Sensitivity of climate change detection and attribution to the characterization of internal climate variability

Imbers, Jara; Lopez, Ana; Huntingford, Chris ORCID: https://orcid.org/0000-0002-5941-7770; Allen, Myles. 2014 Sensitivity of climate change detection and attribution to the characterization of internal climate variability. Journal of Climate, 27 (10). 3477-3491. https://doi.org/10.1175/JCLI-D-12-00622.1

Before downloading, please read NORA policies.

Preview

Text © 2014 American Meteorological Society N513149JA.pdf - Published Version Download (989kB) | Preview

Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1175/JCLI-D-12-00622.1
Programmes:	CEH Topics & Objectives 2009 - 2012 > Biogeochemistry > BGC Topic 2 - Biogeochemistry and Climate System Processes > BGC - 2.3 - Determine land-climate feedback processes to improve climate model predictions
UKCEH and CEH Sections/Science Areas:	Reynard
ISSN:	0894-8755
Additional Keywords:	Climate change, Regression analysis, Statistical techniques, Time series
NORA Subject Terms:	Meteorology and Climatology Atmospheric Sciences
Date made live:	07 Mar 2016 13:49 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/513149

Actions (login required)

View Item

Document Downloads

More statistics for this item...