Detecting human influence on extreme temperatures in China

  • Qiuzi Han Wen
  • , Xuebin Zhang
  • , Ying Xu
  • , Bin Wang

Research output: Contribution to journalArticlepeer-review

102 Scopus citations

Abstract

This study compares observed and model-simulated spatiotemporal patterns of changes in Chinese extreme temperatures during 1961-2007 using an optimal detection method. Four extreme indices, namely annual maximum daily maximum (TXx) and daily minimum (TNx) temperatures and annual minimum daily maximum (TXn) and daily minimum (TNn) temperatures, are studied. Model simulations are conducted with the CanESM2, which include six 5-member ensembles under different historical forcings, i.e., four individual external forcings (greenhouse gases, anthropogenic aerosol, land use change, and solar irradiance), combined effect of natural forcings (solar irradiance and volcanic activity), and combined effect of all external forcings (both natural and anthropogenic forcings). We find that anthropogenic influence is clearly detectable in extreme temperatures over China. Additionally, anthropogenic forcing can also be separated from natural forcing in two-signal analyses. The influence of natural forcings cannot be detected in any analysis. Moreover, there are indications that the effects of greenhouse gases and/or land use change may be separated from other anthropogenic forcings in warm extremes TXx and TNx in joint two-signal analyses. These results suggest that further investigations of roles of individual anthropogenic forcing are justified, particularly in studies of extremely warm temperatures over China. Key Points Anthropogenic influence is clearly detectable in China's extreme temperatures Human influence is separated from that of natural forcing in two-signal analysis Effect of greenhouse gas is separated from that of other anthropogenic forcings.

Original languageEnglish
Pages (from-to)1171-1176
Number of pages6
JournalGeophysical Research Letters
Volume40
Issue number6
DOIs
StatePublished - Mar 28 2013
Externally publishedYes

Keywords

  • detection and attribution
  • extreme temperature
  • optimal fingerprint

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