Integrating remote sensing and machine learning to study the linkages between microalgae, ice Sheet, and climate
Snow and ice play an essential role in regulating the global energy balance via high surface albedo (reflectivity). The presence of light-absorbing impurities, including the abiotic materials like dust and black carbon and the biological impurities dominated by microbial communities, could substantially decrease the albedo of snow and ice. Snow algae and glacier algae are among the primary microbial communities in supraglacial environments, which have been observed in Greenland, Antarctica, Alaska, Svalbard, Himalaya, Siberia, the Rocky Mountains, and the European Alps. It was recently shown that glacial algal blooms have significant impacts on the bare ice albedo in southwest Greenland, where a ‘dark’ ice band appears along the ablation zone during the summer season. Quantifying the distribution and abundance of glacial algae is fundamental for understanding the evolving processes of algal blooms. It has been challenging to monitor the development of glacier algae at a regional scale with temporally frequent observations. In this talk, I will introduce using ocean color satellite data to quantify the spatiotemporal variability of glacier algae, and applying machine learning techniques to model their linkages with ice sheet and climate.
Shujie Wang is an assistant professor in the Department of Geography, and an associate of the Earth and Environmental Sciences Institute and the Institute for Computational and Data Sciences at Penn State University. She received her Ph.D. in geography at the University of Cincinnati, and worked as a postdoctoral research scientist at the Lamont-Doherty Earth Observatory of Columbia University. Her research interest is studying cryospheric processes using remote sensing, machine learning, and numerical modeling methods. Her current research focuses on the impact of supraglacial microbes on ice sheet melting, and the flow dynamics and stability of Antarctic ice shelves.