Abstract
Abstract. The darkening effects of biological impurities on ice and snow have been recognised as a control on the surface energy balance of terrestrial snow, sea ice, glaciers and ice sheets. With a heightened interest in understanding the impacts of a changing climate on snow and ice processes, quantifying the impact of biological impurities on ice and snow albedo (bioalbedo) and its evolution through time is a rapidly growing field of research. However, rigorous quantification of bioalbedo has remained elusive because of difficulties in isolating the biological contribution to ice albedo from that of inorganic impurities and the variable optical properties of the ice itself. For this reason, isolation of the biological signature in reflectance data obtained from aerial/orbital platforms has not been achieved, even when ground-based biological measurements have been available. This paper provides the cell-specific optical properties that are required to model the spectral signatures and broadband darkening of ice. Applying radiative transfer theory, these properties provide the physical basis needed to link biological and glaciological ground measurements with remotely sensed reflectance data. Using these new capabilities we confirm that biological impurities can influence ice albedo, then we identify 10 challenges to the measurement of bioalbedo in the field with the aim of improving future experimental designs to better quantify bioalbedo feedbacks. These challenges are (1) ambiguity in terminology, (2) characterising snow or ice optical properties, (3) characterising solar irradiance, (4) determining optical properties of cells, (5) measuring biomass, (6) characterising vertical distribution of cells, (7) characterising abiotic impurities, (8) surface anisotropy, (9) measuring indirect albedo feedbacks, and (10) measurement and instrument configurations. This paper aims to provide a broad audience of glaciologists and biologists with an overview of radiative transfer and albedo that could support future experimental design.
Highlights
The presence of biological impurities in the cryosphere has been known for more than a century, with scientific interest dating back to 1676 when Van Leeuwenhoek described microbial life in snow samples
Physical modelling establishes a functional relationship between snow and ice properties and surface radiance, enabling both predictions of albedo change given changes in optical properties and surface parameter retrieval from spectral reflectance, and allows us to quantify the bioalbedo of a complex mixture of ice, biotic and abiotic impurities
The two stream approach is computationally efficient and able to accurately predict spectral reflectance hemispherically; a multistream approach is required to predict directional reflectance. These models are well validated for clean snow (Grenfell et al, 1994) and have been applied to snow contaminated with black carbon and dust (Painter et al, 2007; Flanner et al, 2007; Gardner and Sharp, 2010; Brandt et al, 2011; Kaspari et al, 2015), their performance for impurity-laden snow has been questioned (Skiles et al, 2017)
Summary
The presence of biological impurities in the cryosphere has been known for more than a century, with scientific interest dating back to 1676 when Van Leeuwenhoek described microbial life in snow samples. Takeuchi (2002), Takeuchi and Kohshima (2004) and Takeuchi et al (2015) showed the albedo reduction resulting from biotic and abiotic impurities to vary between different glaciers, signifying that the mass and optical properties of abiotic impurities such as dust are crucial determinants of surface albedo This may be relevant on the western Greenland Ice Sheet, where high concentrations of dust may be outcropping from melting Holocene ice (Bøggild et al, 2010; Wientjes et al, 2010, 2011). A physical modelling approach is required to link ground measurements to spectral data and to integrate effects of both biological and nonbiological impurities being present in and on the ice. Physical modelling establishes a functional relationship between snow and ice properties and surface radiance, enabling both predictions of albedo change given changes in optical properties and surface parameter retrieval from spectral reflectance, and allows us to quantify the bioalbedo of a complex mixture of ice, biotic and abiotic impurities. The discussion of empirical bioalbedo studies will proceed by detailed examination of 10 distinct challenges, the effects of which are quantified using the model where possible
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