Sensitivity of Climate Change Detection and Attribution to the Characterization of Internal Climate Variability

Jara Imbers, Ana Lopez, Chris Huntingford, Myles Allen

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


The Intergovernmental Panel on Climate Change's (IPCC) "very likely" statement that anthropogenic emissions are affecting climate is based on a statistical detection and attribution methodology that strongly depends on the characterization of internal climate variability. In this paper, the authors test the robustness of this statement in the case of global mean surface air temperature, under different representations of such variability. The contributions of the different natural and anthropogenic forcings to the global mean surface air temperature response are computed using a box diffusion model. Representations of internal climate variability are explored using simple stochastic models that nevertheless span a representative range of plausible temporal autocorrelation structures, including the short-memory first-order autoregressive [AR(1)] process and the long-memory fractionally differencing process. The authors find that, independently of the representation chosen, the greenhouse gas signal remains statistically significant under the detection model employed in this paper. The results support the robustness of the IPCC detection and attribution statement for global mean temperature change under different characterizations of internal variability, but they also suggest that a wider variety of robustness tests, other than simple comparisons of residual variance, should be performed when dealing with other climate variables and/or different spatial scales. © 2014 American Meteorological Society.
Original languageEnglish (US)
Pages (from-to)3477-3491
Number of pages15
JournalJournal of Climate
Issue number10
StatePublished - May 2014
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUK-C1-013-04
Acknowledgements: This work was based on work supported in part by Award KUK-C1-013-04 made by King Abdulah University of Science and Technology (KAUST). M. R. A. acknowledges financial support from NOAA/DoE International Detection and Attribution Group (IDAG). A. L. was funded by the ESRC Centre for Climate Change Economics and Policy, funded by the Economic and Social Research Council and Munich Re. The authors also thank Dr. Ingram and one of the referees for valuable discussions.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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