AmeriFlux , Yesterday , Today and Tomorrow

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AmeriFlux, Yesterday, Today and Tomorrow

Dennis Baldocchi, UC BerkeleyMargaret Torn and Deb Agarwal, Lawrence Berkeley National LabBev Law, Oregon State UniversityTom Boden, Oak Ridge National Laboratory

Cezanne1

AmeriFlux, circa 2012Growth in the Network

Data from Bai Yang and Tom Boden

Age of Flux Sites, and the Length of their Data ArchivePros and Cons of a Sparse Flux NetworkProsCovers Most Climate and Ecological SpacesLong-Term Operation Experiences Extreme Events, Gradual Climate Change, and DisturbanceGradients of Sites across Landscapes and Regions Span Range of Environmental and Ecological Forcing VariablesClusters of Sites examine effects of Land Use Change, Management, and Disturbance (fire, drought, insects, logging, thinning, fertilizer, flooding, woody encroachment)Robust Statistics due to Over-SamplingConsCant Cover All Physical and Ecological Spaces or Complex TerrainCurrent Record is too Short to Detect Climate or CO2-Induced Trends Flux Depends on Vegetation in the FootprintBias Errors at Night, Under Low WindsThe Type of Network Affects the Type of ScienceSparse Network of Intense Super-Sites and Clusters of Sites, Producing Mechanistic Information can Test, Validate and Parameterize Process and Mechanistic ModelsDenser and More Extensive Network of Less-Expensive Sites can Assist in Statistical and Spatial Up-Scaling of Fluxes with Remote Sensing

Climate Space of AmeriFlux SitesYang et al 2008, JGR Biogeosciences

AmeriFlux Sites, Circa 2003, and Ecosystem/Climate RepresentativenessHargrove, Hoffman and Law, 2003 Eos

Representativeness of AmeriFlux, Circa 2008(blue is good!)Yang et al. 2008 JGR Biogeosciences

Basis of a Successful Flux NetworkIt Takes People (Scientists, Postdocs, Students and Technicians)

Social Network that Facilitates Meetings, Workshops, Shared Leadership and a Shared/Central Data BaseThis Fosters Getting to Know Each Other, Collaboration, Communication, Common Vision, Shared Goals, And Joint Authorship of Synthesis PapersJan Steen, So the Old Sing, So Twitter the Young', c.166510Past and Current Leadership

Dave Hollinger, Chair1997-2001Bev Law, Chair2001-2011

Margaret TornAmeriFlux PI, 2012-Tom BodenAmeriFlux Data Archive

Published Use of AmeriFlux Data

184 Papers linked to key word AmeriFlux

These Papers have been cited over 7000 Times246 Papers linked to key word FluxnetIssues of standardization, or not?

Know Thy Site

Ray LeuningMost Flux Instruments are Very Good; Pick the Instrument System that is Most Appropriate to YourWeather and ClimateOpen-Path CO2 Fluxes were 1.7% Higher than Closed Path Fluxes

Schmidt et al. 2012, JGR Biogeosciences

Site Calibration with Roving Standard

Schmidt et al 2012 JGR BiogeosciencesExtrinsic ContributionsData Contribute to Producing Better Models via Validation, Parameterization, Data-Assimilation & Defining Functional ResponsesLand-Vegetation-Atmosphere-ClimateEnergy Partitioning, Albedo, Energy Forcing, Land UseRemote Sensing, Light Use Efficiency ModelsRegional and Global GPP modelsEcosystem and Biogeochemical Cycling Carbon Cycle, Disturbance, Phenology, Environmental Change, Plant Functional TypesHydrology Evaporation , Soil Moisture, Ground-Water, DroughtLessons Learned

Whats in the Data?Magnitudes and Trends in Annual C and H2O Fluxes, by Plant Functional Type and Climate SpaceLight-Use, Temperature, Rain Response FunctionsEmergent-Scale PropertiesDiffuse LightRain PulsesDrought and Ground Water AccessDisturbanceInsect DefoliationFire, Logging and ThinningDrought and MortalityBioPhysical ForcingsAlbedo and TemperatureEnergy Partitioning with Land Use

C Fluxes are a Function of Time Since Disturbances, as well as Weather, Structure and FunctionUrbanski et al. 2007 JGR Biogeosciences

Gilmanov et al 2010 Range Ecology & ManagementLight Response Curves of CO2 Flux are Quasi-Linear,Deviating from Monteiths Classic Paper and Impacting the Interpretation of C Flux with Remote Sensing

Niyogi et al 2004 GRLLight Use Efficiency INCREASES with the Fraction of Diffuse Light

Response Functions from Elevation/Climate GradientsAnderson-Teixeira et al. 2010 GCB

Respiration is a function of Temperature, Soil Moisture, Growth, Rain PulsesAnd Temperature AcclimationXu et al. 2004 Global Biogeochemical Cycles

