ameriflux , yesterday , today and tomorrow
DESCRIPTION
AmeriFlux , Yesterday , Today and Tomorrow. Dennis Baldocchi, UC Berkeley Margaret Torn and Deb Agarwal, Lawrence Berkeley National Lab Bev Law, Oregon State University Tom Boden, Oak Ridge National Laboratory. AmeriFlux , circa 2012. Growth in the Network. - PowerPoint PPT PresentationTRANSCRIPT
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AmeriFlux, Yesterday, Today and Tomorrow
Dennis Baldocchi, UC BerkeleyMargaret Torn and Deb Agarwal, Lawrence Berkeley National
LabBev Law, Oregon State University
Tom Boden, Oak Ridge National Laboratory
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AmeriFlux, circa 2012
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Growth in the Network
Data from Bai Yang and Tom Boden
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Age of Flux Sites, and the Length of their Data Archive
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Pros and Cons of a Sparse Flux Network
• Pros– Covers Most Climate and Ecological Spaces– Long-Term Operation Experiences Extreme Events, Gradual Climate
Change, and Disturbance– Gradients of Sites across Landscapes and Regions Span Range of
Environmental and Ecological Forcing Variables– Clusters 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-Sampling• Cons
– Can’t Cover All Physical and Ecological Spaces or Complex Terrain– Current Record is too Short to Detect Climate or CO2-Induced Trends – Flux Depends on Vegetation in the Footprint– Bias Errors at Night, Under Low Winds
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The Type of Network Affects the Type of Science
• Sparse Network of Intense Super-Sites and Clusters of Sites, Producing Mechanistic Information can Test, Validate and Parameterize Process and Mechanistic Models
• Denser and More Extensive Network of Less-Expensive Sites can Assist in Statistical and Spatial Up-Scaling of Fluxes with Remote Sensing
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Climate Space of AmeriFlux Sites
Yang et al 2008, JGR Biogeosciences
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AmeriFlux Sites, Circa 2003, and Ecosystem/Climate Representativeness
Hargrove, Hoffman and Law, 2003 Eos
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Representativeness of AmeriFlux, Circa 2008(blue is good!)
Yang et al. 2008 JGR Biogeosciences
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Basis of a Successful Flux Network
It Takes People (Scientists, Postdocs, Students and Technicians)
Social Network that Facilitates Meetings, Workshops, Shared Leadership and a Shared/Central Data Base
This Fosters Getting to Know Each Other, Collaboration, Communication, Common Vision, Shared Goals,
And Joint Authorship of Synthesis Papers
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Past and Current Leadership
Dave Hollinger, Chair1997-2001
Bev Law, Chair2001-2011
Margaret TornAmeriFlux PI, 2012-
Tom BodenAmeriFlux Data Archive
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Published Use of AmeriFlux Data
184 Papers linked to key word ‘AmeriFlux’
These Papers have been cited over 7000 Times
246 Papers linked to key word ‘Fluxnet’
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Issues of standardization, or not?
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‘Know Thy Site’
Ray Leuning
Most Flux Instruments are Very Good; Pick the Instrument System that is Most Appropriate to Your
Weather and Climate
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Open-Path CO2 Fluxes were 1.7% Higher than Closed Path Fluxes
Schmidt et al. 2012, JGR Biogeosciences
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Site Calibration with Roving Standard
Schmidt et al 2012 JGR Biogeosciences
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Extrinsic Contributions• Data Contribute to Producing Better Models via Validation,
Parameterization, Data-Assimilation & Defining Functional Responses– Land-Vegetation-Atmosphere-Climate
• Energy Partitioning, Albedo, Energy Forcing, Land Use– Remote Sensing, Light Use Efficiency Models
• Regional and Global GPP models– Ecosystem and Biogeochemical Cycling
• Carbon Cycle, Disturbance, Phenology, Environmental Change, Plant Functional Types
– Hydrology • Evaporation , Soil Moisture, Ground-Water, Drought
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Lessons Learned
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What’s in the Data?• Magnitudes and Trends in Annual C and H2O Fluxes, by Plant Functional
Type and Climate Space• Light-Use, Temperature, Rain Response Functions• Emergent-Scale Properties
– Diffuse Light– Rain Pulses– Drought and Ground Water Access
• Disturbance– Insect Defoliation– Fire, Logging and Thinning– Drought and Mortality
• BioPhysical Forcings– Albedo and Temperature– Energy Partitioning with Land Use
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Harvard Forest
Year
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Car
bon
Flux
Den
sity
, gC
m-2
y-1
-600
-400
-200
0
800
1000
1200
1400
1600
1800
GPPNEEReco
C Fluxes are a Function of Time Since Disturbances, as well as Weather, Structure and Function
Urbanski et al. 2007 JGR Biogeosciences
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Gilmanov et al 2010 Range Ecology & Management
Light Response Curves of CO2 Flux are Quasi-Linear,Deviating from Monteith’s Classic Paper and
Impacting the Interpretation of C Flux with Remote Sensing
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Niyogi et al 2004 GRL
Light Use Efficiency INCREASES with the Fraction of Diffuse Light
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Response Functions from Elevation/Climate Gradients
Anderson-Teixeira et al. 2010 GCB
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Respiration is a function of Temperature, Soil Moisture, Growth, Rain Pulses
And Temperature Acclimation
Xu et al. 2004 Global Biogeochemical Cycles
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Rain-Induced Pulses in Respiration:Long –Term Studies Capture More Pulses, Better Statistics
Ma et al. 2012 AgForMet
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Disturbance, Fire and Thinning
Dore et al. 2012 GCB
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Insect Defoliation, 2007
Clark et al. 2010 GCB
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Disturbance DynamicsC Flux = f(time since disturbance)
Amiro et al. 2010 JGR Biogeosci
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Flux Phenology
Gonsamo et al 2012 JGR Biogeosci
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Satellite vs Flux Phenology
Gonsamo et al 2012 JGR Biogeosci
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It’s Not only CO2!Effects of Precipitation and Energy on Evaporation
Williams et al. 2012 WRR
MI Budyko
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Schwalm et al 2012 Nature Geoscience
Long-Term Studies can Assess Links between Drought and Fluxes
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Schwalm et al 2012 Nature Geoscience
Net Negative Effects on Carbon and Water Fluxes are Strong: What about 2012?
