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

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Presentation on theme: "CSIRO LAND and WATER GCMs Validation Towards Realistic Impacts Assessment Data Mpelasoka F., Bates B., Jones R. and Whetton P. Knowledge CSIRO Atmospheric."— Presentation transcript:

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

2 CSIRO LAND and WATER GCMs validation is hard and perhaps even a poorly defined problem  Increasing confusions and uncertainties as models become complex  Increasing confusions and uncertainties as models become complex (Rind, 1999; Petersen, 2000)  Inadequacy of traditional objective skill measures for diagnostics relevant to impacts assessment CSIRO Atmospheric Research | CSIRO Atmospheric Research Creditability of GCMs

3 CSIRO LAND and WATERObjectives 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

4 CSIRO LAND and WATER Strategy: Eigen-Analysis Based Validation Singular Spectral Analysis (SSA) Scheme  Local assessment of time series structure Common Principal Components (CPCs) Model  Variability assessment of spatial and temporal eigenvalues CSIRO Atmospheric Research | CSIRO Atmospheric Research

5 CSIRO LAND and WATER Basic SSA Scheme STEP 1 + STEP 2 : DECOMPOSITION STAGE STEP 3 + STEP 4: RECONSTRUCTION STAGE CSIRO Atmospheric Research | CSIRO Atmospheric Research

6 CSIRO LAND and WATER CPCs 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 CSIRO Atmospheric Research | CSIRO Atmospheric Research

7 CSIRO LAND and WATER GCMs 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 | CSIRO Atmospheric Research

8 CSIRO LAND and WATER Test Site l Murray-Darling (M-D) Basin –Area = 1 060 000 km 2 –Mean Precip = 508 000 GL/Yr –Runoff = 23 850 GL/Yr l Most precipitation is evaporated | CSIRO Atmospheric Research

9 CSIRO LAND and WATER Seasonal Distribution M-D Basin: JJA 1971- 2000 Potential Evaporation ObservedMk3 GCM | CSIRO Atmospheric Research

10 CSIRO LAND and WATER Seasonal Distribution M-D Basin: JJA Precip 1971-2000 ObservedMk3 GCM | CSIRO Atmospheric Research

11 CSIRO LAND and WATER Seasonal Distribution M-D Basin: JJA Precipitation 1971-2000 ObservedHadCM3 GCM | CSIRO Atmospheric Research

12 CSIRO LAND and WATER Seasonal Distribution M-D Basin: DJF Tmax 1971-2000 ObservedHadCM3 GCM | CSIRO Atmospheric Research

13 CSIRO LAND and WATER Local Assessment (SSA) Local Assessment (SSA) Bourke: JJA precip series structure (1971-2000) Observed Mk3 GCM CSIRO Atmospheric Research | CSIRO Atmospheric Research

14 CSIRO LAND and WATER Local Assessment: ‘Base’ Signal Observed Mk3 GCM Bourke: reconstructed JJA 1971-2000 precip ‘BASE’ signal CSIRO Atmospheric Research | CSIRO Atmospheric Research

15 CSIRO LAND and WATER Local Assessment (Q-Q Plots) Bourke: Independent comparison of base and perturbations JJA precip signal distributions ‘BASE’ signals‘PERTURBATIONS’ | CSIRO Atmospheric Research

16 CSIRO LAND and WATER Spatial Variability: Partial Eigenvalue Spectrum Partial eigenvalues of M-D Basin observed precip (95% confidence limits) versus partial eigenvalues of CSIRO Mk3 simulation DJF precip variance JJA precip variance | CSIRO Atmospheric Research

17 CSIRO LAND and WATER Concluding Remarks Averages-based validation -Tends to mask much needed detail relevant to realistic impact assessment (variability and extremes) -Different explanation might account for the same observations Eigen-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 | CSIRO Atmospheric Research

18 CSIRO LAND and WATER Acknowledgements l Climate Impacts LINK Project, UK (HadCM3 data provision) (HadCM3 data provision) l Janice Bathols and Harvey Davies - CSIRO, AR (NAP software application support) l Lorraine Bates and Geoff Hodgson - CSIRO, LW (GIS technical advisory support) | CSIRO Atmospheric Research


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