Using a Global Flux Network—FLUXNET— to Study the Breathing of the Terrestrial Biosphere
Dennis BaldocchiESPM/Ecosystem Science Div.
University of California, Berkeley
AsiaFlux, Seoul, Korea Nov. 17, 2008
Contemporary CO2 Record
Mauna LoaKeeling data
year
1950 1960 1970 1980 1990 2000 2010
CO
2 (pp
m)
300
310
320
330
340
350
360
370
380
Methods To Assess Terrestrial Carbon Fluxes at Landscape to Continental Scales, and Across
Multiple Time Scales
GCM InversionModeling
Remote Sensing/MODIS
Eddy Flux Measurements/FLUXNET
Forest/Biomass Inventories
Biogeochemical/Ecosystem Dynamics Modeling
Physiological Measurements/Manipulation Expts.
Objectives
• Time– Annual Integration– Seasonal Dynamics– Inter-Annual Variability– Disturbance/Chronosequence
• Processes– Photosynthesis = f(Q,T,functional type)– Respiration = f(T, growth, ppt, q)
• Space• Other Uses and Application
– Ecosystem Modeling
Temporal Dynamics of C Fluxes
• Hour• Day• Month• Season• Year• Multiple Years • Pulses
• Lags• Switches
FN (gC m-2 y-1)
-1500 -1000 -500 0 500 1000 1500
p(x)
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
mean: -182.9 gC m-2 y-1
std dev: 269.5n: 506
Baldocchi, Austral J Botany, 2008
Probability Distribution of Published NEE Measurements, Integrated Annually
Baldocchi, Austral J Botany, 2008
Does Net Ecosystem Carbon Exchange Scale with Photosynthesis?
FA (gC m-2 y-1)
0 500 1000 1500 2000 2500 3000 3500 4000
F N (g
C m
-2 y
-1)
-1000
-750
-500
-250
0
250
500
750
1000
Ecosystems with greatest GPP don’t necessarily experience greatest NEE
FA (gC m-2 y-1)
0 500 1000 1500 2000 2500 3000 3500 4000
F R (g
C m
-2 y
-1)
0
500
1000
1500
2000
2500
3000
3500
4000
UndisturbedDisturbed by Logging, Fire, Drainage, Mowing
Baldocchi, Austral J Botany, 2008
Ecosystem Respiration Scales Tightly with Ecosystem Photosynthesis, But Is with Offset by Disturbance
Length of Growing Season, days
50 100 150 200 250 300 350
F N (g
C m
-2 y
r-1)
-1000
-800
-600
-400
-200
0
200
Temperate and Boreal Deciduous Forests Deciduous and Evergreen Savanna
Baldocchi, Austral J Botany, 2008
Net Ecosystem Carbon Exchange Scales with Length of Growing Season
Harvard Forest, 1991-2004
Year
1990 1992 1994 1996 1998 2000 2002 2004 2006
NE
E (g
C m
-2 d
-1)
-10
-8
-6
-4
-2
0
2
4
6
8
10
Data of Wofsy, Munger, Goulden, et al.
Decadal Plus Time Series of NEE:Flux version of the Keeling’s Mauna Loa Graph
Harvard Forest
Year
1990 1992 1994 1996 1998 2000 2002 2004 2006
Car
bon
Flux
(gC
m-2
y-1
)
-600
-400
-200
01000
1200
1400
1600
1800
FNFAFR
Interannual Variation and Long Term Trends in Net Ecosystem Carbon Exchange (FN), Photosynthesis (FA) and Respiration (FR)
Urbanski et al 2007 JGR
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 2003 European Drought/Heat Stress
Interannual Variability in FN
d FA/dt (gC m-2 y-2)
-750 -500 -250 0 250 500 750 1000
d F R
/dt (
gC m
-2 y
-2)
-750
-500
-250
0
250
500
750
1000Coefficients:b[0] -4.496b[1] 0.704r ² 0.607n =164
Baldocchi, Austral J Botany, 2008
Interannual Variations in Photosynthesis and Respiration are Coupled
Interannual Variability in NEE is tiny across the Global Network
FLUXNET Network, 75 sites
NEE (gC m-2 y-1)
-1000 -800 -600 -400 -200 0 200 400 600
p(N
EE
)
0.00
0.05
0.10
0.15
0.20
0.25
2002: -220 +/- 35.2 gC m-2 y-1
2003: -238 +/- 39.92004: -243 +/- 39.7 2005: -237 +/- 38.7
Interannual Variability in GPP is tiny across the Global Network, too
FLUXNET Network, 75 sites
GPP (gC m-2 y-1)
0 500 1000 1500 2000 2500 3000 3500
p(G
PP
)
0.00
0.05
0.10
0.15
0.20
0.25
2002: 1117 +/- 74.0gC m-2 y-1
2003: 1103 +/- 67.82004: 1162 +/- 77.02005: 1133 +/- 70.1
FLUXNET database
precipitation
0 500 1000 1500 2000 2500
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
2003: 721 +/- 59 mm2004: 855 +/- 542005: 806 +/- 46
Rg (GJ m-2 y-1)
1 2 3 4 5 6 7 8
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
2003: 4.70 +/- 0.129 GJ m-2 y-1
2004: 4.67 +/- 0.1322005: 4.59 +/- 0.135
Little Change in Abiotic Drivers--annual Rg, ppt --across Network
FLUXNET, 75 sites
Tair, C
-10 -5 0 5 10 15 20 25 30
0.00
0.05
0.10
0.15
0.20
0.25
2003: 8.86 +/- 0.82 C2004: 8.2 +/- 0.86 2005: 8.84 +/- 0.78
Complicating Dynamical Factors
• Switches– Phenology– Drought– Frost/Freeze
• Pulses– Rain– Litterfall
