Asilomar FLUXNET Workshop
Dennis Baldocchi
University of California, Berkeley
Bag of Tools To Assess Terrestrial Carbon Budgets
at Across Scales
Global and Regional
Inversion Modeling
Eddy Flux
Measurements/
FLUXNET
Remote Sensing/
MODIS
Forest/Biomass
Inventories
Biogeochemical/Ecosystem Modeling
Philosophy
• We are Scientists, Not Experimentalists or
Modelers
• We need multiple constraints to understand the
‘breathing of the biosphere’ because it is a
complex, non-linear process that spans 14 orders
of magnitude in time and space
• We are applying the Linux model to science, open
access/open source
– Improves the product via its use by multiple agents
– It is built on trust, sharing, collaboration and two-way
interaction
Ideas
• Produce and Provide Data to parameterize and refine
complex, coupled models
• Validate Models at Tower Sites, via time series
• Validate Models across climate and ecosystem
gradients
• Develop value added products, e.g. grid average fluxes
• Use models to diagnose limits with measurements
• Use Models to ask and answer scientific questions
pertaining to the ‘breathing of the biosphere’ across a
spectrum of time and space scales
Data and Results
Probability Distribution of Published NEE Measurements, Integrated Annually
0.07
0.06
0.05
mean: -182.9 gC m-2 y-1
std dev: 269.5
n: 506
p(x)
0.04
0.03
0.02
0.01
0.00
-1500
-1000
-500
0
FN (gC m-2 y-1)
Baldocchi, Austral J Botany, 2008
500
1000
1500
Interannual Variability in NEE is tiny across the Global Network
0.25
FLUXNET Network, 75 sites
2002: -220 +/- 35.2 gC m-2 y-1
2003: -238 +/- 39.9
2004: -243 +/- 39.7
2005: -237 +/- 38.7
0.20
p(NEE)
0.15
0.10
0.05
0.00
-1000
-800
-600
-400
-200
0
NEE (gC m-2 y-1)
200
400
600
Interannual Variability in GPP is tiny across the Global Network, too
2002: 1117 +/- 74.0gC m
2003: 1103 +/- 67.8
2004: 1162 +/- 77.0
2005: 1133 +/- 70.1
-2 -1
y
0.25
FLUXNET Network, 75 sites
p(GPP)
0.20
0.15
0.10
0.05
0.00
0
500
1000
1500
2000
GPP (gC m-2 y-1)
2500
3000
3500
FLUXNET, 75 sites
0.25
2003: 8.86 +/- 0.82 C
2004: 8.2 +/- 0.86
2005: 8.84 +/- 0.78
0.20
Little Change in Abiotic Drivers-annual Rg, ppt --across Network
pdf
0.15
0.10
0.05
0.00
-10
-5
0
5
10
15
20
25
30
Tair, C
2003: 4.70 +/- 0.129 GJ m-2 y-1
2004: 4.67 +/- 0.132
2005: 4.59 +/- 0.135
FLUXNET database
0.35
0.35
0.30
0.30
2003: 721 +/- 59 mm
2004: 855 +/- 54
2005: 806 +/- 46
0.25
0.25
0.20
pdf
pdf
0.20
0.15
0.15
0.10
0.10
0.05
0.05
0.00
1
0.00
0
500
1000
1500
precipitation
2000
2500
2
3
4
5
Rg (GJ m-2 y-1)
6
7
8
Does Net Ecosystem Carbon Exchange Scale with Photosynthesis?
1000
750
250
-2
-1
FN (gC m y )
500
0
-250
-500
-750
-1000
0
500
1000
1500
2000
2500
3000
3500
4000
FA (gC m-2 y-1)
Ecosystems with greatest GPP don’t necessarily experience greatest NEE
Baldocchi, Austral J Botany, 2008
Ecosystem Respiration Scales Tightly with Ecosystem Photosynthesis,
But Is with Offset by Disturbance
4000
Undisturbed
Disturbed by Logging, Fire, Drainage, Mowing
3500
FR (gC m-2 y-1)
3000
2500
2000
1500
1000
500
0
0
500
1000
1500
2000
2500
FA (gC m-2 y-1)
Baldocchi, Austral J Botany, 2008
3000
3500
4000
Net Ecosystem Carbon Exchange Scales with Length of Growing Season
Temperate and Boreal Deciduous Forests
Deciduous and Evergreen Savanna
200
FN (gC m-2 yr-1)
0
-200
-400
-600
-800
-1000
50
100
150
200
250
300
Length of Growing Season, days
Baldocchi, Austral J Botany, 2008
350
Decadal Plus Time Series of NEE:
Flux version of the Keeling’s Mauna Loa Graph
10
8
Harvard Forest, 1991-2004
6
-2
-1
NEE (gC m d )
4
2
0
-2
-4
-6
-8
-10
1990
1992
1994
1996
1998
Year
Data of Wofsy, Munger, Goulden, et al.
