AmeriFlux , Yesterday , Today and Tomorrow

1. AmeriFlux, Yesterday, Today and Tomorrow Dennis Baldocchi, UC Berkeley Margaret Torn and Deb Agarwal, Lawrence Berkeley National Lab Bev Law, Oregon State University Tom Boden, Oak Ridge National Laboratory

2. AmeriFlux, circa 2012

3. Growth in the Network Data from Bai Yang and Tom Boden

4. Age of Flux Sites, and the Length of their Data Archive

5. Pros and Cons of a Sparse Flux Network • Pros • Covers Most Climate and Ecological Spaces • Long-Term Operation Experiences Extreme Events, Gradual Climate Change, and Disturbance • Gradients of Sites across Landscapes and Regions Span Range of Environmental and Ecological Forcing Variables • Clusters of Sites examine effects of Land Use Change, Management, and Disturbance (fire, drought, insects, logging, thinning, fertilizer, flooding, woody encroachment) • Robust Statistics due to Over-Sampling • Cons • Can’t Cover All Physical and Ecological Spaces or Complex Terrain • Current Record is too Short to Detect Climate or CO2-Induced Trends • Flux Depends on Vegetation in the Footprint • Bias Errors at Night, Under Low Winds

6. The Type of Network Affects the Type of Science • Sparse Network of Intense Super-Sites and Clusters of Sites, Producing Mechanistic Information can Test, Validate and Parameterize Process and Mechanistic Models • Denser and More Extensive Network of Less-Expensive Sites can Assist in Statistical and Spatial Up-Scaling of Fluxes with Remote Sensing

7. Climate Space of AmeriFlux Sites Yang et al 2008, JGR Biogeosciences

8. AmeriFlux Sites, Circa 2003, and Ecosystem/Climate Representativeness Hargrove, Hoffman and Law, 2003 Eos

9. Representativeness of AmeriFlux, Circa 2008 (blue is good!) Yang et al. 2008 JGR Biogeosciences

10. Basis of a Successful Flux Network It Takes People (Scientists, Postdocs, Students and Technicians) Social Network that Facilitates Meetings, Workshops, Shared Leadership and a Shared/Central Data Base This Fosters Getting to Know Each Other, Collaboration, Communication, Common Vision, Shared Goals, And Joint Authorship of Synthesis Papers

11. Past and Current Leadership Dave Hollinger, Chair 1997-2001 Bev Law, Chair 2001-2011 Margaret Torn AmeriFlux PI, 2012- Tom Boden AmeriFlux Data Archive

12. Published Use of AmeriFlux Data 184 Papers linked to key word ‘AmeriFlux’ These Papers have been cited over 7000 Times 246 Papers linked to key word ‘Fluxnet’

13. Issues of standardization, or not?

14. ‘Know Thy Site’ Ray Leuning Most Flux Instruments are Very Good; Pick the Instrument System that is Most Appropriate to Your Weather and Climate

15. Open-Path CO2 Fluxes were 1.7% Higher than Closed Path Fluxes Schmidt et al. 2012, JGR Biogeosciences

16. Site Calibration with Roving Standard Schmidt et al 2012 JGR Biogeosciences

17. Extrinsic Contributions • Data Contribute to Producing Better Models via Validation, Parameterization, Data-Assimilation & Defining Functional Responses • Land-Vegetation-Atmosphere-Climate • Energy Partitioning, Albedo, Energy Forcing, Land Use • Remote Sensing, Light Use Efficiency Models • Regional and Global GPP models • Ecosystem and Biogeochemical Cycling • Carbon Cycle, Disturbance, Phenology, Environmental Change, Plant Functional Types • Hydrology • Evaporation , Soil Moisture, Ground-Water, Drought

18. Lessons Learned

19. What’s in the Data? • Magnitudes and Trends in Annual C and H2O Fluxes, by Plant Functional Type and Climate Space • Light-Use, Temperature, Rain Response Functions • Emergent-Scale Properties • Diffuse Light • Rain Pulses • Drought and Ground Water Access • Disturbance • Insect Defoliation • Fire, Logging and Thinning • Drought and Mortality • BioPhysicalForcings • Albedo and Temperature • Energy Partitioning with Land Use

20. C Fluxes are a Function of Time Since Disturbances, as well as Weather, Structure and Function Urbanski et al. 2007 JGR Biogeosciences

21. Light Response Curves of CO2 Flux are Quasi-Linear, Deviating from Monteith’s Classic Paper and Impacting the Interpretation of C Flux with Remote Sensing Gilmanov et al 2010 Range Ecology & Management

