A predictive model for the spectral bioalbedo of snow

Abstract

We present 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 with varying diameters and pigment concentrations, and then estimates the effect of the algae on snow melt. 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, which is then convolved with incoming spectral irradiance to provide albedo. Albedo is then used to drive a point-surface energy balance model to calculate snow pack melt rate. The model is used to investigate the sensitivity of snow to algal biomass and pigmentation, including subsurface algal blooms. The model is then used to recreate real spectral albedo data from the High Sierra (California, USA) and broadband albedo data Mittivakkat Gletscher (SE Greenland). Finally, spectral ‘signatures’ are identified that could be used to identify biology in snow and ice from remotely sensed spectral reflectance data. Our simulations indicate that algal blooms can influence snowpack albedo and melt rate, but also highlight that “indirect” feedbacks related to their presence are a key uncertainty that must be investigated