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.

通过基于神经网络的模拟器，本研究在未来的气候变化情景下，建立了海平面变化与地球重力、旋转和形变（GRD）效应之间的关联。通过采用机器学习模型输出的非线性回归后处理技术，我们推导出了对模拟的海平面变化的预测区间，并展示了神经网络模拟器在准确性和计算效率上的优势。