1. Using Biophysical Models and Eddy Covariance Measurements to Ask (and Answer) Questions About Biosphere-Atmosphere Interactions Dennis Baldocchi Biometeorology Lab ESPM University of California, Berkeley

2. Conservation of Mass, e.g. Solving the Bathtub Problem The ‘Level’ in a Tub depends on the Fluxes IN and OUT of the Tub What is the State of the Atmosphere?

3. Isotopic exchange Courtesy of Jose Fuentes, UVa Trace Gas and Energy Fluxes between Land and the Atmosphere

4. Quantifying Sources and Sinks Biology: – Leaf area density, a(z) – internal conc, C i – stomatal resistance, r s Physics: – Boundary layer resistance, r b – Scalar conc, C(z)

5. Biogeophysical-Ecohydrological View

6. Controlling Processes and Linkages: Roles of Time and Space Scales

7. Sub-Grid Variability: What Errors arise from Averaging?

8. Eddy Covariance Technique Oak Savanna Annual Grassland Peatland/Pasture Temperate Deciduous Forest Boreal Conifer Forest Crops

9. FLUXNET: From Sea to Shining Sea 400+ Sites, circa 2007

10. Physiology Photosynthesis Stomatal Conductance Transpiration Micrometeorology Leaf/Soil Energy Balance Radiative Transfer Lagrangian Turbulent Transfer CANVeg MODEL

11. Key Attributes of CanVeg Seasonality – Leaf Area Index – Photosynthetic Capacity (V cmax ) Model parameters based on Site Measurements and EcoPhysiological Rules and Scaling Functions – Stomatal Conductance scales with Photosynthesis – J max and R d scale with V cmax Multilayer Framework – Computes Fluxes (non-linear functions) on the basis of a leaf’s local environment – Considers Sun and Shade Leaf Fraction Leaf Clumping Leaf Inclination Angle Non-local Turbulent Transport and Counter-Gradient Transfer

12. ESPM 228, Advanced Topics in Micromet and Biomet Models Must Consider Seasonality in Leaf Area Index and Photosynthetic Capacity, V cmax Wilson et al. 2001 Tree Physiol

13. Results and Discussion

14. ESPM 111 Ecosystem Ecology Can Principles from a Global Network Produce Insights about Global-Scale Fluxes? What is the Upper Bound of GPP and its Variability? Bottom-Up: Counting Productivity on leaves, plant by plant, species by species Top-Down: GPP Scales with Energy

15. 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

16. ESPM 111 Ecosystem Ecology Upper-Bound on Global Gross Primary Productivity Global GPP is ~ 120 * 10 15 gC y -1 Solar Constant, S* (1366 W m -2 ) – Ave across disk of Earth S*/4 Transmission of sunlight through the atmosphere (1-0.17=0.83) Conversion of shortwave to visible sunlight (0.5) Conversion of visible light from energy to photon flux density in moles of quanta (4.6/10 6 ) – Mean photosynthetic photon flux density, Q p Fraction of absorbed Q p (1-0.1=0.9) Photosynthetic efficiency, a (0.02) Arable Land area (~ 100 * 10 12 m 2 ) Length of daylight (12 hours * 60 minutes * 60 seconds = 43200 s/day) Length of growing season (180 days) Gram of carbon per mole (12) GPP = 1366*0.83*0.5*4.6*0.9*0.02*100*10 12 *43200*180*12/4=108*10 15 gC y -1

17. Random Sampling Error Reaches Equilibrium with > 60 Sites

18. Interannual Variability in GPP is small, and not significantly different, across the Global Network

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

20. Answering Questions with Models Roles of Structure and Function Leaf Angles and Clumping Leaf Area Index Photosynthetic Capacity Phenology Roles of Microclimate Conditions on Mass and Energy Exchange – Diffuse Light – Humidity – Temperature Sub-Grid Parameterization, Energy Balance Closure and Scaling – Insights from a 2-D, ‘Wet’ DaisyWorld

21. CO 2 Flux Model Test: Hourly to Annual Time Scales

22. Time Scales of Interannual Variability Baldocchi et al., 2001 Ecological Modeling

23. ESPM 111 Ecosystem Ecology Role of Proper Model Abstraction

24. How Long should one Measure Fluxes?: Decadal Power Spectrum of CO 2 and Water Vapor Fluxes

25. Emergent Processes: Impact of Leaf Clumping on Canopy Light Response Curves

26. Interaction between Clumping and Leaf Area

27. ESPM 228 Adv Topics Micromet & Biomet clumpedrandomsphericalerectophileplanophile NEE (gC m -2 a -1 )-577-354-720-1126-224 E (MJ m -2 a -1 ) 16901551177420231473 H (MJ m -2 a -1 )10961032109511711008 How Sensitive are Fluxes to Leaf Inclination Angle Distribution?

