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  • Perkins, S.E., and A.J. Pitman, 2007: Reducing uncertainty in the range of climate projections from the AR4 climate. Climatic Change. Submitted.

Regional climate projections using climate models commonly use an “all-model”
ensemble based on data sets such as the Intergovernmental Panel on Climate
Change’s (IPCC) 4th Assessment (AR4). Some regional assessments have omitted
models based on specific criteria. We use a criteria based on the capacity of climate
models to simulate the observed probability density function calculated using daily
data, model-by-model and region-by-region for each of the AR4 models over
Australia. We demonstrate that by omitting those climate models with relatively weak
skill in simulating the observed probability density function of maximum and
minimum temperature and precipitation difference, regional projections are obtained.
Once relatively weak models are omitted, the projected increase in mean maximum
and mean minimum temperatures increase, but the change in the annual maximum
and minimum temperatures is lower amongst the better models. There is little impact
on mean precipitation but the better models simulate a larger increase in the annual
rainfall event combined with a large decrease in the number of rain days. We
therefore show that omitting weaker models based on a quantitative skill score
derived using 20th Century observations provides a means to reduce uncertainty in
climate model projections. Since the weaker models appear to bias the amount of
mean warming towards lower increases, bias annual maximum temperatures to
excessive warming and bias precipitation such that the amount of the annual rainfall
event is under-estimated we suggest that our approach would lead to improved
regional scale estimates of future climate change.


Last Updated: 2007-12-12

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