CAREER: Accelerating Probabilistic Predictions of Sea-level Rise with Deep Learning

University Of Montana, Missoula MT

Investigators

Abstract

A new paradigm has recently emerged in predicting changes to Earth’s glaciers that emphasizes accounting for unknowns with respect to ice physics and future climate in order to construct a range of plausible futures. Such probability-based bounds are essential in preparing for sea level rise, ecological changes, and other climate feedbacks in which understanding both best- and worst-case scenarios is of practical importance. This task is computationally difficult: glacier models are expensive, particularly when it can take months or years to run the many thousands of simulations required to completely characterize possible outcomes. However, understanding this distribution is essential in meeting the grand challenge of building a sustainable future. This project will use deep learning to construct surrogate models, replacements for ice sheet model components that approximate the original model, but which are much less computationally costly. In addition, this project will convene a summer school and develop educational models to integrate the research and educational components of the work. In particular, this project will use geometric and generative deep learning in tandem with novel applications of classic techniques in high-performance computing to build an approximate (or surrogate) model that is hundreds of times faster than traditional ice sheet models. Such a speedup will in turn allow researchers to explore an unprecedented range of future scenarios and meet the challenges of ice modeling for understanding future global impacts. In addition, a nine-day summer school will bring together instructors and students from around the globe to learn and share effective methods for applying deep learning to problems in glaciology. To help high schoolers learn the skills needed to understand climate change and its uncertainties and to use computers to tackle the associated scientific, economic, and policy challenges, we will develop educational modules tailored towards high school students exploring the intersection of programming, data, and global change. This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences. 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.