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.

In this work, we report on strategies and results of our initial approach for modernization of Titan2D code. Titan2D is a geophysical mass flow simulation code designed for modeling of volcanic flows, debris avalanches and landslides over a realistic terrain model. It solves an underlying hyperbolic system of partial differential equations using parallel adaptive mesh Godunov scheme. The following work was done during code refactoring and modernization. To facilitate user input two level python interface was developed. Such design permits large changes in C++ and Python low-level while maintaining stable high-level interface exposed to the end user. Multiple diverged forks implementing different material models were merged back together. Data storage layout was changed from a linked list of structures to a structure of arrays representation for better memory access and in preparation for further work on better utilization of vectorized instruction. Existing MPI parallelization was augmented with OpenMP parallelization. The performance of a hash table used to store mesh elements and nodes references was improved by switching from a linked list for overflow entries to dynamic arrays allowing the implementation of the binary search algorithm. The introduction of the new data layout made possible to reduce the number of hash table look-ups by replacing them with direct use of indexes from the storage class. The modifications lead to 8-9 times performance improvement for serial execution.

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 km2 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).

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Abstract. Glacier velocity measurements are essential to understand ice flow mechanics, monitor natural hazards, and make accurate projections of future sea-level rise. Despite these important applications, the method most commonly used to derive glacier velocity maps, feature tracking, relies on empirical parameter choices that rarely account for glacier physics or uncertainty. Here we test two statistics- and physics-based metrics to assess velocity maps from a range of existing feature-tracking workflows at Kaskawulsh Glacier, Canada. Based on inter-comparisons with ground-truth data, velocity maps with metrics falling within our recommended ranges contain fewer erroneous measurements and more spatially correlated noise than velocity maps with metrics that deviate from those ranges. Thus, these metric ranges are suitable for refining feature-tracking workflows and evaluating the resulting velocity products. We have released an open-source software package for computing and visualizing these metrics, the GLAcier Feature Tracking testkit (GLAFT).