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 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 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 What a Tower Sees Schulze, 2006 Biogeosciences What the Atmosphere Sees Eddy Covariance Reality 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 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 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 Time Series Analysis: Raw Data Time Series: FingerPrint Time Series: Diurnal Pattern Time Series: Mean Diurnal Pattern Night time Biased Respiration CO 2 Storage Flux Deciduous Broadleaved Forests Fourier Transforms Time Series: Spectral Analysis Baldocchi et al., 2001 AgForMet Stoy et al Tree Physiol Time Series: Interannual Variability Data of Wofsy, Munger, Goulden, Harvard Univ Knohl et al Max Planck, Jena Intern-annual Lag Effects Due to Drought/Heat Stress Processes Canopy Photosynthesis Light Temperature Soil Moisture Functional Type Ecosystem Respiration Temperature Soil Moisture Photosynthesis From E. Falge Concepts: NEE and Environmental Drivers 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 Light and Photosynthesis: Leaves, Canopies and Emerging Processes CO 2 uptake-Light Response Curve: Crops Linear Function and High r 2 (~0.90) Function is Non-Linear and Low r 2 (~0.50) CO 2 uptake-Light Response Curve: Forest CO 2 flux vs Sunlight at different LAI Xu and Baldocchi, 2003, AgForMet Use Theory to Interpret Complex Field Data Patterns Leuning et al. 1995, PCE A c vs Q p : Daily Sums Become Linear!? Role of Averaging Period: Hourly vs Daily Sims et al. AgForMet, 2005 Sims et al 2005, AgForMet Role of Averaging Period: Snap Shot vs Daily Integral Canopy Light Response Curves: Effect of Diffuse Light CO 2 Flux and Diffuse Radiation Niyogi et al., GRL 2004 C Fluxes and Remote Sensing: NPP and NDVI of a Grassland Xu, Gilmanov, Baldocchi Rahman et al 2005 GRL Linking Water and Carbon: Potential to assess G c with Remote Sensing Xu + DDB Land Surface Water Index (LSWI) plotted with daily NEE for 2004/2005 PRI and NEE Land Surface Water Index LSWI = (860 - 1640)/(860 + 1640) PRI = ( 570 ) / ( 570 ) Falk, Baldocchi, Ma Partitioning Carbon Fluxes Law and Ryan, 2005, Biogeochemistry Kuzyakov, 2006 De-Convolving Soil Respiration From E. Falge Deconstructing NEP: Flux Partitioning into R eco and GPP Xu and Baldocchi Falge et al Ecosystem Respiration Xu + Baldocchi, AgForMet 2003 Is Q 10 Conservative? Environmental Controls on Respiration Xu + Baldocchi, AgForMet 2003 Rains Pulse do not have Equal Impacts Xu, Baldocchi Agri For Meteorol, 2004 Rain Pulses: Heterotrophic Respiration Respiration time Constant & ppt Xu + DDB Tonzi Open areas Tang, Baldocchi, Xu, Global Change Biology, 2005 Respiration and Photosynthesis Lags and Leads in Ps and Resp: Diurnal Tang et al, Global Change Biology 2005. Cross-Site Analyses What is Wrong with this Picture? Valentini et al., 2000, Nature Longitudinal Gradients across Continents in T and ppt Break the Relationship Falge et al., 2002 Law et al 2002 AgForMet Temperature Acclimation Falge et al; Baldocchi et al. Respiration: Temperature and acclimation Analyst: Enquist et al. 2003, Nature Atkin Spatial Gradients: NEE and Length of Growing Season Re vs GPP Data of Pilegaard et al. Soil Temperature: An Objective Indicator of Phenology?? 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 Disturbance and Carbon Fluxes Amiro et al., 2006 Coursolle et al. 2006