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Abstract There is no consensus on how quickly the earth's ice sheets are melting due to global warming, nor on the ramifications to sea level rise. Due to its potential effects on coastal populations and global economies, sea level rise is a grave concern, making ice melt rates an important area of study. The ice‐sheet science community consists of two groups that perform related but distinct kinds of research: a data community, and a model building community. The data community characterizes past and current states of the ice sheets by assembling data from field and satellite observations. The modeling community forecasts the rate of ice‐sheet decline with computational models validated against observations. Although observational data and models depend on one another, these two groups are not well integrated. Better coordination between data collection efforts and modeling efforts is imperative if we are to improve our understanding of ice sheet loss rates. We present a new science gateway,GHub, a collaboration space for ice sheet scientists. This web‐accessible gateway will host datasets and modeling workflows, and provide access to codes that enable tool building by the ice sheet science community. Using GHub, we will collect and centralize existing datasets, creating data products that more completely catalog the ice sheets of Greenland and Antarctica. We will build workflows for model validation and uncertainty quantification, extending existing ice sheet models. Finally, we will host existing community codes, enabling scientists to build new tools utilizing them. With this new cyberinfrastructure, ice sheet scientists will gain integrated tools to quantify the rate and extent of sea level rise, benefitting human societies around the globe. 

Abstract In Southeast Greenland, summer melt and high winter snowfall rates give rise to firn aquifers: vast stores of meltwater buried beneath the ice-sheet surface. Previous detailed studies of a single Greenland firn aquifer site suggest that the water drains into crevasses, but this is not known at a regional scale. We develop and use a tool in Ghub, an online gateway of shared datasets, tools and supercomputing resources for glaciology, to identify crevasses from elevation data collected by NASA's Airborne Topographic Mapper across 29000 km 2 of Southeast Greenland. We find crevasses within 3 km of the previously mapped downglacier boundary of the firn aquifer at 20 of 25 flightline crossings. Our data suggest that crevasses widen until they reach the downglacier boundary of the firn aquifer, implying that crevasses collect firn-aquifer water, but we did not find this trend with statistical significance. The median crevasse width, 27 meters, implies an aspect ratio consistent with the crevasses reaching the bed. Our results support the idea that most water in Southeast Greenland firn aquifers drains through crevasses. Less common fates are discharge at the ice-sheet surface (3 of 25 sites) and refreezing at the aquifer bottom (1 of 25 sites). 

Probabilistic hazard assessments for studying overland pyroclastic flows or atmospheric ash clouds under short timelines of an evolving crisis, require using the best science available unhampered by complicated and slow manual workflows. Although deterministic mathematical models are available, in most cases, parameters and initial conditions for the equations are usually only known within a prescribed range of uncertainty. For the construction of probabilistic hazard assessments, accurate outputs and propagation of the inherent input uncertainty to quantities of interest are needed to estimate necessary probabilities based on numerous runs of the underlying deterministic model. Characterizing the uncertainty in system states due to parametric and input uncertainty, simultaneously, requires using ensemble based methods to explore the full parameter and input spaces. Complex tasks, such as running thousands of instances of a deterministic model with parameter and input uncertainty require a High Performance Computing infrastructure and skilled personnel that may not be readily available to the policy makers responsible for making informed risk mitigation decisions. For efficiency, programming tasks required for executing ensemble simulations need to run in parallel, leading to twin computational challenges of managing large amounts of data and performing CPU intensive processing. The resulting flow of work requires complex sequences of tasks, interactions, and exchanges of data, hence the automatic management of these workflows are essential. Here we discuss a computer infrastructure, methodology and tools which enable scientists and other members of the volcanology research community to develop workflows for construction of probabilistic hazard maps using remotely accessed computing through a web portal.