Algal growth and weathering crust state drive variability in western Greenland Ice Sheet ice albedo

Abstract

Abstract. One of the primary controls upon the melting of the Greenland Ice Sheet (GrIS) is albedo, a measure of how much solar radiation that hits a surface is reflected without being absorbed. Lower-albedo snow and ice surfaces therefore warm more quickly. There is a major difference in the albedo of snow-covered versus bare-ice surfaces, but observations also show that there is substantial spatio-temporal variability of up to ∼0.4 in bare-ice albedo. Variability in bare-ice albedo has been attributed to a number of processes including the accumulation of light-absorbing impurities (LAIs) and the changing physical properties of the near-surface ice. However, the combined impact of these processes upon albedo remains poorly constrained. Here we use field observations to show that pigmented glacier algae are ubiquitous and cause surface darkening both within and outside the south-west GrIS “dark zone” but that other factors including modification of the ice surface by algal bloom presence, surface topography and weathering crust state are also important in determining patterns of daily albedo variability. We further use observations from an unmanned aerial system (UAS) to examine the scale gap in albedo between ground versus remotely sensed measurements made by Sentinel-2 (S-2) and MODIS. S-2 observations provide a highly conservative estimate of algal bloom presence because algal blooms occur in patches much smaller than the ground resolution of S-2 data. Nevertheless, the bare-ice albedo distribution at the scale of 20 m×20 m S-2 pixels is generally unimodal and unskewed. Conversely, bare-ice surfaces have a left-skewed albedo distribution at MODIS MOD10A1 scales. Thus, when MOD10A1 observations are used as input to energy balance modelling, meltwater production can be underestimated by ∼2 %. Our study highlights that (1) the impact of the weathering crust state is of similar importance to the direct darkening role of light-absorbing impurities upon ice albedo and (2) there is a spatial-scale dependency in albedo measurement which reduces detection of real changes at coarser resolutions.

Quantifying spatiotemporal variability of glacier algal blooms and the impact on surface albedo in southwestern Greenland

“…The MOD10A1 data include broadband albedo estimated based on the MOD09GA product. We used the version 6 data, which are greatly improved in sensor calibration, cloud detection, and aerosol retrieval and correction relative to version 5 (Casey et al, 2017;Lyapustin et al, 2014;Toller et al, 2013). Version 6 data are recommended for assessing temporal variability of surface albedo since they are corrected for sensor degradation issues that impacted earlier versions (Casey et al, 2017).…”

“…Recent studies have revealed a significant impact of glacier algal blooms on bare ice albedo in Greenland (Stibal et al, 2017;Tedstone et al, 2020;Williamson et al, 2018). Along the ablation zone over the southwestern Greenland Ice Sheet, a dark ice band appears every summer season (Shimada et al, 2016;Tedstone et al, 2017).…”

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

“…However, current remote sensing estimates of vegetation biomass and distribution are biased towards plants on exposed ground 1,23,24 and often exclude snow algae from analysis as their spectral profile precludes the use of classical vegetation indices. 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 .…”

Greater Greenland Ice Sheet contribution to global sea level rise in CMIP6

“…Sixty percent of this recent increase in GrIS sea-level contribution is due to enhanced surface runoff 4,6 and GrIS surface processes will also play an important role in a warming climate 2 . Its future mass loss rate strongly depends on the future global temperature rise and therefore anthropogenic greenhouse-gas emission rates 2,[7][8][9] , but also on the strength of regional factors such as the meltalbedo feedback 10,11 , glacier algae growth [12][13][14][15] , cloud phase feedbacks 9,[16][17][18][19] , and atmospheric circulation changes 8,[20][21][22][23][24] . Global climate models (GCMs) of the Climate Model Intercomparison Project 5th Phase (CMIP5) show a clear signal of above average temperature rise in the Arctic in various different emission scenarios [25][26][27] .…”