Biogeosciences
Biogeosciences
BG
Articles | Volume 17, issue 7
Articles | Volume 17, issue 7
https://doi.org/10.5194/bg-17-1821-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-17-1821-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 7
Research article
 | 
03 Apr 2020
Research article |  | 03 Apr 2020

Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis

Didier G. Leibovici, Shaun Quegan, Edward Comyn-Platt, Garry Hayman, Maria Val Martin, Mathieu Guimberteau, Arsène Druel, Dan Zhu, and Philippe Ciais

Viewed

Total article views: 2,131 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,485 596 50 2,131 54 42
  • HTML: 1,485
  • PDF: 596
  • XML: 50
  • Total: 2,131
  • BibTeX: 54
  • EndNote: 42
Views and downloads (calculated since 09 Jul 2019)
Cumulative views and downloads (calculated since 09 Jul 2019)

Viewed (geographical distribution)

Total article views: 2,131 (including HTML, PDF, and XML) Thereof 1,953 with geography defined and 178 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Discussed (final revised paper)

Latest update: 25 Apr 2024
Download
Short summary
Analysing the impact of environmental changes due to climate change, e.g. geographical spread of climate-sensitive infections (CSIs) and agriculture crop modelling, may require land surface modelling (LSM) to predict future land surface conditions. There are multiple LSMs to choose from. The paper proposes a multivariate spatio-temporal data science method to understand the inherent uncertainties in four LSMs and the variations between them in Nordic areas for the net primary production.
Read more
Altmetrics
Final-revised paper
Preprint