Calibrating an ice sheet model using high-dimensional binary spatial data

TL;DR

This paper introduces a novel calibration method for high-dimensional binary spatial data from ice sheet models, improving uncertainty quantification and projections of sea level rise.

Contribution

The paper presents a new calibration approach for binary spatial data using dimension reduction, enabling effective parameter estimation for ice sheet models.

Findings

Successfully calibrated the PSU3D-ICE model with 499 ensemble members.

Improved characterization of parameter uncertainty in ice sheet modeling.

Enhanced projections of sea level rise considering data-model discrepancies.

Abstract

Rapid retreat of ice in the Amundsen Sea sector of West Antarctica may cause drastic sea level rise, posing significant risks to populations in low-lying coastal regions. Calibration of computer models representing the behavior of the West Antarctic Ice Sheet is key for informative projections of future sea level rise. However, both the relevant observations and the model output are high-dimensional binary spatial data; existing computer model calibration methods are unable to handle such data. Here we present a novel calibration method for computer models whose output is in the form of binary spatial data. To mitigate the computational and inferential challenges posed by our approach, we apply a generalized principal component based dimension reduction method. To demonstrate the utility of our method, we calibrate the PSU3D-ICE model by comparing the output from a 499-member perturbed-parameter ensemble with observations from the Amundsen Sea sector of the ice sheet. Our methods help rigorously characterize the parameter uncertainty even in the presence of systematic data-model discrepancies and dependence in the errors. Our method also helps inform environmental risk analyses by contributing to improved projections of sea level rise from the ice sheets.