DOI: 10.1007/s10584-016-1726-x
Scopus记录号: 2-s2.0-84976310081
论文题名: Multi-model ensemble projections of future extreme temperature change using a statistical downscaling method in south eastern Australia
作者: Wang B. ; Liu D.L. ; Macadam I. ; Alexander L.V. ; Abramowitz G. ; Yu Q.
刊名: Climatic Change
ISSN: 0165-0009
EISSN: 1573-1480
出版年: 2016
卷: 138, 期: 2018-01-02 起始页码: 85
结束页码: 98
语种: 英语
Scopus关键词: Agriculture
; Climate change
; Uncertainty analysis
; Coupled Model Intercomparison Project
; Extreme temperature events
; Extreme temperature indices
; General circulation model
; Maximum and minimum temperatures
; South-eastern Australia
; Statistical downscaling
; Uncertainty estimates
; Climate models
; agricultural ecosystem
; climate change
; climate effect
; crop yield
; future prospect
; general circulation model
; statistical analysis
; temperature anomaly
; uncertainty analysis
; warming
; wheat
; Australia
; Triticum aestivum
英文摘要: 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. © 2016, Springer Science+Business Media Dordrecht. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/84229
Appears in Collections: 气候减缓与适应 气候变化事实与影响
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作者单位: School of Life Sciences, Faculty of Science, University of Technology Sydney, PO Box 123, Broadway, Ultimo, NSW, Australia; NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW, Australia; Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, NSW, Australia; Now at Met Office, FitzRoy Road, Exeter, United Kingdom
Recommended Citation:
Wang B.,Liu D.L.,Macadam I.,et al. Multi-model ensemble projections of future extreme temperature change using a statistical downscaling method in south eastern Australia[J]. Climatic Change,2016-01-01,138(2018-01-02)