AmeriFlux, Yesterday, Today and Tomorrow Dennis Baldocchi, UC Berkeley Margaret Torn and Deb...
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AmeriFlux, Yesterday, Today and Tomorrow Dennis Baldocchi, UC Berkeley Margaret Torn and Deb Agarwal, Lawrence Berkeley National Lab Bev Law, Oregon State
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
Slide 2
AmeriFlux, circa 2012
Slide 3
Growth in the Network Data from Bai Yang and Tom Boden
Slide 4
Age of Flux Sites, and the Length of their Data Archive
Slide 5
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 Cant
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
Slide 6
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
Slide 7
Climate Space of AmeriFlux Sites Yang et al 2008, JGR
Biogeosciences
Slide 8
AmeriFlux Sites, Circa 2003, and Ecosystem/Climate
Representativeness Hargrove, Hoffman and Law, 2003 Eos
Slide 9
Representativeness of AmeriFlux, Circa 2008 (blue is good!)
Yang et al. 2008 JGR Biogeosciences
Slide 10
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
Slide 11
Past and Current Leadership Dave Hollinger, Chair 1997-2001 Bev
Law, Chair 2001-2011 Margaret Torn AmeriFlux PI, 2012- Tom Boden
AmeriFlux Data Archive
Slide 12
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
Slide 13
Issues of standardization, or not?
Slide 14
Know Thy Site Ray Leuning Most Flux Instruments are Very Good;
Pick the Instrument System that is Most Appropriate to Your Weather
and Climate
Slide 15
Open-Path CO 2 Fluxes were 1.7% Higher than Closed Path Fluxes
Schmidt et al. 2012, JGR Biogeosciences
Slide 16
Site Calibration with Roving Standard Schmidt et al 2012 JGR
Biogeosciences
Slide 17
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
Slide 18
Lessons Learned
Slide 19
Whats 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
Slide 20
C Fluxes are a Function of Time Since Disturbances, as well as
Weather, Structure and Function Urbanski et al. 2007 JGR
Biogeosciences
Slide 21
Gilmanov et al 2010 Range Ecology & Management Light
Response Curves of CO 2 Flux are Quasi-Linear, Deviating from
Monteiths Classic Paper and Impacting the Interpretation of C Flux
with Remote Sensing
Slide 22
Niyogi et al 2004 GRL Light Use Efficiency INCREASES with the
Fraction of Diffuse Light
Slide 23
Response Functions from Elevation/Climate Gradients
Anderson-Teixeira et al. 2010 GCB
Slide 24
Respiration is a function of Temperature, Soil Moisture,
Growth, Rain Pulses And Temperature Acclimation Xu et al. 2004
Global Biogeochemical Cycles
Slide 25
Rain-Induced Pulses in Respiration: Long Term Studies Capture
More Pulses, Better Statistics Ma et al. 2012 AgForMet
Slide 26
Disturbance, Fire and Thinning Dore et al. 2012 GCB
Slide 27
Insect Defoliation, 2007 Clark et al. 2010 GCB
Slide 28
Disturbance Dynamics C Flux = f(time since disturbance) Amiro
et al. 2010 JGR Biogeosci
Slide 29
Flux Phenology Gonsamo et al 2012 JGR Biogeosci
Slide 30
Satellite vs Flux Phenology Gonsamo et al 2012 JGR
Biogeosci
Slide 31
Its Not only CO2! Effects of Precipitation and Energy on
Evaporation Williams et al. 2012 WRR MI Budyko
Slide 32
Schwalm et al 2012 Nature Geoscience Long-Term Studies can
Assess Links between Drought and Fluxes
Slide 33
Schwalm et al 2012 Nature Geoscience Net Negative Effects on
Carbon and Water Fluxes are Strong: What about 2012?
Slide 34
Lee et al Nature 2011 Land Use and Climate Forests are warmer
than nearby Grasslands
Slide 35
Light Use Efficiency Models: Upscale Fluxes from Towers to
Regions Yuan et al. 2007, AgForMetHeinsch et al 2006 IEEE
Slide 36
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
Slide 37
Seasonal Maps of NEE, via Regression Tree Analysis, on
AmeriFlux and Modis Data Xiao et al. 2008 AgForMet
Slide 38
Chen et al 2011 Biogeosciences What is the Truth?; How Good is
Good-Enough?
Slide 39
Regional Estimates of Fire, Drought, Hurricanes on NEE Xiao et
al. 2011 AgForMet
Slide 40
Krinner et al 2005 GBC Using Flux Data to Validate Dynamic
Vegetation Models-ORCHIDEE
Slide 41
Data-Model Fusion/Assimilation Sacks et al. 2006 GCB
Slide 42
Model Hierarchy Testing: How Much Detail is Needed? Bonan et al
2012 JGR Biogeosci
Slide 43
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;
Slide 44
Its Not Just About CO 2 : Significant change in albedo with 3
disturbance types OHalloran et al 2012 GCB 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. Beetle effect occurs mostly after snags fall
HurricaneFireBeetles
Slide 45
Hollinger et al 2009 Global Change Biology Albedo Scales with
Nitrogen We can Use Albedo to Parameterize N and Ps Capacity in
Models!
Slide 46
The Albedo-N Correlation may be Spurious Knyazikhin 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 reflectance variations 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 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
Slide 47
Validating and Improving Climate Drivers, like Net Radiation
Fields Jin et al 2011 RSE
Slide 48
Radiation and Evaporation Maps
Slide 49
Miller et al 2007 Adv Water Res Testing Ecohydrology Theories
for Soil Moisture
Slide 50
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
Slide 51
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
Slide 52
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