• Emergent Processes– Diffuse Light/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:q(-)GPP(-); Re(-)
Clouds:PAR(-) GPP=f(PAR)(+)
June, 2003
Time (hour)
0 5 10 15 20 25 30
Flux
Den
sity
-10
-8
-6
-4
-2
0
6
7
soil respirationcanopy photosynthesis
Soil Respiration Lags Photosynthesis onHourly Scale
Tang et al. 2006, GCB
July, Rsoil-Ps lag
lag (30 min)
-30 -20 -10 0 10 20 30la
g co
rrel
atio
n-1.0
-0.8
-0.6
-0.4
-0.2
0.0
Emergent Scale Process:CO2 Flux and Diffuse Radiation
Niyogi et al., GRL 2004
• We are poised to see effects of Cleaner/Dirtier Skies and Next Volcano
FLUXNET 2007 Database
GPP at 2% efficiency and 365 day Growing Season
Potential and Real Rates of Gross Carbon Uptake by Vegetation:Most Locations Never Reach Upper Potential
tropics
GPP at 2% efficiency and 182.5 day Growing Season
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
E. Falge et al 2002 AgForMet; Baldocchi et al 2001 BAMS
Optimal NEE: Acclimation with Temperature
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??
Baldocchi et al. Int J. Biomet, 2005
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
8 day means
Daily g
ross
CO
2 flux
(m
mol m
-2 d
ay-1
)
0
200
400
600
800
1000
1200
1400
16008 day meansDaily n
et C
O2 flux
(m
mol m
-2 d
ay-1
)
-400
-200
0
200
400
600
800
r2 = 0.92
8 day means
Daily g
ross
LUE
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
Daily n
et C
O2 flux
(m
mol 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
Daily g
ross
CO
2 flux
(m
mol 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 0.03Daily g
ross
LUE
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
Do Snap-Shot C Fluxes, inferred from Remote Sensing, Relate to Daily C Flux Integrals?
Spatial Variations in C Fluxes
Xiao et al. 2008, AgForMet
Xiao and Baldocchi, unpublished
Upscaling Flux Data with Remote Sensing DataPublished, Global: -182 gC m-2 y-1
Map, US: -189 gC m-2 y-1
Baldocchi, White, Schwartz, unpublished
Spatialize Phenology with Transformation Using Climate Map
Mean Air Temperature, C
4 6 8 10 12 14 16 18
Day
of N
EE =
060
80
100
120
140
160
Coefficients:b[0]: 169.3b[1]: -4.84r ²: 0.691
White, Baldocchi and Schwartz, unpublished
Flux Based Phenology Patterns with Match well with data from Phenology Network
Limits to Landscape Classification by Functional Type
• Stand Age/Disturbance• Biodiversity• Fire• Logging• Insects/Pathogens• Management/Plantations• Kyoto Forests
Conifer Forests, Canada and Pacific Northwest
Stand Age After Disturbance
1 10 100 1000
F N (g
C m
-2 y
-1)
-600
-400
-200
0
200
400
600
800
1000
Time Since Disturbance Affects Net Ecosystem Carbon Exchange
Baldocchi, Austral J Botany, 2008 Data of teams lead by Amiro, Dunn, Paw U, Goulden
Other Activities and Uses of Fluxnet Data
• Landuse• Ecosystem Modeling• EcoHydrology• Biodiversity• Climate
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
Kucharik et al., 2006 Ecol Modeling
Ecosystem Model Testing and Development
Seasonality of Photosynthetic Capacity
Wang et al, 2007 GCB
Optimizing Seasonality of Vcmax improves Prediction of Fluxes
Wang et al, 2007 GCB
‘A continuing challenge to long-term Earth observations is the prejudice against science that is not directly aimed at hypothesis testing. At a time when the planet is being propelled by human action into another climate regime with incalculable social and environmental costs, we cannot afford such a rigid view of the scientific enterprise. The only way to figure out what is happening to our planet is to measure it, and this means tracking changes decade after decade and poring over the records. A point of diminishing scientific returns has never been realized in what is now known as the "Keeling Curve," the Mauna Loa CO2 record’.
Ralph Keeling, Science March 28, 2008
A Continuing Challenge and Task:Measure Ecosystem Processes on Ecosystem Timescales
Acknowledgements
• Leadership– Riccardo Valentini, Steve Running, Bev Law
• Data Preparation: FLUXNET-2007– Dario Papale, Markus Reichstein, Catharine Van Ingen,
Deb Agarwal, Tom Boden, Bob Cook, Susan Holliday, Bruce Wilson, +++
• FLUXNET Office @ Berkeley– Eva Falge, Lianhong Gu, Matthias Falk, Rodrigo Vargas,
Youngryel Ryu• Networks
– AmeriFlux, CarboEurope, AsiaFlux, ChinaFlux, Fluxnet Canada, OzFlux, +++
• Agencies– NSF/RCN, ILEAPS, DOE/TCP, NASA, Microsoft, ++++