Advances in Statistical Climatology, Meteorology and Oceanography
Advances in Statistical Climatology, Meteorology and Oceanography
ASCMO
Articles | Volume 8, issue 2
Articles | Volume 8, issue 2
https://doi.org/10.5194/ascmo-8-249-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-8-249-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 8, issue 2
14 Dec 2022
 | 14 Dec 2022

Evaluation of simulated responses to climate forcings: a flexible statistical framework using confirmatory factor analysis and structural equation modelling – Part 2: Numerical experiment

Katarina Lashgari, Anders Moberg, and Gudrun Brattström

Viewed

Total article views: 2,140 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,836 232 72 2,140 120 68 131
  • HTML: 1,836
  • PDF: 232
  • XML: 72
  • Total: 2,140
  • Supplement: 120
  • BibTeX: 68
  • EndNote: 131
Views and downloads (calculated since 14 Dec 2022)
Cumulative views and downloads (calculated since 14 Dec 2022)

Viewed (geographical distribution)

Total article views: 2,122 (including HTML, PDF, and XML) Thereof 2,122 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 Feb 2026
Download
Short summary
The performance of a new statistical framework containing various structural equation modelling (SEM) models is evaluated in a pseudo-proxy experiment in comparison with the performance of statistical models used in many detection and attribution studies. Each statistical model was fitted to seven continental-scale regional temperature data sets. The results indicated the SEM specification is the most appropriate for describing the underlying latent structure of the simulated data analysed.
Read more
Share