Advances in Statistical Climatology, Meteorology and Oceanography
Advances in Statistical Climatology, Meteorology and Oceanography
ASCMO
Articles | Volume 6, issue 2
Articles | Volume 6, issue 2
https://doi.org/10.5194/ascmo-6-223-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-6-223-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 6, issue 2
22 Dec 2020
 | 22 Dec 2020

A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence

Viewed

Total article views: 5,582 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
4,306 1,160 116 5,582 410 129 161
  • HTML: 4,306
  • PDF: 1,160
  • XML: 116
  • Total: 5,582
  • Supplement: 410
  • BibTeX: 129
  • EndNote: 161
Views and downloads (calculated since 22 Dec 2020)
Cumulative views and downloads (calculated since 22 Dec 2020)

Viewed (geographical distribution)

Total article views: 5,479 (including HTML, PDF, and XML) Thereof 5,471 with geography defined and 8 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Nov 2025
Download
Short summary
Uncertainties in land model projections are important to understand in order to build confidence in Earth system modeling. In this paper, we introduce a framework for estimating uncertain land model parameters with machine learning. This method increases the computational efficiency of this process relative to traditional hand tuning approaches and provides objective methods to assess the results. We further identify key processes and parameters that are important for accurate land modeling.
Read more
Share