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.


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