Past and future changes in temperature extremes in Australia: a global context Workshop on metrics and methodologies of estimation of extreme climate events, Paris 27 th – 29 th September 2010 Lisa Alexander, Climate Change Research Centre, UNSW, Sydney, Australia

The “land of drought and flooding rains” Population predominantly live in temperate zone and in sub-tropical zones in the east and south-west Climate strongly modulated by ENSO variability Sustained period of drought in south over the last decade Extremely high temperatures

Highest recorded temperatures StateTemperature (°C) DatePlace nameLatitudeLongitude South Australia Oodnadatta Western Australia Mardie New South Wales Menindee Queensland Birdsville Victoria Hopetoun Northern Territory Finke Tasmania Scamander

2009 heatwaves

Average winter Tmin increase has led to a substantial decrease in probability of temperatures < 1°C Mean Mean probability °C°C Have temperatures become more extreme? Source: Nicholls and Alexander, 2007 Illustrative example for Melbourne

Seasonal trends in temperature Tmin/TN90pTmax/TX90p Means vs extremes1957–2005 Only statistically significant trends are shown in colour Triangles represent increasing/decreasing (upward/downward) trends in the upper 10 th percentile at individual stations The size of the triangle reflects the magnitude of the trend Bold indicates statistically significant change

Spatial correlations Trends in extremes generally well correlated with trends in means across Australia in every season Absolute trends in extremes greater than mean trends when averaged across Australia summer winter Extreme temperature Mean temperature MaxMin

Example method:- Angular distance weights for i th station, w i, which are defined as:- where f is the correlation function: L is the decorrelation length scale θ is the bearing k sums over all stations within circle of influence m adjusts function decay Scaling issues Need to define minimum number of stations for a gridbox calculation Search Radius

Calculation of decorrelation length scale, L m=1 m=4 m=10 Station correlations are averaged into 100km bins within 5 latitude bands 2 nd order polynomial is fitted L is defined where the fitted function falls below 1/e L is calculated for each season and year for each index Source: Alexander et al. 2006

How good are gridding methods for extreme temperatures? ActualSimulated

Errors associated with gridding temperature extremes

Homogeneity issues

Trends in some temperature extreme indices,

Comparison with other regions Source: Caesar et al USA Europe China Russia Australia

Comparison of observed and modelled timeseries

Observed vs modelled trends, 1957 to 1999 IndexObsMulti-model Warm nights1.11 ± (0.48/1.87) Frost days-0.89 ± (-1.46/0.22) Extreme temperature range ± (-0.29/0.31) Heat wave duration 7.05 ± (-0.31/0.91)

The importance of natural variability Model (SSTNAT)Obs Reds where La Niña warmer than El Niño – crosses where difference significant versus ENSO and global daily maximum Tmax

Extreme Tmax Australia Observed hottest day max 0.5°C - 1°C warmer during El Niño than La Niña SSTNAT hottest day max up to 0.3°C cooler during El Niño than La Niña ALL hottest day max up to 0.6°C cooler during El Niño than La Niña

Anthropogenic versus natural forcing Two models (CCSM/PCM) with output from different forcings Results show that some temperature extremes are inconsistent with natural-only forcings

Future projections for different scenarios Source: Alexander and Arblaster (2009)

Future changes minus (A1B) Multi-model agreement across most of Australia for large significant increases in warm nights and heat wave duration

Changes scale with strength of emissions IndexAust/Global (A1B) B1/A1BA2/A1B Warm nights Frost days Extreme temperature range Heat wave duration

Conclusions Over the last 50 years Australia has seen trends in temperature extremes associated with warming (the exception is northwest Australia in DJF) Work is ongoing on how to best address issues of scale Natural climate variability is important and models do not appear to capture some important processes Anthropogenic forcing is also important in capturing trends and model simulations indicate continued warming of temperature extremes in the future The magnitude of future changes appears to scale with strength of emissions