28. ESPM 228 Adv Topics Micromet & Biomet V cmax (73)V cmax (50)% difference NEE (gC m-2 a-1)-577-454-21.3 E (MJ m -2 a -1 ) 16901584-6.3 H (MJ m -2 a -1 )109611999.3 Carbon, Water and Sensible Heat Exchange scale with Photosynthetic Capacity

29. ESPM 228 Adv Topics Micromet & Biomet 0.1 m0.01m0.001 m NEE (gC m-2 a -1 )-577-588-586 E (MJ m -2 a -1 ) 169016521615 H (MJ m -2 a -1 )109611641202 Leaf Size has a Modest Effect on Carbon & Water Exchange, But a Large Effect on Sensible Heat Exchange

30. Are VOCs a Large Source of Carbon?

31. Net Ecosystem Carbon Exchange scales with Growing Season Length Baldocchi et al, 2001 Ecological Modeling

32. Baldocchi et al., 2005 Int J Biomet. Soil Temperature: An Objective Indicator of Phenology??

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

34. Baldocchi, White, Schwartz, unpublished Spatialize Phenology with Transformation Using Climate Map

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

36. How do Sky Conditions Affect Net Carbon Exchange (NEE)?: Data Baldocchi, 1997 PCENiyogi et al., GRL 2004

37. More Diffuse than Direct Light is Intercepted

39. Knohl and Baldocchi, 2008 JGR Biogeosci The ‘Diffuse-Light Enhancement’ is a function of LAI

40. Knohl and Baldocchi, 2008 JGR Biogeosci There are Trade-Offs between Reducing Light Amount (with Clouds and Aerosol) and Increasing Light Use Efficiency

41. Canopy Photosynthesis and Aerosols: Impact on Daily & Annual Time Scales, II referencedirect radiation, - 20% % difference NEE (gC m -2 a -1 )-577-65513.5 E (MJ m -2 a -1 ) 169017292.3 H (MJ m -2 a -1 )10961058-3.5

42. Simple Model suggests A/T decreases with increasing  or Ci/Ca

43. Water Use Efficiency: C i /C a, Vapor Pressure Deficit and Diffuse Light Fraction But Complex feedbacks among Ci/Ca, humidity and diffuse light need to be considered! Knohl and Baldocchi, unpublished

44. How Do Changes in vpd and C i /C a conspire to affect A/T?

45. In toto (considering coupled energy balance feedbacks) A/T increases with C i /C a

46. A/T, Stable Isotope Discrimination and Diffuse Light Knohl and Baldocchi, unpublished

47. ESPM 111 Ecosystem Ecology Leaf Size and Extinction Major Extinction at Triassic-Jurassic Boundary during period of Elevated Greenhouse effect – 4 fold increase in CO 2 – 3 to 4 C temperature increase – 99% species turnover of megaflora with leaves > 5 cm – 10% species turnover of flora with leaves < 0.5 cm – Small Leaves are more effective in transferring heat and experiencing lethal surface temperatures McElwain et al Science, 1999

48. Why are Leaves Certain Sizes? Biophysics as an Evolutionary Filter Leaf Temperature and Leaf Morphology

49. Leaf size, CO 2 and Temperature: Why are oak leaves small in CA and large in TN?

50. Leaf Temperature and Isotopes? Helliker and Richter 2008 Nature

51. Sub-Grid Variability: Lessons Derived from Wet DaisyWorld Latent Heat Exchange Map

52. Baldocchi et al, 2005 Tellus Spatial Variation in subGrid T and LE follows Power Law Scaling

53. Sub-Grid Scaling Errors in ET

54. Conclusions Biophysical Model aids in understanding the impact of diffuse light on photosynthesis, isoprene emission, water use efficiency and stable isotope discrimination A cellular automata, energy balance model shows that spatial averaging of energy balance drivers can produce huge errors in grid-scale energy fluxes and can explain lack of energy balance closure

55. Acknowledgements Funding – NASA, DOE/TCP, NIGEC/WESTGEC, NSF, Microsoft Eddy Covariance Measurements – Kell Wilson, Bev Law, Alexander Knohl FLUXNET – Eva Falge, Lianhong Gu, Deb Agarwal et al Canopy Modeling and Diffuse Light – Alexander Knohl, Lianhong Gu, Kell Wilson Isoprene – Peter Harley, Jose Fuentes, Dave Bowling, Russ Monson & Alex Guenther 13 C Isotopes – Alexander Knohl, Dave Bowling, Russ Monson