GEORGIA: A Graph Neural Network Based EmulatOR for Glacial Isostatic Adjustment.

Glacial isostatic adjustment (GIA) modeling is not only useful for understanding past relative sea‐level change but also for projecting future sea‐level change due to ongoing land deformation. However, GIA model predictions are subject to a range of uncertainties, most notably due to uncertainty in the input ice history. An effective way to reduce this uncertainty is to perform data‐model comparisons over a large ensemble of possible ice histories, but this is often impossible due to computational limitations. Here we address this problem by building a deep‐learning‐based GIA emulator that can mimic the behavior of a physics‐based GIA model while being computationally cheap to evaluate. Assuming a single 1‐D Earth rheology, our emulator shows 0.54 m mean absolute error on 150 out‐of‐sample testing data with <0.5 s emulation time. Using this emulator, two illustrative applications related to the calculation of barystatic sea level are provided for use by the sea‐level community. Plain Language Summary: Piecing together the history of ice sheet change during past glacial cycles is not only important for understanding past sea‐level change but also for predicting how ongoing glacial rebound contributes to future sea‐level change. Traditionally, a physics‐based "sea‐level model" is used to predict the sea‐level change associated with a particular reconstruction of past ice sheet change and compare the results with geological records of past sea level. However, a fundamental limitation of this approach is the need to compute sea‐level change for a large number of plausible ice histories, which is often prohibited by the computational resources required to repeatedly solve the complex physical equations. In this paper, we describe a machine‐learning‐based statistical model that can mimic the behavior of a physics‐based sea‐level model. This statistical model is computationally cheap and we demonstrate that it is able to accurately predict global sea‐level change for a suite of 150 "unseen" ice histories. Our statistical model predicts sea‐level change 100–1,000 times faster than a physics‐based model, making it an ideal tool for investigating and improving our understanding of global ice sheet change. Key Points: The first attempt to build a deep‐learning based Glacial isostatic adjustment (GIA) emulator that can accurately predict global sea‐level change based on a given ice modelThis emulator (GEORGIA) can predict global sea‐level change history within 0.5 s with minor emulation errorThis GIA emulator along with two illustrative applications are available for use by the wider sea‐level community [ABSTRACT FROM AUTHOR]