Advances in Statistical Climatology, Meteorology and Oceanography
Advances in Statistical Climatology, Meteorology and Oceanography
ASCMO
Articles | Volume 6, issue 2
Articles | Volume 6, issue 2
https://doi.org/10.5194/ascmo-6-91-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-6-91-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 6, issue 2
17 Sep 2020
 | 17 Sep 2020

A new energy-balance approach to linear filtering for estimating effective radiative forcing from temperature time series

Donald P. Cummins, David B. Stephenson, and Peter A. Stott

Viewed

Total article views: 1,584 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,117 365 102 1,584 64 66
  • HTML: 1,117
  • PDF: 365
  • XML: 102
  • Total: 1,584
  • BibTeX: 64
  • EndNote: 66
Views and downloads (calculated since 17 Sep 2020)
Cumulative views and downloads (calculated since 17 Sep 2020)

Viewed (geographical distribution)

Total article views: 1,533 (including HTML, PDF, and XML) Thereof 1,533 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 30 Dec 2024
Download
Short summary
We have developed a novel and fast statistical method for diagnosing effective radiative forcing (ERF), a measure of the net effect of greenhouse gas emissions on Earth's energy budget. Our method works by inverting a recursive digital filter energy balance representation of global climate models and has been successfully validated using simulated data from UK Met Office climate models. We have estimated time series of historical ERF by applying our method to the global temperature record.
Read more