Using an Uncertainty Quantification Framework to Calibrate the Runoff Generation Scheme in E3SM Land Model V1

Abstract

Abstract. Runoff is a critical component of the terrestrial water cycle and Earth System Models (ESMs) are essential tools to study its spatio-temporal variability. Runoff schemes in ESMs typically include many parameters so model calibration is necessary to improve the accuracy of simulated runoff. However, runoff calibration at global scale is challenging because of the high computational cost and the lack of reliable observational datasets. In this study, we calibrated 11 runoff relevant parameters in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) using an uncertainty quantification framework. First, the Polynomial Chaos Expansion machinery with Bayesian Compressed Sensing is used to construct computationally inexpensive surrogate models for ELM-simulated runoff at 0.5° × 0.5° for 1991–2010. The main methodological advance in this work is the construction of surrogates for the error metric between ELM and the benchmark data, facilitating efficient calibration and avoiding the more conventional, but challenging, construction of high-dimensional surrogates for ELM itself. Second, the Sobol index sensitivity analysis is performed using the surrogate models to identify the most sensitive parameters, and our results show that in most regions ELM-simulated runoff is strongly sensitive to 3 of the 11 uncertain parameters. Third, a Bayesian method is used to infer the optimal values of the most sensitive parameters using an observation-based global runoff dataset as the benchmark. Our results show that model performance is significantly improved with the inferred parameter values. Although the parametric uncertainty of simulated runoff is reduced after the parameter inference, it remains comparable to the multi-model ensemble uncertainty represented by the global hydrological models in ISMIP2a. Additionally, the annual global runoff trend during the simulation period is not well constrained by the inferred parameter values, suggesting the importance of including parametric uncertainty in future runoff projections.

Coupled Lake-Atmosphere-Land Physics Uncertainties in a Great Lakes Regional Climate Model

“…Additionally, other parameterizations should become more important for different QoIs that we intend to look at in future work, such as microphysics (MP) for precipitation (Qian et al, 2015), among others. We also aim to extend the current framework to parametric calibration using observations (e.g., Lu, Ricciuto, Stoyanov, & Gu, 2018;Xu et al, 2022) in a subsequent study.…”

“…To appropriately deal with the inevitable presence of this epistemic uncertainty, the weather and climate modeling fields have employed the use of Perturbed Physics/Parameter Ensembles (PPE; Bellprat, Kotlarski, Lüthi, & Schär, 2012a;Eidhammer et al, 2024). Several PPE-based studies have investigated processes in the atmosphere (Bellprat et al, 2012a;Qian et al, 2015Qian et al, , 2018Qian et al, , 2024, land surface processes (Ricciuto et al, 2018;Xu et al, 2022), and oceans (Huber & Zanna, 2017). Land-atmosphere interactions were investigated in C. Wang et al (2021).…”