Abstract Atmospheric particulate matter (PM) as light‐absorbing particles (LAPs) deposited to snow cover can result in early onset and rapid snow melting, challenging management of downstream water resources. We identified LAPs in 38 snow samples (water years 2013–2016) from the mountainous Upper Colorado River basin by comparing among laboratory‐measured spectral reflectance, chemical, physical, and magnetic properties. Dust sample reflectance, averaged over the wavelength range of 0.35–2.50 μm, varied by a factor of 1.9 (range, 0.2300–0.4444) and was suppressed mainly by three components: (a) carbonaceous matter measured as total organic carbon (1.6–22.5 wt. %) including inferred black carbon, natural organic matter, and carbon‐based synthetic, black road‐tire‐wear particles, (b) dark rock and mineral particles, indicated by amounts of magnetite (0.11–0.37 wt. %) as their proxy, and (c) ferric oxide minerals identified by reflectance spectroscopy and magnetic properties. Fundamental compositional differences were associated with different iron oxide groups defined by dominant hematite, goethite, or magnetite. These differences in iron oxide mineralogy are attributed to temporally varying source‐area contributions implying strong interannual changes in regional source behavior, dust‐storm frequency, and (or) transport tracks. Observations of dust‐storm activity in the western U.S. and particle‐size averages for all samples (median, 25 μm) indicated that regional dust from deserts dominated mineral‐dust masses. Fugitive contaminants, nevertheless, contributed important amounts of LAPs from many types of anthropogenic sources.
more »
« less
Dust Deposited on Snow Cover in the San Juan Mountains, Colorado, 2011–2016: Compositional Variability Bearing on Snow‐Melt Effects
Abstract Light‐absorbing particles in atmospheric dust deposited on snow cover (dust‐on‐snow, DOS) diminish albedo and accelerate the timing and rate of snow melt. Identification of these particles and their effects is relevant to snow‐radiation modeling and water‐resource management. Laboratory‐measured reflectance of DOS samples from the San Juan Mountains (USA) were compared with DOS mass loading, particle sizes, iron mineralogy, carbonaceous matter type and content, and chemical compositions. Samples were collected each spring for water years 2011–2016, when individual dust layers had merged into one (all layers merged) at the snow surface. Average reflectance values of the six samples were 0.2153 (sd, 0.0331) across the visible wavelength region (0.4–0.7 μm) and 0.3570 (sd, 0.0498) over the full‐measurement range (0.4–2.50 μm). Reflectance values correlated inversely to concentrations of ferric oxide, organic carbon (1.4–10 wt.%), magnetite (0.05–0.13 wt.%), and silt (PM63‐3.9;median grain sizes averaged 21.4 μm) but lacked correspondence to total iron and PM10contents. Measurements of reflectance and Mössbauer spectra and magnetic properties indicated that microcrystalline hematite and nano‐size goethite were primarily responsible for diminished visible reflectance. Positive correlations between organic carbon and metals attributed to fossil‐fuel combustion, with observations from electron microscopy, indicated that some carbonaceous matter occurred as black carbon. Magnetite was a surrogate for related light‐absorbing minerals, dark rock particles, and contaminants. Similar analyses of DOS from other areas would help evaluate the influences of varied dust sources, wind‐storm patterns, and anthropogenic inputs on snow melt and water resources in and beyond the Colorado River Basin.
