Calibration and Propagation of Model Structural Error for E3SM Land Model


Model structural error is often the dominant component of predictive uncertainty budget in climate models. We develop a general framework for a probabilistic representation of the structural error inside the model, followed by a simultaneous calibration of physical inputs and parameters representing the structural error. The resulting embedded model-error strategy conserves physical constraints, allows meaningful predictions of a full set of output quantities of interest (QoIs), disambiguates model error from data noise, and leads to predictions with attributable uncertainties. The approach is further enhanced to include a spatio-temporal model surrogate with Karhunen-Loeve and polynomial chaos representations, providing dimensionality reduction with quantifiable uncertainty, and augmenting the predictive variance. The developed workflow is implemented in UQ Toolkit ( The method is demonstrated for E3SM (Energy Exascale Earth System Model) land model calibration given FLUXNET observations, highlighting the need for burdening physical parameters with stochasticity due to forcing factors.

Dec 9, 2019
San Francisco, CA