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Analysis of Continental Flux Estimates for Regional Model Performance Evaluation

This study compares flux tower records to regional and site-level model runs to assess model validity. The analysis includes evaluating seasonal and interannual patterns, mean biases, and model rankings based on NEE, R, and GPP.

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Analysis of Continental Flux Estimates for Regional Model Performance Evaluation

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  1. Joint Site-Regional Analysis Brett Raczka & Ken Davis, Penn State UniversityMay 15, 2009 • Objective • Attempt to utilize flux tower records to evaluate the validity of continental flux estimates submitted to the regional interim synthesis activity. • Methods (only partly completed) • Compare flux tower records to extracts from regional model runs, and to site-level model runs, with special attention to models run at both regional and site level. • Evaluate seasonal and interannual patterns, and mean bias. • Consider NEE, R and GPP. • Draw conclusions re: regional model performance. Diagnose causes of discrepancies.

  2. Joint Site-Regional Analysis • Conclusions to date: • Regional model NEE output presents a systematic positive bias as compared to site level runs • Site level model runs produce higher annual NEE variability (sigma) than regional runs, more representative of observed annual NEE • Site level annual NEE produce higher correlation (R) to observed annual NEE than regional model runs • CLM-CN, CLM-CASA, CASA-GFED consistently ranked highest among model skill statistics for regional runs • ECOSYS, CanIBIS consistently ranked highest “ “ for site level runs Brett Raczka & Ken Davis, Penn State University

  3. Future work: Joint Site-Regional Analysis • Add additional sites and models (cutoff date?) • Take comments from community and modify analyses • Enlist help from the I-synthesis community in understanding the causes of observed model behaviors • Expand analyses to the seasonal cycle, and R and GPP • Publish results • Possibly contribute some portion of the site analyses to site-only papers. Brett Raczka & Ken Davis, Penn State University

  4. Interim Synthesis: Inter-annual NEE Carbon Source • Point #1: Systematic difference between site and regional extract model runs (DLEM, ORCHIDEE & SIB were only models run in both modes) • Regional model runs for DLEM & ORCHIDEE present a consistent positive bias compared to site model runs for DLEM & ORCHIDEE • Source(s) of Annual NEE difference? - Different driver (met) data - Different site characteristics (soil or vegetation) - Equilibrium?, Regional model averaging Carbon Sink Carbon Source Carbon Sink Brett Raczka & Ken Davis, Penn State University

  5. Interim Synthesis: Inter-annual NEE Regional Model Run • Correlation Coefficient (R) Point #2: For regional model runs R has wide spread i.e. R values distributed across -1 to 1 across all sites and models (1st figure) Point #3: For site model runs R has considerable spread as well, but there are exceptions: Mer (Peatland) UMB (DBF) Me2 (Pond. Pine) –anticorrelated Obs (Old Black Spruce) (2nd Fig.) Site Model Run Brett Raczka & Ken Davis, Penn State University

  6. Interim Synthesis: Inter-annual NEE Regional Model Run • σobservations vs. σmodel Point #4: For regional model runs, in general: σobs >>σmodel, eg: Let (Grass), Ca1 (ENF), Ne3 Corn/Soy (1st Figure) Point #5: For site model runs, in general: σobs ~σmodel (2nd Figure) Why? - Does regional averaging reduce variability either for NEE or driver data input? -Reminiscent of large variability for atmospheric inversions compared to forward model runs over continent Site Model Run Brett Raczka & Ken Davis, Penn State University

  7. Interim Synthesis: Inter-annual NEE Regional Model Run • RMSD (centered pattern difference) Point #6: For regional model runs, no site-model combination that performed ‘well’ i.e. small RMSD. Inter-annual pattern not being captured (1st figure) Point #7: For site model runs, several sites show small RMSD, eg. UMB, Mer, Obs & Ho1. (2nd figure) Not consistent across PFT however. Site Model Run Brett Raczka & Ken Davis, Penn State University

  8. Interim Synthesis: Inter-annual NEE Regional Model Runs • Overall Model Rankings Ranking Criteria: • Bias (Observed mean - model mean) • Total RMSD (Includes both bias and centered RMSD) • R (Correlation Coefficient) Two methods to rank: • Based on average statistic value across all sites • Based on average ranking value across all sites Regional Model Runs Brett Raczka & Ken Davis, Penn State University

  9. Interim Synthesis: Inter-annual NEE Regional Model Runs Brett Raczka & Ken Davis, Penn State University

  10. Interim Synthesis: Inter-annual NEE Site Model Runs Site Model Runs Brett Raczka & Ken Davis, Penn State University

  11. Interim Synthesis: Inter-annual NEE Site Model Runs Brett Raczka & Ken Davis, Penn State University

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