A0925
Title: Emulation and calibration of an Arctic sea ice model with spatial outputs
Authors: Yawen Guan - Colorado State University (United States) [presenting]
Deborah Sulsky - University of New Mexico (United States)
Abstract: Arctic sea ice plays a critical role in the global climate system. Physical models of sea ice simulate key characteristics such as thickness, concentration, and motion, offering valuable insights into its behavior and future projections. However, these models often exhibit large parametric uncertainties due to poorly constrained input parameters. Statistical calibration provides a formal framework for estimating these parameters using observational data while also quantifying the uncertainty in model projections. Calibrating sea ice models poses unique challenges, as both model output and observational data are high-dimensional multivariate spatial fields. A hierarchical latent variable model is presented that leverages principal component analysis to capture spatial dependence and radial basis functions to model discrepancies between simulations and observations. This method is demonstrated through the calibration of MPAS-Sea ice, the sea ice component of the E3SM, using satellite observations of Arctic sea ice.
