CSIRO LAND and WATER
C S I R O
GCMs Validation Towards GCMs Validation Towards Realistic Impacts AssessmentRealistic Impacts Assessment
DataData
Mpelasoka F., Bates B., Jones R. and Whetton P.Mpelasoka F., Bates B., Jones R. and Whetton P.
KnowledgeKnowledge
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GCMs validation is hard and perhaps even a poorly defined problem
Increasing confusions and uncertainties Increasing confusions and uncertainties as models become complex as models become complex (Rind, 1999; Petersen, 2000)
Inadequacy of traditional objective skill measures for diagnostics relevant to impacts assessment
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Creditability of GCMs
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ObjectivesObjectives
To validate GCMs in terms of signals interpretable in the context of climate impacts
To examine time series structures for climate elements of interest
To evaluate covariance across spatial and temporal scales at which impacts occur
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Strategy: Eigen-AnalysisStrategy: Eigen-Analysis Based Based ValidationValidation
Singular Spectral Analysis (SSA) Scheme
Local assessment of time series structure
Common Principal Components (CPCs) Model
Variability assessment of spatial and temporal eigenvalues
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Basic SSA SchemeBasic SSA Scheme
1-DSeries
STEP 1:Embedding
Process
STEP 2:Singular ValueDecomposition(SDV) Process
STEP 3:GroupingProcess
STEP 4:Diagonal Averaging
Process
Input SignalL-D
SeriesWindow = L
TrajectoryMatrix
rank-one-bi-
orthogonalmatrices
severalresultantmatrices
Decomposed initial seriesinto
additive components
STEP 1 + STEP 2 :STEP 1 + STEP 2 :DECOMPOSITION STAGEDECOMPOSITION STAGE
STEP 3 + STEP 4:STEP 3 + STEP 4:RECONSTRUCTION STAGERECONSTRUCTION STAGE
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CPCs ModelCPCs Model
Covariance matrices have different eigenvalues but identical eigenvectors (Flurry, 1984)
Implies multiple data sets share common components, but each set has different eigenvalues associated with those components
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GCMs and DataGCMs and Data
GCMs: Mk3 (CSIRO, Australia) and HadCM3 (Hadley Centre, UK)– Mk3 horizontal resolution: 3.73 x 3.75 deg– HadCM3 horizontal resolution: 2.50 x 3.75 deg
Data: Daily series of gridded simulated and observed variables for 1971-2000
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Test SiteTest Site
Murray-Darling (M-D) Basin– Area = 1 060 000 km2
– Mean Precip = 508 000 GL/Yr
– Runoff = 23 850 GL/Yr
Most precipitation is evaporated
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Seasonal DistributionSeasonal Distribution
M-D Basin: M-D Basin: JJA 1971- 2000 Potential Evaporation JJA 1971- 2000 Potential Evaporation
Observed Mk3 GCM
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Seasonal DistributionSeasonal Distribution
M-D Basin: JJA Precip 1971-2000M-D Basin: JJA Precip 1971-2000
Observed Mk3 GCM
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Seasonal DistributionSeasonal Distribution
M-D Basin: JJA Precipitation 1971-2000M-D Basin: JJA Precipitation 1971-2000
Observed HadCM3 GCM
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Seasonal DistributionSeasonal Distribution
M-D Basin: DJF Tmax 1971-2000M-D Basin: DJF Tmax 1971-2000
Observed HadCM3 GCM
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Local Assessment (SSA)Local Assessment (SSA)
Bourke: JJA precip series structure (1971-2000)Bourke: JJA precip series structure (1971-2000)
ObservedObserved Mk3 GCMMk3 GCM
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Reconstructed seriesAverage(26.432%)
1 308 614 921 1227 1534 1840 2147 2453 2760
0.807
0.815
0.824
0.832
0.841
0.849
0.857
0.866
0.874
0.883
0.891
0.899
0.908
0.916
1(1.586%)
1 308 614 921 1227 1534 1840 2147 2453 2760
-0.86
-0.74
-0.62
-0.50
-0.38
-0.26
-0.14
-0.02
0.10
0.22
0.35
0.47
0.59
0.71
2(1.529%)
1 308 614 921 1227 1534 1840 2147 2453 2760
-0.57
-0.44
-0.32
-0.19
-0.06
0.06
0.19
0.31
0.44
0.56
0.69
0.81
0.94
1.06
5(1.223%)
1 308 614 921 1227 1534 1840 2147 2453 2760
-0.18
-0.09
0.01
0.10
0.19
0.28
0.38
0.47
0.56
0.65
0.74
0.84
0.93
1.02
Reconstructed seriesAverage(11.383%)
1 308 614 921 1227 1534 1840 2147 2453 2760
0.851
0.861
0.872
0.883
0.893
0.904
0.915
0.925
0.936
0.947
0.957
0.968
0.979
0.989
3(0.546%)
1 308 614 921 1227 1534 1840 2147 2453 2760
-0.99
-0.88
-0.77
-0.65
-0.54
-0.43
-0.32
-0.21
-0.09
0.02
0.13
0.24
0.36
0.47
4(0.497%)
1 308 614 921 1227 1534 1840 2147 2453 2760
-0.22
-0.17
-0.12
-0.08
-0.03
0.01
0.06
0.10
0.15
0.19
0.24
0.29
0.33
0.38
7(0.369%)
1 308 614 921 1227 1534 1840 2147 2453 2760
-0.162
-0.131
-0.099
-0.068
-0.036
-0.005
0.026
0.058
0.089
0.121
0.152
0.183
0.215
0.246
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Local Assessment: ‘Base’ SignalLocal Assessment: ‘Base’ Signal
ObservedObserved
Mk3 GCMMk3 GCM
Bourke: reconstructed JJA 1971-2000 precip Bourke: reconstructed JJA 1971-2000 precip ‘BASE’ signal‘BASE’ signal
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Reconstructed SeriesInitial(recon.)
