An attribution analysis of extreme temperature changes is conducted using updated observations (HadEX2) and multi-model climate simulation (CMIP5) datasets for an extended period of 1951–2010. Compared to previous HadEX/CMIP3-based results, which identified human contributions to the observed warming of extreme temperatures on global and regional scales, the current results provide better agreement with observations, particularly for the intensification of warm extremes. Removing the influence of two major modes of natural internal variability (the Arctic Oscillation and Pacific Decadal Oscillation) from observations further improves attribution results, reducing the model-observation discrepancy in cold extremes. An optimal fingerprinting technique is used to compare observed changes in annual extreme temperature indices of coldest night and day (TNn, TXn) and warmest night and day (TNx, TXx) with multi-model simulated changes that were simulated under natural-plus-anthropogenic and natural-only (NAT) forcings. Extreme indices are standardized for better intercomparisons between datasets and locations prior to analysis and averaged over spatial domains from global to continental regions following a previous study. Results confirm previous HadEX/CMIP3-based results in which anthropogenic (ANT) signals are robustly detected in the increase in global mean and northern continental regional means of the four indices of extreme temperatures. The detected ANT signals are also clearly separable from the response to NAT forcing, and results are generally insensitive to the use of different model samples as well as different data availability.
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Alexander LV et al (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res 111:D05109
Allen MR, Stott PA (2003) Estimating signal amplitudes in optimal fingerprinting, part I: theory. Clim Dyn 21:477–491
Allen MR, Tett SFB (1999) Checking for model consistency in optimal fingerprinting. Clim Dyn 15:419–434
Bindoff NL et al (2013) Detection and attribution of climate change: from global to regional. In: Stocker TF et al (eds) The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK and New York, NY, USA
Brown SJ, Caesar J, Ferro CAT (2008) Global changes in extreme daily temperature since 1950. J Geophys Res 113:D05115. doi:10.1029/2006JD008091
Christidis N, Stott PA, Brown SJ (2011) The role of human activity in the recent warming of extremely warm daytime temperatures. J Clim 24:1922–1930
Christidis N, Stott PA, Hegerl GC, Betts RA (2013) The role of land use change in the recent warming of daily extreme temperatures. Geophys Res Lett 40:589–594
Christidis N, Stott PA, Zwiers FW (2014) Fast-track attribution assessments based on pre-computed estimates of changes in the odds of warm extremes. Clim Dyn. doi:10.1007/s00382-014-2408-x
Donat MG, Alexander LV (2012) The shifting probability distribution of global daytime and night-time temperatures. Geophys Res Lett 39:L14707. doi:10.1029/2012GL052459
Donat MG et al (2013) Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: the HadEX2 dataset. J Geophys Res Atmos 118:2098–2118
Hartmann DL et al (2013) Observations: atmosphere and surface. In: Stocker TF et al (eds) Climate change 2013. The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK and New York, NY, USA
Huntingford C, Stott PA, Allen MR, Lambert FH (2006) Incorporating model uncertainty into attribution of observed temperature change. Geophys Res Lett 33:L05710. doi:10.1029/2005GL024831
Jones GS, Stott PA, Christidis N (2013) Attribution of observed historical near-surface temperature variations to anthropogenic and natural causes using CMIP5 simulations. J Geophys Res 118:4001–4024
Meehl GA et al (2007) The WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull Am Meteorol Soc 88:1383–1394
Meehl GA, Teng H, Arblaster JM (2014) Climate model simulations of the observed early-2000s hiatus of global warming. Nat Clim Change 4:898–902. doi:10.1038/nclimate2357
Min SK, Zhang XB, Zwiers FW, Hegerl GC (2011) Human contribution to more-intense precipitation extremes. Nature 470:378–381
Min SK, Zhang X, Zwiers FW, Shiogama H, Tung YS, Wehner M (2013) Multimodel detection and attribution of extreme temperature changes. J Clim 26:7430–7451
Morak S, Hegerl GC, Christidis N (2013) Detectable changes in the frequency of temperature extremes. J Clim 26:1561–1574
Mueller B, Seneviratne SI (2011) Hot days induced by precipitation deficits at the global scale. Proc Natl Acad Sci USA 109:12398–12403
Portmann RW, Solomon S, Hegerl GC (2009) Spatial and seasonal patterns in climate change, temperatures, and precipitation across the United States. Proc Natl Acad Sci USA 106:7324–7329. doi:10.1073/pnas.0808533106
Ren G, Zhou Y (2014) Urbanization effect on trends of extreme temperature indices of national stations over mainland China, 1961–2008. J Clim 27:2340–2360
Ribes A, Planton S, Terray L (2013) Application of regularised optimal fingerprinting to attribution. Part I: method, properties and idealised analysis. Clim Dyn 41:2817–2836
Seneviratne SI et al (2012) Changes in climate extremes and their impacts on the natural physical environment. In: Field CB et al (eds) Managing the risks of extreme events and disasters to advance climate change adaptation. A special report of working groups I and II of the IPCC. Cambridge University Press, Cambridge, UK, and New York, NY, USA
Seneviratne SI, Donat MG, Mueller B, Alexander LV (2014) No pause in the increase of hot temperature extremes. Nat Clim Change 4:161–163
Sillmann J, Donat MG, Fyfe JC, Zwiers FW (2014) Observed and simulated temperature extremes during the recent warming hiatus. Environ Res Lett 9:064023. doi:10.1088/1748-9326/9/6/064023
Sun Y, Zhang X, Zwiers FW, Song L, Wan H, Hu T, Yin H, Ren G (2014) Rapid increase in the risk of extreme summer heat in Eastern China. Nat Clim Change 4:1082–1085. doi:10.1038/nclimate2410
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498
Trenberth KE, Fasullo JT (2013) An apparent hiatus in global warming? Earth’s Fut 1:19–32. doi:10.1002/2013EF000165
Trenberth KE, Fasullo JT, Branstator G, Phillips AS (2014) Seasonal aspects of the recent pause in surface warming. Nat Clim Change 4:911–916. doi:10.1038/nclimate2341
Zhang XB, Wan H, Zwiers FW, Hegerl GC, Min SK (2013) Attribution intensification of precipitation extremes to human influence. Geophys Res Lett 40:5252–5257. doi:10.1002/grl.51010
Zwiers FW, Zhang XB, Feng Y (2011) Anthropogenic influence on long return period daily temperature extremes at regional scales. J Clim 24:881–892
We thank the CLIMDEX Project team (www.climdex.org) for providing HadEX2 data. 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 of this paper) for producing and making available their model output. This study is supported by the Environment Canada. SKM was funded by the Korean Meteorological Administration Research and Development Grant 2013-3180.
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Kim, YH., Min, SK., Zhang, X. et al. Attribution of extreme temperature changes during 1951–2010. Clim Dyn 46, 1769–1782 (2016). https://doi.org/10.1007/s00382-015-2674-2
- Detection and attribution
- Extreme temperature
- Anthropogenic forcing
- Natural variability
- CMIP5 models