The first physical model for the spectral ‘bioalbedo’ of snow, which predicts the spectral reflectance of snow packs contaminated with variable concentrations of red snow algae of varying size and pigment concentrations have been developed. The results have been published in the paper A predictive model for the spectral “bioalbedo” of snow by J.M. Cook (University of Sheffiled) et al., including Nansen Center CEO Prof. Sebastian Mernild in Journal of Geophysical Research. The model and estimates are used to study the effect of the algae on melting of snow applied for a glacier at Greenland.

At work on heavily discolored ice in the ‘dark zone’ on the Greenland Ice Sheet in 2010. Courtesy: Black and Bloom Team.

The bio-optical model estimates the absorption coefficient of individual cells, a radiative transfer scheme calculates the spectral reflectance of snow contaminated with algal cells and a point-surface energy balance model driven by the spectrally-integrated surface reflectance value calculates melt. For the first time, a predictive model of snowmelt that takes into account snow algal blooms and can simulate fluctuations in the concentration of nine key pigments. The developed model provide bio-optical, albedo and melt predictions, given input values for cell size, pigment concentration, biomass loading in the snow, snow physical properties and meteorological variables. The model demonstrates that algae melt snow via a ‘bioalbedo’ effect and also quantifies the direct impact of biomass and pigment concentration on albedo and snow melt rate.

The simulated melt rate (mm w.e. per day) for two sites (MIT17 and 19, with highest concentration at the latter) at the Mittivakat Gletcher (Greenland) and clean snow. In (A) albedo used to drive the model in a broadband albedo (400-2200 nm) in (B) albedo values over the visible spectral range.

The model is used to investigate the sensitivity of snow to algal biomass and pigmentation, including subsurface algal blooms in the snow layer. The results are demonstrated using data from real algal blooms on the Mittivakkat Gletscher (Greenland) to quantify the impact algae in the snow on melt of the glacier surface. The model was also used to simulate spectral reflectance patterns from algal snow, demonstrating the potential for use of remotely-sensed spectral reflectance data for detecting life in the cryosphere and estimation of their impact on the snow melt.

Citation: Cook, JM., AJ. Hodson, AJ. Taggart, SH. Mernild, and the Black and Bloom Team (2017): A predictive model for the spectral “bioalbedo” of snow. J. Geophysical Research, doi: 10.1002/2016JF003932.