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

Data from Bai Yang and Tom Boden

Age of Flux Sites, and the Length of their Data Archive

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

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

Climate Space of AmeriFlux Sites
Yang et al 2008, JGR Biogeosciences

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

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

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

Dave Hollinger, Chair
1997-2001
Bev Law, Chair
2001-2011
Margaret Torn
AmeriFlux PI, 2012-
Tom Boden
AmeriFlux
Data Archive

184 Papers linked to key word
‘AmeriFlux’
These Papers have been cited over 7000 Times
246 Papers linked to key word
‘Fluxnet’

Ray Leuning
Most Flux Instruments are Very Good;
Pick the Instrument System that is Most Appropriate to Your
Weather and Climate

Open-Path CO
2
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 Biogeosciences

• 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

• 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

C Fluxes are a Function of Time Since Disturbances, as well as Weather, Structure and Function
GPP
NEE
Reco
Harvard Forest
1800
1600
1400
1200
1000
800
0
-200
-400
-600
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Year
Urbanski et al. 2007 JGR Biogeosciences

Light Response Curves of CO
2
Flux are Quasi-Linear,
Deviating from Monteith’s Classic Paper and
Impacting the Interpretation of C Flux with Remote
Sensing
Gilmanov et al 2010 Range Ecology & Management

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

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

Respiration is a function of
Temperature, Soil Moisture, Growth, Rain Pulses
And Temperature Acclimation
Xu et al. 2004 Global Biogeochemical Cycles

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

Disturbance, Fire and Thinning
Dore et al. 2012 GCB

Insect Defoliation, 2007
Clark et al. 2010 GCB

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

Gonsamo et al 2012 JGR Biogeosci

Satellite vs Flux Phenology
Gonsamo et al 2012 JGR Biogeosci

It’s Not only CO2!
Effects of Precipitation and Energy on Evaporation
MI Budyko
Williams et al. 2012 WRR

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

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

Land Use and Climate
Forests are warmer than nearby
Grasslands
Lee et al Nature 2011

Light Use Efficiency Models:
Upscale Fluxes from Towers to Regions
GPP ( )

PAR
Yuan et al. 2007, AgForMet
0 f T s f

Heinsch et al 2006 IEEE
0 f VPD f T min
)

C and Water fluxes Derived from Satellite-Snap Shots Scale with Daily Integrated Fluxes from Eddy Covariance
Sims et al 2005 AgForMet
Ryu et al. 2011 AgForMet

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

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

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

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

Data-Model Fusion/Assimilation
Sacks et al. 2006 GCB

Model Hierarchy Testing: How Much Detail is Needed?
Bonan et al 2012 JGR Biogeosci

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;
Richardson et al, 2012 GCB

2
Significant change in albedo with 3 disturbance types
Hurricane Beetles Fire
Beetle effect occurs mostly after snags fall
Albedo change produces radiative forcing of same magnitude as CO
2
forcing in case studies of forest mortality from hurricane defoliation, pine beetles,
and fire.
O’Halloran et al 2012 GCB

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

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.
• an increase in the amount of absorbing foliar constituents enhances absorption and correspondingly decreases canopy reflectance
• 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

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

Radiation and Evaporation Maps
Jin et al 2011 RSE

Testing Ecohydrology Theories for Soil Moisture
Miller et al 2007 Adv Water Res

• 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

Importance of Site Metadata,
A Plea for more LIDAR data to Test New Satellite Products and
Force 3D Ecosystem Dynamic Models
Simard et al 2011 JGR Biogeosciences Medvigy et al 2009 JGR Biogeoscience

• 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

Registered AmeriFlux Sites