Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 12, issue 9
Articles | Volume 12, issue 9
https://doi.org/10.5194/gmd-12-4053-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-4053-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 9
Methods for assessment of models
 | 
17 Sep 2019
Methods for assessment of models |  | 17 Sep 2019

Detecting causality signal in instrumental measurements and climate model simulations: global warming case study

Mikhail Y. Verbitsky, Michael E. Mann, Byron A. Steinman, and Dmitry M. Volobuev

Viewed

Total article views: 4,031 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,942 986 103 4,031 150 192
  • HTML: 2,942
  • PDF: 986
  • XML: 103
  • Total: 4,031
  • BibTeX: 150
  • EndNote: 192
Views and downloads (calculated since 27 Mar 2019)
Cumulative views and downloads (calculated since 27 Mar 2019)

Viewed (geographical distribution)

Total article views: 4,031 (including HTML, PDF, and XML) Thereof 3,667 with geography defined and 364 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 18 May 2026
Download
Short summary
In this study, we propose an additional climate model validation procedure that assesses whether causality signals between model drivers and responses are consistent with those observed in nature. Specifically, we suggest the method of conditional dispersion as the best approach to directly measure the causality between model forcing and response. Our results show that there is a strong causal signal from the carbon dioxide series to the global temperature series.
Read more
Share