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Site Synthesis Objectives

Results of the North American Carbon Program Site-level Interim Synthesis Model Data Comparison (NACP Site Synthesis).

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Site Synthesis Objectives

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  1. Results of the North American Carbon Program Site-level Interim Synthesis Model Data Comparison (NACP Site Synthesis) Ken Davis, Deborah Huntzinger, Dan Riciutto, Kevin Schaefer, Peter Thornton, Deb Agarwal, Arlyn Andrews, Altaf Arain, Ian Baker, Dennis Baldocchi, Alan Barr, Paul Bolstad, Peter Curtis, Michael Dietze, Danilo Dragoni, Mathias Goeckede, Robert Grant, Lianhong Gu, Dave Hollinger, Gabriel Katul, Tony King, Chris Kucharik, Beverly Law, Shugang Liu, Erandi Lokupitiya, Yiqi Luo, Roser Matamala, Russ Monson, J Munger, Walter Oechel, Ram Oren, Philippe Peylin, David Price, Andrew Richardson, Bill Riley, Michael Sprintsin, Hanqin Tian, Christina Tonitto, Margaret Torn, Catharine van Ingen, Shashi Verma, Tao Wang, and Ensheng Weng

  2. Site Synthesis Objectives Are the various measurement and modeling estimates of carbon fluxes consistent with each other - and if not, why? • Quantify model and observation uncertainty • 47 flux tower sites; 25 models • Gap-filled observed weather • Observed fluxes and ancillary biological data • January 7-9: Full workshop at ORNL • February 17-20: NACP All-Investigators Mtg

  3. Flux Tower Sites

  4. Participating Models Results from 15 models 9 simulations per site

  5. Sources of Uncertainty • Observations • Random error • Systematic error • Models • Structure • Parameter • Initial conditions • Meteorological Forcing • Footprint

  6. Uncertainty at Inter-annual Time Scale Annual total NEE at Howland site, Maine

  7. Disturbance Uncertainty ORCHIDEE at 1850 burn site, Manitoba

  8. Uncertainty at Seasonal Time Scale Average seasonal cycle at Howland site, Maine

  9. Uncertainty at Diurnal Time Scale Mead rain-fed corn-soy rotation site (Nebraska) Soybean Year Corn Year

  10. Observed Flux Uncertainty (Based on Richardson et al., 2006, Ag. For. Met. 136:1-18)

  11. Conclusions • Site Synthesis is a powerful dataset for model evaluation and improvement • Next: rigorous statistical analysis to quantify uncertainty

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