1. FLUXNET: Measuring CO 2 and Water Vapor Fluxes Across a Global Network Dennis Baldocchi ESPM/Ecosystem Science Div. University of California, Berkeley IndoFlux, Chennai, India, July 2006

2. FLUXNET: From Sea to Shining Sea 379 Sites, circa 2006

4. Global distribution of Flux Towers with Respect to Climate

5. Evolution of FLUXNET Measure Annual Cycle of NEE –Micromet issues of Detrending, Transfer Functions, Flux Sampling and Measurements, Gap-filling, Error Assessment Measure and Interpret Intra-annual Variation of NEE –Flux partitioning (GPP & R eco ); assessment of metadata,e.g. V cmax, soil respiration, LAI, biomass inventories. Measure and Interpret Inter-annual variations of NEE Measure NEE over multiple Land-Use Classes –crops, grasslands, deciduous and evergreen broadleaf and conifer forests –Disturbance: logging, biodiversity and fire Manipulative Studies –Nitrogen and H 2 O additions Measure NEE over Representative Areas –Scaling Flux Information of Footprint to MODIS pixel

6. Successes Mountains of data from a spectrum of canopy roughness conditions, functional types and climate spaces have been collected A Model for Data Sharing –FLUXNET Web Site, a venue for distributing Primary, Value-added and Meta-Data products Value-Added Products have been produced –Development of Gap-Filling Techniques –Production of Gap-Filled Daily and Annual Sums Data for Validating and Improving SVAT models used for weather, climate, biogeochemistry and ecosystem dynamics Collaboration & Synthesis through Workshops and Hosting Visitors –Building a Collaborative, Cooperative, Multi-Disciplinary & International Community of Researchers –Characterizing Annual C Fluxes –Environmental Controls on NEE Training New and Next Generation of Scientists, Postdocs, Students

7. ‘ Failures’/’Un-resolved’ Issues Not Measuring Night-time Fluxes Well Not Measuring Fluxes over Complex terrain and during Advection Well ImPerfect U* correction –New Gu Algorithm ImPerfect Flux Partitioning –Works Better on Longer Time Scales ImPerfect Energy Balance Closure –Could be ‘red-herring’ Need Better Outreach and Training Needs Expansion into other Regions –India –Africa

8. Visions with a Flux Measurement Network Processes –Canopy-Scale Response Functions Emergent Processes –Flux Partitioning, NEP=GPP-R eco Acclimation Time –Daily/Seasonal Dynamics –Pulses, Lags, Switches –Intra- + Interannual Variability –Stand Age/Disturbance Space –Climate/Structure/Function –Coherence/Gradients –Upscaling with Remote Sensing New Directions

9. Probability Statistics of NEE

10. Light and Photosynthesis: Emergent Processes at Leaf and Canopy Scales

11. Volcanoes, Aerosols + NEE

12. CO 2 Flux and Diffuse Radiation Niyogi et al., GRL 2004

13. Photosynthesis-Respiration Processed by Falge

14. Analysis of E. Falge NEE: Acclimation with Temperature

15. Linking Water and Carbon: Potential to assess G c with Remote Sensing Xu + DDB

16. Temporal Dynamics of C Fluxes Hour Day Month Season Year Multiple Years Pulses Lags Switches

17. Complicating Dynamical Factors Switches/Pulses –Rain –Phenology/Length of Season –Frost/Freezing Emergent Processes –Clouds & LUE Acclimation Lags Stand Age/Disturbance

18. Decadal Plus Time Series of NEE: Flux version of the Keeling’s Mauna Loa Graph Data of Wofsy, Munger, Goulden et al.

19.  Re vs  GPP

20. Knohl et al Max Planck, Jena Lag Effects Due to Drought/Heat Stress

21. Data of Pilegaard et al. Soil Temperature: An Objective Indicator of Phenology??

22. Data of: ddb, Wofsy, Pilegaard, Curtis, Black, Fuentes, Valentini, Knohl, Yamamoto. Granier, Schmid Baldocchi et al. Int J. Biomet, in press Soil Temperature: An Objective Measure of Phenology, part 2

23. Spatial Variations in C Fluxes

24. Spatial Gradients: NEE and Length of Growing Season

27. Tower vs Satellite NDVI Falk et al., to be submitted

28. Sims et al 2005 AgForMet

29. Heinsch et al. IEEE 2006

30. Global MODIS Test

31. Limits to Landscape Classification by Functional Type Stand Age/Disturbance Biodiversity Fire Logging Insects/Pathogens Management/Plantations Kyoto Forests

32. Effects of Stand Age: After Logging Law et al. 2003 Global Change Biology

33. Biodiversity and Evaporation Baldocchi, 2004: Data from Black, Schmid, Wofsy, Baldocchi, Fuentes

34. Value of Flux Networks Documenting Change in Ecosystem Metabolism –Network acts as ‘canary in the mine’ Produces Large and Long Data Sets –Reduced Sampling Error –Robust Dataset for Model Development Study Spectra of Time Scales –Capture Pulses and Lags Study Gradient of Climates, Structure and Function Field of Dreams: ‘Build it and they will Come’ –Better Integrated Research Studies