New AGU paper: Microbes change the colour and chemistry of Antarctic snow

In recent decades there has been a significant increase in snow melt on the Antarctic Peninsula and therefore more ‘wet snow’ containing liquid water. This wet snow is a microbial habitat In our new paper, we show that distance from the sea controls microbial abundance and diversity. Near the coast, rock debris and marine fauna fertilize the snow with nutrients allowing striking algal blooms of red and green to develop, which alter the absorption of visible light in the snowpack. This happens to a lesser extent further inland where there is less fertilization.

Figure showing the location of the field sites on the Antarctic Peninsula at two scales (A/B), plus close up views of the red snow algal patches (C/D).

A particularly interesting finding is that the absorption of visible light by carotenoid pigments has greatest influence at the surface of the snow pack whereas chlorophyll is most influential beneath the surface. Higher concentrations of dissolved inorganic carbon and carbon dioxde were measured in interstitial air near the coast compared to inland and a close association was found between chlorophyll and dissolved organic carbon. These observations suggest in situ production of carbon that can support more diverse microbial life, including species originating in nearby terrestrial and marine habitats.

Reflected light from clean snow, snow with green algae and snow with red algae.


These observations will help to predict microbial processes including carbon exchange between snow, atmosphere, ocean and soils occurring in the fastest-warming part of the Antarctic, where snowmelt has already doubled since the mid-twentieth century and is expected to double again by 2050.


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.