22. Light Use Efficiency INCREASES with the Fraction of Diffuse Light Niyogi et al 2004 GRL

23. Response Functions from Elevation/Climate Gradients Anderson-Teixeira et al. 2010 GCB

24. Respiration is a function of Temperature, Soil Moisture, Growth, Rain Pulses And Temperature Acclimation Xu et al. 2004 Global Biogeochemical Cycles

25. Rain-Induced Pulses in Respiration: Long –Term Studies Capture More Pulses, Better Statistics Ma et al. 2012 AgForMet

26. Disturbance, Fire and Thinning Dore et al. 2012 GCB

27. Insect Defoliation, 2007 Clark et al. 2010 GCB

28. Disturbance DynamicsC Flux = f(time since disturbance) Amiro et al. 2010 JGR Biogeosci

29. Flux Phenology Gonsamo et al 2012 JGR Biogeosci

30. Satellite vs Flux Phenology Gonsamo et al 2012 JGR Biogeosci

31. It’s Not only CO2!Effects of Precipitation and Energy on Evaporation MI Budyko Williams et al. 2012 WRR

32. Long-Term Studies can Assess Links between Drought and Fluxes Schwalm et al 2012 Nature Geoscience

33. Net Negative Effects on Carbon and Water Fluxes are Strong: What about 2012? Schwalm et al 2012 Nature Geoscience

34. Land Use and Climate Forests are warmer than nearby Grasslands Lee et al Nature 2011

35. Light Use Efficiency Models:Upscale Fluxes from Towers to Regions Yuan et al. 2007, AgForMet Heinsch et al 2006 IEEE

36. C and Water fluxes Derived from Satellite-Snap Shots Scale with Daily Integrated Fluxes from Eddy Covariance Sims et al 2005 AgForMet Ryu et al. 2011 AgForMet

37. Seasonal Maps of NEE, via Regression Tree Analysis, on AmeriFlux and Modis Data Xiao et al. 2008 AgForMet

38. What is the Truth?; How Good is Good-Enough? Chen et al 2011 Biogeosciences

39. Regional Estimates of Fire, Drought, Hurricanes on NEE Xiao et al. 2011 AgForMet

40. Using Flux Data to Validate Dynamic Vegetation Models-ORCHIDEE Krinner et al 2005 GBC

41. Data-Model Fusion/Assimilation Sacks et al. 2006 GCB

42. Model Hierarchy Testing: How Much Detail is Needed? Bonan et al 2012 JGR Biogeosci

43. Testing Phenology Predictions in Ecosystem-Dynamic Models The total bias in modeled annual GEP was +35 ± 365 g C m-2 yr-1 for deciduous forests +70 ± 335 g C m-2 yr-1 for evergreen forests across all sites, models, and years; Richardson et al, 2012 GCB

44. It‘s Not Just About CO2:Significant change in albedo with 3 disturbance types Hurricane Beetles Fire Albedo change produces radiative forcing of same magnitude as CO2 forcing in case studies of forest mortality from hurricane defoliation, pine beetles, and fire. Beetle effect occurs mostly after snags fall O’Halloran et al 2012 GCB

45. Albedo Scales with Nitrogen We can Use Albedo to Parameterize N and Ps Capacity in Models! Hollinger et al 2009 Global Change Biology

46. The Albedo-N Correlation may be Spurious Knyazikhin et al 2012 PNAS report that the previously reported correlation is an artifact—it is a consequence of variations in canopy structure, rather than of %N. • an increase in the amount of absorbing foliar constituents enhances absorption and correspondingly decreases canopy reflectance • When the BRF data are corrected for canopy-structure effects, the residual reflectance • variations are negatively related to %N at all wavelengths in the interval 423–855 nm. To inferleafbiochemicalconstituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710–790 nm provide critical information for correction of structural influences

47. Validating and Improving Climate Drivers, like Net Radiation Fields Jin et al 2011 RSE

48. Radiation and Evaporation Maps Jin et al 2011 RSE

49. Testing Ecohydrology Theories for Soil Moisture Miller et al 2007 Adv Water Res

50. Current and Future Collaborations • COSMOS and Soil Moisture Fields • Validation of Satellite based estimates of CO2, LIDAR, Albedo, and Soil Moisture (SMOS, SMAP, AIRMOSS) • Priors for CO2-Satellite Inversions (GOSAT, OCO) • Data-Model Assimilation • Phenology and Pheno-Camera Networks • FLUXNET and NEON