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

Wang, B; Liu, DL; Macadam, I; Alexander, LV; Abramowitz, G; Yu, Q

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

Wang, B Liu, DL Macadam, I Alexander, LV Abramowitz, G Yu, Q

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Climatic Change, 2016, 138, (1-2), pp. 85-98
Issue Date:: 2016-06-27

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Field	Value	Language
dc.contributor.author	Wang, B
dc.contributor.author	Liu, DL
dc.contributor.author	Macadam, I
dc.contributor.author	Alexander, LV
dc.contributor.author	Abramowitz, G
dc.contributor.author	Yu, Q https://orcid.org/0000-0001-6950-1821
dc.date.accessioned	2022-03-21T04:43:29Z
dc.date.available	2022-03-21T04:43:29Z
dc.date.issued	2016-06-27
dc.identifier.citation	Climatic Change, 2016, 138, (1-2), pp. 85-98
dc.identifier.issn	0165-0009
dc.identifier.issn	1573-1480
dc.identifier.uri	http://hdl.handle.net/10453/155416
dc.description.abstract	© 2016 Springer Science+Business Media DordrechtProjections 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.
dc.language	English
dc.publisher	Springer
dc.relation	NSW Department of Primary Industries
dc.relation.ispartof	Climatic Change
dc.relation.isbasedon	10.1007/s10584-016-1726-x
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Meteorology & Atmospheric Sciences
dc.title	Multi-model ensemble projections of future extreme temperature change using a statistical downscaling method in south eastern Australia
dc.type	Journal Article
utslib.citation.volume	138
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Students
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2022-03-21T04:43:27Z
pubs.issue	1-2
pubs.publication-status	Published
pubs.volume	138
utslib.citation.issue	1-2

Abstract:

© 2016 Springer Science+Business Media DordrechtProjections 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.

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http://hdl.handle.net/10453/155416