Glacier algae accelerate melt rates on the south-western Greenland Ice Sheet

Abstract

Abstract. Melting of the Greenland Ice Sheet (GrIS) is the largest single contributor to eustatic sea level and is amplified by the growth of pigmented algae on the ice surface, which increases solar radiation absorption. This biological albedo-reducing effect and its impact upon sea level rise has not previously been quantified. Here, we combine field spectroscopy with a radiative-transfer model, supervised classification of unmanned aerial vehicle (UAV) and satellite remote-sensing data, and runoff modelling to calculate biologically driven ice surface ablation. We demonstrate that algal growth led to an additional 4.4–6.0 Gt of runoff from bare ice in the south-western sector of the GrIS in summer 2017, representing 10 %–13 % of the total. In localized patches with high biomass accumulation, algae accelerated melting by up to 26.15±3.77 % (standard error, SE). The year 2017 was a high-albedo year, so we also extended our analysis to the particularly low-albedo 2016 melt season. The runoff from the south-western bare-ice zone attributed to algae was much higher in 2016 at 8.8–12.2 Gt, although the proportion of the total runoff contributed by algae was similar at 9 %–13 %. Across a 10 000 km2 area around our field site, algae covered similar proportions of the exposed bare ice zone in both years (57.99 % in 2016 and 58.89 % in 2017), but more of the algal ice was classed as “high biomass” in 2016 (8.35 %) than 2017 (2.54 %). This interannual comparison demonstrates a positive feedback where more widespread, higher-biomass algal blooms are expected to form in high-melt years where the winter snowpack retreats further and earlier, providing a larger area for bloom development and also enhancing the provision of nutrients and liquid water liberated from melting ice. Our analysis confirms the importance of this biological albedo feedback and that its omission from predictive models leads to the systematic underestimation of Greenland's future sea level contribution, especially because both the bare-ice zones available for algal colonization and the length of the biological growth season are set to expand in the future.

Glacier algae foster ice-albedo feedback in the European Alps

“…20.3 × 10 4 cells ml −1 ) is comparable with those found in South-West Greenland 24,50 . Thus, we might expect that the algae density observed on Vadret da Morteratsch Glacier will have an effect on optical properties similar to that already observed on polar glaciers 24,35 .…”

“…These studies highlighted the presence of different species of algae on snow and ice, and their role in the albedo decrease. While recent literature focusing on the southwest margin of the Greenland Ice Sheet identified a strong impact of glacier algae on the optical properties of ice 18,24,26,34,35 , this phenomenon remains largely unexplored in the European Alps.…”

“…Sentinel-2 is particularly suited for mapping spatial and temporal variability of the cryosphere at fine scale 44 , while Sentinel-3 allows a broader perspective 43 . Some studies have already exploited these data for mapping algae distribution in Maritime Antarctica 33 and Southwest Greenland 25,35 .…”

Remote sensing reveals Antarctic green snow algae as important terrestrial carbon sink

“…Efforts to use remote sensing to identify and quantify snow algae have to date focused on the Northern Hemisphere, with early work using airborne hyperspectral imaging 25 and newer predictive models developed for quantifying biomass and the bioalbedo (the impact of biological impurities on ice and snow albedo) of snow and ice [26][27][28] . Several studies have used satellite observations to investigate snow and ice algae on larger scales [29][30][31] , implicating algal blooms as significant drivers for darkening and enhancing melt of the Greenland ice sheet 31 . Current spectral and spatial resolution of freely available multispectral satellite imagery limits the study of most snow and ice algae to presence detection through classification models or assessing relatively small, ground validated areas.…”

Applications of Unmanned Aerial Vehicles in Cryosphere: Latest Advances and Prospects

“…Though the populations were counted three separate times, this leaves comfortable room for human error, creating a desire for a machine learning process to be attempted in future studies. In one novel study, Cook et al [109] applied a random forest supervised classification using directional reflectance measurements as training data to a low-flown (30 m above ground) multispectral orthomosaic to map algae on the Greenland ice sheet. Combined with additional satellite and in situ measurements, they documented the positive albedo feedback effect contributed by surface algae in Western Greenland (responsible for 6%-9% of total runoff from the western sector in summer 2016).…”