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Study the effect of climate change on frost days

PI: Bertrand Timbal
Institution: Australian Bureau of Meteorology
This study will focus on quantifying both the effect of mean and variance changes in the impact of climate change on extremes, looking at one particular case.

The area of interest is a large river basin inland of Australia: the Murray-Darling Basin, with the following property:
Important network of climate stations (40) with long daily records (50 years+)
Agricultural area strongly impacted by frost
Observed frost occurrence during winter months range from 10% to 1% (range of extremes)

Direct model outputs (DMO) will be compared with a downscaling technique (hence the requirement for atmospheric large-scale forcing) developed for daily temperature extremes (Tmin and Tmax) in the region (Timbal and McAvaney, Climate Dyn., 17, 947-963, 2001) in three steps:
Comparison of the DMO and downscaled GCMs reproduction of frost days for the current climate (i.e. Is there a benefit in using a statistical downscaling method for local climate extremes?)
Future scenarios influence on frost days with both DMO and downscaling approach (i.e. Is it important to reproduce the current PDF properly to infer future change for climate extremes?)
Use of the range of GCMs simulations to compare the mean and variance effects (i.e. Comparison of downscaled PDFs vs. PDF shifted by the mean warming produced by the GCMs: how important is the variance effect?)

The focus will be on the impact of modelsí sensitivity and variability more than the importance of scenarios. Therefore, once set of experiments in the following list is sufficient with at least 20 years of daily data, the one with the largest number of GCMs (and the largest number of members) participating would be the most suitable:
Control (2), CO2 doubling (8) and quadrupling (9)
Current climate (3) and future scenarios (either A2, A1B or B1) at the end of the CO2 increase and after a period of stabilisation
  • Timbal, B., E. Fernandez and Z. Li, 2008: Generalization of a statistical downscaling model to provide local climate change projections for Australia. Environmental Modelling and Software, doi:10.1016/j.envsoft.2008.07.007. Abstract. Full Article. Edit.
  • Timbal, B., P. Hope and S. Charles, 2008: Evaluating the consistency between statistically downscaled and global dynamical model climate change projections. Journal of Climate, 21(22), 6052-6059. Abstract. Edit.

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