Emulation with uncertainty quantification of regional sea-level change caused by the Antarctic Ice Sheet

TL;DR

This paper develops neural-network emulators to efficiently predict regional sea-level changes caused by Antarctic Ice Sheet mass variations, incorporating uncertainty quantification and achieving significant computational speedups.

Contribution

It introduces neural-network emulators for sea-level change projection that are both accurate and computationally efficient, with a novel uncertainty quantification method.

Findings

Emulators achieve accuracy comparable to baseline machine learning models.

Prediction intervals are well-calibrated for uncertainty estimation.

Neural-network emulators are approximately 100 times faster than traditional solvers.

Abstract

Projecting sea-level change in 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 high computational cost and time. Here we build neural-network emulators of 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 based on datasets produced using a numerical solver for the static sea-level equation and published ISMIP6-2100 ice-sheet model simulations referenced in the IPCC AR6 report. We show that the neural-network emulators have an accuracy that is competitive with baseline machine learning emulators. In order to quantify uncertainty, we derive well-calibrated prediction intervals for simulated sea-level change via a linear regression postprocessing technique that uses (nonlinear) machine learning model outputs, a technique that has previously been applied to numerical climate models. We also demonstrate substantial gains in computational efficiency: a feedforward neural-network emulator exhibits on the order of 100 times speedup in comparison to the numerical sea-level equation solver that is used for training.