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Advancing earth system model calibration: a diffusion-based method

by Yanfang Liu, Dan Lu, Guannan Zhang, Feng Bao, Zezhong Zhang
Publication Type
Conference Paper
Book Title
ICLR 2024 Workshop: Tackling Climate Change with Machine Learning
Publication Date
Page Numbers
1 to 10
Publisher Location
District of Columbia, United States of America
Conference Name
International Conference on Learning Representations (ICLR)
Conference Location
Vienna, Austria
Conference Sponsor
Conference Date

Understanding of climate impact on ecosystems globally requires site-specific model calibration. Here we introduce a novel diffusion-based uncertainty quantification (DBUQ) method for efficient model calibration. DBUQ is a score-based diffusion model that leverages Monte Carlo simulation to estimate the score function and evaluates a simple neural network to quickly generate samples for approximating parameter posterior distributions. DBUQ is stable, efficient, and can effectively calibrate the model given diverse observations, thereby enabling rapid and site-specific model calibration on a global scale. This capability significantly advances Earth system modeling and our understanding of climate impacts on Earth systems. We demonstrate DBUQ's capability in E3SM land model calibration at the Missouri Ozark AmeriFlux forest site. Both synthetic and real-data applications indicate that DBUQ produces accurate parameter posterior distributions similar to those generated by Markov Chain Monte Carlo sampling but with 30X less computing time. This efficiency marks a significant stride in model calibration, paving the way for more effective and timely climate impact analyses.