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Probabilistic Predictions of Climate Change in Australia using the Reliability Ensemble Average (REA) of CMIP3 Model Simulations. Dr A.F. Moise & Dr D. Hudson Bureau of Meteorology Research Centre Melbourne, Australia [email protected]. Insert title here. Overview. Methodology: REA CMIP3 - PowerPoint PPT Presentation
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Probabilistic Predictions of Climate Probabilistic Predictions of Climate Change in Australia using the Reliability Change in Australia using the Reliability Ensemble Average (REA) of CMIP3 Model Ensemble Average (REA) of CMIP3 Model SimulationsSimulations
Dr A.F. Moise & Dr D. HudsonBureau of Meteorology Research CentreMelbourne, [email protected]
ICCC, Hong Kong, May 2007 2
Overview
Methodology: REA CMIP3 Areas under study Results: REA for DJF, JJA Temperature, Precipitation Changes across SRES scenarios Methodology: probabilistic projections Threshold probabilities PDF’s for Australian regions Reliability contribution of CMIP3 models Summary
Acknowledgement
This activity is supported by the Australian Greenhouse Office.
ReferencesGiorgi, F., and L. Mearns, 2002. Journal of Climate, 15, 1141-1158.Giorgi, F., and L. Mearns, 2003. Geophysical Research Letters, 30 (12), art. no 1629, doi:10.1029/2003GL017130.
Milestones
Methodology
Model reliability is a function of model bias (B) AND the distance (D) from the REA average
= Natural variability
Model is “reliable” (Ri=1) when its bias and distance from the REA mean are within natural variability.
Weighted ensemble average and RMSD (weighted by model reliability Ri)
i i
i ii
R
TRT~
ii
iii
T R
TTR 2)~
(~
REA-mean REA-rmsd
)()(,,ii
iDiBi DabsBabsRRR
εT = Max{30yr-runMean[detrended(20th century observed T time series)]} – Min{[(…..)]}
RB: performance criterion RD: convergence criterion
If |BTJ| < εT then RB,I = 1
If |BTJ| < εT then RB,I = 1
CMIP 3 models used
Models
OBS
1981-2000and
2081-2100
- NCC high quality monthly data set
Regions of analysis
Maps of Australia and southern Africa (1=Gabon, 2=Congo, 3=Dem.Rep. Congo, 4=Tanzania, Rwanda, Burundi, Uganda, 5=Kenya, 6=Angola, 7=Zambia, 8=Malawi, 9=Mozambique, 10=Namibia, 11=Botswana, 12=Zimbabwe, 13=Madagascar, 14=South Africa, Lesotho, Swaziland).
Also shown are the regions analysed separately.
Results – DJF Temperature – SRESA2
REA-mean
Simple-mean
REA-rmsd
Simple-rmsd
Rb
Rd
R
NatVar
(3.9 oC)
(0.5)
(0.3 oC)(0.9 oC)
(0.6 oC)
(3.9 oC)
(0.6)
(0.4)
Results – JJA Temperature – SRESA2
REA-mean
Simple-mean
REA-rmsd
Simple-rmsd
Rb
Rd
R
NatVar
(3.8 oC)
(0.5)
(0.3 oC)(0.7 oC)
(0.4 oC)
(3.7 oC)
(0.7)
(0.3)
Results – DJF Precipitation – SRESA2
REA-mean
Simple-mean
REA-rmsd
Simple-rmsd
Rb
Rd
R
NatVar
(0.0 mm/d)
(0.8)
(0.6 mm/d)(0.4 mm/d)
(0.4 mm/d)
(0.0 mm/d)
(0.9)
(0.7)
Results – JJA Precipitation – SRESA2
REA-mean
Simple-mean
REA-rmsd
Simple-rmsd
Rb
Rd
R
NatVar
(-0.1 mm/d)
(0.8)
(0.2 mm/d)(0.2 mm/d)
(0.1 mm/d)
(-0.1 mm/d)
(0.9)
(0.7)
Averaged changes across scenariosDJF Australia REA results
-0.08
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
1 2 3 4
Temperature change (deg C)
Pre
cip
itatio
n c
ha
ng
e (
mm
/da
y)
SWWA MDB TROP
A1B
B1
B1
A2
B1
A2
A1B
A1B
A2
JJA Australia REA results
-0.3
-0.25
-0.2
-0.15
-0.1
-0.05
0
1 2 3 4 5
Temperature change (deg C)
Pre
cip
itatio
n c
ha
ng
e (
mm
/da
y)
SWWA MDB TROP
A1B
B1
B1
A2
B1
A2
A1B
A1B
A2
JJA
DJF
Predictions and Probabilities - Method
N
j j
ii
R
RmP
1
)(Probabilities of regional climate change:
Assume: each models’ reliability Ri is an indicator of the likelihood of its simulation the change simulated by a more reliable model is more likely to occur!
i ii
TT mPmP thi )()( thi TT
Threshold probability = summing over all P(mi) exceeding a given
threshold of climate change.
