A Variational LSTM Emulator of Sea Level Contribution From the Antarctic Ice Sheet

Abstract The Antarctic ice sheet (AIS) will be a dominant contributor to global mean sea level rise in the 21st century but remains a major source of uncertainty. The Ice Sheet Model Intercomparison for CMIP6 (ISMIP6) is an ensemble of continental‐scale models for studying the evolution of the AIS a...

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Bibliographic Details
Published in:Journal of Advances in Modeling Earth Systems
Main Authors: Peter Van Katwyk, Baylor Fox‐Kemper, Hélène Seroussi, Sophie Nowicki, Karianne J. Bergen
Format: Article in Journal/Newspaper
Language:English
Published: American Geophysical Union (AGU) 2023
Subjects:
emulator
sea level
ISMIP6
uncertainty quantification
neural network
machine learning
Physical geography
GB3-5030
Oceanography
GC1-1581
Antarctic
The Antarctic
Antarc*
Ice Sheet
Online Access:https://doi.org/10.1029/2023MS003899
https://doaj.org/article/6948fa27b84a4de998af4318b04c3116
Description
Summary:Abstract The Antarctic ice sheet (AIS) will be a dominant contributor to global mean sea level rise in the 21st century but remains a major source of uncertainty. The Ice Sheet Model Intercomparison for CMIP6 (ISMIP6) is an ensemble of continental‐scale models for studying the evolution of the AIS and projecting its future contribution to sea level. Due to their complexity and computational cost, ISMIP6 simulations are sparse and generated infrequently. Emulators are smaller‐scale models that approximate ISMs and enable experimentation and exploration into the drivers of sea level change. We introduce a neural network (NN) emulator to approximate the ISMIP6 ensemble, using a variational Long Short‐Term Memory (LSTM) with Monte Carlo dropout to quantify single‐projection uncertainty. The proposed NN emulator is compared to a Gaussian Process (GP) emulator on four criteria: accuracy of point estimates and predictive distributions of individual model projections, approximation of the ensemble projections, and model training time. The NN predicts more accurately on single projections, with a mean absolute error of 0.46 mm Sea Level Equivalent (SLE) versus 0.73 mm SLE for the GP, and has more accurate uncertainty estimates. The NN emulator also better approximates the ensemble distribution of ISMIP6 model projections, with a Kullback‐Leibler divergence of 18.26 versus 199.14 for GP at the projection year 2100. The NN enables more accurate experimentation with a reduced runtime, offering a new tool for understanding the important role of regional precipitation, ice sheet drainage systems, and interannual and longer timescale dynamics.

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