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

Author

Listed:
  • Bin Wang

    (University of Technology Sydney
    Wagga Wagga Agricultural Institute)

  • De Li Liu

    (Wagga Wagga Agricultural Institute)

  • Ian Macadam

    (University of New South Wales
    Now at Met Office, FitzRoy Road)

  • Lisa V. Alexander

    (University of New South Wales)

  • Gab Abramowitz

    (University of New South Wales)

  • Qiang Yu

    (University of Technology Sydney)

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.

Suggested Citation

  • Bin Wang & De Li Liu & Ian Macadam & Lisa V. Alexander & Gab Abramowitz & Qiang Yu, 2016. "Multi-model ensemble projections of future extreme temperature change using a statistical downscaling method in south eastern Australia," Climatic Change, Springer, vol. 138(1), pages 85-98, September.
    • Handle: RePEc:spr:climat:v:138:y:2016:i:1:d:10.1007_s10584-016-1726-x
    DOI: 10.1007/s10584-016-1726-x
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    Cited by:

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    2. Li, Siyi & Wang, Bin & Feng, Puyu & Liu, De Li & Li, Linchao & Shi, Lijie & Yu, Qiang, 2022. "Assessing climate vulnerability of historical wheat yield in south-eastern Australia's wheat belt," Agricultural Systems, Elsevier, vol. 196(C).
    3. Bin Wang & De Li Liu & Cathy Waters & Qiang Yu, 2018. "Quantifying sources of uncertainty in projected wheat yield changes under climate change in eastern Australia," Climatic Change, Springer, vol. 151(2), pages 259-273, November.
    4. Wang, Bin & Feng, Puyu & Chen, Chao & Liu, De Li & Waters, Cathy & Yu, Qiang, 2019. "Designing wheat ideotypes to cope with future changing climate in South-Eastern Australia," Agricultural Systems, Elsevier, vol. 170(C), pages 9-18.
    5. Ennan Zheng & Mengting Qin & Peng Chen & Tianyu Xu & Zhongxue Zhang, 2022. "Climate Change Affects the Utilization of Light and Heat Resources in Paddy Field on the Songnen Plain, China," Agriculture, MDPI, vol. 12(10), pages 1-19, October.
    6. Dengpan Xiao & Huizi Bai & De Li Liu, 2018. "Impact of Future Climate Change on Wheat Production: A Simulated Case for China’s Wheat System," Sustainability, MDPI, vol. 10(4), pages 1-15, April.

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