Collaborative Research: Frameworks: Ghub as a Community-Driven Data-Model Framework for Ice-Sheet Science

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Sea level rise is challenging societies around the globe. Planning for future sea level rise in the US is critical for national security, public health, and socioeconomic stability. However, current predictions of sea level rise remain uncertain, because the future behavior of melting ice sheets - a primary cause of sea level rise - is not well understood. A recent United Nations report (IPCC Special Report on the Ocean and Cryosphere in a Changing Climate) summarized two startling facts: (i) Recent sea level rise acceleration is due to increased ice loss from the Greenland and Antarctic ice sheets; and (ii) Uncertainty related to ice-sheet instability arises from limited observations, incomplete representation of ice-sheet processes in current models, and evolving understanding of the complex interactions between the atmosphere, ocean and ice sheets. Improving our ability to forecast the health of ice sheets and hence, predictions of future sea level rise, requires a large, long-lasting collective effort among ice sheet scientists working closely with scientists from the modeling and remote sensing disciplines. One challenge in this collective effort is the range of disciplines and approaches to ice-sheet science - the degree of specialization is an obstacle to efficient collaborative work. This project will lower the barriers among sub-disciplines in ice-sheet science by creating and promoting a centralized web-based hub, called “Ghub,” where datasets and tools will be made accessible to the full range of ice sheet science fields of study. Ghub is accessible to all interested scientists and lay personnel. Use of Ghub includes access to datasets, analysis tools, and cloud computing power, as well as the ability to develop and share new tools within the Ghub environment. Several avenues of outreach and education as part of the Ghub project are specifically aimed at framing ice-sheet science for general audiences, and including students from underrepresented groups.The urgency in reducing uncertainties of near-term sea level rise relies on improved modeling of ice-sheet response to climate change. Predicting future ice-sheet change requires a tremendous effort across a range of disciplines in ice-sheet science including expertise in observational data, paleoglaciology ("paleo") data, numerical ice sheet modeling, and widespread use of emerging methodologies for learning from the data, such as machine learning. However, significant knowledge and disciplinary barriers make collaboration between data and model groups the exception rather than the norm. Most modeling groups write their own tools to ingest data and analyze output, newer and larger observational datasets are not being fully taken advantage of by the modeling community, and paleo data critical for constraining model representation of ice sheet history are largely inaccessible to modelers. The diverse disciplinary approaches to ice-sheet science has led to bottlenecks that slow the response to the developing crisis. Coordination between data generators and modelers is critical for testing data-driven hypotheses, providing mechanistic explanations for past ice-sheet change, and incorporating newly understood physical processes and validating models to improve their predictive ability. Solving the urgent problem of unoptimized collaboration requires a novel, integrated, trans-disciplinary program that lowers barriers across the distinct approaches to ice-sheet science. Fostering collaboration between disciplines will lead to a transformational leap in ice-sheet and sea-level science. To make the leap, we must improve the efficiency in collaboration among traditionally disparate approaches to the problem. We will develop a community-building scientific and educational cyberinfrastructure framework including models and data processing tools, to enable coordination and synergistic exchange between ice-sheet scientific communities. The new cyberinfrastructure will be a significant bridge that connects the numerical ice-sheet modeling community with rapidly growing observational datasets of past and present ice-sheet states that will ultimately improve predictions of sea level rise. The GHub cyberinfrastructure will also be a template for organizing disparate scientific communities to address urgent societal needs in a timely fashion.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.