CSIRO LAND and WATER GCMs Validation Towards Realistic Impacts Assessment Data Mpelasoka F., Bates B., Jones R. and Whetton P. Knowledge CSIRO Atmospheric

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

| CSIRO Atmospheric ResearchCSIRO Atmospheric Research


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


Creditability of GCMs


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

| CSIRO Atmospheric Research


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



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



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



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



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



Seasonal DistributionSeasonal Distribution

M-D Basin: M-D Basin: JJA 1971- 2000 Potential Evaporation JJA 1971- 2000 Potential Evaporation

Observed Mk3 GCM




M-D Basin: JJA Precip 1971-2000M-D Basin: JJA Precip 1971-2000

Observed Mk3 GCM




M-D Basin: JJA Precipitation 1971-2000M-D Basin: JJA Precipitation 1971-2000

Observed HadCM3 GCM




M-D Basin: DJF Tmax 1971-2000M-D Basin: DJF Tmax 1971-2000

Observed HadCM3 GCM



Local Assessment (SSA)Local Assessment (SSA)

Bourke: JJA precip series structure (1971-2000)Bourke: JJA precip series structure (1971-2000)

ObservedObserved Mk3 GCMMk3 GCM


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


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


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


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)



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



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



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))


Documents

CSIRO LAND and WATER GCMs Validation Towards Realistic Impacts Assessment Data Mpelasoka F., Bates B., Jones R. and Whetton P. Knowledge CSIRO Atmospheric