FLUXNET after 10 Years: Synthesizing CO 2 and Water Vapor Fluxes From Across a Global Network
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FLUXNET after 10 Years: Synthesizing CO2 and Water Vapor Fluxes
From Across a Global Network
Dennis BaldocchiESPM/Ecosystem Science Div.
University of California, Berkeley
ILEAPS, Boulder, Jan 2006
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FLUXNET: From Sea to Shining Sea379 Sites, circa 2006
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Global distribution of Flux Towers with Respect to Climate
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Evolution of FLUXNET
• Measure Annual Cycle of NEE– Micromet issues of Detrending, Transfer Functions, Flux Sampling
and Measurements, Gap-filling, Error Assessment• Measure and Interpret Intra-annual Variation of NEE
– Flux partitioning (GPP & Reco); assessment of metadata,e.g. Vcmax, soil respiration, LAI, biomass inventories.
• Measure and Interpret Inter-annual variations of NEE• Measure NEE over multiple Land-Use Classes
– crops, grasslands, deciduous and evergreen broadleaf and conifer forests
– Disturbance, logging, biodiversity and fire• Manipulative Studies
– Nitrogen and H2O additions• Measure NEE over Representative Areas
– Scaling Flux Information of Footprint to MODIS pixel
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Successes
• Mountains of data from a spectrum of canopy roughness conditions, functional types and climate spaces have been collected
• A Model for Data Sharing– FLUXNET Web Site, a venue for distributing Primary, Value-added and Meta-
Data products• Value-Added Products have been produced
– Development of Gap-Filling Techniques– Production of Gap-Filled Daily and Annual Sums
• Data for Validating and Improving SVAT models used for weather, climate, biogeochemistry and ecosystem dynamics
• Collaboration & Synthesis through Workshops and Hosting Visitors– Building a Collaborative, Cooperative, Multi-Disciplinary & International
Community of Researchers– Characterizing Annual C Fluxes– Environmental Controls on NEE
• Training New and Next Generation of Scientists, Postdocs, Students
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‘Failures’/’Un-resolved’ Issues
• Not Measuring Night-time Fluxes Well• Not Measuring Fluxes over Complex terrain and during
Advection Well• ImPerfect U* correction
– New Gu Algorithm• ImPerfect Flux Partitioning
– Works Better on Longer Time Scales• ImPerfect Energy Balance Closure
– Could be ‘red-herring’ based on recent several talks at a SSSA workshop
• Need Better Outreach and Training
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Visions with a Flux Measurement Network
• Processes– Canopy-Scale Response Functions
• Emergent Processes– Flux Partitioning, NEP=GPP-Reco
• Acclimation• Time
– Daily/Seasonal Dynamics– Pulses, Lags, Switches– Intra- + Interannual Variability– Stand Age/Disturbance
• Space– Climate/Structure/Function– Coherence/Gradients– Upscaling with Remote Sensing
• New Directions
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Probability Statistics of NEE
Col 4 vs Col 5
NEE (gC m-2 y-1)
-1500 -1000 -500 0 500 1000
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16Published data (275 site years)Median: -229 gC m-2 y-1
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Light and Photosynthesis:
Emergent Processes at Leaf and Canopy Scales
