FLUXNET after 10 Years: Synthesizing CO 2 and Water Vapor Fluxes From Across a Global Network

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 Sea379 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. Soil Temperature: An Objective Indicator of Phenology?? Data of Pilegaard et al.

24. 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

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 • 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. 2003 Global Change Biology

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