Publications

Wang, SJ; Tedesco, M; Alexander, P; Xu, M; Fettweis, X (2020). Quantifying spatiotemporal variability of glacier algal blooms and the impact on surface albedo in southwestern Greenland. CRYOSPHERE, 14(8), 2687-2713.

Abstract
Albedo reduction due to light-absorbing impurities can substantially enhance ice sheet surface melt by increasing surface absorption of solar energy. Glacier algae have been suggested to play a critical role in darkening the ablation zone in southwestern Greenland. It was very recently found that the Sentinel-3 Ocean and Land Colour Instrument (OLCI) band ratio R-709nm/R-673nm can characterize the spatial patterns of glacier algal blooms. However, Sentinel-3 was launched in 2016, and current data are only available over three melting seasons (2016-2019). Here, we demonstrate the capability of the MEdium Resolution Imaging Spectrometer (MERIS) for mapping glacier algae from space and extend the quantification of glacier algal blooms over southwestern Greenland back to the period 2004-2011. Several band ratio indices (MERIS chlorophyll a indices and the impurity index) were computed and compared with each other. The results indicate that the MERIS two-band ratio index (2BDA) R-709nm/R-665nm is very effective in capturing the spatial distribution and temporal dynamics of glacier algal growth on bare ice in July and August. We analyzed the interannual (2004-2011) and summer (July-August) trends of algal distribution and found significant seasonal and interannual increases in glacier algae close to the Jakobshavn Isbrae Glacier and along the middle dark zone between the altitudes of 1200 and 1400 m. Using broadband albedo data from the Moderate Resolution Imaging Spectroradiometer (MODIS), we quantified the impact of glacier algal growth on bare ice albedo, finding a significant correlation between algal development and albedo reduction over algae-abundant areas. Our analysis indicates the strong potential for the satellite algal index to be used to reduce bare ice albedo biases in regional climate model simulations.

DOI:
10.5194/tc-14-2687-2020

ISSN:
1994-0416