A Multi-Fidelity Tensor Emulator for Spatiotemporal Outputs: Emulation of Arctic Sea Ice Dynamics

TL;DR

This paper introduces a multi-fidelity tensor emulator that efficiently combines low- and high-resolution simulations to accurately predict complex spatiotemporal Arctic sea ice dynamics while significantly reducing computational costs.

Contribution

The paper presents a novel multi-fidelity emulator integrating tensor decomposition, Gaussian processes, and discrepancy modeling for scalable, accurate spatiotemporal emulation of earth system models.

Findings

Lower prediction error compared to single-fidelity models

Reduced uncertainty in emulation results

Effective handling of large-scale spatiotemporal data

Abstract

Numerical models are widely used to simulate the earth system, but they are computationally expensive and often depend on many uncertain input parameters. Their effective use requires calibration and uncertainty quantification, which typically involve running the model across many input configurations and therefore incur substantial computational cost. Statistical emulation provides a practical alternative for efficiently exploring model behavior. We are motivated by the Arctic sea ice component of the Energy Exascale Earth System Model (MPAS-Seaice), which generates large spatiotemporal outputs at multiple spatial resolutions, with high-resolution (or high-fidelity, HF) simulations being more accurate but computationally more expensive than lower-resolution (low-fidelity, LF) simulations. Multi-fidelity (MF) emulation integrates information across resolutions to construct efficient and accurate surrogate models, yet existing approaches struggle to scale to large spatiotemporal data. We develop an MF emulator that combines tensor decomposition for dimensionality reduction, Gaussian process priors for flexible function approximation, and an additive discrepancy model to capture systematic differences between LF and HF data. The proposed framework enables scalable emulation while maintaining accurate predictions and well-calibrated uncertainty for complex spatiotemporal fields, and consistently achieves lower prediction error and reduced uncertainty than LF-only and HF-only models in both simulation studies and MPAS-Seaice analysis. By leveraging the complementary strengths of LF and HF data and using an efficient tensor decomposition approach, our emulator greatly reduces computational expense, making it well suited for large-scale simulation tasks involving complex physical models.