1. Comparing Simulated and Observed Gross Primary Productivity Kevin Schaefer, Altaf Arain, Alan Barr, Jing Chen, Ken Davis, Dimitre Dimitrov, Ni Golaz, Timothy Hilton, David Hollinger, Elyn Humphreys, Benjamin Poulter, Brett Raczka, Andrew Richardson, Alok Sahoo, Christopher Schwalm, Peter Thornton, Rodrigo Vargas, Hans Verbeeck, Chris Williams NACP Synthesis Management Team Ameriflux and Fluxnet Canada Investigators Modeling Team Investigators

2. Objectives Quantify how well models simulate GPPQuantify how well models simulate GPP Identify sources of errorIdentify sources of error

3. 32 Flux Tower Sites

4. 21 Models, 3 MODIS, 2 Model Mean

5. Model Runs Gap-filled observed weatherGap-filled observed weather Steady stateSteady state Observed NEE partitioned into GPP & respirationObserved NEE partitioned into GPP & respiration GPP UncertaintyGPP Uncertainty Random Random U* filtering U* filtering Gap-filling Gap-filling Partitioning Partitioning

6. Model-Data Comparison Daily average GPPDaily average GPP Performance MeasuresPerformance Measures Chi-squared statistic Chi-squared statistic Root Mean Squared Error Root Mean Squared Error Normalized Mean Absolute Error Normalized Mean Absolute Error Bias Bias

7. Overall Model Performance Model Mean MODIS Optimized Model C5 C5.1 Daily GPP Taylor Plot Standard Deviation Ratio GPP Ratio

8. Typical GPP Date (Year) GPP (  mol m -2 s -1 ) Monthly Average GPP for CA-Ca1 Observed Simulated

9. Daily Average Shortwave Radiation (W m -2 ) Light Use Efficiency Curves GPP (  mol m -2 s -1 ) Daily Average GPP for CA-Ca1 Observed Simulated

10. Daily Average Temperature (°C) Temperature Response Curves GPP (  mol m -2 s -1 ) Daily Average GPP for CA-Ca1 Observed Simulated

11. Conclusions Models don’t simulate GPP wellModels don’t simulate GPP well Bias in seasonal amplitudeBias in seasonal amplitude Improve LUEImprove LUE Improve Temperature ResponseImprove Temperature Response

12. Acknowledgments Funding provided by NASA, NOAA, and NSFFunding provided by NASA, NOAA, and NSF

13. Statistics Chi-squared Root Mean Square Error Bias B > 0 model greater than data RMSE  0 perfect fit with data  2 ~ 1 model matches data within uncertainty Normalized Mean Absolute Error NMAE  0 perfect fit with data

14. Overall Performance by Model

15. Ratio of Annual GPP Amplitude Simulated:Observed Amplitude Ratio (-) Percent Years (  )

16. GPP Total Uncertainty for CA-Ca1 Date (year) GPP Uncertainty (  mol m -2 s -1 )

17. Daily Average GPP GPP Uncertainty for CA-Ca1 Date (year) GPP Uncertainty (  mol m -2 s -1 )