1 76 151 226 301 376 451 526 601 676 751 826 901 976 1051 1126 1201 1276 1351 1426 1501 1576 1651 1726 1801 1876 1951 2026 2101 2176 2251 2326 2401 2476 2551 2626 2701
0.10
0.29
0.48
0.67
0.87
1.06
1.25
1.44
1.63
1.82
2.01
2.21
2.40
2.59
2.78
Reconstructed SeriesInitial(recon.)
1 76 151 226 301 376 451 526 601 676 751 826 901 976 1051 1126 1201 1276 1351 1426 1501 1576 1651 1726 1801 1876 1951 2026 2101 2176 2251 2326 2401 2476 2551 2626 27010.10
0.23
0.36
0.50
0.63
0.76
0.89
1.02
1.16
1.29
1.42
1.55
1.69
1.82
1.95
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Local Assessment (Q-Q Plots)Local Assessment (Q-Q Plots)
Bourke: Independent comparison of base and Bourke: Independent comparison of base and perturbations JJA precip signal distributionsperturbations JJA precip signal distributions
‘BASE’ signals ‘PERTURBATIONS’
0 1 2 3 4
0
1
2
3
4
Q-QPlot
MK
3 P
reci
pita
tion
(mm
/day
)
OBS Precipitation (mm/day)-10 0 10 20 30 40
-10
0
10
20
30
40
Q-QPlot
MK
3 P
reci
p' (
mm
/da
y)
OBS Precip' (mm/day)
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Spatial Variability: Partial Eigenvalue SpectrumSpatial Variability: Partial Eigenvalue Spectrum
Partial eigenvalues of M-D Basin observed precip Partial eigenvalues of M-D Basin observed precip (95% confidence limits) versus partial eigenvalues of (95% confidence limits) versus partial eigenvalues of
CSIRO Mk3 simulationCSIRO Mk3 simulation
DJF precip varianceDJF precip variance JJA precip varianceJJA precip variance
0 1 2 3 4 5 6 7 8 9-50
0
50
100
150
200
250
300
350
400 OBS MK3
Pa
rtia
l Eig
enva
lue
Principal Component
0 1 2 3 4 5 6 7 8 9-20
0
20
40
60
80
100
OBS MK3
Par
tial E
igen
valu
e
Principal component
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Concluding RemarksConcluding RemarksAverages-based validationAverages-based validation
- Tends to mask much needed detail relevant to Tends to mask much needed detail relevant to realistic impact assessment (variability and realistic impact assessment (variability and extremes)extremes)
- Different explanation might account for the same observations
Eigen-analysis based validationEigen-analysis based validation
– Considers structure and variability across a spectrum of spatial (global, regional, local) and temporal (inter-decadal, inter-annual, seasonal) scales
– Pinpoints the causes of mismatches between observations and GCM outputs, leading to GCM improvement
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AcknowledgementsAcknowledgements
Climate Impacts LINK Project, UK Climate Impacts LINK Project, UK ((HadCM3 data provisionHadCM3 data provision))
Janice Bathols and Harvey Davies - CSIRO, Janice Bathols and Harvey Davies - CSIRO, AR AR ((NAP software application supportNAP software application support))
Lorraine Bates and Geoff Hodgson - CSIRO, Lorraine Bates and Geoff Hodgson - CSIRO, LW LW ((GIS technical advisory supportGIS technical advisory support))
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