D208 Oak leaf, forest floorTleaf: 25o CCO2 : 360 ppm
Qpar (mol m-2 s-1)
0 200 400 600 800 1000 1200 1400 1600 1800
A ( m
ol m-2 s-1 )
0
2
4
6
8
10
12datamodel
model: clumped leaves
PPFD (mol m-2 s-1)
0 500 1000 1500 2000
F c (
mol
m-2
s-1)
-40
-30
-20
-10
0
10measured
(a)
0 500 1000 1500 2000-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
absorbed PAR (µmol m-2 s-1)
WHEAT
F c (mg
m-2 s-1 )
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Volcanoes, Aerosols + NEE
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CO2 Flux and Diffuse Radiation
Niyogi et al., GRL 2004
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Photosynthesis-Respiration
Processed by Falge
North America + Europe
Reco (gC m-2 yr-1)
0 200 400 600 800 1000 1200 1400 1600
GP
P (g
C m
-2 y
r-1)
0
200
400
600
800
1000
1200
1400
1600
b[1] 1.18r ² 0.893
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Mean Summer Temperature (C)5 10 15 20 25 30
Tem
pera
ture
Opt
imum
fo
r Can
opy
CO 2 upt
ake
(C)
5
10
15
20
25
30
35
b[0] 3.192b[1] 0.923r ² 0.830
Analysis of E. Falge
NEE: Acclimation with TemperatureNEE: Acclimation with Temperature
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Respiration: Temperature and acclimation
Analyst: Enquist et al. 2003, Nature
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GPP RH/Ca (mol m-2s-1)
0.00 0.01 0.02 0.03 0.04 0.05 0.06
g c (m
ol m
-2s-1
)
0.0
0.2
0.4
0.6
0.8
Linking Water and Carbon:Potential to assess Gc with Remote Sensing
Xu + DDB
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0
10
20
30
40Stomatal Conductance SurveyDennis Baldocchi; UC Berkeley
Bal
l-Ber
ry
0
10
20
30
40
FLUXNET Data"B
all-B
erry
"
Processed by M. Falk
An Example of Scale Invariance
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Temporal Dynamics of C Fluxes
• Hour• Day• Month• Season• Year• Multiple Years • Pulses
• Lags• Switches
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Decadal Plus Time Series of NEE:Flux version of the Keeling’s Mauna Loa Graph
Harvard Forest
Year
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
CO
2 Fl
ux D
ensi
ty (g
C m
-2 d
-1)
-12
-8
-4
0
4
8 NEERecoGEE
Data of Wofsy, Munger, Goulden et al.
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Complicating Dynamical Factors
• Switches/Pulses– Rain– Phenology/Length of
Season– Frost/Freezing
• Emergent Processes– Clouds & LUE
• Acclimation• Lags • Stand Age/Disturbance
Temperate Broadleaved Deciduous Forest
Day
0 50 100 150 200 250 300 350
NE
E (g
C m
-2 d
-1)
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
LAI=0GPP=0;Litterfall (+)Reco=f(litterfall)(+)
snow:Tsoil(+)
GPP=0; Reco(+)
no snow
Tsoil (-)
Reco (-)
GPP=f(LAI, Vcm
ax )
late spring
early spring
Drought:(-)GPP(-); Re(-)
Clouds:PAR(-) GPP=f(PAR)(+)
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Re vs GPPInterannual Variability in NEE
d GPP/dt
-300 -200 -100 0 100 200 300
d R
eco/
dt
-300
-200
-100
0
100
200
300
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2002 2003 2004 2005
NE
E[g
C m
-2 w
eek-
1 ]
-80
-70
-60
-50
-40
-30
-20
-10
0
10
20
HainichLeinefelde
Knohl et al Max Planck, Jena
Lag Effects Due to Drought/Heat Stress
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Soroe, DenmarkBeech Forest1997
day
0 50 100 150 200 250 300 350-10
-5
0
5
10
15
20
NEE, gC m-2 d-1
Tair, recursive filter, oC
Tsoil, oC
Data of Pilegaard et al.
Soil Temperature: An Objective Indicator of Phenology??
