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Climatic Change

, Volume 138, Issue 1–2, pp 85–98 | Cite as

Multi-model ensemble projections of future extreme temperature change using a statistical downscaling method in south eastern Australia

  • Bin Wang
  • De Li Liu
  • Ian Macadam
  • Lisa V. Alexander
  • Gab Abramowitz
  • Qiang Yu
Article

Abstract

Projections of changes in temperature extremes are critical to assess the potential impacts of climate change on agricultural and ecological systems. Statistical downscaling can be used to efficiently downscale output from a large number of general circulation models (GCMs) to a fine temporal and spatial scale, providing the opportunity for future projections of extreme temperature events. This paper presents an analysis of extreme temperature data downscaled from 7 GCMs selected from the Coupled Model Intercomparison Project phase 5 (CMIP5) using a skill score based on spatial patterns of climatological means of daily maximum and minimum temperature. Data for scenarios RCP4.5 and RCP8.5 for the New South Wales (NSW) wheat belt, south eastern Australia, have been analysed. The results show that downscaled data from most of the GCMs reproduces the correct sign of recent trends in all the extreme temperature indices (except the index for cold days) for 1961–2000. An independence weighted mean method is used to calculate uncertainty estimates, which shows that multi-model ensemble projections produce a consistent trend compared to the observations in most extreme indices. Great warming occurs in the east and northeast of the NSW wheat belt by 2061–2100 and increases the risk of exposure to hot days around wheat flowering date, which might result in farmers needing to reconsider wheat varieties suited to maintain yield. This analysis provides a first overview of projected changes in climate extremes from an ensemble of 7 CMIP5 GCM models with statistical downscaling data in the NSW wheat belt.

Keywords

Extreme Index Cold Night Warm Night Extreme Temperature Event Statistical Downscaling Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The senior author acknowledges that the Chinese Scholarship Council provided the scholarship and NSW Department of Industry provided office facilities for conducting this work. We acknowledge the modelling groups, Program for Climate Model Diagnosis and Intercomparison and the WCRP’s Working Group on Coupled Modelling for their roles in making available the WCRP CMIP5 multi-model dataset. Support of this dataset is provided by the Office of Science, US Department of Energy. This work was partly funded by the ARC Centre of Excellence for Climate System Science (grant CE110001028). We greatly appreciate the anonymous reviewers for their constructive comments on the early version of this manuscript.

Supplementary material

10584_2016_1726_MOESM1_ESM.docx (1.2 mb)
ESM 1 (DOCX 1.24 mb)

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Bin Wang
    • 1
    • 2
  • De Li Liu
    • 2
  • Ian Macadam
    • 3
    • 4
  • Lisa V. Alexander
    • 3
  • Gab Abramowitz
    • 3
  • Qiang Yu
    • 1
  1. 1.School of Life Sciences, Faculty of ScienceUniversity of Technology SydneyUltimoAustralia
  2. 2.NSW Department of Primary IndustriesWagga Wagga Agricultural InstituteWagga WaggaAustralia
  3. 3.Climate Change Research Centre and ARC Centre of Excellence for Climate System ScienceUniversity of New South WalesSydneyAustralia
  4. 4.Now at Met Office, FitzRoy RoadExeterUK

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