Climate Dynamics

, Volume 41, Issue 11–12, pp 2837–2853 | Cite as

Application of regularised optimal fingerprinting to attribution. Part II: application to global near-surface temperature

Article

Abstract

Attribution of global near-surface temperature changes is revisited using simulations from the coupled model intercomparison project 5 and methodological improvements from the regularised optimal fingerprinting approach. The analysis of global mean temperature shows that changes can be robustly detected and attributed to anthropogenic influence. However, the differences between results from individual models and observations are found to be larger than the simulated internal variability in several cases. Discrimination between greenhouse gases and other anthropogenic forcings, based on the global mean only, is more difficult due to collinearity of temporal response patterns. Using spatio-temporal data provides less robust conclusions with respect to detection and attribution, as the results tend to deteriorate as the spatial resolution increases. More importantly, some inconsistencies between individual models and observations are found in this case. Such behaviour is not observed in a perfect model framework, where pseudo-observations and the expected response patterns are provided by the same model. However, using response patterns from a model other than the one used for pseudo-observations may lead to the same behaviour as real observations. Our results suggest that additional sources of uncertainty, such as modeling uncertainty or observational uncertainty, should not be neglected in detection and attribution.

Keywords

Detection Attribution Climate change Optimal fingerprints Global temperature 

Supplementary material

382_2013_1736_MOESM1_ESM.eps (1.5 mb)
2-forcing attribution analysis based on global average time-series and HadCRUT3 observations. Same as Figure 1, based on HadCRUT3 observations instead of the median HadCRUT4 dataset. EPS (1501 KB)
382_2013_1736_MOESM2_ESM.eps (1.7 mb)
3-forcing attribution analysis based on global average time-series and HadCRUT3 observations. Same as Figure 2, based on HadCRUT3 observations instead of the median HadCRUT4 dataset. EPS (1754 KB)
382_2013_1736_MOESM3_ESM.eps (733 kb)
Results from the spatio-temporal analysis, as a function of the spatial resolution. Same as Figure 3, based on HadCRUT3 observations instead of the median HadCRUT4 dataset. EPS (733 KB)
382_2013_1736_MOESM4_ESM.eps (1.7 mb)
Reconstruction of the global mean temperature based on 3-forcing ROF attribution analysis at T4-resolution. ROF is applied to HadCRUT4 data at T4-resolution, as in Figure 3, bottom right. The middle panels illustrate how the global mean temperature time-series is reconstructed in this analysis. Results are shown for ten climate models from the CMIP5 database, in three-forcing analysis: ANT (red) + AER (green) + NAT (blue). Caption for each panel is identical to that used in Figure 1. Scaling factors (left) and RCT p-values (right) are the same as shown in Figure 3, bottom right. EPS (1787 KB)
382_2013_1736_MOESM5_ESM.eps (3 mb)
2-forcing cohort framework analysis, Part I. Same as Figure 5, in a 2-forcing analysis, based on a wider set of climate models used as pseudo-observations (rows), or to provide response pattern estimates (columns). EPS (3079 KB)
382_2013_1736_MOESM6_ESM.eps (1.4 mb)
2-forcing cohort framework analysis, Part II. Same as Figure 6, in a 2-forcing analysis, based on a wider set of climate models used as pseudo-observations (rows), or to provide response pattern estimates (columns). EPS (1478 KB)
382_2013_1736_MOESM7_ESM.eps (4.3 mb)
3-forcing cohort framework analysis, Part I. Same as Figure 5, based on a wider set of climate models used as pseudo-observations (rows), or to provide response pattern estimates (columns). EPS (4436 KB)
382_2013_1736_MOESM8_ESM.eps (1.5 mb)
3-forcing cohort framework analysis, Part II. Same as Figure 6, based on a wider set of climate models used as pseudo-observations (rows), or to provide response pattern estimates (columns). EPS (1553 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.CNRM-GAME, Météo France, CNRSToulouseFrance
  2. 2.SUC, CERFACS-CNRS URA1875ToulouseFrance

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