Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 12
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

Viewed

Total article views: 3,338 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,554 714 70 3,338 227 61 70
  • HTML: 2,554
  • PDF: 714
  • XML: 70
  • Total: 3,338
  • Supplement: 227
  • BibTeX: 61
  • EndNote: 70
Views and downloads (calculated since 16 Dec 2021)
Cumulative views and downloads (calculated since 16 Dec 2021)

Viewed (geographical distribution)

Total article views: 3,338 (including HTML, PDF, and XML) Thereof 3,169 with geography defined and 169 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 05 Jul 2025
Download
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.
Read more
Share