AmeriFlux, Yesterday, Today and Tomorrow Dennis Baldocchi, UC Berkeley Margaret Torn and Deb...
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- Slide 1
- 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
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- Slide 55
- Registered AmeriFlux Sites