Applying the MEMS 3.0 Model Across Contrasting Grazing US Ecoregions and Management: Model Uncertainty and Evaluation

Grazinglands store substantial soil organic carbon (SOC), yet their potential to act as net carbon (C) sinks depends on management-driven net ecosystem CO2 exchange (NEE). Process-based models capable of representing contrasting management strategies are essential for evaluating mitigation potential. However, the uncertainty inherent in model outputs is often overlooked, which can limit the reliability of predictions. Here, we apply the MEMS 3.0 model to quantify uncertainty in SOC and NEE predictions and evaluate model performance across diverse U.S. grazing ecoregions. We conducted a Bayesian calibration with observations of SOC and its fractions, and annual NEE measurements from six NEON grazing sites. Model evaluation was performed with an independent dataset collected from four experimental sites located in Oklahoma, Michigan, and Wyoming USA under prescriptive and adaptive grazing management treatments. The model performed well in predicting baseline SOC and estimating weekly ecosystem fluxes across sites. Although annual NEE estimates exhibited some discrepancies relative to flux-tower observations, ~90% of measured fluxes fell within simulated posterior predictive intervals. Moreover, the model is consistent with the flux-observations, demonstrating no significant treatment differences between prescriptive and adaptive grazing treatments at the Oklahoma and Wyoming sites. We demonstrated that MEMS 3.0 can represent SOC dynamics and ecosystem fluxes in grazinglands across contrasting climates. Our results show that neglecting uncertainty in measured and simulated fluxes can lead to misleading model-data comparisons. These findings highlights the importance of uncertainty quantification for robust interpretation of predicted grazing management outcomes and for supporting credible climate change mitigation and C accounting frameworks.