IGSNRR OpenIR
Using multi-model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia
Wang, Bin1; Zheng, Lihong2; Liu, De Li1,3,4; Ji, Fei5; Clark, Anthony6; Yu, Qiang7,8,9
2018-11-15
Source PublicationINTERNATIONAL JOURNAL OF CLIMATOLOGY
ISSN0899-8418
Volume38Issue:13Pages:4891-4902
Corresponding AuthorWang, Bin(bin.a.wang@dpi.nsw.gov.au)
AbstractGlobal 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.
KeywordGCMs machine learning multi-model ensemble random forest support vector machine
DOI10.1002/joc.5705
WOS KeywordORGANIC-CARBON STOCKS ; EASTERN AUSTRALIA ; RANDOM FORESTS ; CHANGE IMPACTS ; PROJECTIONS ; INDEPENDENCE ; PREDICTION ; SCENARIOS ; ALGORITHM ; NETWORKS
Indexed BySCI
Language英语
WOS Research AreaMeteorology & Atmospheric Sciences
WOS SubjectMeteorology & Atmospheric Sciences
WOS IDWOS:000450222100015
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/52487
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWang, Bin
Affiliation1.NSW Dept Primary Ind, Wagga Wagga Agr Inst, Wagga Wagga, NSW 2650, Australia
2.Charles Sturt Univ, Sch Comp & Math, Wagga Wagga, NSW, Australia
3.Univ New South Wales, Climate Change Res Ctr, Sydney, NSW, Australia
4.Univ New South Wales, ARC Ctr Excellence Climate Extremes, Sydney, NSW, Australia
5.NSW Off Environm & Heritage, Dept Planning & Environm, Sydney, NSW, Australia
6.NSW Dept Primary Ind, Orange Agr Inst, Orange, NSW, Australia
7.Univ Technol Sydney, Sch Life Sci, Fac Sci, Sydney, NSW, Australia
8.Univ Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China
9.Northwest A&F Univ, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling, Shaanxi, Peoples R China
Recommended Citation
GB/T 7714
Wang, Bin,Zheng, Lihong,Liu, De Li,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,2018,38(13):4891-4902.
APA Wang, Bin,Zheng, Lihong,Liu, De Li,Ji, Fei,Clark, Anthony,&Yu, Qiang.(2018).Using multi-model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia.INTERNATIONAL JOURNAL OF CLIMATOLOGY,38(13),4891-4902.
MLA Wang, Bin,et al."Using multi-model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia".INTERNATIONAL JOURNAL OF CLIMATOLOGY 38.13(2018):4891-4902.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Bin]'s Articles
[Zheng, Lihong]'s Articles
[Liu, De Li]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Bin]'s Articles
[Zheng, Lihong]'s Articles
[Liu, De Li]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Bin]'s Articles
[Zheng, Lihong]'s Articles
[Liu, De Li]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.