more »
« less
- Award ID(s):
- 1642268
- PAR ID:
- 10456355
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- Volume:
- 125
- Issue:
- 7
- ISSN:
- 2169-897X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract. The Snow, Ice, and Aerosol Radiative (SNICAR) model has been used in various capacities over the last 15 years to model the spectral albedo of snow with light-absorbing constituents (LACs). Recent studies have extended the model to include an adding-doubling two-stream solver and representations of non-spherical ice particles; carbon dioxide snow; snow algae; and new types of mineral dust, volcanic ash, and brown carbon. New options also exist for ice refractive indices and solar-zenith-angle-dependent surface spectral irradiances used to derive broadband albedo. The model spectral range was also extended deeper into the ultraviolet for studies of extraterrestrial and high-altitude cryospheric surfaces. Until now, however, these improvements and capabilities have not been merged into a unified code base. Here, we document the formulation and evaluation of the publicly available SNICAR-ADv3 source code, web-based model, and accompanying library of constituent optical properties. The use of non-spherical ice grains, which scatter less strongly into the forward direction, reduces the simulated albedo perturbations from LACs by ∼9 %–31 %, depending on which of the three available non-spherical shapes are applied. The model compares very well against measurements of snow albedo from seven studies, though key properties affecting snow albedo are not fully constrained with measurements, including ice effective grain size of the top sub-millimeter of the snowpack, mixing state of LACs with respect to ice grains, and site-specific LAC optical properties. The new default ice refractive indices produce extremely high pure snow albedo (>0.99) in the blue and ultraviolet part of the spectrum, with such values only measured in Antarctica so far. More work is needed particularly in the representation of snow algae, including experimental verification of how different pigment expressions and algal cell concentrations affect snow albedo. Representations and measurements of the influence of liquid water on spectral snow albedo are also needed.more » « less
-
Abstract Cold seasons in Arctic ecosystems are increasingly important to the annual carbon balance of these vulnerable ecosystems. Arctic winters are largely harsh and inaccessible leading historic data gaps during that time. Until recently, cold seasons have been assumed to have negligible impacts on the annual carbon balance but as data coverage increases and the Arctic warms, the cold season has been shown to account for over half of annual methane (CH4) emissions and can offset summer photosynthetic carbon dioxide (CO2) uptake. Freeze–thaw cycle dynamics play a critical role in controlling cold season CO2and CH4loss, but the relationship has not been extensively studied. Here, we analyze freeze–thaw processes through in situ CO2and CH4fluxes in conjunction with soil cores for physical structure and porewater samples for redox biogeochemistry. We find a movement of water toward freezing fronts in soil cores, leaving air spaces in soils, which allows for rapid infiltration of oxygen‐rich snow melt in spring as shown by oxidized iron in porewater. The snow melt period coincides with rising ecosystem respiration and can offset up to 41% of the summer CO2uptake. Our study highlights this important seasonal process and shows spring greenhouse gas emissions are largely due to production from respiration instead of only bursts of stored gases. Further warming is projected to result in increases of snowpack and deeper thaws, which could increase this ecosystem respiration dominate snow melt period causing larger greenhouse gas losses during spring.more » « less
-
Abstract We extend a stochastic aerosol‐snow albedo model to explicitly simulate dust internally/externally mixed with snow grains of different shapes and for the first time quantify the combined effects of dust‐snow internal mixing and snow nonsphericity on snow optical properties and albedo. Dust‐snow internal/external mixing significantly enhances snow single‐scattering coalbedo and absorption at wavelengths of <1.0 μm, with stronger enhancements for internal mixing (relative to external mixing) and higher dust concentrations but very weak dependence on snow size and shape variabilities. Compared with pure snow, dust‐snow internal mixing reduces snow albedo substantially at wavelengths of <1.0 μm, with stronger reductions for higher dust concentrations, larger snow sizes, and spherical (relative to nonspherical) snow shapes. Compared to internal mixing, dust‐snow external mixing generally shows similar spectral patterns of albedo reductions and effects of snow size and shape. However, relative to external mixing, dust‐snow internal mixing enhances the magnitude of albedo reductions by 10%–30% (10%–230%) at the visible (near‐infrared) band. This relative enhancement is stronger as snow grains become larger or nonspherical, with comparable influences from snow size and shape. Moreover, for dust‐snow external and internal mixing, nonspherical snow grains have up to ~45% weaker albedo reductions than spherical grains, depending on snow size, dust concentration, and wavelength. The interactive effect of dust‐snow mixing state and snow shape highlights the importance of accounting for these two factors concurrently in snow modeling. For application to land/climate models, we develop parameterizations for dust effects on snow optical properties and albedo with high accuracy.more » « less
-
null (Ed.)Abstract Effective snow grain radius ( r e ) is mapped at high resolution using near-infrared hyperspectral imaging (NIR-HSI). The NIR-HSI method can be used to quantify r e spatial variability, change in r e due to metamorphism, and visualize water percolation in the snowpack. Results are presented for three different laboratory-prepared snow samples (homogeneous, ice lens, fine grains over coarse grains), the sidewalls of which were imaged before and after melt induced by a solar lamp. The spectral reflectance in each ~3 mm pixel was inverted for r e using the scaled band area of the ice absorption feature centered at 1030 nm, producing r e maps consisting of 54 740 pixels. All snow samples exhibited grain coarsening post-melt as the result of wet snow metamorphism, which is quantified by the change in r e distributions from pre- and post-melt images. The NIR-HSI method was compared to r e retrievals from a field spectrometer and X-ray computed microtomography (micro-CT), resulting in the spectrometer having the same mean r e and micro-CT having 23.9% higher mean r e than the hyperspectral imager. As compact hyperspectral imagers become more widely available, this method may be a valuable tool for assessing r e spatial variability and snow metamorphism in field and laboratory settings.more » « less