Ice Model Calibration using Diffusion Models

Music City Center 

Rapid changes in the cryosphere can affect climate change, such as global sea-level rise. Computer models are useful for understanding the behavior of Antarctic ice sheets and can be used to study their impact on rising sea levels. However, uncertainty quantification of model parameters is challenging because the model outputs and observations are high-dimensional and spatially correlated. Furthermore, they are semicontinuous with an excess of zeros. To address these challenges, we propose a diffusion model-based emulator that can accurately generate the pseudodata across various parameter settings. Since the resulting likelihood from the emulator is intractable, we propose an approximate Bayesian computation method with a Siamese network. The Siamese network is trained to determine whether images generated by the emulator with proposed parameters closely resemble observational data based on the similarity of their features. We apply our method to calibrate the computer model for the West Antarctic Ice Sheet data to generate future projections of sea level rise based on modern ice sheet observations, where the current approaches are infeasible due to the aforementioned challenges.

Ice model calibration

diffusion model

approximate Bayesian computation

Siamese network

semicontinuous spatial data 

Section on Statistics and the Environment