Using multi-model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia
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)