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

Handle URI:
http://hdl.handle.net/10754/599596
Title:
Sensitivity of Climate Change Detection and Attribution to the Characterization of Internal Climate Variability
Authors:
Imbers, Jara; Lopez, Ana; Huntingford, Chris; Allen, Myles
Abstract:
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.
Citation:
Imbers J, Lopez A, Huntingford C, Allen M (2014) Sensitivity of Climate Change Detection and Attribution to the Characterization of Internal Climate Variability. J Climate 27: 3477–3491. Available: http://dx.doi.org/10.1175/JCLI-D-12-00622.1.
Publisher:
American Meteorological Society
Journal:
Journal of Climate
KAUST Grant Number:
KUK-C1-013-04
Issue Date:
May-2014
DOI:
10.1175/JCLI-D-12-00622.1
Type:
Article
ISSN:
0894-8755; 1520-0442
Sponsors:
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.
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Full metadata record

DC FieldValue Language
dc.contributor.authorImbers, Jaraen
dc.contributor.authorLopez, Anaen
dc.contributor.authorHuntingford, Chrisen
dc.contributor.authorAllen, Mylesen
dc.date.accessioned2016-02-28T05:54:01Zen
dc.date.available2016-02-28T05:54:01Zen
dc.date.issued2014-05en
dc.identifier.citationImbers J, Lopez A, Huntingford C, Allen M (2014) Sensitivity of Climate Change Detection and Attribution to the Characterization of Internal Climate Variability. J Climate 27: 3477–3491. Available: http://dx.doi.org/10.1175/JCLI-D-12-00622.1.en
dc.identifier.issn0894-8755en
dc.identifier.issn1520-0442en
dc.identifier.doi10.1175/JCLI-D-12-00622.1en
dc.identifier.urihttp://hdl.handle.net/10754/599596en
dc.description.abstractThe 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.en
dc.description.sponsorshipThis 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.en
dc.publisherAmerican Meteorological Societyen
dc.titleSensitivity of Climate Change Detection and Attribution to the Characterization of Internal Climate Variabilityen
dc.typeArticleen
dc.identifier.journalJournal of Climateen
dc.contributor.institutionUniversity of Oxford, Oxford, United Kingdomen
dc.contributor.institutionLondon School of Economics and Political Science, London, United Kingdomen
dc.contributor.institutionCentre for Ecology & Hydrology, Oxfordshire, United Kingdomen
kaust.grant.numberKUK-C1-013-04en
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