CR 29:41-52 (2005)  -  doi:10.3354/cr029041

Probabilistic distributions of regional climate change and their application in risk analysis of wheat production

Qunying Luo1,*, Roger N. Jones2, Martin Williams1, Brett Bryan3, William Bellotti4

1Department of Geographical and Environmental Studies, University of Adelaide, North Terrace, Adelaide,South Australia 5005, Australia
2Climate Impact Group, CSIRO Atmospheric Research, Private Bag 1, Aspendale, Victoria 3195, Australia
3Policy and Economic Research Unit, CSIRO Land and Water, Private Bag 2, Glen Osmond, South Australia 5064, Australia
4School of Agriculture & Wine, University of Adelaide, South Australia 5371, Australia

ABSTRACT: Downscaled outputs from 9 climate models and information from the 2000 Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) were used to construct probability distributions of regional climate change for Roseworthy, South Australia. The construction of probability distribution for regional climate change involved the identification, quantification and treatment of uncertainties from greenhouse gas (GHG) emission scenarios, climate sensitivity and local climate change. Monte Carlo random sampling techniques were applied to component ranges of uncertainty defined by quantified upper and lower limits, assuming uniform probability over each range. Construction of resulting probability distributions of regional climate provided a framework for risk analysis. These probabilities were applied to the Agricultural Production System sIMulator (APSIM)-Wheat model to evaluate potential wheat production at Roseworthy for the year 2080 through the identification of critical yield thresholds. The conditional probability of not meeting the critical yield threshold increased from 27% under baseline conditions to 45% under the median probability for the year 2080, indicating less profitable wheat production in the study area.

KEY WORDS: Probability distribution functions · Monte Carlo random sampling · Uncertainty management · Climate change · APSIM-Wheat model · Risk analysis · Wheat production

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