USAP-DC

Uncertainty in projections of future sea level rise comes, in part, from ice-sheet melting under the influence of unpredictable variations in ocean and atmospheric temperature near ice sheets. Using state-of-the-art modeling techniques, the Antarctic Ice Sheet Large Ensemble (AISLENS) Project will estimate the range of possible Antarctic Ice Sheet melt during the recent past and over the next several centuries that could result from such climate variations. The AISLENS Project will also facilitate research by providing modeling output as an open product to the broader climate and glaciology communities. The project will support an early career faculty member, and interdisciplinary training for a graduate student, postdoctoral fellow and undergraduate student. As a part of this project, an undergraduate course on "Sea Level Rise and Coastal Engineering" will be also developed, bringing together Earth Science and Civil Engineering students in an interdisciplinary setting and contributing to their education in sea level science and coastal adaptation. This will be done in the geographic context of the Southeastern US, the region of most concentrated vulnerability to sea-level rise in the US. The primary goal of the proposed research is to understand and quantify the role of internal climate variability in driving ice loss from the Antarctic Ice Sheet over the recent past and into the future. The AISLENS Project will encompass hundreds of simulations of Antarctic ice sheet evolution from 1950 to 2300 forced by realistic variations in climate, including snowfall and melt from fluctuating oceanic and atmospheric temperatures. Plausible realizations of Antarctic climate forcing will be generated from stochastic emulation of output from the Energy Exascale Earth System Model (E3SM) under past and future emissions scenarios. These realizations of variable climate will be used to force the MPAS Albany Land Ice (MALI) model, a state-of-the-art model of ice flow in the Antarctic Ice Sheet. In this project, AISLENS will be used to conduct uncertainty and attribution analyses. In the uncertainty analysis, the evolution of ensemble spread in simulations of the future evolution of the Antarctic Ice Sheet will be systematically decomposed to determine which temporal and spatial scales of climate variability contribute the most to future ice-sheet projection uncertainty. In the attribution analysis, a range of satellite-based observations of recent Antarctic ice loss will be compared to the envelope of internal variability of Antarctic ice loss simulated in AISLENS simulations encompassing the recent past. This analysis will provide context to recent observations indicating significant variability of Antarctic climate forcing and provide a possible path forward for conducting robust statistical inference studies for observed ice-sheet changes. 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.

Person	Role
Robel, Alexander	Investigator and contact

Antarctic Glaciology	Award # 1947882
Antarctic Integrated System Science	Award # 1947882

USAP-1947882_1

None in the Database

1. Robel, A. A., Pegler, S. S., Catania, G., Felikson, D., & Simkins, L. M. (2022). Ambiguous stability of glaciers at bed peaks. Journal of Glaciology, 1–8. (doi:10.1017/jog.2022.31)
2. Christian, J. E., Robel, A. A., & Catania, G. (2022). A probabilistic framework for quantifying the role of anthropogenic climate change in marine-terminating glacier retreats. The Cryosphere, 16(7), 2725–2743. (doi:10.5194/tc-16-2725-2022)
3. Christian, J. E., Robel, A. A., & Catania, G. (2022). A probabilistic framework for quantifying the role of anthropogenic climate change in marine-terminating glacier retreats. (doi:10.5194/tc-2021-394)
4. Muruganandham, S., Robel, A. A., Hoffman, M. J., & Price, S. F. (2023). Statistical Generation of Ocean Forcing With Spatiotemporal Variability for Ice Sheet Models. Computing in Science & Engineering, 25(3), 30–41. (doi:10.1109/mcse.2023.3300908)