Rain-Induced Pulses in Respiration:Long Term Studies Capture More Pulses, Better StatisticsMa et al. 2012 AgForMet

Disturbance, Fire and Thinning

Dore et al. 2012 GCB

Insect Defoliation, 2007

Clark et al. 2010 GCB

Disturbance DynamicsC Flux = f(time since disturbance)Amiro et al. 2010 JGR Biogeosci

Flux Phenology

Gonsamo et al 2012 JGR Biogeosci

Satellite vs Flux PhenologyGonsamo et al 2012 JGR Biogeosci

Its Not only CO2!Effects of Precipitation and Energy on Evaporation

Williams et al. 2012 WRR

MI Budyko

Schwalm et al 2012 Nature GeoscienceLong-Term Studies can Assess Links between Drought and Fluxes

Schwalm et al 2012 Nature GeoscienceNet Negative Effects on Carbon and Water Fluxes are Strong: What about 2012?

Lee et al Nature 2011Land Use and ClimateForests are warmer than nearbyGrasslands

Light Use Efficiency Models:Upscale Fluxes from Towers to Regions

Yuan et al. 2007, AgForMet

Heinsch et al 2006 IEEE

Sims et al 2005 AgForMetC and Water fluxes Derived from Satellite-Snap Shots Scale with Daily Integrated Fluxes from Eddy Covariance

Ryu et al. 2011 AgForMet

Seasonal Maps of NEE, via Regression Tree Analysis, on AmeriFlux and Modis DataXiao et al. 2008 AgForMet

Also see Chen et al 2011 Biogeosciences, TEM37

Chen et al 2011 BiogeosciencesWhat is the Truth?; How Good is Good-Enough?

Regional Estimates of Fire, Drought, Hurricanes on NEEXiao et al. 2011 AgForMet

Krinner et al 2005 GBCUsing Flux Data to Validate Dynamic Vegetation Models-ORCHIDEE

Data-Model Fusion/AssimilationSacks et al. 2006 GCB

Model Hierarchy Testing: How Much Detail is Needed?

Bonan et al 2012 JGR Biogeosci

Richardson et al, 2012 GCBTesting Phenology Predictions in Ecosystem-Dynamic ModelsThe total bias in modeled annual GEP was+35 365 g C m-2 yr-1 for deciduous forests

+70 335 g C m-2 yr-1 for evergreen forests across all sites, models, and years;

Its Not Just About CO2:Significant change in albedo with 3 disturbance types

OHalloran et al 2012 GCBAlbedo change produces radiative forcing of same magnitude as CO2 forcing in case studies of forest mortality from hurricane defoliation, pine beetles, and fire.Beetle effect occurs mostly after snags fallHurricaneFireBeetles

Change in net radiative forcing over different time frames since disturbance, and spatial scales (hurricane short time, small scale)Change in NBP and albedo are almost mirror images (comparable)44

Hollinger et al 2009 Global Change BiologyAlbedo Scales with NitrogenWe can Use Albedo to Parameterize N and Ps Capacity in Models!

The Albedo-N Correlation may be SpuriousKnyazikhin et al 2012 PNAS report that the previously reported correlation is an artifactit is a consequence of variations in canopy structure, rather than of %N.When the BRF data are corrected for canopy-structure effects, the residual reflectancevariations are negatively related to %N at all wavelengths in the interval 423855 nm.To infer leaf biochemical constituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710790 nmprovide critical information for correction of structural influencesan increase in the amount of absorbing foliar constituents enhances absorption and correspondingly decreases canopy reflectance

Validating and Improving Climate Drivers, like Net Radiation FieldsJin et al 2011 RSE

Jin et al 2011 RSERadiation and Evaporation Maps

Miller et al 2007 Adv Water ResTesting Ecohydrology Theories for Soil Moisture

Current and Future CollaborationsCOSMOS and Soil Moisture FieldsValidation of Satellite based estimates of CO2, LIDAR, Albedo, and Soil Moisture (SMOS, SMAP, AIRMOSS)Priors for CO2-Satellite Inversions (GOSAT, OCO)Data-Model AssimilationPhenology and Pheno-Camera NetworksFLUXNET and NEON

Simard et al 2011 JGR BiogeosciencesImportance of Site Metadata, A Plea for more LIDAR data to Test New Satellite Products and Force 3D Ecosystem Dynamic Models

Medvigy et al 2009 JGR BiogeoscienceAmeriFlux PlansDOE grant to LBL to Manage 10-12 Long Term Clusters of Flux TowersEnsure Cohort of Long Term Sites Extend into the Future to Address Ecological and Climate Questions on their Native Time ScalesContinue Operation of Roving Calibration system to All AmeriFlux SitesCentral Data Archiving, Processing and Data DistributionOpen Access, Prompt Submission, Uniform ProcessingSpare Sensors for Emergencies

Registered AmeriFlux Sites