Testing the robustness of the anthropogenic climate change detection statements using different empirical models

Handle URI:
http://hdl.handle.net/10754/599869
Title:
Testing the robustness of the anthropogenic climate change detection statements using different empirical models
Authors:
Imbers, J.; Lopez, A.; Huntingford, C.; Allen, M. R.
Abstract:
This paper aims to test the robustness of the detection and attribution of anthropogenic climate change using four different empirical models that were previously developed to explain the observed global mean temperature changes over the last few decades. These studies postulated that the main drivers of these changes included not only the usual natural forcings, such as solar and volcanic, and anthropogenic forcings, such as greenhouse gases and sulfates, but also other known Earth system oscillations such as El Niño Southern Oscillation (ENSO) or the Atlantic Multidecadal Oscillation (AMO). In this paper, we consider these signals, or forced responses, and test whether or not the anthropogenic signal can be robustly detected under different assumptions for the internal variability of the climate system. We assume that the internal variability of the global mean surface temperature can be described by simple stochastic models that explore a wide range of plausible temporal autocorrelations, ranging from short memory processes exemplified by an AR(1) model to long memory processes, represented by a fractional differenced model. In all instances, we conclude that human-induced changes to atmospheric gas composition is affecting global mean surface temperature changes. ©2013. American Geophysical Union. All Rights Reserved.
Citation:
Imbers J, Lopez A, Huntingford C, Allen MR (2013) Testing the robustness of the anthropogenic climate change detection statements using different empirical models. J Geophys Res Atmos 118: 3192–3199. Available: http://dx.doi.org/10.1002/jgrd.50296.
Publisher:
Wiley-Blackwell
Journal:
Journal of Geophysical Research: Atmospheres
KAUST Grant Number:
KUK-C1-013-04
Issue Date:
27-Apr-2013
DOI:
10.1002/jgrd.50296
Type:
Article
ISSN:
2169-897X
Sponsors:
This work was based on work supported in part by Award No. KUK-C1-013-04, made by King Abdulah University of Science and Technology (KAUST). 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 the authors of the four studies analyzed in this paper for providing their data for their analysis.
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Full metadata record

DC FieldValue Language
dc.contributor.authorImbers, J.en
dc.contributor.authorLopez, A.en
dc.contributor.authorHuntingford, C.en
dc.contributor.authorAllen, M. R.en
dc.date.accessioned2016-02-28T06:31:19Zen
dc.date.available2016-02-28T06:31:19Zen
dc.date.issued2013-04-27en
dc.identifier.citationImbers J, Lopez A, Huntingford C, Allen MR (2013) Testing the robustness of the anthropogenic climate change detection statements using different empirical models. J Geophys Res Atmos 118: 3192–3199. Available: http://dx.doi.org/10.1002/jgrd.50296.en
dc.identifier.issn2169-897Xen
dc.identifier.doi10.1002/jgrd.50296en
dc.identifier.urihttp://hdl.handle.net/10754/599869en
dc.description.abstractThis paper aims to test the robustness of the detection and attribution of anthropogenic climate change using four different empirical models that were previously developed to explain the observed global mean temperature changes over the last few decades. These studies postulated that the main drivers of these changes included not only the usual natural forcings, such as solar and volcanic, and anthropogenic forcings, such as greenhouse gases and sulfates, but also other known Earth system oscillations such as El Niño Southern Oscillation (ENSO) or the Atlantic Multidecadal Oscillation (AMO). In this paper, we consider these signals, or forced responses, and test whether or not the anthropogenic signal can be robustly detected under different assumptions for the internal variability of the climate system. We assume that the internal variability of the global mean surface temperature can be described by simple stochastic models that explore a wide range of plausible temporal autocorrelations, ranging from short memory processes exemplified by an AR(1) model to long memory processes, represented by a fractional differenced model. In all instances, we conclude that human-induced changes to atmospheric gas composition is affecting global mean surface temperature changes. ©2013. American Geophysical Union. All Rights Reserved.en
dc.description.sponsorshipThis work was based on work supported in part by Award No. KUK-C1-013-04, made by King Abdulah University of Science and Technology (KAUST). 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 the authors of the four studies analyzed in this paper for providing their data for their analysis.en
dc.publisherWiley-Blackwellen
dc.subjectclimate changeen
dc.subjectdetection and attributionen
dc.subjectinternal climate variabilityen
dc.titleTesting the robustness of the anthropogenic climate change detection statements using different empirical modelsen
dc.typeArticleen
dc.identifier.journalJournal of Geophysical Research: Atmospheresen
dc.contributor.institutionDepartment of Atmospheric Oceanic Planetary Physics; University of Oxford; Oxford UKen
dc.contributor.institutionCenter for the Analysis of time series and CCCEP; London School of Economics; London UKen
dc.contributor.institutionCentre for Ecology and Hydrology; Wallingford UKen
kaust.grant.numberKUK-C1-013-04en
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