A Diffusion‐Based Uncertainty Quantification Method to Advance E3SM Land Model Calibration

• Published in: Journal of geophysical research. Machine learning and computation Vol. 1; no. 3
• Main Authors: Lu, Dan, Liu, Yanfang, Zhang, Zezhong, Bao, Feng, Zhang, Guannan
• Format: Journal Article
• Language: English
• Published: 01.09.2024
• DOI: 10.1029/2024JH000234
• ISSN: 2993-5210

Calibrating land surface models and accurately quantifying their uncertainty are crucial for improving the reliability of simulations of complex environmental processes. This, in turn, advances our predictive understanding of ecosystems and supports climate‐resilient decision‐making. Traditional calibration methods, however, face challenges of high computational costs and difficulties in accurately quantifying parameter uncertainties. To address these issues, we develop a diffusion‐based uncertainty quantification (DBUQ) method. Unlike conventional generative diffusion methods, which are computationally expensive and memory‐intensive, DBUQ innovates by formulating a parameterized generative model and approximates this model through supervised learning, which enables quick generation of parameter posterior samples to quantify its uncertainty. DBUQ is effective, efficient, and general‐purpose, making it suitable for site‐specific ecosystem model calibration and broadly applicable for parameter uncertainty quantification across various earth system models. In this study, we applied DBUQ to calibrate the Energy Exascale Earth System Model land model at the Missouri Ozark AmeriFlux forest site. Results indicated that DBUQ produced accurate parameter posterior distributions similar to those from Markov Chain Monte Carlo sampling but with 30 times less computing time. This significant improvement in efficiency suggests that DBUQ can enable rapid, site‐level model calibration at a global scale, enhancing our predictive understanding of climate impacts on terrestrial ecosystems. Plain Language Summary Land surface models are essential for simulating environmental processes and aiding climate‐resilient decision‐making. Traditionally, calibrating these models has been costly and time‐consuming. To address this, we developed a new method called diffusion‐based uncertainty quantification (DBUQ), which is faster and less memory‐intensive than previous methods. Using supervised learning, DBUQ quickly generates samples to accurately approximate parameter posterior distributions. We tested this method on the Energy Exascale Earth System Model land model at the Missouri Ozark AmeriFlux forest site, finding that it can produce results similar to traditional methods but 30 times faster. This efficiency suggests that DBUQ could revolutionize the calibration of land surface models globally, enhancing our predictive understanding of climate impacts on ecosystems. Key Points A novel diffusion model‐based uncertainty quantification method was developed for efficient model calibration This method produced accurate parameter posterior distributions comparable to those from Markov Chain Monte Carlo sampling, but 30 times faster The method performs amortized Bayesian inference and can be broadly applied to accelerate earth system model calibration