Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo

Cook, Joseph M.; Hodson, Andrew J.; Gardner, Alex; Flanner, M.; Tedstone, Andrew; Williamson, Christopher; Irvine‐Fynn, Tristram; Nilsson, Johan; Bryant, Robert G.; Tranter, Martyn

doi:10.5194/tc-11-2611-2017

Cited by 76 publications

(91 citation statements)

References 109 publications

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“…Smoother, wetter ice surfaces that develop in concert with increasing glacier algae abundance produce fewer opportunities for high-angle scattering of photons and significant potential for indirect enhancement of the biological albedo reduction effect (11,42). Our findings confirm the importance of both direct and indirect impacts of glacier algal blooms for processes of GrIS surface darkening.…”

Section: Secondary Phenolic Pigmentation Dominates Light Absorptionsupporting

confidence: 71%

“…6E), significant deviation from a 1:1 relationship was apparent at higher algal concentrations (F 1,494 = 6.54 × 10 32 , P < 0.001), with MODIS-derived BBA consistently lower as compared to modeled BBA. Additional to the direct impacts of glacier algae on surface ice albedo through pigment-mediated energy capture (this study), indirect perturbations of surface ice physics are also likely important (11,42,43).…”

Section: Secondary Phenolic Pigmentation Dominates Light Absorptionmentioning

confidence: 84%

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Algal photophysiology drives darkening and melt of the Greenland Ice Sheet

Williamson

Cook²,

Tedstone

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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134

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Blooms of Zygnematophycean "glacier algae" lower the bare ice albedo of the Greenland Ice Sheet (GrIS), amplifying summer energy absorption at the ice surface and enhancing meltwater runoff from the largest cryospheric contributor to contemporary sea-level rise. Here, we provide a step change in current understanding of algal-driven ice sheet darkening through quantification of the photophysiological mechanisms that allow glacier algae to thrive on and darken the bare ice surface. Significant secondary phenolic pigmentation (11 times the cellular content of chlorophyll a) enables glacier algae to tolerate extreme irradiance (up to ∼4,000 μmol photons·m −2 ·s −1 ) while simultaneously repurposing captured ultraviolet and short-wave radiation for melt generation. Total cellular energy absorption is increased 50-fold by phenolic pigmentation, while glacier algal chloroplasts positioned beneath shading pigments remain low-light-adapted (E k ∼46 μmol photons·m −2 ·s −1 ) and dependent upon typical nonphotochemical quenching mechanisms for photoregulation. On the GrIS, glacier algae direct only ∼1 to 2.4% of incident energy to photochemistry versus 48 to 65% to ice surface melting, contributing an additional ∼1.86 cm water equivalent surface melt per day in patches of high algal abundance (∼10 4 cells·mL −1 ). At the regional scale, surface darkening is driven by the direct and indirect impacts of glacier algae on ice albedo, with a significant negative relationship between broadband albedo (Moderate Resolution Imaging Spectroradiometer [MODIS]) and glacier algal biomass (R 2 = 0.75, n = 149), indicating that up to 75% of the variability in albedo across the southwestern GrIS may be attributable to the presence of glacier algae.Greenland Ice Sheet | glacier algae | photophysiology | melt | cryosphere

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Section: Secondary Phenolic Pigmentation Dominates Light Absorptionsupporting

confidence: 71%

Section: Secondary Phenolic Pigmentation Dominates Light Absorptionmentioning

confidence: 84%

Algal photophysiology drives darkening and melt of the Greenland Ice Sheet

Williamson

Cook²,

Tedstone

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

134

View full text Add to dashboard Cite

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“…There are two possible factors affecting carrying capacity: (1) the reduction in physical space available for microbial growth (i.e. the volume of liquid melt water, McKindsey et al, 2006) and (2) the exhaustion of resources for the algae on the snow surface (Cui and Lawson, 1982). The maximum algal cell volume on surface snow (depth = 2 cm) at Site A (day 214) was substantially smaller compared to the total volume of liquid water in the surface snow obtained from the water content (0.19 mL m −2 vs. 500 mL m −2 ); this indicates that the physical space for algal growth in the habitat is not a factor affecting carrying capacity in this study.…”

