Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 3
Articles | Volume 17, issue 3
https://doi.org/10.5194/gmd-17-1133-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-1133-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 3
Methods for assessment of models
 | 
12 Feb 2024
Methods for assessment of models |  | 12 Feb 2024

Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data

Jonathan Hobbs, Matthias Katzfuss, Hai Nguyen, Vineet Yadav, and Junjie Liu

Viewed

Total article views: 4,104 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,744 1,176 184 4,104 346 169 216
  • HTML: 2,744
  • PDF: 1,176
  • XML: 184
  • Total: 4,104
  • Supplement: 346
  • BibTeX: 169
  • EndNote: 216
Views and downloads (calculated since 14 Nov 2022)
Cumulative views and downloads (calculated since 14 Nov 2022)

Viewed (geographical distribution)

Total article views: 4,104 (including HTML, PDF, and XML) Thereof 4,033 with geography defined and 71 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 06 May 2026
Download
Short summary
The cycling of carbon among the land, oceans, and atmosphere is a closely monitored process in the global climate system. These exchanges between the atmosphere and the surface can be quantified using a combination of atmospheric carbon dioxide observations and computer models. This study presents a statistical method for investigating the similarities and differences in the estimated surface–atmosphere carbon exchange when different computer model assumptions are invoked. 
Read more
Share