Microstructural characterization of snow, firn and ice

Baker, Ian

doi:10.1098/rsta.2018.0162

Cited by 20 publications

(15 citation statements)

References 116 publications

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“…It was “dry drilled”—meaning that no drilling fluid was used to prevent lithostatic collapse of the borehole. Baker ( 2019 ) acquired firn cores to a depth of ∼85 m to study firn densification mechanisms and microstructural evolution (Ian Baker, personal communication; see also Lomonaco et al , 2011 ). At the end of their campaign, the borehole was capped with a cover to prevent snow accumulation in the borehole.…”

Section: Methodsmentioning

confidence: 99%

“…The Greenland ice sheet consists of meteorically derived ice that can serve as an analogue for other icy locations in the Solar System. The high altitude (3211 m) and low temperatures (−48°C average winter minimum to −11°C average summer maximum) at the top of the ice sheet represent a textbook example of dry (minimal liquid) deposition, compression, and modification of grains to form ice (see Lomonaco et al , 2011 ; Baker, 2019 ). At the surface, the ice is porous and consists of a series of interlocked grains known as firn, whereas deeper, the grains become altered as the pore spaces close off and firn converts to nonporous glacial ice.…”

Section: Introductionmentioning

confidence: 99%

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Subsurface In Situ Detection of Microbes and Diverse Organic Matter Hotspots in the Greenland Ice Sheet

et al. 2020

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We used a deep-ultraviolet fluorescence mapping spectrometer, coupled to a drill system, to scan from the surface to 105 m depth into the Greenland ice sheet. The scan included firn and glacial ice and demonstrated that the instrument is able to determine small (mm) and large (cm) scale regions of organic matter concentration and discriminate spectral types of organic matter at high resolution. Both a linear point cloud scanning mode and a raster mapping mode were used to detect and localize microbial and organic matter “hotspots” embedded in the ice. Our instrument revealed diverse spectral signatures. Most hotspots were <20 mm in diameter, clearly isolated from other hotspots, and distributed stochastically; there was no evidence of layering in the ice at the fine scales examined (100 μm per pixel). The spectral signatures were consistent with organic matter fluorescence from microbes, lignins, fused-ring aromatic molecules, including polycyclic aromatic hydrocarbons, and biologically derived materials such as fulvic acids. In situ detection of organic matter hotspots in ice prevents loss of spatial information and signal dilution when compared with traditional bulk analysis of ice core meltwaters. Our methodology could be useful for detecting microbial and organic hotspots in terrestrial icy environments and on future missions to the Ocean Worlds of our Solar System.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Subsurface In Situ Detection of Microbes and Diverse Organic Matter Hotspots in the Greenland Ice Sheet

et al. 2020

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“…Snow properties such as snow albedo, Snow Grain Size (SGS), Snow Particle Shape (SPS), Specific Surface Area (SSA), snow purity (Warren and Wiscombe, 1980;Painter et al, 2003;Hansen and Nazarenko, 2004;Taillandier et al, 2007;Gallet et al, 2009;Battaglia et al, 2010;Gardner et al, 2010;Domine et al, 2011;Liu et al, 2012;Qu et al, 2015;Baker et al, 2019;Pohl et al, 2020a) show large variabilities temporally and spatially (Kukla et al, 1986). They play important roles in the global radiation budget, which is critical to some well-known phenomenon such as the Arctic amplification (Serreze and Francis, 2006;Domine et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Ice crystal shape observed from field measurements and derived from satellite observations. For scientists working in a laboratory or on campaign-based studies, the best way to get an image of snow is to use an X-ray microtomography or confocal scanning optical microscope/scanning electron microscope (Hagenmuller et al, 2016;Baker et al, 2019;Personal communication with Dr. Ian Baker). In a field measurement and its related application areas (e.g., calculation of snow albedo), a spherical shape assumption is widely used because it is easier to derive other snow properties such as SSAs and snow albedo based on this assumption, compared to other more complicated shapes (see Appendix).…”

mentioning

confidence: 99%

The retrieval of snow properties from SLSTR/Sentinel-3 – part 1: method description and sensitivity study

Mei¹,

Розанов²,

Pohl³

et al. 2020

Preprint

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Abstract. The eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm has been applied on the Top-Of-Atmosphere reflectance measured by the Sea and Land Surface Temperature Radiometer (SLSTR) instrument onboard Sentinel-3 to derive snow properties: Snow Grain Size (SGS), Snow Particle Shape (SPS) and Specific Surface Area (SSA) under cloud-free conditions. This is the first part of the paper, to describe the retrieval method and the sensitivity study. Nine pre-defined ice crystal particle shapes (aggregate of 8 columns, Drontal, hollow bullet rosettes, hollow column, plate, aggregate of 5 plates, aggregate of 10 plates, solid bullet rosettes, column) are used to describe the snow optical properties. The optimal SGS and SPS are estimated iteratively utilizing a Look-Up-Table (LUT) approach. The SSA is then calculated using another pre-calculated LUT for the retrieved SGS and SPS. The optical properties (e.g., phase function) of the ice crystals can reproduce the wavelength-dependent/angular-dependent snow reflectance features, compared to laboratory measurements. A comprehensive study to understand the impact of aerosol, ice crystal shape, ice crystal surface roughness, and cloud contamination on the retrieval accuracy of snow properties has been performed based on SCIATRAN radiative transfer simulations. The main findings are (1) Snow angular and spectral reflectance feature can be described by the predefined ice crystal properties only when both SGS and SPS can be optimally and iteratively obtained; (2) The impact of ice crystal surface roughness plays minor effects on the retrieval results; (3) SGS and SSA show an inverse linear relationship; (4) The retrieval of SSA assuming non-convex particle shape, compared to convex particle (e.g. sphere) shows larger results; (5) Aerosol/cloud contamination due to unperfected atmospheric correction and cloud screening introduces underestimation of SGS, inaccurate SPS and overestimation of SSA.

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“…Baker [38] continues the discussion on the structure focusing on techniques used to characterize the microstructures of snow, firn (multi-year snow) and ice on a larger scale. These techniques, some of which also reveal the location of impurities in the samples, include: transmission electron microscopy, synchrotron-based X-ray topography, cold-stage scanning electron microscopy coupled with energy dispersive X-ray spectroscopy, electron channelling patterns and electron backscatter diffraction (EBSD); cold-stage confocal scanning optical microscopy coupled with Raman spectroscopy; and micro X-ray computed tomography.…”

mentioning

confidence: 99%