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

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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.

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Acknowledgments

We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1) for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We acknowledge Sophie Tyteca for great technical help on the data pre-processing.

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Authors and Affiliations

  1. CNRM-GAME, Météo France, CNRS, 42 avenue G. Coriolis, Toulouse, 31057, France

    Aurélien Ribes

  2. SUC, CERFACS-CNRS URA1875, 42 avenue G. Coriolis, Toulouse, 31057, France

    Laurent Terray

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  1. Aurélien Ribes
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  2. Laurent Terray
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Correspondence to Aurélien Ribes.

Electronic supplementary material

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382_2013_1736_MOESM1_ESM.eps

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

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

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

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

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

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

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

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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Ribes, A., Terray, L. Application of regularised optimal fingerprinting to attribution. Part II: application to global near-surface temperature. Clim Dyn 41, 2837–2853 (2013). https://doi.org/10.1007/s00382-013-1736-6

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  • DOI: https://doi.org/10.1007/s00382-013-1736-6

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