Application of regularised optimal fingerprinting to attribution. Part II: application to global near-surface temperature
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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.
KeywordsDetection Attribution Climate change Optimal fingerprints Global temperature
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.
- Drost F, Karoly D (2012) Evaluating global climate responses to different forcings using simple indices. Geophys Res Lett. doi: 10.1029/2012GL052667
- Gillett N, Arora V, Flato G, Scinocca J, von Salzen K (2012) Improved constraints on 21st-century warming derived using 160 years of temperature observations. Geophys Res Lett 39(L01704). doi: 10.1029/2011GL050226
- Hasselmann K (1979) On the signal-to-noise problem in atmospheric response studies. In: Shaw DB (ed) Meteorology over the tropical oceans. Royal Meteorological Society, pp 251–259Google Scholar
- Hegerl G, Zwiers F, Braconnot P, Gillet N, Luo Y, Marengo Orsini J, Nicholls N, Penner J, Stott P (2007) Understanding and attributing climate change. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.Google Scholar
- Morice C, Kennedy J, Rayner N, Jones PD (2012) Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the hadcrut4 data set. J Geophys Res 117(D8). doi: 10.1029/2011JD017187
- Ribes A, Terray L, Planton S (2013) Application of regularised optimal fingerprinting to attribution. Part I: method, properties and idealised analysis. Clim Dyn. doi: 10.1007/s00382-013-1735-7