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June 2020 A fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheet
Ben Seiyon Lee, Murali Haran, Robert W. Fuller, David Pollard, Klaus Keller
Ann. Appl. Stat. 14(2): 605-634 (June 2020). DOI: 10.1214/19-AOAS1305


We consider the scientifically challenging and policy-relevant task of understanding the past and projecting the future dynamics of the Antarctic ice sheet. The Antarctic ice sheet has shown a highly nonlinear threshold response to past climate forcings. Triggering such a threshold response through anthropogenic greenhouse gas emissions would drive drastic and potentially fast sea level rise with important implications for coastal flood risks. Previous studies have combined information from ice sheet models and observations to calibrate model parameters. These studies have broken important new ground but have either adopted simple ice sheet models or have limited the number of parameters to allow for the use of more complex models. These limitations are largely due to the computational challenges posed by calibration as models become more computationally intensive or when the number of parameters increases.

Here, we propose a method to alleviate this problem: a fast sequential Monte Carlo method that takes advantage of the massive parallelization afforded by modern high-performance computing systems. We use simulated examples to demonstrate how our sample-based approach provides accurate approximations to the posterior distributions of the calibrated parameters. The drastic reduction in computational times enables us to provide new insights into important scientific questions, for example, the impact of Pliocene era data and prior parameter information on sea level projections. These studies would be computationally prohibitive with other computational approaches for calibration such as Markov chain Monte Carlo or emulation-based methods. We also find considerable differences in the distributions of sea level projections when we account for a larger number of uncertain parameters. For example, based on the same ice sheet model and data set, the 99th percentile of the Antarctic ice sheet contribution to sea level rise in 2300 increases from 6.5 m to 13.1 m when we increase the number of calibrated parameters from three to 11. With previous calibration methods, it would be challenging to go beyond five parameters. This work provides an important next step toward improving the uncertainty quantification of complex, computationally intensive and decision-relevant models.


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Ben Seiyon Lee. Murali Haran. Robert W. Fuller. David Pollard. Klaus Keller. "A fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheet." Ann. Appl. Stat. 14 (2) 605 - 634, June 2020.


Received: 1 March 2019; Revised: 1 October 2019; Published: June 2020
First available in Project Euclid: 29 June 2020

zbMATH: 07239876
MathSciNet: MR4117822
Digital Object Identifier: 10.1214/19-AOAS1305

Rights: Copyright © 2020 Institute of Mathematical Statistics


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Vol.14 • No. 2 • June 2020
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