globalchange  > 气候减缓与适应
DOI: 10.1002/joc.5705
论文题名:
Using multi-model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia
作者: Wang B.; Zheng L.; Liu D.L.; Ji F.; Clark A.; Yu Q.
刊名: International Journal of Climatology
ISSN: 8998418
出版年: 2018
卷: 38, 期:13
起始页码: 4891
结束页码: 4902
语种: 英语
英文关键词: GCMs ; machine learning ; multi-model ensemble ; random forest ; support vector machine
Scopus关键词: Artificial intelligence ; Bayesian networks ; Climate change ; Decision trees ; Learning systems ; Object oriented programming ; Rain ; Support vector machines ; Bayesian model averaging ; Climate change impact ; GCMs ; Global climate model ; Machine learning methods ; Multi-model ensemble ; Performance criterion ; Random forests ; Climate models ; air temperature ; climate change ; climate effect ; climate modeling ; CMIP ; ensemble forecasting ; rainfall ; support vector machine ; Australia
英文摘要: Global climate models (GCMs) are useful tools for assessing climate change impacts on temperature and rainfall. Although climate data from various GCMs have been increasingly used in climate change impact studies, GCMs configurations and module characteristics vary from one to another. Therefore, it is crucial to assess different GCMs to confirm the extent to which they can reproduce the observed temperature and rainfall. Rather than assessing the interdependence of each GCM, the purpose of this study is to compare the capacity of four different multi-model ensemble (MME) methods (random forest [RF], support vector machine [SVM], Bayesian model averaging [BMA] and the arithmetic ensemble mean [EM]) in reproducing observed monthly rainfall and temperature. Of these four methods, the RF and SVM demonstrated a significant improvement over EM and BMA in terms of performance criteria. The relative importance of each GCM based on the RF ensemble in reproducing rainfall and temperature could also be ranked. We compared the GCMs importance and Taylor skill score and found that their correlation was 0.95 for temperature and 0.54 for rainfall. Our results also demonstrated that the number of GCMs ensemble simulations could be reduced from 33 to 25 in RF model while maintaining predictive error less than 2%. Having such a representative subset of simulations could reduce computational costs for climate impact modelling and maintain the quality of ensemble at the same time. We conclude that machine learning MME could be efficient and useful with improved accuracy in reproducing historical climate variables. © 2018 Royal Meteorological Society
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/116764
Appears in Collections:气候减缓与适应

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作者单位: NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW, Australia; School of Computing and Mathematics, Charles Sturt University, Wagga Wagga, NSW, Australia; Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW, Australia; NSW Office of Environment and Heritage, Department of Planning and Environment, Sydney, NSW, Australia; NSW Department of Primary Industries, Orange Agricultural Institute, Orange, NSW, Australia; Faculty of Science, School of Life Sciences, University of Technology Sydney, Sydney, NSW, Australia; Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, University of Chinese Academy of Sciences, Beijing, China; State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling, China

Recommended Citation:
Wang B.,Zheng L.,Liu D.L.,et al. Using multi-model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia[J]. International Journal of Climatology,2018-01-01,38(13)
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