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

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Cited articles

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Aldrin, M., Holden, M., Guttorp, P., Skeie, R. B., Myhre, G., and Berntsen, T. K.: Bayesian estimation of climate sensitivity based on a simple climate model fitted to observations of hemispheric temperatures and global ocean heat content, Environmetrics, 23, 253–271,, 2012. a
Allen, M. R. and Stott, P. A.: Estimating Signal Amplitudes in Optimal Fingerprinting, Part I: Theory, Clim. Dynam., 21, 477–491,, 2003. a
Allen, M. R. and Tett, S. F. B.: Checking for Model Consistency in Optimal Fingerprinting, Clim. Dynam., 15, 419–434,, 1999. a
Álvarez, M., Luengo, D., and Lawrence, N. D.: Latent Force Models, in: Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, edited by: van Dyk, D. and Welling, M., vol. 5 of Proceedings of Machine Learning Research, pp. 9–16, PMLR, Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA, available at: (last access: 10 May 2020), 2009. a
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.