1. FLUXNET after 10 Years: Synthesizing CO2 and Water Vapor Fluxes From Across a Global Network
Dennis Baldocchi
ESPM/Ecosystem Science Div.
University of California, Berkeley
ILEAPS, Boulder, Jan 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 & Reco); assessment of metadata,e.g. Vcmax, 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 H2O 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’ based on recent several talks at a SSSA workshop
Need Better Outreach and Training

8. Visions with a Flux Measurement Network
Processes
Canopy-Scale Response Functions
Emergent Processes
Flux Partitioning, NEP=GPP-Reco
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. CO2 Flux and Diffuse Radiation
Niyogi et al., GRL 2004

13. Photosynthesis-Respiration
Processed by Falge

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

15. Respiration: Temperature and acclimation
Analyst: Enquist et al. 2003, Nature

16. Linking Water and Carbon: Potential to assess Gc with Remote Sensing
Xu + DDB

17. An Example of Scale Invariance
Processed by M. Falk

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

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

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

21. DRe vs DGPP

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

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

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

25. Spatial Gradients: NEE and Length of Growing Season
Coherent response among sites, impact of length of growing season. Does not account for interannual variability at a site, due to snow cover, drought, cloudy vs clear summers etc.

26. Spatial Variations in C Fluxes

27. Sims et al 2005 AgForMet

28. Global MODIS Test
Heinsch et al. in press

29. Testbed for Ecohydrological Theory
Miller et al, Adv. Water Research, submitted

30. Value of Flux Networks 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

31. Future Directions Administrative Scientific
ReOrganize FLUXNET with Multiple/International Funding Sources
Scientific
NEE in Urban and Suburban, Africa, India, Latin America and High Arctic Environments
Coupling CO2, Trace Gas Deposition/Emission (O3, voc) and Methane Fluxes
Adopting New Technology (TDL, wireless networks) to embellish flux measurements
Couple tower data with Real-time Data Assimilation Models.
Boundary Layer Budgets using Fluxes and High Precision CO2 measurements
Spectral reflectance measurements across the network
Spatial-Temporal Network-Scale Analysis
Real-time Data Assimilation
Matching Footprints of Tower and Pixels
Model Lags, Switches and Pulses
Using Fluxnet data to assess problems in
Ecology, Ecohydrology, Biogeochemistry, Biogeography, Remote Sensing, Global Modeling, Biodiversity

33. Validating MODIS
Falk, Ma, Baldocchi, unpublished

34. Heinsch et al. submitted

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

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

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

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