Generalization of a statistical downscaling model to provide local climate change projections for Australia |
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| Authors: | B. Timbal E. Fernandez Z. Li |
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| Affiliation: | 1. NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, PMB, Wagga Wagga, NSW 2650, Australia;2. Graham Centre for Agricultural Innovation (An alliance between NSW Department of Primary Industries and Charles Sturt University), Wagga Wagga, NSW 2650, Australia;3. Melbourne School of Land & Environment and Future Farm Industries Cooperative Research Centre, University of Melbourne, Australia;4. Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, NSW 2052, Australia;5. Department of Parks and Wildlife Western Australia, Department of Agriculture and Food Western Australia and Future Farm Industries Cooperative Research Centre, Perth, WA, Australia;6. Plant Biology and Climate Change Cluster, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia;7. International Crops Research Institute for the Semi-Arid Tropics, Patancheru, India;1. Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom;2. Anhui Province Meteorological Observatory, Hefei, China;3. Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China;1. Institute of Geography and Geology, University of Wuerzburg, Am Hubland, 97074 Wuerzburg, Germany;2. Institute of Water Resources Management, Hydrology and Agricultural Hydraulic Engineering, Leibniz University of Hannover, Appelstr. 9 A, 30167 Hannover, Germany;3. Xinjiang Climate Centre, 46 Jianguolu Road, Urumqi 830002 Xinjiang, China;4. Nanjing University, 22, Hankou Road, 210093 Nanjing, China;5. Max Planck Institute for Meteorology, Bundesstrasse 53, 20146 Hamburg, Germany;1. Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Via C. Colombo 112, 00147 Rome, Italy;2. Karolinska Institutet, Institute of Environmental Medicine, Stockholm, Sweden;3. Department of Environmental Health, Harvard T. H. Chan School of Public Health, Cambridge, MA, USA;4. Stockholm County Council, Centre for Occupational and Environmental Medicine, Stockholm, Sweden;5. Italian National Institute for Environmental Protection and Research, Rome, Italy;6. National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Greenbelt, MD, USA;7. Civil and Environmental Engineering, Technion, Haifa, Israel;8. Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel;9. Swiss Tropical and Public Health Institute, Basel, Switzerland;10. University of Basel, Basel, Switzerland |
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| Abstract: | Climate change information required for impact studies is of a much finer spatial scale than climate models can directly provide. Statistical downscaling models (SDMs) are commonly used to fill this scale gap. SDMs are based on the view that the regional climate is conditioned by two factors: (1) the large-scale climatic state and (2) local physiographic features. An SDM based on an analogue approach has been developed within the Australian Bureau of Meteorology and applied to six regions covering the southern half of Australia. Six surface predictands (daily minimum and maximum temperature and dew-point temperature, daily total rainfall and pan evaporation) were modelled. The skill of the SDMs is evaluated by comparing reconstructed and observed series using a range of metrics: first two moments of the series, the ability to reproduce day-to-day and inter-annual variability, and long-term trends. Once optimised, the SDMs are applied to a selection of global climate models which contributed to the Intergovernmental Panel on Climate Change 4th assessment report released in 2007. A user-friendly graphical interface has been developed to facilitate dissemination of the SDM results and provides a range of options for users to obtain tailored information. Once the projections are calculated for the places of interest, graphical outputs are displayed and can be downloaded jointly with the underlying data, allowing the user to use the data in their own application. |
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