A predictive model for the spectral “bioalbedo” of snow

J M Cook,A J Taggart,S H Mernild,M Tranter,A J Hodson

doi:10.1002/2016jf003932

Abstract

AbstractWe 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.

Highlights

Snow has a more variable albedo than any other surface on Earth, ranging from as high as 0.98 for fresh, clean snow to 0.3 in the visible wave band for snow heavily laden with light-absorbing impurities [Flanner and Zender, 2006; Painter et al, 2013]
We model the impact of subsurface blooms on snowpack albedo and melt rates and discuss potential spectral biosignatures that might facilitate remote sensing of snow algae
Despite the current lack of data, we were able to apply our model to two partial data sets—one spectral and one broadband—filling missing data with surrogates derived from wider literature. We present these comparisons for two reasons: first, to show that the model is capable of accurately recreating empirical spectral reflectance data given realistic input data; second, since we started with measured spectra and have inferred values for several input parameters, we suggest that the model can be inverted to determine biomass and pigment concentrations

Summary

Introduction

Snow has a more variable albedo than any other surface on Earth, ranging from as high as 0.98 for fresh, clean snow to 0.3 in the visible wave band for snow heavily laden with light-absorbing impurities [Flanner and Zender, 2006; Painter et al, 2013] This variability exerts an important influence on global climate via the snow-albedo feedback [Budyko, 1969]. The effect of biological impurities (“bioalbedo”) has yet to be isolated from other impurities and snow grain metamorphosis effects despite being recognized as potentially important for snow spectral albedo [Painter et al, 2001; Benning et al, 2014] These effects have yet to be incorporated into a predictive radiative transfer model (RTM). This means that we cannot properly characterize snow albedo in general climate models because an appropriate, physically based understanding of the sensitivity of snow albedo to the full suite of biotic, biogenic, and abiotic impurities is lacking

Results

Discussion

Conclusion