past and future changes in temperature extremes in australia: a global context workshop on metrics...
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Past and future changes in temperature extremes in
Australia: a global contextWorkshop on metrics and methodologies of estimation of extreme
climate events, Paris 27th – 29th 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
State Temperature (°C)
Date Place name
Latitude Longitude
South Australia
50.7 02.01.1960
Oodnadatta
-27.56 135.45
Western Australia
50.5 19.02.1998
Mardie -21.19 115.98
New South Wales
49.7 10.01.1939
Menindee -32.39 142.42
Queensland
49.5 24.12.1972
Birdsville -25.90 139.35
Victoria 48.8 07.02.2009
Hopetoun -35.72 142.36
Northern Territory
48.3 01.01.1960
Finke -25.58 134.57
Tasmania 42.2 30.01.2009
Scamander
-41.46 148.26
Average winter Tmin increase has led to a substantial decrease in probability of temperatures < 1°C
Mean 1957-1980
Mean 1981-2005
prob
abili
ty
6.9 7.6
°C
Have temperatures become more extreme?
Source: Nicholls and Alexander, 2007
Illustrative example for Melbourne
Seasonal trends in temperature
Tmin/TN90p Tmax/TX90p
Means vs extremes1957–2005
Only statistically significant trends are shown in colour
Triangles represent increasing/decreasing (upward/downward) trends in the upper 10th percentile at individual stations
The size of the triangle reflects the magnitude of the trend
Bold indicatesstatistically 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
winterEx
tre
me
te
mp
era
ture
Mean temperature
Max Min
Example method:- Angular distance weights for ith station, wi, which are defined as:-
where f is the correlation function:L is the decorrelation length scaleθ is the bearingk sums over all stations within circle of influencem adjusts function decay
Scaling issues
k ,
)cos(11 ki
f
ff
k
k
ikk
iim
m
m
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
•2nd 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
Lri
ief /
Source: Alexander et al. 2006
Observed vs modelled trends, 1957 to 1999
Index Obs Multi-model
Warm nights 1.11 ±0.06
1.15 (0.48/1.87)
Frost days -0.89 ±0.07
-0.19 (-1.46/0.22)
Extreme temperature range
-0.19 ±0.02
0.04 (-0.29/0.31)
Heat wave duration
7.05 ±0.33
0.26 (-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 changes 2080-2099 minus 1980-1999 (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
Index Aust/Global (A1B)
B1/A1B A2/A1B
Warm nights 0.86 0.65 1.11
Frost days 0.45 0.86 1.15
Extreme temperature range
-0.53 0.58 2.14
Heat wave duration
0.30 0.50 1.40
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