New TCD paper: Dark ice on Greenland Ice Sheet

Our new discussion paper, led by Black and Bloom PDRA Andrew Tedstone, examines in detail why there is a stripe of dark, fast-melting ice on the Greenland Ice Sheet, particularly in the south-west. This ‘dark zone’ is clearly visible in satellite imagery of the Greenland Ice Sheet and is important because darker ice melts faster. It is crucial to understand what causes the ice to be dark there because if it grows or darkens in a warming climate then we can expect the deglaciation of Greenland to accelerate more than is currently predicted. There are two main competing hypotheses that could explain the presence of the dark zone: 1) dust melting out from ancient ice is darkening the ice; 2) algae are growing on the ice sheet and changing its colour.

An aerial view of the Black and Bloom Camp at S6 (Greenland Ice Sheet) in 2016, in the heart of the ‘dark zone’.

The paper shows that the dark zone changes its shape, size and duration each year. This appears to be most strongly controlled by the sensible heat flux (air temperature) between June and August, number of days with air temperatures above zero, and timing of the snow-line retreat.

This figure from the paper shows the extent of the dark zone between June and August each year between 2000 – 2016.

These findings provide some insights into which surface processes are most likely to explain the dynamics of the dark zone. The spatial distribution of the dark ice is best explained by the melting out of dust particles from ancient ice, although these particles are not dark enough to explain the colour change of the dark zone. However, these dusts may be crucial nutrients and substrates for ice algae, suggesting that the dusts control where the dark zone is, and the algae determine how dark it gets. Our other recent TCD paper showed how algae can darken ice and snow; however, there are also meteorological conditions required for algal growth including sufficient sunlight and liquid water. We suggest in the paper that the most likely hypothesis is that dust melts out from ancient ice and stimulates the growth of algae when meteorology allows it. Algae need the dust to grow, and the dust is not dark without the algae.

Bioalbedo: new model and TCD paper

I’m very pleased to report our new paper is now in open discussion in The Cryosphere. The paper presents a new model for predicting the spectral bioalbedo of snow and ice, which confirms that ice algae on ice surfaces can change its colour and by doing so enhance its melt rate (“bioalbedo”). We also used the model to critique the techniques used to measure bioalbedo in the field. The model is based on the SNow ICe and Atmosphere Radiative model (SNICAR), but adapted to interface with a mixing model for pigments in algal cells. We refer to the coupled models as BioSNICAR.

The darkening effect of algal growth (bioalbedo) in the visible wavelengths can be seen in this UAV image of our 2016 field camp at S6 on the Greenland Ice Sheet

The model uses Mie theory to work out the optical properties of individual algal cells with refractive indices calculated using a pigment mixing model. The user can decide how much of each pigment the cell contains, the cell size, the biomass concentration in each of n vertical layers, the snow/ice optical properties, angle and spectral distribution of incoming sunlight and the mass concentration, optical properties and distribution of inorganic impurities including mineral dusts and black carbon (soot). From this information, the model predicts the albedo of the surface for each wavelength in the solar spectrum. This can then be used to inform an energy balance model to see how much melt results from changes to any of the input values, including growth or pigmentation of algae.

This figure shows the effect of algal cells with different pigmentation on spectral albedo of snow/ice. In A) the cells have 1.5% chlorophyll a and 10% photoprotective carotenoids, B) 1.5% chlorophyll a and 5% photoprotective carotenoids, C) 1.5% chlorophyll a and 1% photoprotective carotenoids and D) 1.5% chlorophyll a only. These percentages are % total cell dry mass. The biomass is shown in the legend, which applies to all four subplots.


The model shows that smaller cells with photoprotective pigments have the greatest albedo-reducing effect. The model experiments suggest that in most cases algal cells have a greater albedo-reducing effect than mineral dusts (depending upon optical properties) but less than soot.


A) An equal biomass concentration with varying vertical distribution in the snow/ice; B) mineral dusts in varying mass concentrations doing a good job of recreating the ‘red-edge’ (see previous post); ) mineral dusts obscuring the spectral signature of algal cells; D) the effect of water in interstitial pore spaces. Ice grains are 1000 microns in diameter and the legend refers to the thickness of liquid water coating around the grains (microns). Note the shift of the absorption feature centred at 1030 nm towards shorter wavelengths when more water is present.


As well as making predictions about albedo change, the modelling is useful for designing field experiments, as it can quantify the error resulting from certain practises, such as using devices with limited wavelength ranges, or neglecting to characterise the vertical distribution of cells. I’ll cover this in some further posts. The most important thing is metadata collection, since standardising this enables the measurement conditions to be as transparent as possible and encourages complementarity between different projects. Importantly, following a protocol for albedo measurements and collecting sufficient metadata will make it easier to couple ground measurements to satellite data. We outline two key procedures: hemispheric albedo measurement, and hemispherical-conical reflectance factor measurement. To accompany the discussion in our paper, we’ve produced some metadata collection sheets that might be useful to other researchers making albedo measurements in the field (download here: metadata sheets) and made our code and data available in an open repository.