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The Big Picture

The Big Picture. To assess the Global Carbon Budget we need information that is ‘Everywhere, All of the Time’ Many Complementary Methods exist, Each with specific, scale-dependent Pros and Cons, so the Overall Premise is Impossible for any stand alone method

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The Big Picture

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  1. The Big Picture • To assess the Global Carbon Budget we need information that is ‘Everywhere, All of the Time’ • Many Complementary Methods exist, Each with specific, scale-dependent Pros and Cons, so the Overall Premise is Impossible for any stand alone method • Multi-Faceted, Integrative C Flux program is Needed • Statistics can be our ‘Friend’ and May Relieve us from Being ‘Everywhere’ • To Address Phenology, Seasonaly Dynamics, Intermittency and Trends we still nee to measure ‘All the Time’. Dennis Baldocchi, University of California, Berkeley

  2. Methods To Assess Terrestrial Carbon Budgets at Landscape to Continental Scales, and Across Multiple Time Scales GCM Inversion Modeling Eddy Flux Measurements/ FLUXNET Remote Sensing/ MODIS Forest/Biomass Inventories Physiological Measurements/ Manipulation Expts. Biogeochemical/ Ecosystem Dynamics Modeling

  3. From point to globe via integration with remote sensing (and gridded metorology) forestinventoryplot century Forest/soil inventories decade Landsurface remote sensing Eddycovariancetowers talltowerobser- vatories remote sensingof CO2 year Temporal scale month week day hour local 0.1 1 10 100 1000 10 000 global Countries plot/site EU Spatial scale [km] From: Markus Reichstein, MPI

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

  5. How many Towers are needed to estimate mean NEE, GPP and assess Interannual Variability, at the Global Scale? Green Plants Abhor a Vacuum, Most Use C3 Photosynthesis, so we May Not need to be Everywhere, All of the Time We Need about 75 towers to produce Robust and Invariant Statistics

  6. Statistical Samping of Interannual variability of C fluxes with a Network Baldocchi et al., in prep

  7. Flux-Derived Global GPP: 123 +/- 8 PgC/y Beer et al, Science, 2010

  8. Upscale NEP, Globally, Explicitly Compute GPP = f(T, ppt) Compute Reco = f(GPP, Disturbance) Compute NEP = GPP-Reco FLUXNET Synthesis Baldocchi, 2008, Aust J Botany Reco = 101 + 0.7468 * GPP Reco, disturbed= 434.99 + 0.922 * GPP

  9. <NEE> = -129 gC m-2 y-1 SNEE = -17.5 PgC/y!! Implies too Large NEE (|-700 gC m-2 y-1| Fluxes in Tropics Ignores C losses from Disturbance and Land Use Change

  10. Carbon Balance if Randomly Disturb C pools

  11. Remote Sensing of NPP: Up and down PAR, LED, Pyranometer, 4 band Net Radiometer Phenology, fpar, LAI, APAR, N, Vcmax

  12. Upward Looking Camera Hemispherical Camera Web Camera

  13. Phenology with Upward-Facing Digital Camera Ryu et al, in prep

  14. Falk, Ma and Baldocchi, unpublished ESPM 111 Ecosystem Ecology

  15. Ground Based, Time Series of Hyper-Spectral Reflectance Measurements, in Conjunction with Flux Measurements Can be Used to Design Future Satellites Ryu et al. Agricultural and Forest Meteorology, in review

  16. Spectrally-Selective Vegetation Indices Track Seasonality of C Fluxes Well Ryu et al. Agricultural and Forest Meteorology, in review

  17. Vegetation Indices can be Used to Predict GPP with Light Use Efficiency Models Ryu et al. Agricultural and Forest Meteorology, in review

  18. UpScaling of FluxNetworks

  19. What We can Do: Is Precision Good Enough for Treaties? Xiao et al 2010, Global Change Biology

  20. Map of Gross Primary Productivity Derived from Regression Tree Algorithms Derived from Flux Network, Satellite Remote Sensing and Climate Data Xiao et al 2010, Global Change Biology Youngryel Ryu and D. Baldocchi, unpublished

  21. Xiao et al 2010, Global Change Biology

  22. Net Ecosystem C Exchange spring summer autumn winter Xiao et al. 2008, AgForMet

  23. Upscale GPP and NEE to the Biome Scale area-averaged fluxes of NEE and GPP were -150 and 932 gC m-2 y-1 net and gross carbon fluxes equal -8.6 and 53.8 TgC y-1 Jingfeng Xiao and D Baldocchi

  24. Coupled Energy Balance-Photosynthesis Sun/Shade Model driven by MODIS, Implemented with Cloud-Computing System Youngryel Ryu and D. Baldocchi, unpublished

  25. Coupled Energy Balance-Photosynthesis Sun/Shade Model driven by MODIS Youngryel Ryu and D. Baldocchi, unpublished

  26. Conclusion • Much Promise in Coupling Remote Sensing and Flux Network Information to produce State, Continental and Global Maps of Net and Gross Carbon Fluxes • How Good is Good Enough? • Errors are still on the Order of +/- 8 PgC/y so we may not have the resolution to detect inter-annual variability and may need Decades to Detect Trends in this Noisy System • Perspective, Drawdown during Glacial-Inter-Glacial was 20 TgC/y of the Land-Ocean System

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