Using a surrogate-assisted Bayesian framework to calibrate the runoff-generation scheme in the Energy Exascale Earth System Model (E3SM) v1

Xu, Donghui; Bisht, Gautam; Sargsyan, Khachik; Liao, Chang; Leung, L. Ruby

doi:https://doi.org/10.5194/gmd-15-5021-2022

Articles | Volume 15, issue 12

https://doi.org/10.5194/gmd-15-5021-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-15-5021-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 12

Model evaluation paper

|

01 Jul 2022

Model evaluation paper |

| 01 Jul 2022

Using a surrogate-assisted Bayesian framework to calibrate the runoff-generation scheme in the Energy Exascale Earth System Model (E3SM) v1

Donghui Xu, Gautam Bisht, Khachik Sargsyan, Chang Liao, and L. Ruby Leung

Supplement

https://doi.org/10.5194/gmd-15-5021-2022-supplement

Data sets

Data for "Using an Uncertainty Quantification Framework to Calibrate the Runoff Generation Scheme in E3SM Land Model V1" Donghui Xu https://doi.org/10.5281/zenodo.5815730

Global Runoff Reconstruction Gionata Ghiggi, Lukas Gudmundsson, and Vincent Humphrey https://doi.org/10.6084/m9.figshare.9228176.v2

Model code and software

Code for "Using an Uncertainty Quantification Framework to Calibrate the Runoff Generation Scheme in E3SM Land Model V1" Donghui Xu https://doi.org/10.5281/zenodo.5815500

The International Land Model Benchmarking (ILAMB) System: Design, Theory, and Implementation (doi:10.18139/ILAMB.v002.00/1251621) N. Collier, F. M. Hoffman, D. M. Lawrence, G. Keppel-Aleks, C. D. Koven, W. J. Riley, M. Mu, and J. T. Randerson https://doi.org/10.1029/2018MS001354

Short summary

The runoff outputs in Earth system model simulations involve high uncertainty, which needs to be constrained by parameter calibration. In this work, we used a surrogate-assisted Bayesian framework to efficiently calibrate the runoff-generation processes in the Energy Exascale Earth System Model v1 at a global scale. The model performance was improved compared to the default parameter after calibration, and the associated parametric uncertainty was significantly constrained.