1. Using a Global Flux Network—FLUXNET— to Study the Breathing of the Terrestrial Biosphere Dennis Baldocchi ESPM/Ecosystem Science Div. University of California, Berkeley Collaborators Rodrigo Vargas, Youngryel Ryu, Markus Reichstein, Dario Papale, Andrew Richardson, Deb Agarwal Catharine van Ingen, Bob Cook and Regional Networks ILEAPS 2009 Melbourne, Australia

2. Objectives Network Background Statistical Representativeness –Global Statistics on NEE, GPP, Reco –Network Size –Is the Statistical Distribution of Carbon Fluxes Invariant at the Global Scale? Emergent Processes –Coupling between Photosynthesis and Respiration –PhotoDegradation, a New Process to Consider –Fluxes and Phenology Space –Regional Flux Maps –Phenology Greenwave

3. Network of Regional Flux Networks

4. FLUXNET: From Sea to Shining Sea 500+ Sites, circa 2009

6. Eddy Covariance Technique Direct In situ Quasi-Continuous Extended Footprint

7. FLUXNET DataBase Web sites –www.fluxnet.ornl.gov/fluxnet/index.cfm –www.fluxdata.org QA/QC –Data Standardization (names, units) –Version Control Derived Flux Quantities –Gap-Filling –U* corrections for periods of low turbulence –Daily, Monthly, Annual Integrals Flux Partitioning –Converting NEE into GPP and Reco Compiling Multifaceted Site MetaData Data Distribution –Search/Manipulate/Archive

8. Standard Processing Procedure Vs non-standard, but Expert Based

9. Results and Discussion

10. Over-Arching Questions relating to Statistical Representativeness As the sparse Network has grown, can it provide a Statistically- Representative sample of NEE, GPP and Reco to infer Global Behavior?, e.g. Polls sample only a small fraction of the population to generate political opinion If mean Solar inputs and Climate conditions are invariant, on an annual and global basis,are NEE, GPP and Reco constant, too?; e.g. global GPP scales with solar radiation which is constant Can Processes derived from a Sparse-Network be Upscaled with Remote Sensing and Climate Maps?; e.g. We don’t need to be everywhere all the time; We can use Bayes Theorem and climate records to upscale.

11. Global distribution of Flux Towers Covers Climate Space Well Can we Integrate Fluxes across Climate Space, Rather than Cartesian Space?

12. Apply Bayes Theorem to FLUXNET? Estimate Global flux by Integrating p(Flux|climate) across Globally-gridded Climate space p(flux) from FLUXNET p(climate|flux) prior from FLUXNET p(climate) from climate database

13. Baldocchi, Austral J Botany, 2008 Probability Distribution of Published NEE Measurements, Integrated Annually

14. Updated PDF with 164 extra site-years yields an insignificant change in the mean (P= 0.68)

15. Xiao and Baldocchi, unpublished Upscaling Flux Data with Remote Sensing Data Published, Global: -182 gC m -2 y -1 Map, US: -189 gC m-2 y-1

16. How many Towers are needed to estimate mean NEE, And assess Interannual Variability, at the Global Scale? We Need about 75 towers to produce robust Statistics

17. Little Change in Abiotic Drivers--annual Rg, ppt --across Network

18. Interannual Variability in NEE is small (between -220 to -243 gC m -2 y -1 ) across the Global Network

19. Interannual Variability in GPP is small, too, across the Global Network (1103 to 1162 gC m -2 y -1 )

20. FLUXNET 2007 Database GPP at 2% efficiency and 365 day Growing Season Potential and Real Rates of Gross Carbon Uptake by Vegetation: Most Locations Never Reach Upper Potential tropics GPP at 2% efficiency and 182.5 day Growing Season

21. Baldocchi, Austral J Botany, 2008 Does Net Ecosystem Carbon Exchange Scale with Photosynthesis? Ecosystems with greatest GPP don’t necessarily experience greatest NEE

22. Baldocchi, Austral J Botany, 2008 Ecosystem Respiration Scales Tightly with Ecosystem Photosynthesis, But Is with Offset by Disturbance

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

24. Interannual Variation and Long Term Trends in Net Ecosystem Carbon Exchange (F N ), Photosynthesis (F A ) and Respiration (F R ) Urbanski et al 2007 JGR

25. Baldocchi, Austral J Botany, 2008 Interannual Variations in Photosynthesis and Respiration are Coupled

26. What Happens to the Grass? October June

27. PhotoDegradation is a New and Emerging Process Affecting C Cycle in Semi-arid Grasslands Baldocchi and Ma, unpublished

28. PhotoDegradation: A Process Not in Coupled Carbon-Climate Models Baldocchi, Ma, Rutledge, unpublished

29. More Carbon is Lost with Rain Pulse on Dry Matter that has Been PhotoDegraded than Dry Matter in the Shade Xu, Baldocchi, Tang, 2004 Global Biogeochem Cycles

30. Phenology

31. Ryu et al. in prep Seasonality in GPP follows NDVI and Defines Growing Season

32. Baldocchi, Austral J Botany, 2008 Net Ecosystem Carbon Exchange Scales with Length of Growing Season

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

34. Baldocchi et al. Int J. Biomet, 2005 Soil Temperature: An Objective Measure of Phenology, part 2

35. Baldocchi, unpublished Spatialize Phenology with Transformation Using Climate Map

36. White, Baldocchi and Schwartz, unpublished Flux Based Phenology Patterns with Match well with data from Phenology Network

37. Spatial Variations in C Fluxes with Remote Sensing

38. Sims et al 2005 AgForMet Do Snap-Shot C Fluxes, inferred from Remote Sensing, Relate to Daily C Flux Integrals?

39. Spatial Variations in C Fluxes Xiao et al. 2008, AgForMet spring summer autumn winter

40. Conclusions A sparse Global Network of Flux Towers produces statistically robust findings that are representative of broad climate and ecological zones that may integrated to validate models at grid/landscape/regional scales Any Statistical Scaling Approach based on Climate must consider Disturbance and Stand Age, too. A global Network of Flux Towers is discovering a number of emergent scale processes relating to the controls in inter-annual variability, controls by light, and phenology

41. Acknowledgments NSF, Research Coordinated Networks Microsoft, Regional Networks, ILEAPS World-wide Collection of Collaborators and Colleagues, Technicians, Postdocs and Students

43. Filtered: annual NPP_GPP_fqc > 0.5, minimum 2 years of data per site GPPRecoNEE NMeanLS MeanMeanLS MeanMeanLS Mean 19972512471178982989-265-190 199825126210951048965-214-131 19993612671163987953-280-211 200044129811281041945-257-184 200160126211781022975-241-205 200284127911771066963-214-215 20039811781185971933-206-253 200413111761213934952-244-263 200513311441216899951-245-265 20069612021198922948-280-251 1 S.D.523757162742 A. Richardson & D. Baldocchi, unpublished ANOVA analysis of the annual sums