globalchange  > 气候减缓与适应
DOI: 10.1007/s10584-017-1906-3
Scopus记录号: 2-s2.0-85011284946
论文题名:
Investigating differences between event-as-class and probability density-based attribution statements with emerging climate change
作者: Harrington L.J.
刊名: Climatic Change
ISSN: 0165-0009
EISSN: 1573-1480
出版年: 2017
卷: 141, 期:4
起始页码: 641
结束页码: 654
语种: 英语
Scopus关键词: Probability ; Probability density function ; Probability distributions ; Signal to noise ratio ; Uncertainty analysis ; Climate system ; Future climate ; Human influences ; Multi-model ensemble ; Probability densities ; Probability of occurrence ; Research communities ; Uncertainty estimates ; Climate change ; anthropogenic effect ; climate change ; climate modeling ; extreme event ; nature-society relations ; probability ; probability density function ; research work ; signal-to-noise ratio
英文摘要: There is significant public and scientific interest in understanding whether and to what extent the severity and frequency of extreme events have increased in response to human influences on the climate system. As the science underpinning the field of event attribution continues to rapidly develop, there are growing expectations of faster and more accurate attribution statements to be delivered, even in the days to weeks after an extreme event occurs. As the research community looks to respond, a variety of approaches have been suggested, each with varying levels of conditioning to the observed state of the climate when the event of interest has occurred. One such approach to utilise unconditioned multi-model ensembles requires pre-computing estimates of the change in probability of occurrence for a wide range of possible ‘events’. In this study, we consider differences between event-as-class attribution statements with changes in the probability density of the distribution at the event threshold of interest. For the majority of extreme event attribution studies, it is likely that the two metrics are comparable once uncertainty estimates are considered. However, results show these two metrics can produce divergent answers from each other for moderate climatological anomalies if the present-day climate distribution experiences a substantial change in the underlying signal-to-noise ratio. As the emergent signals of climate change becomes increasingly clear, this study highlights the need for clear and explicit framing in the context of applying pre-computed attribution statements, particularly if attribution perspectives are to be included within the framework of future climate services. © 2017, Springer Science+Business Media Dordrecht.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/84049
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: New Zealand Climate Change Research Institute, School of Geography, Environment and Earth Sciences, Victoria University of Wellington, Wellington, New Zealand

Recommended Citation:
Harrington L.J.. Investigating differences between event-as-class and probability density-based attribution statements with emerging climate change[J]. Climatic Change,2017-01-01,141(4)
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