FLUXNET after 10 Years: Synthesizing CO2 and Water Vapor Fluxes From Across a Global Network Dennis Baldocchi ESPM/Ecosystem Science Div. University of California, Berkeley ILEAPS, Boulder, Jan 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 & Reco); assessment of metadata,e.g. Vcmax, 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 H2O 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’ based on recent several talks at a SSSA workshop Need Better Outreach and Training

Visions with a Flux Measurement Network Processes Canopy-Scale Response Functions Emergent Processes Flux Partitioning, NEP=GPP-Reco 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

CO2 Flux and Diffuse Radiation Niyogi et al., GRL 2004

Photosynthesis-Respiration Processed by Falge

NEE: Acclimation with Temperature Analysis of E. Falge

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

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

An Example of Scale Invariance Processed by M. Falk

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

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

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

DRe vs DGPP

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

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

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

Spatial Gradients: NEE and Length of Growing Season Coherent response among sites, impact of length of growing season. Does not account for interannual variability at a site, due to snow cover, drought, cloudy vs clear summers etc.

Spatial Variations in C Fluxes

Sims et al 2005 AgForMet

Global MODIS Test Heinsch et al. in press

Testbed for Ecohydrological Theory Miller et al, Adv. Water Research, submitted

Value of Flux Networks 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

Future Directions Administrative Scientific ReOrganize FLUXNET with Multiple/International Funding Sources Scientific NEE in Urban and Suburban, Africa, India, Latin America and High Arctic Environments Coupling CO2, Trace Gas Deposition/Emission (O3, voc) and Methane Fluxes Adopting New Technology (TDL, wireless networks) to embellish flux measurements Couple tower data with Real-time Data Assimilation Models. Boundary Layer Budgets using Fluxes and High Precision CO2 measurements Spectral reflectance measurements across the network Spatial-Temporal Network-Scale Analysis Real-time Data Assimilation Matching Footprints of Tower and Pixels Model Lags, Switches and Pulses Using Fluxnet data to assess problems in Ecology, Ecohydrology, Biogeochemistry, Biogeography, Remote Sensing, Global Modeling, Biodiversity

Validating MODIS Falk, Ma, Baldocchi, unpublished

Heinsch et al. submitted

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

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. 2003 Global Change Biology

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