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Data of: ddb, Wofsy, Pilegaard, Curtis, Black, Fuentes, Valentini, Knohl, Yamamoto. Granier, SchmidBaldocchi et al. Int J. Biomet, in press
Soil Temperature: An Objective Measure of Phenology, part 2
Temperate Deciduous Forests
Day, Tsoil >Tair
70 80 90 100 110 120 130 140 150 160
Day
NE
E=0
70
80
90
100
110
120
130
140
150
160
DenmarkTennesseeIndianaMichiganOntarioCaliforniaFranceMassachusettsGermanyItalyJapan
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Spatial Gradients:NEE and Length of Growing Season
Broad-Leaved Forests
Length of Growing Season
100 150 200 250
NEE
(gC
m-2
yr-1
)
-800
-700
-600
-500
-400
-300
-200
-100
0
100Japan
DenmarkItaly
Massachusetts, USA
Belgium
Tennessee, USAPrince Albert, CANADA
Ontario
Indiana, USA
Michigan, USA
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Spatial Variations in C Fluxes
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8 day means
Dai
ly g
ross
CO
2 flu
x
(mm
ol m
-2 d
ay-1
)
0
200
400
600
800
1000
1200
1400
16008 day means
Dai
ly n
et C
O2 f
lux
(mm
ol m
-2 d
ay-1
)
-400
-200
0
200
400
600
800
r2 = 0.92
8 day means
Dai
ly g
ross
LU
E
0.00
0.01
0.02
0.03
r2 = 0.65
Single clear days
AM net CO2 flux (mmol m-2 hr-1)
0 20 40 60 80 100 120
Dai
ly n
et C
O2 f
lux
(mm
ol m
-2 d
ay-1
)
-400
-200
0
200
400
600
800Single clear days
AM gross CO2 flux (mmol m-2 hr-1)
0 20 40 60 80 100 120 140
Dai
ly g
ross
CO
2 flu
x
(mm
ol m
-2 d
ay-1
)
0
200
400
600
800
1000
1200
1400
1600
r2 = 0.88
Single clear days
AM gross LUE
0.00 0.01 0.02 0.03
Dai
ly g
ross
LU
E
0.00
0.01
0.02
0.03
r2 = 0.73
r2 = 0.64
r2 = 0.56
Evergreen needleleaf forestDeciduous broadleaf forestGrassland and woody savanna
a b c
d e f
Sims et al 2005 AgForMet
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Heinsch et al. in press
Global MODIS Test
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Testbed for Ecohydrological Theory
Miller et al, Adv. Water Research, submitted
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Value of Flux Networks
• Produces Large and Long Data Sets– Reduced Sampling Error– Robust Dataset for Model Development
• Study Spectra of Time Scales– Capture Pulses and Lags
• Study Gradient of Climates, Structure and Function• Field of Dreams: ‘Build it and they will Come’
– Better Integrated Research Studies
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Future Directions
• Administrative– ReOrganize FLUXNET with Multiple/International Funding Sources
• Scientific
– NEE in Urban and Suburban, Africa, India, Latin America and High Arctic Environments– Coupling CO2, Trace Gas Deposition/Emission (O3, voc) and Methane Fluxes– Adopting New Technology (TDL, wireless networks) to embellish flux measurements– Couple tower data with Real-time Data Assimilation Models.– Boundary Layer Budgets using Fluxes and High Precision CO2 measurements– Spectral reflectance measurements across the network– Spatial-Temporal Network-Scale Analysis– Real-time Data Assimilation– Matching Footprints of Tower and Pixels– Model Lags, Switches and Pulses– Using Fluxnet data to assess problems in
• Ecology, Ecohydrology, Biogeochemistry, Biogeography, Remote Sensing, Global Modeling, Biodiversity
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Validating MODIS
Falk, Ma, Baldocchi, unpublished
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Heinsch et al. submitted
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Tower vs Satellite NDVI
0.00 0.25 0.50 0.75 1.000.00
0.25
0.50
0.75
1.00
NDVIMODIS
ND
VI TO
WE
R
conifers deciduous
crops grassland
Falk et al., to be submitted
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Limits to Landscape Classification by Functional Type
• Stand Age/Disturbance• Biodiversity• Fire• Logging• Insects/Pathogens• Management/Plantations• Kyoto Forests
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Effects of Stand Age:After Logging
Law et al. 2003 Global Change Biology
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Biodiversity and Evaporation
Temperate/Boreal Broadleaved ForestsSummer Growing Season
Number of Dominant Tree Species (> 5% of area or biomass survey)
1 2 3 4 5 6 7 8
E/
Eeq
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
Baldocchi, 2004: Data from Black, Schmid, Wofsy, Baldocchi, Fuentes