Systematic differences in future 20 year temperature extremes in AR4 model projections over Australia as a function of model skill

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The projection of temperature extremes by climate models participating in the Intergovernmental Panel on Climate Change Fourth Assessment Report (AR4) are examined regionally over Australia. Minimum and maximum temperature extremes are defined as the 20 year return value calculated using extreme value theory. Three measures of model evaluation, a means-based, a distribution-based [via probability density functions (PDFs)] and an extreme-based (via the tails of PDFs) method, are used to compare daily model data to observed daily data over various climatic regions for a 20 year period. Model ensembles consisting of the better' and poorer' models determined by each measure of skill are created for each region. These are compared with an all-model ensemble to examine the difference in more skilled ensemble projections of temperature extremes in the A2 (high emissions) scenario for 2046-2065 and 2081-2100. If either of the distribution-based evaluation methods were used to distinguish models, the higher skilled models projected smaller increases in the 20 year return values than the all-model ensemble for both maximum temperature and minimum temperature. For some regions, the 90% confidence intervals of the better and poorer ensemble ranges did not overlap, indicating that projections are statistically significantly different. We show that the means-based evaluation produces less consistent results to the two distribution-based evaluation methods. We conclude that specific AR4 models, shown to be relatively poor over most regions of Australia by different skill metrics, bias the projected increase in the 20 year temperature extremes towards higher values. We also suggest that performance in simulating the mean climate is an unreliable measure of climate model capacity used to select models for projecting changes in extremes over Australia. Copyright (c) 2012 Royal Meteorological Society
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Perkins, Sarah
Pitman, Andrew
Sisson, Scott
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UNSW Faculty
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