Articles | Volume 6, issue 2
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 91–102, 2020
https://doi.org/10.5194/ascmo-6-91-2020
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 91–102, 2020
https://doi.org/10.5194/ascmo-6-91-2020

  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 et al.

Data sets

Forcings, Feedbacks and Climate Sensitivity in HadGEM3-GC3.1 and UKESM1 (https://github.com/timothyandrews/HadGEM3-ERF-Timeseries) T. Andrews, M.~B. Andrews, A. Bodas-Salcedo, G. S. Jones, T. Kulhbrodt, J. Manners, M. B. Menary, J. Ridley, M. A. Ringer, A. A. Sellar, C. A. Senior, and Y. Tang https://doi.org/10.1029/2019MS001866

Model code and software

Coverage Bias in the {HadCRUT4} Temperature Series and its Impact on Recent Temperature Trends (https://www-users.york.ac.uk/~kdc3/papers/coverage2013/had4_krig_annual_v2_0_0.txt) K. Cowtan and R. G. Way https://doi.org/10.1002/qj.2297

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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.