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September 2022 Ice model calibration using semicontinuous spatial data
Won Chang, Bledar A. Konomi, Georgios Karagiannis, Yawen Guan, Murali Haran
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Ann. Appl. Stat. 16(3): 1937-1961 (September 2022). DOI: 10.1214/21-AOAS1577

Abstract

Rapid changes in Earth’s cryosphere caused by human activity can lead to significant environmental impacts. Computer models provide a useful tool for understanding the behavior and projecting the future of Arctic and Antarctic ice sheets. However, these models are typically subject to large parametric uncertainties, due to poorly constrained model input parameters that govern the behavior of simulated ice sheets. Computer model calibration provides a formal statistical framework to infer parameters, using observational data, and to quantify the uncertainty in projections due to the uncertainty in these parameters. Calibration of ice sheet models is often challenging because the relevant model output and observational data take the form of semicontinuous spatial data with a point mass at zero and a right-skewed continuous distribution for positive values. Current calibration approaches cannot handle such data. Here, we introduce a hierarchical latent variable model that handles binary spatial patterns and positive continuous spatial patterns as separate components. To overcome challenges due to high dimensionality, we use likelihood-based generalized principal component analysis to impose low-dimensional structures on the latent variables for spatial dependence. We apply our methodology to calibrate a physical model for the Antarctic ice sheet and demonstrate that we can overcome the aforementioned modeling and computational challenges. As a result of our calibration, we obtain improved future ice-volume change projections.

Funding Statement

This material was based upon work partially supported by the National Science Foundation under Grant DMS-1638521 to the Statistical and Applied Mathematical Sciences Institute (SAMSI).

Acknowledgments

The authors are grateful to Dr. David Pollard for the helpful discussion. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Citation

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Won Chang. Bledar A. Konomi. Georgios Karagiannis. Yawen Guan. Murali Haran. "Ice model calibration using semicontinuous spatial data." Ann. Appl. Stat. 16 (3) 1937 - 1961, September 2022. https://doi.org/10.1214/21-AOAS1577

Information

Received: 1 November 2020; Revised: 1 October 2021; Published: September 2022
First available in Project Euclid: 19 July 2022

Digital Object Identifier: 10.1214/21-AOAS1577

Keywords: computer model calibration , computer model emulation , Dimension reduction , semicontinuous spatial data , West Antarctic Ice Sheet

Rights: Copyright © 2022 Institute of Mathematical Statistics

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Vol.16 • No. 3 • September 2022
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