Skip to main content
U.S. flag

An official website of the United States government

Publication Date
1 May 2023

Statistical Generation of Ocean Forcing With Spatiotemporal Variability for Ice Sheet Models

Authors

Author

Melting of ice at the base of floating ice shelves that fringe the Antarctic ice sheet has been identified as a significant source of uncertainty in sea level rise projections. Part of this uncertainty derives from chaotic internal variability of the coupled ocean-atmosphere system. For numerical ice sheet model projections, this uncertainty has not previously been quantified because of the prohibitive computational expense of running large climate model ensembles. Here, we develop and demonstrate a technique that generates independent realizations of internal climate variability from a single climate model simulation. Building on previous developments in model emulation, this technique uses empirical orthogonal function decomposition and Fourier-phase randomization to generate statistically consistent realizations of spatiotemporal variability fields for the target climate variable. The method facilitates efficient sampling of a wide range of climate trajectories, which can also be incorporated within ice sheet or other physical models to represent feedback processes. 

Muruganandham, Shivaprakash, Alexander A. Robel, Matthew J. Hoffman, and Stephen F. Price. 2023. “Statistical Generation Of Ocean Forcing With Spatiotemporal Variability For Ice Sheet Models”. Computing In Science &Amp; Engineering 25 (3). Institute of Electrical and Electronics Engineers (IEEE): 30-41. doi:10.1109/mcse.2023.3300908.
Funding Program Area(s)
Additional Resources:
ALCC (ASCR Leadership Computing Challenge)
NERSC (National Energy Research Scientific Computing Center)