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Lee et al Nature 2011
Land Use and Climate
Forests are warmer than nearbyGrasslands
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Light Use Efficiency Models:Upscale Fluxes from Towers to Regions
Yuan et al. 2007, AgForMet Heinsch et al 2006 IEEE
0 ( ) ( )sf T f 0 min( ) ( )f VPD f T
( )GPP fPAR NVDVI PAR
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Sims et al 2005 AgForMet
C and Water fluxes Derived from Satellite-Snap Shots Scale with Daily Integrated Fluxes from Eddy Covariance
Ryu et al. 2011 AgForMet
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Seasonal Maps of NEE, via Regression Tree Analysis, on AmeriFlux and Modis Data
Xiao et al. 2008 AgForMet
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Chen et al 2011 Biogeosciences
What is the Truth?; How Good is Good-Enough?
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Regional Estimates of Fire, Drought, Hurricanes on NEE
Xiao et al. 2011 AgForMet
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Krinner et al 2005 GBC
Using Flux Data to Validate Dynamic Vegetation Models-ORCHIDEE
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Data-Model Fusion/Assimilation
Sacks et al. 2006 GCB
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Model Hierarchy Testing: How Much Detail is Needed?
Bonan et al 2012 JGR Biogeosci
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Richardson et al, 2012 GCB
Testing Phenology Predictions in Ecosystem-Dynamic Models
The 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;
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It‘s Not Just About CO2:Significant change in albedo with 3 disturbance types
O’Halloran et al 2012 GCB
Albedo 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 fall
Hurricane FireBeetles
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Hollinger et al 2009 Global Change Biology
Albedo Scales with NitrogenWe can Use Albedo to Parameterize N and Ps Capacity in Models!
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The Albedo-N Correlation may be Spurious
Knyazikhin et al 2012 PNAS report that the previously reported correlation is an artifact—it is a consequence of variations in canopy structure, rather than of %N.
• When the BRF data are corrected for canopy-structure effects, the residual reflectance
• variations are negatively related to %N at all wavelengths in the interval 423–855 nm.
To infer leaf biochemical constituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710–790 nm
provide critical information for correction of structural influences
• an increase in the amount of absorbing foliar constituents enhances absorption and correspondingly decreases canopy reflectance
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Validating and Improving Climate Drivers, like Net Radiation Fields
Jin et al 2011 RSE
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Jin et al 2011 RSE
Radiation and Evaporation Maps
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Miller et al 2007 Adv Water Res
Testing Ecohydrology Theories for Soil Moisture
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Current and Future Collaborations
• COSMOS and Soil Moisture Fields• Validation of Satellite based estimates of CO2,
LIDAR, Albedo, and Soil Moisture (SMOS, SMAP, AIRMOSS)
• Priors for CO2-Satellite Inversions (GOSAT, OCO)• Data-Model Assimilation• Phenology and Pheno-Camera Networks• FLUXNET and NEON
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Simard et al 2011 JGR Biogeosciences
Importance 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 Biogeoscience
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AmeriFlux Plans
• DOE grant to LBL to Manage 10-12 Long Term Clusters of Flux Towers– Ensure Cohort of Long Term Sites Extend into the Future to
Address Ecological and Climate Questions on their Native Time Scales
• Continue Operation of Roving ‘Calibration’ system to All AmeriFlux Sites
• Central Data Archiving, Processing and Data Distribution– Open Access, Prompt Submission, Uniform Processing
• Spare Sensors for Emergencies
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