Emulation With Uncertainty Quantification of Regional Sea‐Level Change Caused by the Antarctic Ice Sheet
[1];
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- University of Missouri Columbia MO USA, Los Alamos National Laboratory Los Alamos NM USA
- Los Alamos National Laboratory Los Alamos NM USA
- University of New Hampshire Durham NH USA
- Los Alamos National Laboratory Los Alamos NM USA, Now at Jet Propulsion Laboratory Pasadena CA USA
- University of Missouri Columbia MO USA
Abstract Projecting regional sea‐level change under various climate‐change scenarios typically involves running forward simulations of the Earth's gravitational, rotational and deformational (GRD) response to ice‐mass change, which requires substantial computational cost if applied to probabilistic frameworks requiring thousands to millions of samples. Here we build emulators of regional sea‐level change at 27 coastal locations, due to the GRD effects associated with future Antarctic Ice Sheet mass change over the 21st century. The emulators are evaluated against a numerical sea‐level model applied to an ensemble of ice‐sheet model simulations of the Antarctic Ice Sheet through 2100. We build a physics‐based emulator using a recent sensitivity kernel approach and compare it to machine learning based emulators (neural network and conditional variational autoencoder methods). In order to quantify uncertainty, we derive well‐calibrated prediction intervals for regional sea‐level change via split‐conformal inference and linear regression, and show that Monte Carlo dropout does not yield well‐calibrated uncertainties in this instance. We also demonstrate substantial gains in computational efficiency using both the physics‐based emulator and neural networks in comparison to the numerical model for the complete regional sea‐level solution. Overall, we find the physics‐based emulator modestly outperforms the machine learning emulators for this problem.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Other Publication Sponsor: LANL Applied Machine Learning Fellowship; USDOE
- Grant/Contract Number:
- 89233218CNA000001; AC02-05CH11231
- OSTI ID:
- 2566279
- Report Number(s):
- LA-UR-24-26311; e2024JH000349
- Journal Information:
- Journal of Geophysical Research. Machine Learning and Computation (Online), Journal Name: Journal of Geophysical Research. Machine Learning and Computation (Online) Journal Issue: 2 Vol. 2; ISSN 2993-5210
- Publisher:
- American Geophysical Union (AGU)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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