Climate Dynamics

, Volume 46, Issue 5–6, pp 1769–1782 | Cite as

Attribution of extreme temperature changes during 1951–2010

  • Yeon-Hee Kim
  • Seung-Ki MinEmail author
  • Xuebin Zhang
  • Francis Zwiers
  • Lisa V. Alexander
  • Markus G. Donat
  • Yu-Shiang Tung


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.


Detection and attribution Extreme temperature Anthropogenic forcing Natural variability CMIP5 models 



We thank the CLIMDEX Project team ( 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Yeon-Hee Kim
    • 1
  • Seung-Ki Min
    • 1
    Email author
  • Xuebin Zhang
    • 2
  • Francis Zwiers
    • 3
  • Lisa V. Alexander
    • 4
  • Markus G. Donat
    • 4
  • Yu-Shiang Tung
    • 5
  1. 1.School of Environmental Science and EngineeringPohang University of Science and TechnologyPohangKorea
  2. 2.Climate Research DivisionEnvironment CanadaTorontoCanada
  3. 3.Pacific Climate Impacts ConsortiumVictoriaCanada
  4. 4.Climate Change Research Centre and ARC Centre of Excellence for Climate System ScienceUniversity of New South WalesSydneyAustralia
  5. 5.National Science and Technology Center for Disaster ReductionTaipeiTaiwan

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