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Our key findings

Key findings

Since the Black and Bloom project started in 2015, we have made considerable progress understanding how particles and microbial processes darken and accelerate the melting of the Greenland Ice Sheet. This page provides a summary of some of our main published findings (newest first). A full list of published outputs is available here. The project has also been featured in the media and elsewhere.


We investigated the relative abundance of dissolved inorganic and dissolved organic macronutrients (specifically nitrogen and phosphorous) in darkening surface ice environments. Based on samples taken from three distinct ice surfaces, we concluded that the dissolved organic phase dominates surface ice environments and that factors other than macronutrient limitation control the extent and magnitude of the glacier algae blooms

Published in: Holland AT, Williamson CJ, Sgouridis F, Tedstone AJ, McCutcheon J, Cook JM, Poniecka E, Yallop ML, Tranter M, Anesio AM & The Black & Bloom Group (2019). Dissolved organic nutrients dominate melting surface ice of the Dark Zone (Greenland Ice Sheet). Biogeosciences, 16, 3283–3296. DOI:10.5194/bg-16-3283-2019

We studied the variability in the abundance and production of bacteria during a single melt season on the Greenland Ice Sheet. Across the supraglacial environment, production of bacteria within the surface layer of windblown dust (cryoconite) was at least four times greater than on surface ice, confirming that cryoconite holes are the true “hot spots” of bacterial activity.

Published in: Nicholes M, Williamson CJ, Tranter M, Holland A, Yallop ML, Anesio AM and Poniecka E (2019). Bacterial dynamics in supra glacial habitats of the Greenland Ice Sheet. Frontiers in Microbiology, DOI:10.3389/fmicb.2019.01366


We presented the first analysis of ice algal communities from samples collected on the Greenland ice sheet using next generation DNA sequencing to classify the organisms present. The data revealed an extremely low algal diversity with the dominant species exhibiting a site-specific distribution, which may be linked to differences in temperatures and extent of melting. Our results helped to better understand the distribution patterns of ice algal communities that play a crucial role in the Greenland ecosystem.

Published in: Lutz S , Anesio AM, Jorge Villar SE et al. Variations of algal communities cause darkening of a Greenland glacier. FEMS Microbiology Ecology. 2014;89:402–14. DOI:10.1099/mgen.0.000159

We used laboratory analyses to detect, for the first time, a low oxygen (anoxic) zone within the layer of powdery windblown dust (cryoconite) that builds up on the surface of ice sheets, revealing potentially suitable oxygen-free conditions for anaerobic microorganisms and  processes. A six-month incubation of cryoconite samples showed a peak in anaerobic activity after forty days with a steady state reached about four months. We concluded that anaerobic microorganisms, which have received little attention to date, should therefore be considered an important component of the cryoconite ecosystem.

Published in: Poniecka EA, Bagshaw EA, Tranter M, Sass H, Williamson CJ, Anesio AM and the Black and Bloom Team (2018) Rapid development of anoxic niches in supraglacial ecosystems, Arctic, Antarctic and Alpine Research, 50:1, DOI:10.1080/15230430.2017.1420859

We performed a ‘space-for-time’ assessment of an ice algal bloom from samples collected during the 2016 melt season from the bare ice zone in south-western Greenland. A variety of light-capturing and light-protective pigments were quantified and shown to increase in line with algal biomass. By advancing our understanding of ice algal bloom development, this study marked an important step toward projecting bloom occurrence and impacts into the future.

Published in: Williamson CJ, Anesio AM, Cook J, Tedstone A, Poniecka E, Holland A, Fagan D, Tranter M and Yallop ML (2018)., Ice algal bloom development on the surface of the Greenland Ice Sheet, FEMS Microbiology Ecology, Volume 94, Issue 3, March 2018, fiy025, DOI:10.1093/femsec/fiy025


We used satellite imagery to examine dark ice in the south-west of the ice sheet each year from 2000 to 2016. We found that not only does dark ice extent vary significantly between years, but so too does its duration, how dark it is and the timing of its first appearance. We also used climate model outputs to investigate possible climatological controls on dark ice. Using these findings, we suggested that whilst the location of dark ice is best explained by impurities melting out of the underlying ice, this alone does not drive dark ice dynamics. Instead, it might enable the growth of ice algae which cause visible surface darkening, but only when meltwater and sunlight (for photosynthesis) are also available. We concluded that further field studies are required to understand the growth of ice algae and links between algae growth and impurities.

Published in: Tedstone AJ, Bamber JL, Cook JM, Williamson CJ, Fettweis X, Hodson AJ and Tranter M (2017). Dark ice dynamics of the south-west Greenland Ice Sheet, The Cryosphere, 11, 2491-2506, DOI:10.5194/tc-11-2491-2017.

We defined a physical model that enables bioalbedo – biological growth that darkens snow and ice, causing it to melt faster – to be quantified from first principles. From this, we confirmed that biological impurities can influence ice albedo and identify 10 challenges to the measurement of bioalbedo in the field with the aim of improving future experimental design.

Published in: Cook JM, Hodson AJ, Gardner AS, Flanner M, Tedstone AJ, Williamson C, Irvine-Fynn, TDL, Nilsson J, Bryant R and Tranter M (2017). Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo, The Cryosphere, 11, 2611-2632, DOI:10.5194/tc-11-2611-2017.

We developed the first numerical model that predicts the changing colour of the snow and its overall reflectivity given information about the snow physics, biology and incoming sunlight, and showed that this information can be used to quantify melt rates.

Published in: Cook JM, Hodson AJ, Taggart AJ, Mernild SH and Tranter M (2017), A predictive model for the spectral “bioalbedo” of snow,Journal of Geophysical Research: Earth Surface, 122, 434– 454, DOI:10.1002/2016JF003932

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