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Fluxnet 2009 Progress

Fluxnet 2009 Progress. Dennis Baldocchi, Rodrigo Vargas, Youngryel Ryu, Markus Reichstein, Dario Papale, Deb Agarwal, Catharine Van Ingen. AmeriFlux 2009. FLUXNET: From Sea to Shining Sea 500+ Sites, circa 2009. Global distribution of Flux Towers Covers Climate Space Well.

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Fluxnet 2009 Progress

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  1. Fluxnet 2009 Progress Dennis Baldocchi, Rodrigo Vargas, Youngryel Ryu, Markus Reichstein, Dario Papale, Deb Agarwal, Catharine Van Ingen AmeriFlux 2009

  2. FLUXNET: From Sea to Shining Sea500+ Sites, circa 2009

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

  4. FLUXNET Community Outreach • NewsLetter, FluxLetter • Asilomar Workshop • Distributed Searchable Database, www.fluxdata.org • Fluxnet Visitors • Paul Stoy, Sebastiaan Luysaaert, Josep Penuelas, Bart Kruijt

  5. Fluxnet Modeling and Data Workshop Asilomar Conference • Questions/Topics: What is the FLUXNET Measurement Community providing to the Modeling Community? • What information and data products do modelers need from the FLUXNET measurement community? • How can sensitivity runs from land surface models help us interpret flux data across climate gradients and plant functional types? • Future composition of FLUXNET

  6. Data Archive, Synthesis, Searchable and Manipulative Database www.fluxdata.org

  7. Progress on the ‘LaThuile’ Synthesis Papers

  8. Water Use Efficiency, Coupling Water and Carbon Fluxes Beer et al. 2009. Global Biogeochemical Cycles

  9. Scales of Flux Variance Paul Stoy et al, Biogeosciences, Submitted

  10. Role of Mycorrhyzae and C Fluxes Vargas et al. New Phytologist, in press

  11. Results and Discussion

  12. Emerging Ideas, Science Beyond Routine Flux Measurements • Continental/Global Upscaling in Time/Space • Flux Spectra across scales of Hours to Decade • PhotoDegradation • Site MetaData Syntheses • Leaf clumping, albedo • Model Data Assimilation

  13. Towards Continental and Global Representativeness The Network is not like Acupuncture (credit M Reichstein). Fluxes from Towers represent far beyond their geographical domain. But we are not Everywhere, All the Time, so We must rely on partnerships with Remote Sensing and Meteorological Data to Upscale

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

  15. Using Flux Data to produce Global ET maps, V1 ET (mm H2O y-1) Fig.9 Global Evapotranspiration (ET) driven by interpolated MERRA meteorological data and 0.5º×0.6º MODIS data averaged from 2000 to 2003. Wenping Yuan

  16. Using Flux data to produce Global ET maps, v2 Martin Jung

  17. 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

  18. How Big Does the Network Need to Be?

  19. 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 • 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. • If mean Solar inputs and Climate conditions are invariant, on an annual and a global-basis,are NEE, GPP and Reco constant, too?; e.g. global GPP scales with solar radiation which is constant

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

  21. Probability Distribution of Published NEE Measurements, Integrated Annually

  22. Probability Distribution of Published GPP Measurements, Integrated Annually Global GPP = 1033 * 110 1012 m2 = 113.6 PgC/y

  23. Joint pdf GPP, Solar Radiation and Temperature E[GPP]= 1237 gC m-2 y-1~136 PgC/y

  24. What Happens to the Grass? June October

  25. Vaira Ranch, 2007

  26. PhotoDegradation Baldocchi, Ma, Rutledge

  27. Remote Sensing of Canopy Structure and GPP

  28. Remote Sensing and Ecosystem Metabolism

  29. VI vs GPP when including all data. LED spectral region (white box) looks showing good correlation, but the high correlation region is large. White rectangle box indicates LED spectral region

  30. Incorporating Soil Evaporation Scheme in CABLE Improves Model Performance Williams et al. 2009. Biogeosciences

  31. Vargas et al New Phytologist, in press

  32. Priestley-Taylor and Surface Conductance Chris Williams

  33. Testing Budyko Chris Williams: EcoHydrology

  34. And, WUE scales with LAI and Soil Moisture Beer et al. 2009. Global Biogeochemical Cycles

  35. Apparent clumping index can constrain true clumping index Ryu, Nilson, Kobayashi, Sonnentag, Baldocchi (to be submitted)

  36. Albedo and Nutrition Hollinger et al 2009 Global Change Biology

  37. Heat absorbed Heat reflected Albedo and Climate Forcing Savannas Integrated annual error, or departure in the shortwave energy budget, for each site as derived from the calculated biome mean albedo. Grasslands Evergreen Deciduous Croplands Tom O’Hallaran

  38. Optimizing Seasonality of Vcmax improves Prediction of Fluxes Wang et al, 2007 GCB

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