= probability of a temperature change exceeding ΔTth
where
PDFs = derivative of P(mi) )(
)(
T
mP i
Threshold probability
SRESA2 - Precipitation - JJAExample:
SWWA
Threshold probability – area averaged
TEMPERATURE
SRESB1 SRESA1B SRESA2
>2 >3 >4 >5 >2 >3 >4 >5 >2 >3 >4 >5
swwa 13 1 0 0 100 19 2 0 100 93 17 1 DJF mdb 23 6 0 0 99 35 8 1 99 77 35 6 tropics 37 1 0 0 97 38 11 0 99 92 40 3 swwa 15 0 0 0 79 8 0 0 100 42 1 0 JJA mdb 49 0 0 0 99 34 1 0 100 84 27 1 tropics 78 2 0 0 100 70 1 0 100 98 79 4 PRECIPITATION SRESB1 SRESA1B SRESA2 <-0.2 <-0.1 >0.1 >0.2 <-0.2 <-0.1 >0.1 >0.2 <-0.2 <-0.1 >0.1 >0.2 swwa 3 10 18 1 8 27 26 1 11 29 13 3 DJF mdb 12 30 36 22 39 59 18 16 15 30 30 22 tropics 25 37 44 38 37 40 43 38 34 42 50 46 swwa 44 67 0 0 65 88 0 0 72 83 2 0 JJA mdb 12 35 5 2 31 63 4 0 55 71 9 3 tropics 1 6 2 1 1 11 1 0 3 13 6 1
PDF’s for sres-A2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0 1 2 3 4 5 6
Temperature Change (oC)
Pro
ba
ilit
y D
en
sit
y
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0 1 2 3 4 5 6
Temperature Change (oC)
Pro
ba
ilit
y D
en
sit
y
All_ozswwamdbtropics
0
0.5
1
1.5
2
2.5
-1.6 -1.2 -0.8 -0.4 0 0.4 0.8 1.2 1.6
Precipitation Change (mm/day)
Pro
ba
ilit
y D
en
sit
y
0
0.5
1
1.5
2
2.5
-1.6 -1.2 -0.8 -0.4 0 0.4 0.8 1.2 1.6
Precipitation Change (mm/day)
Pro
ba
ilit
y D
en
sit
y
Reliability Contributions (%) - Australia
Normalised contributions (in %) to the overall model reliability for each CGCM. Good model performance and convergence leads to higher
contribution. If all models were equal, they would contribute 8.3% each.
ccs
m
cn
rm
csi
r
gfd
l
gfd
l_cm
2_
1
in
m
ip
sl_
cm4
mir
ocm
mp
i_ec
ha
m5
mri
pcm
uk
mo_
ha
dcm
3
Precipitation all_oz 9 6 8 13 8 11 7 4 13 9 4 7 swwa 10 9 4 12 11 11 5 2 14 12 3 6 mdb 4 7 7 11 13 9 7 4 13 10 6 8
DJF
tropics 11 6 8 12 6 10 4 8 12 8 5 9
all_oz 8 9 7 10 11 7 10 5 11 9 6 7 swwa 13 5 8 8 8 5 10 18 8 4 8 6 mdb 8 7 10 11 11 7 7 5 10 6 7 10
JJA
tropics 9 7 6 11 12 8 11 4 12 9 5 4 Temperature
all_oz 13 8 8 12 9 11 4 7 6 10 3 10 swwa 8 13 13 11 11 10 2 11 8 4 3 6 mdb 11 9 13 13 14 5 3 9 2 7 6 9
DJF
tropics 5 2 7 9 5 15 5 9 12 17 1 13
all_oz 4 3 2 11 13 3 14 16 21 4 1 8 swwa 2 4 3 7 8 3 13 30 11 11 1 6 mdb 2 3 2 8 12 2 11 15 28 6 1 10
JJA
tropics 11 2 1 15 18 2 11 8 15 4 1 13
i i
i ii
R
TRT~
REA mean
Summary
• REA is a useful tool to determine regional climate change from an ensemble of model simulations.
• Provides a means of producing probabilistic climate change predictions.
• Significantly lowers RMSD of mean climate change.• Obtain ‘skill measure’ of models through reliability analysis.• Summary for Australia:
– Magnitude of ΔT in winter is similar to summer.– No significant rainfall changes in DJF.– Significant decreases in rainfall in JJA over SWWA, MDB– On average, RD consistently better than RB – Resulting PDFs vary in shape depending on region (e.g. bi-
modal vs uni- modal, width)• Same analysis has been repeated over southern Africa (see
coming paper for details).
ACCSP
Australian Climate Change Science Programme Supported by the AGO CSIRO Marine and Atmospheric Research BMRC
Launched in October 2007 at GREENHOUSE 2007
Australian Climate Change Projections ReportAustralian Climate Change Projections Report 150 pages + Website access for projections
Any questions?
From: Allen and Ingram, 2002, Nature, 419, 224-232.
Milestones
Overview