The dynamic mass loss due to ICE flow from the antarctic ICE sheet directly into oceans is one of the greatest sources of uncertainty in predicting sea level rise. Over 80% of the antarctic ICE…

This federal grant was awarded by the National Aeronautics and Space Administration (NASA) Shared Services Center to Vanderbilt University, a leading private research university, for a project focused on advancing the understanding of ice shelf and glacier dynamics in Antarctica. The project aims to better comprehend ice shelf and glacier weakening and calving processes, the stability of fractures, and the damage mechanics of marginal shear zones under different flow regimes. The research will utilize remote sensing data, including from the ICESat-2 satellite, to quantify the location and geometry of rifts and surface crevasses, and implement machine learning techniques to automate the identification of these features. The $455,398.00 grant does not have a set-aside designation and is intended to support the project's objectives of improving predictions of sea level rise due to ice mass loss from the Antarctic ice sheet. The award is part of NASA's broader efforts to utilize remote sensing data to enhance understanding of ice sheet, ice shelf, and glacier processes and their impact on ice mass balance and sea level change.

The dynamic mass loss due to ICE flow from the antarctic ICE sheet directly into oceans is one of the greatest sources of uncertainty in predicting sea level rise. Over 80% of the antarctic ICE sheet drains into the ocean through floating ICE shelves, where all mass loss occurs due to roughly equal contributions from basal melting and tabular calving. ICE shelves provide resistance to the flow of upstream grounded glacier ICE into the ocean, so any loss of this resistance or buttressing due to calving could trigger marine ICE cliff instability and increase the flux of grounded ICE flow into the ocean, thereby contributing to sea level rise. Importantly, calving is complicated by ICE-ocean-atmospheric couplings and the major concern is that future warmer climates and oceans could increase calving rates and destabilize glaciers and ICE shelves. The project s overall objective is to better understand ICE shelf and glacier weakening and calving process, the stability of fractures, and the damage mechanics of marginal shear zones under different flow regimes. Our hypothesis is that observable fracture features in ICE are manifestations of thermo-mechanical damage evolution in space and time. To test this hypothesis and address the objective, we will develop an advanced modeling approach that integrates mechanics-based computation with remotely sensed data compiled using statistical analysis and machine learning. We apply our modeling approach to study damage evolution in larsen c and crosson ICE shelves and understand the consequences for its upstream glaciers. We propose to use altimetry data, including icesat-2, ATM (airborne topographic mapper), and lvis (land vegetation, and ICE sensor), as well as high-resolution DEM data from the rema project to measure surface roughness to map shear zones and rifts, derive spot estimates of crevasse depths, and to measure m lange freeboard in rifts. We will implement deep convolutional neural networks, a type of advanced machine learning model, to automate the process of rift and crevasse damage identification from satellite-based images. By combining identified damage feature information with crevasse depth data from icesat-2, we will generate useful datasets or damage-maps for calibration and validation of the inverse and forward computational models. Subsequently, we will study of rift dynamics to better understand the feedback between damage accumulation, rift propagation and ICE rheology. The project addresses the solicitation s focus to utilize remote sensing data to improve our understanding of ICE sheet, ICE shelf, and glacier processes and how these processes affect ICE mass balance and ultimately sea level rise and establish time series of ICE sheet geophysical parameters and investigate trends and variabilities, and their drivers to aid model predictions. The project builds on a database of NASA icebridge, icesat, and icesat-2 observations previously constructed by CO-i smith and will utilize a variety of satellite and airborne based measurements to quantify the location and geometry of rifts and surface crevasses. As a member of the icesat-2 science team, CO-i smith will collaborate with other team members so that our data and code help address the needs of the science team, including quantification of uncertainties in the ATL06 and ATL11 datasets in regions of crevassing, and will work with science team members h. Fricker and b. Lipovsky to integrate their databases of rift locations with our work. The datasets and python code produced by the project will be shared openly following findable, accessible, interoperable, and reusable practices so that they can be used by the icesat-2 science team and the broader community. We will also share our implementation of the computational and machine learning models built using open source.