Journal Article

A Framework for Parametric and Predictive Uncertainty Quantification in the E3SM Land Model: Assessing Site and Observable Generalizability

Serbin, Shawn P., Urban, Nathan M., Jiang, Zhongjing, Kuang, Chongai, Isenberg, Natalie M., Subba, Tamanna, Woo, Hyun‐Myung

2026

Overview

Quantifying parametric uncertainty using observations from individual sites provides a critical foundation for Earth system modeling, serving as a necessary first step before scaling up to regional or global applications. This study introduces a novel computational framework designed to enhance model predictability by reducing parametric uncertainty and assessing site and observable generalizability using various observational constraints. The framework integrates five components: Model Simulation, Statistical Emulation, Global Sensitivity Analysis (GSA), Model Calibration, and Model Prediction. Using the E3SM land model, we simulated site‐level land‐atmosphere carbon and energy fluxes from 2003 to 2007 across five evergreen needleleaf FLUXNET sites, perturbing 26 vegetation‐related model parameters. Gaussian process emulators were employed to expedite GSA and model calibration. Four critical parameters that strongly influence selected land‐atmosphere fluxes were identified by GSA. Bayesian approaches were used to infer parameter probability distributions leveraging synthetic data and FLUXNET observations. The results reveal that posterior parameter distributions vary significantly across different sites and observables within the same plant functional type. Probabilistic predictions indicate that parameters calibrated at one site can enhance predictive accuracy at other sites, although site heterogeneity may sometimes outweigh parametric uncertainty. Additionally, the probabilistic predictions demonstrate that calibration for one variable can also improve predictability for other variables, thereby maximizing predictive capabilities with limited observations. This framework provides a powerful approach for reducing parametric uncertainty in Earth system models and deepening our understanding of carbon dynamics and energy cycles. Its adaptability makes it a valuable tool for broader applications in Earth system modeling. Plain Language Summary Understanding and predicting how carbon and energy move between the land and atmosphere is a key goal in Earth system science. But making accurate predictions can be difficult because many model parameters, key values used in the model's math equations, are uncertain. In this study, we developed a framework to improve predictions by better estimating these parameters at specific sites. We used the E3SM land model (ELM) to simulate carbon and energy fluxes at five evergreen forest sites, and applied a machine learning tool to build a surrogate of ELM to speed up the analysis. This surrogate helped us identify the most important parameters and adjust them using real‐world data from tower‐based flux measurements. We then tested how well parameter values calibrated at one site could be applied to other sites, and whether calibrating the model with one type of observation (like carbon fluxes) could also improve predictions of other variables (like energy fluxes). Our results show that both the location of observations and the type of variable used for calibration affect how well the model performs. This framework offers a practical way to improve land model predictions, especially when observations are limited. Key Points A computational framework for uncertainty quantification is developed for Earth system model (ELM), with applicability to other ELMs Site‐level uncertainty quantification improves model predictability and guides transferable parameterization across sites and observables Synthetic data are used for parameter estimation to disentangle model error and parametric uncertainty

• Publisher: John Wiley & Sons, Inc,American Geophysical Union (AGU)
• Subject: Atmosphere / Atmospheric models / Calibration / Carbon / Data assimilation / Earth / ELM / emulator / ENVIRONMENTAL SCIENCES / global sensitivity analysis / Heterogeneity / Markov analysis / model calibration / Modelling / Parameter estimation / Parameters / Probability theory / Sensitivity analysis / Simulation / Uncertainty / uncertainty quantification / Vegetation