1. Applications of eddy covariance measurements, Part 1: Lecture on Analyzing and Interpreting CO 2 Flux Measurements Dennis Baldocchi ESPM/Ecosystem Science Div. University of California, Berkeley CarboEurope Summer Course, 2006 Namur, Belgium

2. Outline Philosophy/Background Processing Time Series Analysis –Diurnal –Seasonal –Interannual Flux Partitioning –Canopy photosynthesis –Ecosystem Respiration Processes –Photosynthesis f(T,PAR, LAI, soil moisture) –Respiration f(photosynthesis, soil C &N, T, soil moisture, growth) –Functional Type –Disturbance Space –Cross-Site Analyzes

3. Philosophy/Background Philosophy –What, How, Why, Will be? BioPhysical Processes –Meteorology/Microclimate Light, temperature, wind, humidity, pressure –Vegetation Structure (height, leaf area index, leaf size) Physiology (photosynthetic capacity, stomatal conductance) –Soil Roots Microbes Abiotic conditions (soil moisture, temperature, chemistry, texture) Spatial-Temporal Variability –Spatial Vertical (canopy) and Horizontal (footprint, landscape, functional type, disturbance) –Temporal Dynamics Diurnal Seasonal Inter-annual

4. What a Tower Sees

5. Schulze, 2006 Biogeosciences What the Atmosphere Sees

6. Eddy Covariance

7. Reality

8. Real-time Sampling Sample instruments at 10 to 20 Hz, depending on height of sensors and wind speed. f sample = 2 times f cutoff (f=nz/U) Store real-time data on hard disk Process and Compute Means, Variances and Covariances, Skewness and Kurtosis. Compute 30 or 60 minute averages of statistical quantities. Document data and procedures. Diagnose instrument and system performance Look for Spikes and Off-Scale Signals

9. Post Processing, hourly data Compute Means, Covariances, Variances, Skewness and Kurtosis using Reynolds averaging Merge turbulence and meteorological data Apply calibration coefficients and gas law corrections to compute unit-correct flux densities and statistics Apply transfer functions and frequency corrections Compute Storage and Advective fluxes Compute power spectra and co-spectra; examine instrument response and interference effects From the Field to your Dissertation

10. Post Processing, daily data Apply QA/QC and eliminate bad data Fill gaps using gap filling methods Correct nighttime data using such corrections as with well-mixed friction velocity, or check against independent measurements, such as soil respiration chambers Compute daily integrals Think and Read

11. Time Series Analysis: Raw Data

12. Time Series: FingerPrint

13. Time Series: Diurnal Pattern

14. Time Series: Mean Diurnal Pattern

15. Night time Biased Respiration

16. CO 2 Storage ‘Flux’

18. Deciduous Broadleaved Forests

24. Fourier Transforms

25. Time Series: Spectral Analysis Baldocchi et al., 2001 AgForMet

26. Stoy et al. 2005 Tree Physiol

27. Time Series: Interannual Variability Data of Wofsy, Munger, Goulden, Harvard Univ

28. Knohl et al Max Planck, Jena Intern-annual Lag Effects Due to Drought/Heat Stress

29. Processes Canopy Photosynthesis –Light –Temperature –Soil Moisture –Functional Type Ecosystem Respiration –Temperature –Soil Moisture –Photosynthesis

30. From E. Falge Concepts: NEE and Environmental Drivers

31. Pulses, Switches and Lags are Important too! They are Features of Complex Dynamical Systems Biosphere is a Complex Dynamical System –Constituent Processes are Non-linear and Experience Non- Gaussian Forcing –Possess Scale-Emergent Properties –Experiences Variability Across a Spectrum of Time and Space Scales –Solutions are sensitive to initial conditions –Solutions are path dependent –Chaos or Self-Organization can Arise

32. Light and Photosynthesis: Leaves, Canopies and Emerging Processes

33. CO 2 uptake-Light Response Curve: Crops Linear Function and High r 2 (~0.90)

34. Function is Non-Linear and Low r 2 (~0.50) CO 2 uptake-Light Response Curve: Forest

35. CO 2 flux vs Sunlight at different LAI Xu and Baldocchi, 2003, AgForMet

36. Use Theory to Interpret Complex Field Data Patterns

39. Leuning et al. 1995, PCE A c vs Q p : Daily Sums Become Linear!?

40. Role of Averaging Period: Hourly vs Daily Sims et al. AgForMet, 2005

41. Sims et al 2005, AgForMet Role of Averaging Period: Snap Shot vs Daily Integral

42. Canopy Light Response Curves: Effect of Diffuse Light

43. CO 2 Flux and Diffuse Radiation Niyogi et al., GRL 2004

44. C Fluxes and Remote Sensing: NPP and NDVI of a Grassland Xu, Gilmanov, Baldocchi

45. Rahman et al 2005 GRL

46. Linking Water and Carbon: Potential to assess G c with Remote Sensing Xu + DDB

47. Land Surface Water Index (LSWI) plotted with daily NEE for 2004/2005 PRI and NEE Land Surface Water Index LSWI = (ρ860 - ρ1640)/(ρ860 + ρ1640) PRI = (  531 -  570 ) / (  531 +  570 ) Falk, Baldocchi, Ma

48. Partitioning Carbon Fluxes

49. Law and Ryan, 2005, Biogeochemistry

50. Kuzyakov, 2006 De-Convolving Soil Respiration

51. From E. Falge

52. Deconstructing NEP: Flux Partitioning into R eco and GPP Xu and Baldocchi Falge et al

53. Ecosystem Respiration Xu + Baldocchi, AgForMet 2003 Is Q 10 Conservative?

54. Environmental Controls on Respiration Xu + Baldocchi, AgForMet 2003

55. Rains Pulse do not have Equal Impacts Xu, Baldocchi Agri For Meteorol, 2004

56. Rain Pulses: Heterotrophic Respiration

57. Respiration time Constant & ppt Xu + DDB

58. Tonzi Open areas Tang, Baldocchi, Xu, Global Change Biology, 2005 Respiration and Photosynthesis

59. Lags and Leads in Ps and Resp: Diurnal Tang et al, Global Change Biology 2005.

60. Cross-Site Analyses

61. What is Wrong with this Picture? Valentini et al., 2000, Nature

62. Longitudinal Gradients across Continents in T and ppt Break the Relationship

64. Falge et al., 2002

65. Law et al 2002 AgForMet

66. Temperature Acclimation Falge et al; Baldocchi et al.

67. Respiration: Temperature and acclimation Analyst: Enquist et al. 2003, Nature

68. Atkin

69. Spatial Gradients: NEE and Length of Growing Season

70.  Re vs  GPP

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

72. Data of: ddb, Wofsy, Pilegaard, Curtis, Black, Fuentes, Valentini, Knohl, Yamamoto. Granier, Schmid Baldocchi et al. Int J. Biomet, in press Soil Temperature: An Objective Measure of Phenology, part 2

73. Disturbance and Carbon Fluxes Amiro et al., 2006

74. Coursolle et al. 2006