FLUXNET: Measuring CO 2 and Water Vapor Fluxes Across a Global Network Dennis Baldocchi ESPM/Ecosystem Science Div. University of California, Berkeley IndoFlux, Chennai, India, July 2006

FLUXNET: From Sea to Shining Sea 379 Sites, circa 2006

Global distribution of Flux Towers with Respect to Climate

Evolution of FLUXNET Measure Annual Cycle of NEE –Micromet issues of Detrending, Transfer Functions, Flux Sampling and Measurements, Gap-filling, Error Assessment Measure and Interpret Intra-annual Variation of NEE –Flux partitioning (GPP & R eco ); assessment of metadata,e.g. V cmax, soil respiration, LAI, biomass inventories. Measure and Interpret Inter-annual variations of NEE Measure NEE over multiple Land-Use Classes –crops, grasslands, deciduous and evergreen broadleaf and conifer forests –Disturbance: logging, biodiversity and fire Manipulative Studies –Nitrogen and H 2 O additions Measure NEE over Representative Areas –Scaling Flux Information of Footprint to MODIS pixel

Successes Mountains of data from a spectrum of canopy roughness conditions, functional types and climate spaces have been collected A Model for Data Sharing –FLUXNET Web Site, a venue for distributing Primary, Value-added and Meta-Data products Value-Added Products have been produced –Development of Gap-Filling Techniques –Production of Gap-Filled Daily and Annual Sums Data for Validating and Improving SVAT models used for weather, climate, biogeochemistry and ecosystem dynamics Collaboration & Synthesis through Workshops and Hosting Visitors –Building a Collaborative, Cooperative, Multi-Disciplinary & International Community of Researchers –Characterizing Annual C Fluxes –Environmental Controls on NEE Training New and Next Generation of Scientists, Postdocs, Students

‘ Failures’/’Un-resolved’ Issues Not Measuring Night-time Fluxes Well Not Measuring Fluxes over Complex terrain and during Advection Well ImPerfect U* correction –New Gu Algorithm ImPerfect Flux Partitioning –Works Better on Longer Time Scales ImPerfect Energy Balance Closure –Could be ‘red-herring’ Need Better Outreach and Training Needs Expansion into other Regions –India –Africa

Visions with a Flux Measurement Network Processes –Canopy-Scale Response Functions Emergent Processes –Flux Partitioning, NEP=GPP-R eco Acclimation Time –Daily/Seasonal Dynamics –Pulses, Lags, Switches –Intra- + Interannual Variability –Stand Age/Disturbance Space –Climate/Structure/Function –Coherence/Gradients –Upscaling with Remote Sensing New Directions

Probability Statistics of NEE

Light and Photosynthesis: Emergent Processes at Leaf and Canopy Scales

Volcanoes, Aerosols + NEE

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

Photosynthesis-Respiration Processed by Falge

Analysis of E. Falge NEE: Acclimation with Temperature

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

Temporal Dynamics of C Fluxes Hour Day Month Season Year Multiple Years Pulses Lags Switches

Complicating Dynamical Factors Switches/Pulses –Rain –Phenology/Length of Season –Frost/Freezing Emergent Processes –Clouds & LUE Acclimation Lags Stand Age/Disturbance

Decadal Plus Time Series of NEE: Flux version of the Keeling’s Mauna Loa Graph Data of Wofsy, Munger, Goulden et al.

 Re vs  GPP

Knohl et al Max Planck, Jena Lag Effects Due to Drought/Heat Stress

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

Spatial Variations in C Fluxes

Spatial Gradients: NEE and Length of Growing Season

Tower vs Satellite NDVI Falk et al., to be submitted

Sims et al 2005 AgForMet

Heinsch et al. IEEE 2006

Global MODIS Test

Limits to Landscape Classification by Functional Type Stand Age/Disturbance Biodiversity Fire Logging Insects/Pathogens Management/Plantations Kyoto Forests

Effects of Stand Age: After Logging Law et al Global Change Biology

Biodiversity and Evaporation Baldocchi, 2004: Data from Black, Schmid, Wofsy, Baldocchi, Fuentes

Value of Flux Networks Documenting Change in Ecosystem Metabolism –Network acts as ‘canary in the mine’ Produces Large and Long Data Sets –Reduced Sampling Error –Robust Dataset for Model Development Study Spectra of Time Scales –Capture Pulses and Lags Study Gradient of Climates, Structure and Function Field of Dreams: ‘Build it and they will Come’ –Better Integrated Research Studies