forest canopy; the independent variables were crown completeness, crown length, crown width, basal area per hectare, tree height, tree density, and storm size. Ten stands were selected for study from two locations on Vancouver Island. Snow depth was monitored over 24 storms ranging from 1.4 to 38.0 cm. The best simple linear regression models that incorporated forest variables were those for individual storms, with fresh snow expressed as a function of mean crown completeness. The best assessments of a particular stand's capability to intercept snow were made using an equation with both storm size and mean crown completeness as independent variables.

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Numerical Simulation of Powder-Snow Avalanches

Journal of Glaciology ◽

10.3189/s0022143000009485 ◽

1990 ◽

Vol 36 (123) ◽

pp. 229-237 ◽

Cited By ~ 1

Author(s):

Yusuke Fukushima ◽

Gary Parker

Keyword(s):

Large Scale ◽

Ambient Air ◽

Dense Core ◽

Snow Avalanche ◽

Entrainment Rate ◽

Density Currents ◽

Snow Avalanches ◽

Snow Layer ◽

Fresh Snow ◽

Snow Particle

AbstractAppropriate expressions describing the motion of powder-snow avalanches are derived. The model consists of four equations, i.e. the conservation equations of fluid mass, snow-particle mass, momentum of the cloud, and kinetic energy of the turbulence. Insofar as the density difference between the avalanche and the ambient air becomes rather large compared with the density of the ambient air, the Boussinesq approximation, which is typically used to analyze density currents, cannot be adopted in the present case. As opposed to previous models, the total buoyancy of a powder-snow avalanche is allowed to change freely via erosion from and deposition on to a static snow layer on a slope. In the model, the snow-particle entrainment rate from the slope is directly linked to the level of turbulence.A discontinuous, large-scale powder-snow avalanche occurred on 26 January 1986 near Maseguchi, Niigata Prefecture, Japan. The avalanche appears to have had a dense core at its base. The present model is employed to simulate that part of the avalanche above any dense core. The depth of the layer of fresh snow is considered to be an important parameter in the model. The larger the depth of fresh snow, the larger is the concentration of snow attained in the avalanche, and the faster its speed. It is seen that the model provides a reasonable description of the powder-snow avalanche generated near Maseguchi. In particular, the model prediction that a powder-snow avalanche strong enough to reach Maseguchi requires a depth of fresh snow of at least 2 m is in agreement with the observed depth just before the event.

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