Section: Factors Affecting Parameters Of Algal Growth Modelmentioning

confidence: 99%

“…The Malthusian model is based on the assumptions that microbial abundance increases by cell division in all present cells at a constant rate, so that there is no addition or removal of cells in the habitat, and that light, nutrients, and habitable space are unlimited. According to this model, the microbial growth curve is calculated as follows (Cui and Lawson, 1982):…”

Section: Approximation Of the Algal Growth Curve With A Numerical Modelmentioning

confidence: 99%

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Observations and modelling of algal growth on a snowpack in north-western Greenland

Onuma¹,

Takeuchi²,

Tanaka³

et al. 2018

The Cryosphere

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Abstract. Snow algal bloom is a common phenomenon on melting snowpacks in polar and alpine regions and can substantially increase snow melt rates due to the effect of albedo reduction on the snow surface. In order to reproduce algal growth on the snow surface using a numerical model, temporal changes in snow algal abundance were investigated on the Qaanaaq Glacier in north-western Greenland from June to August 2014. Snow algae first appeared at the study sites in late June, which was approximately 94 h after air temperatures exceeded the melting point. Algal abundance increased exponentially after this appearance, but the increasing rate became slow after late July, and finally reached 3.5 × 10 7 cells m −2 in early August. We applied a logistic model to the algal growth curve and found that the algae could be reproduced with an initial cell concentration of 6.9 × 10 2 cells m −2 , a growth rate of 0.42 d −1 , and a carrying capacity of 3.5 × 10 7 cells m −2 on this glacier. This model has the potential to simulate algal blooms from meteorological data sets and to evaluate their impact on the melting of seasonal snowpacks and glaciers.

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Optimal Estimation of Snow and Ice Surface Parameters from Imaging Spectroscopy Measurements

Bohn

Thompson

Carmon

et al. 2021

Preprint

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Snow and ice melt processes are a key in Earth's energy-balance and hydrological cycle. Their quantification facilitates predictions of meltwater runoff as well as distribution and availability of fresh water. They control the balance of the Earth's ice sheets and are acutely sensitive to climate change. These processes decrease the surface reflectance with unique spectral patterns due to the accumulation of liquid water and light absorbing particles (LAP), that require imaging spectroscopy to map and measure. Here we present a new method to retrieve snow grain size, liquid water fraction, and LAP mass mixing ratio from airborne and spaceborne imaging spectroscopy acquisitions. This methodology is based on a simultaneous retrieval of atmospheric and surface parameters using optimal estimation (OE), a retrieval technique which leverages prior knowledge and measurement noise in an inversion that also produces uncertainty estimates. We exploit statistical relationships between surface reflectance spectra and snow and ice properties to estimate their most probable quantities given the reflectance. To test this new algorithm we conducted a sensitivity analysis based on simulated top-of-atmosphere radiance spectra using the upcoming EnMAP orbital imaging spectroscopy mission, demonstrating an accurate estimation performance of snow and ice surface properties. A validation experiment using in-situ measurements of glacier algae mass mixing ratio and surface reflectance from the Greenland Ice Sheet gave uncertainties of ±16.4 µg/g ice and less than 3%, respectively. Finally, we evaluated the retrieval capacity for all snow and ice properties with an AVIRIS-NG acquisition from the Greenland Ice Sheet demonstrating this approach's potential and suitability for upcoming orbital imaging spectroscopy missions.

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Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo

Cited by 76 publications

References 109 publications

Algal photophysiology drives darkening and melt of the Greenland Ice Sheet

Algal photophysiology drives darkening and melt of the Greenland Ice Sheet

Observations and modelling of algal growth on a snowpack in north-western Greenland

Optimal Estimation of Snow and Ice Surface Parameters from Imaging Spectroscopy Measurements

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