Calibrating an Ice Sheet Model Using High-Dimensional Binary Spatial Data

• Main Authors: Chang, Won, Murali Haran, Applegate, Patrick, Pollard, David
• Format: Text
• Language: unknown
• Published: Taylor & Francis 2015
• Subjects: Medicine; Genetics; FOS Biological sciences; Neuroscience; Biotechnology; 59999 Environmental Sciences not elsewhere classified; FOS Earth and related environmental sciences; Immunology; FOS Clinical medicine; 69999 Biological Sciences not elsewhere classified; 80699 Information Systems not elsewhere classified; FOS Computer and information sciences; 19999 Mathematical Sciences not elsewhere classified; FOS Mathematics; Inorganic Chemistry; FOS Chemical sciences; Amundsen Sea; Antarctic; West Antarctic Ice Sheet; West Antarctica; Antarc*; Antarctica; Ice Sheet
• Online Access: https://dx.doi.org/10.6084/m9.figshare.1629327; https://tandf.figshare.com/articles/journal_contribution/Calibrating_an_Ice_Sheet_Model_using_High_dimensional_Binary_Spatial_Data/1629327/2

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. Supplementary materials for this article are available online.