Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 3.480
IF3.480
IF 5-year value: 4.194
IF 5-year
4.194
CiteScore value: 6.7
CiteScore
6.7
SNIP value: 1.143
SNIP1.143
IPP value: 3.65
IPP3.65
SJR value: 1.761
SJR1.761
Scimago H <br class='widget-line-break'>index value: 118
Scimago H
index
118
h5-index value: 60
h5-index60
Download
Short summary
Causal inference promises new insight into biosphere–atmosphere interactions using time series only. To understand the behaviour of a specific method on such data, we used artificial and observation-based data. The observed structures are very interpretable and reveal certain ecosystem-specific behaviour, as only a few relevant links remain, in contrast to pure correlation techniques. Thus, causal inference allows to us gain well-constrained insights into processes and interactions.
Altmetrics
Final-revised paper
Preprint
BG | Articles | Volume 17, issue 4
Biogeosciences, 17, 1033–1061, 2020
https://doi.org/10.5194/bg-17-1033-2020
Biogeosciences, 17, 1033–1061, 2020
https://doi.org/10.5194/bg-17-1033-2020

Research article 26 Feb 2020

Research article | 26 Feb 2020

Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach

Christopher Krich et al.

Viewed

Total article views: 1,769 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,230 519 20 1,769 22 24
  • HTML: 1,230
  • PDF: 519
  • XML: 20
  • Total: 1,769
  • BibTeX: 22
  • EndNote: 24
Views and downloads (calculated since 13 Aug 2019)
Cumulative views and downloads (calculated since 13 Aug 2019)

Viewed (geographical distribution)

Total article views: 1,453 (including HTML, PDF, and XML) Thereof 1,445 with geography defined and 8 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 19 Jan 2021
Publications Copernicus
Download
Short summary
Causal inference promises new insight into biosphere–atmosphere interactions using time series only. To understand the behaviour of a specific method on such data, we used artificial and observation-based data. The observed structures are very interpretable and reveal certain ecosystem-specific behaviour, as only a few relevant links remain, in contrast to pure correlation techniques. Thus, causal inference allows to us gain well-constrained insights into processes and interactions.
Citation
Altmetrics
Final-revised paper
Preprint