AmeriFlux , Yesterday , Today and Tomorrow

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

AmeriFlux, circa 2012

Growth in the Network

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

Past and Current Leadership

Dave Hollinger, Chair1997-2001

Bev 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 Times

246 Papers linked to key word ‘Fluxnet’

Issues of standardization, or not?

‘Know Thy Site’

Ray Leuning

Most Flux Instruments are Very Good; Pick the Instrument System that is Most Appropriate to Your

Weather and Climate

Open-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 Biogeosciences

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

Lessons Learned

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

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

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

Niyogi et al 2004 GRL

Light Use Efficiency INCREASES with the Fraction of Diffuse Light

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 DynamicsC Flux = f(time since disturbance)

Amiro et al. 2010 JGR Biogeosci

Flux Phenology

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

Williams et al. 2012 WRR

MI Budyko

Schwalm et al 2012 Nature Geoscience

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?

Lee et al Nature 2011

Land Use and Climate

Forests are warmer than nearbyGrasslands

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

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

Seasonal Maps of NEE, via Regression Tree Analysis, on AmeriFlux and Modis Data

Xiao et al. 2008 AgForMet

Chen et al 2011 Biogeosciences

What is the Truth?; How Good is Good-Enough?

Regional Estimates of Fire, Drought, Hurricanes on NEE

Xiao et al. 2011 AgForMet

Krinner et al 2005 GBC

Using Flux Data to Validate Dynamic Vegetation Models-ORCHIDEE

Data-Model Fusion/Assimilation

Sacks et al. 2006 GCB

Model Hierarchy Testing: How Much Detail is Needed?

Bonan et al 2012 JGR Biogeosci

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;

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

Hollinger et al 2009 Global Change Biology

Albedo Scales with NitrogenWe can Use Albedo to Parameterize N and Ps Capacity in Models!

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

Validating and Improving Climate Drivers, like Net Radiation Fields

Jin et al 2011 RSE

Jin et al 2011 RSE

Radiation and Evaporation Maps

Miller et al 2007 Adv Water Res

Testing Ecohydrology Theories for Soil Moisture

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

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

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

Registered AmeriFlux Sites