Water on the Antarctic Ice Sheet: Quantifying Surface Melt and Mapping Supraglacial Lakes

NSF logoThis project is funded by NSF grant 1643715


Allen Pope (NSIDC)

Project Summary

Melting of snow and ice at the surface of the Antarctic ice sheet can lead to the formation of meltwater lakes, an important precursor to ice-shelf collapse and accelerated ice-sheet mass loss. Understanding the present state of Antarctic surface melt provides a baseline to gauge how quickly melt impacts could evolve in the future and to reduce uncertainties in estimates of future sea-level rise. This project will use a suite of complimentary measurements from Earth-observing satellites, ground observations, and numerical climate and ice-shelf models to enhance understanding of surface melt and lakes, as well as the processes linking these systems. The project directly supports the scientific training of a postdoctoral associate and several undergraduate researchers. In addition, it will promote public scientific literacy and the broadening of quantitative skills for high-school students through the development and implementation of an educational unit in a partnership with an education and outreach expert and two high school teachers. Accurate prediction of sea-level contributions from Antarctica critically requires understanding current melting and supraglacial lake conditions. This project will quantify Antarctic surface melt and supraglacial lakes, and the linkages between the two phenomena. Scatterometer data will enable generation of a 19-year multi-sensor melt time series. Synthetic aperture radar data will document melt conditions across all Antarctic ice shelves at the highest spatial resolution to date (40 m). Multispectral satellite imagery will be used to delineate and measure the depth of supraglacial lakes--for the first time studying the spatial and temporal variations of Antarctic supraglacial lakes. Melt and lake observations will be compared to identify agreement and disagreement. Melt observations will be used to evaluate biases in a widely used, reanalysis-driven, regional climate model. This model will then be used to examine climatic and glaciological variables associated with supraglacial lakes. Finally, in situ observations and climate model output will drive a numerical model that simulates the entire lifecycle of surface melt and possible subsequent lake formation.

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