A predictive model for the spectral "bioalbedo" of snow. Issue 1 (31st January 2017)

• Record Type: Journal Article
• Authors: Cook, J. M.; Hodson, A. J.; Taggart, A. J.; Mernild, S. H.; Tranter, M.
• Is Part Of: Journal of geophysical research. Volume 122:Issue 1(2017)
• Page Start: 434
• Page End: 454
• Publication Date: 2017-01-31
• Subjects: albedo -- spectral reflectance -- biooptics -- melt -- radiative transfer -- life detection; Geomorphology -- Periodicals; 551.3
• DOI: 10.1002/2016JF003932
• Languages: English
• ISSNs: 2169-9003

Abstract

Abstract: We present the first physical model for the spectral "bioalbedo" of snow, which predicts the spectral reflectance of snowpacks contaminated with variable concentrations of red snow algae with varying diameters and pigment concentrations and then estimates the effect of the algae on snowmelt. The biooptical 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 snowpack 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 (CA, USA) and broadband albedo data from 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 not only indicate that algal blooms can influence snowpack albedo and melt rate but also highlight that "indirect" feedback related to their presence are a key uncertainty that must be investigated. Plain Language Summary: Biological impurities have long been understood to impact melt rates on snow and ice by changing its colour and therefore the amount of solar energy absorbed. This paper presents the first numerical model that predicts the changing colour of the snow and its overall reflectivity given information about the snow physics, biology and incoming sunlight and shows that this information can be used to quantify melt rates. Key Points: A physical model is presented that predicts the spectral albedo and melt impact of algal blooms on snow for the first time "Bioalbedo" is shown to impact the melt rate of snow, and associated indirect feedback are shown to be important Spectral "signatures" are identified that could be used to detect life in snow and ice from remotely sensed spectral reflectance data