EAGER: Exploring a Community Driven Data-model Framework for Testing the Stability of the Greenland Ice Sheet

Lead PI: Dr. Joerg Michael Schaefer

Unit Affiliation: Geochemistry, Lamont-Doherty Earth Observatory (LDEO)

September 2018 - August 2020
Arctic ; Greenland Ice Sheet
Project Type: Research

DESCRIPTION: In the wake of devastating flooding related to recent hurricane strikes on densely inhabited areas, the potential impact of sea level rise has never been more evident. Rising sea level is challenging societies around the globe, but current predictions of future sea level rise contain uncertainty. Greenland's ice, if fully melted, would raise global sea levels more than 7 m. However, at present, we do not have data or models that allow for a definitive consensus view of Greenland Ice Sheet vulnerability to climate change, and our sea level predictions are only as good as our ability to model Greenland Ice Sheet change using computer simulations and the data driving those simulations. Vast datasets of observations and increasingly sophisticated computer models are being employed to solve this problem. Yet, significant knowledge and disciplinary barriers make collaboration between data and model groups the exception rather than the norm. The PIs propose to establish a community-building scientific and educational cyber hub to enable seamless access to data, and coordination and synergy between different scientific disciplines. The overarching goal is to increase the accuracy of predictions of the Greenland Ice Sheet contribution to sea level rise by 2050, 2100 and beyond. The project will support two female PIs (one of which is a young investigator) and partial funding for two graduate students.

Observational datasets of Greenland Ice Sheet change, both from the satellite era and the recent geologic past, are rapidly expanding. Some of these observations have been used for calibration and validation of ice sheet models thereby improving the physical understanding of ice sheet behavior and estimates of future sea level rise. But substantial data-model gaps remain due to the knowledge barrier of understanding and using satellite- and paleo-data and the lack of a standard framework for using available observational datasets in ice sheet modeling experiments. There is significant potential to generate a long-lasting cyberinfrastructure framework with ice sheet data, software tools, online cloud-based execution and educational materials. When combined, this would lead to rapid progress in improving ice sheet modeling capability and decreasing uncertainty in sea level rise forecasting. The PIs will bring together experts in ice sheet observation, data analysis and modeling to guide the creation of a community hub that will enable two-way communication between data generators and modelers. The PIs will pilot software tools necessary to facilitate interoperability among the various data sets and modeling tools and investigate new metrics for model-data intercomparison and model assessment.