Collaborative Research: Decoding & Predicting Greenland's Surface Melt History & Future with Observations, Regional Atmospheric Modeling and GCMs
The investigators plan a three-year effort to improve understanding and prediction of surface melting on the Greenland ice sheet. Satellite remote-sensing, regional climate modeling and nonlinear analysis techniques will be used to: assess variability of observed surface melt occurrence; benchmark model-based melt proxies versus observed melt; diagnose synoptic-scale meteorological/sea-ice controls on melt; and assess future change in melt proxies based on regional models driven by CMIP5 (Coupled Model Intercomparison Project Phase 5) general circulation models (GCMs). Understanding surface melt on polar ice sheets is important because surface melt affects albedo, can produce useful paleoclimatic records, contributes to mass balance through runoff, and is a trigger for ice-shelf collapse leading to ice-flow acceleration and sea-level rise. Improved understanding of the synoptic-meteorological causes of melting, and of the ability of state-of-the-art models to simulate melting accurately, would help assess the effects of future warming on melting, ice-sheet flow and sea level rise. The investigators propose three main research themes to help address these issues: (1) regional-atmospheric-model skill assessments and diagnosis of present synoptic controls on surface melt; (2) application of results from the model skill assessments to GCM-based climate scenarios for estimates of future change; and (3) expanding satellite-based retrieval of surface melt state characterization through retrieval of melt magnitude using a novel fusion of passive microwave and optical/thermal satellite data. In addition to addressing key questions relating Greenland ice sheet balance to sea-level change, the project includes outreach to the public, primarily through a web site, and numerous educational impacts. The project would support undergraduate and graduate students, and involves investigators from a primarily undergraduate institution with a significant population of first-generation college students, and from a Hispanic-serving institution. All data and model results will be archived and made available through the ACADIS data center.