Generalization of a statistical downscaling model to provide local climate change projections for Australia

  • Authors:

  • B. Timbal;E. Fernandez;Z. Li

  • Affiliations:

  • Centre for Australian Weather and Climate Research, Bureau of Meteorology, P.O. Box 1289, Melbourne 3001, Australia;Centre for Australian Weather and Climate Research, Bureau of Meteorology, P.O. Box 1289, Melbourne 3001, Australia;Centre for Australian Weather and Climate Research, Bureau of Meteorology, P.O. Box 1289, Melbourne 3001, Australia

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2009

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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.