Elements: Software. icepack: an open-source glacier flow modeling library in Python

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This project supports the development of a software package named "icepack", that will enable simulations of how glaciers, such as those in Greenland, Antarctica, and mountain ranges around the world, will flow in response to the environment around them. Glaciologists use software tools to run simulations so that they can make predictions of how large the Greenland and Antarctic ice sheets will be in the future. With these predictions, scientists can give policy-makers and the public better predictions on the sea level rise in the coming decades. While the ability to run simulations is essential for advancing our understanding of science, doing so requires a significant programming and scientific expertise. The goal of this project is to lower this barrier to entry. Led by an early career scientist, the team, from University of Washington will develop a tool that is easier to use for researchers and students, whether they are experts or novices. The software applications will be freely available and an open source license.icepack allows for estimating parameters, such as a basal friction or internal rheology, that are not observable via remote sensing. Glaciologists use simulation tools like icepack for (1) exploring aspects of the physics of ice sheets that are not completely understood, (2) drawing inferences from observational data, and (3) making predictions of the future state of the ice sheets in order to estimate future sea-level rise. While modeling is an essential tool for practicing glaciologists, it is still a complex endeavor. In addition to supporting development of more features and improvements to icepack, we will create an extensive set of tutorial materials for a workshop aimed at graduate students and early-career researchers on how to use icepack. Additionally, the investigators will implement novel algorithms for parameter estimation and uncertainty quantification in icepack. These will allow the investigators to leverage the entire time series of observations of the ice sheets, while current algorithms are limited in how much data they can use, and to get a better idea of the statistical spread on estimates of the current and future states of the ice sheets.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Cross-Cutting Program within the NSF Directorate for Geosciences, and the EarthCube Program jointly sponsored by the NSF Directorate for Geosciences and the Office of Advanced Cyberinfrastructure.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.