| NSF Org: |
OPP Office of Polar Programs (OPP) |
| Recipient: |
|
| Initial Amendment Date: | March 24, 2023 |
| Latest Amendment Date: | February 11, 2025 |
| Award Number: | 2319109 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Marc Stieglitz
mstiegli@nsf.gov (703)292-4354 OPP Office of Polar Programs (OPP) GEO Directorate for Geosciences |
| Start Date: | October 1, 2022 |
| End Date: | September 30, 2023 (Estimated) |
| Total Intended Award Amount: | $114,476.00 |
| Total Awarded Amount to Date: | $3,701.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
1700 E COLD SPRING LN BALTIMORE MD US 21251-0001 (443)885-3200 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
1700 E COLD SPRING LN BALTIMORE MD US 21251-0001 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): |
ANS-Arctic Natural Sciences, EarthCube |
| Primary Program Source: |
0100CYXXDB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.050, 47.070, 47.078 |
ABSTRACT
Predicting future sea level relies on improved modeling of how the climate forces the ice sheet to change. This is particularly challenging at the ice-ocean boundary where there are multiple processes occurring simultaneously. At present, no single equation adequately describes the changing ice-ocean boundary, which poses problems for coupling ice sheet models to climate models. This project will improve understanding of how the variables that influence the ice-ocean boundary may change over time and space using machine learning to search for relationships amidst available data. This technique will allow the research team to categorize glacier terminus behavior and identify the relevant parameters forcing change for a particular glacier around the Greenland Ice Sheet. Results of the machine learning exercise will be used to develop an equation to represent ice-ocean interactions in an ice sheet model which will be used to determine future changes to the ice sheet forced by the ocean into the future.
Models of future ice sheet change yield reliable forecasts of sea level rise only when all the critical processes controlling ice sheet evolution are appropriately accounted for. However, many physical processes are currently poorly understood. One such process is ablation (iceberg calving and submarine melt) at the terminus of outlet glaciers, which has been shown to be the dominant control on mass change at particular glaciers. The goal of this project is to improve model forecasts of sea-level from Greenland by using machine learning analyses of glaciological observations to inform physics-based modeling of outlet glaciers, with a focus on the ice-ocean boundary. Machine learning tools will be used to determine what controls changes in terminus position over a range of time scales for all glaciers in Greenland over a period of pronounced historical change (the satellite era). Analysis of the model performance will enable the research team to determine the dominant controls on terminus position for individual and groups of glaciers and to test how well the model performs as new glaciological and environmental data become available. The machine learning model of terminus positions will be used to improve projections of outlet glacier mass change using a physically-based numerical ice flow model. The team will examine how robust model prediction is on various time-scales as more and more data become available over the course of this project. The project will result in refined projections of dynamic loss from the Greenland Ice Sheet, which is important for policy makers needing to make critical infrastructure and resource decisions globally. This goal is a central focus for research within NSF's Office of Polar Programs, NSF's Navigating the New Arctic, and other national (e.g., NASA, NOAA) and international priorities. The project integrates researchers across disciplines, genders, and career stages. Data products and methods produced through this project will be make publicly available and will be useful to the broader scientific community.
This project is co-funded by a collaboration between the Directorate for Geosciences and Office of Advanced Cyberinfrastructure to support AI/ML and open science activities 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.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project improved our understanding of what is driving changes on the Greenland Ice Sheet, focusing on the processes that are causing glaciers to retreat --- this is currently a major source of uncertainty in our understanding of how the ice sheet will evolve in the future. As part of this project, a machine-learning model was trained on a vast variety of satellite and model data and used to identify what is controlling glacier retreat.
The project has results in a new application of machine learning within cryospheric sciences, which is still an underutilized tool within this scientific discipline. The results will inform improvements in physical models of the ice sheet, which will improve our projections of how the ice sheet will evolve over the next century. These kinds of models are used by the Intergovernmental Panel on Climate Change (IPCC) in their Assessment Report (AR) process, which produces synthesized, consensus projections of how sea level will change around the world.
As far as broader impacts, the bulk of work within the research project was conducted by a PhD student, who was able to develop critical skills in machine learning and glaciology that will be applied beyond this project. The student had the opportunity to present results from the work at U.S. and international conferences and is preparing a peer-reviewed manuscript.
Last Modified: 02/26/2025
Modified by: Denis Felikson
Please report errors in award information by writing to: awardsearch@nsf.gov.