2000
2002
2004
2006
Interannual Variation and Long Term Trends
in Net Ecosystem Carbon Exchange (FN), Photosynthesis (FA) and Respiration (FR)
Harvard Forest
1800
Carbon Flux (gC m-2 y-1)
1600
1400
1200
FN
1000
0
FA
FR
-200
-400
-600
1990
1992
1994
1996
1998
Year
Urbanski et al 2007 JGR
2000
2002
2004
2006
Lag Effects Due to 2003 European
Drought/Heat Stress
20
10
NEE
[g C m-2 week-1]
0
-10
-20
-30
-40
-50
-60
-70
Hainich
Leinefelde
-80
2002
Knohl et al Max Planck, Jena
2003
2004
2005
Potential and Real Rates of Gross Carbon Uptake by Vegetation:
Most Locations Never Reach Upper Potential
GPP at 2% efficiency and 365 day
Growing Season
tropics
GPP at 2% efficiency and
182.5 day Growing Season
FLUXNET 2007 Database
Emergent Scale Process:
CO2 Flux and Diffuse Radiation
•We are poised to see effects of Cleaner/Dirtier Skies and Next Volcano
Niyogi et al., GRL 2004
Time Since Disturbance Affects Net Ecosystem Carbon Exchange
Conifer Forests, Canada and Pacific Northwest
1000
800
FN (gC m-2 y-1)
600
400
200
0
-200
-400
-600
1
10
100
1000
Stand Age After Disturbance
Baldocchi, Austral J Botany, 2008
Data of teams lead by Amiro, Dunn, Paw U, Goulden
Role of Proper Model Abstraction
ESPM 111 Ecosystem Ecology
Emergent Processes: Impact of Leaf Clumping on
Canopy Light Response Curves
Deciduous forest
-2 -1
Fc (mol m s )
-40
(a)
-30
-20
-10
0
model: spherical leaves
10
0
500
1000
1500
2000
2500
-2 -1
Fc (mol m s )
-40
-30
(b)
-20
-10
measured
model: clumped leaves
0
10
0
500
1000
PPFD
1500
-2
(mol m
2000
-1
s
)
2500
Scales of Interannual Variability
Walker Branch Watershed, TN: 1981-2001
CANOAK
1
year
130 days
nSnee/nee
0.1
0.01
7 years
0.001
0.0001
0.0001
0.001
0.01
Frequency (1/day)
0.1
1
Seasonality of Photosynthetic Capacity
Wang et al, 2007 GCB
Optimizing Seasonality of Vcmax improves Prediction of Fluxes
Wang et al, 2007 GCB
Flux data Aren’t always the Perfect Truth
10
5
-1
-2
mol m s
CO2 Flux Density
0
-5
-10
-15
-20
-25
0
4
8
12
16
20
24
time (hours)
Ne: measured (-4.84 gC m-2 day-1)
Ne: computed (-5.09 gC m-2 day-1)
-2
-1
Fwpl+Storage: measured (-5.96 gC m day )
-2
-1
Fwpl: measured (-6.12 gC m day )
Test Model Response Functions with Data
Canoak vs Deciduous Forests
1800
GPP(gC m-2 y-1)
1600
1400
1200
1000
800
CANOAK, Oak Ridge
Deciduous Forests, FLUXNET
600
600
800
1000
1200
Reco (gC m-2 y-1)
1400
1600
1800
NEE and Growing Season Length
Temperate Deciduous Forests
0
-100
NEE (g C m-2 year-1)
-200
-300
-400
-500
-600
CANOAK, Oak Ridge, TN
Published Measurements, r2=0.89
-700
-800
120
140
160
180
200
Days with NEE < 0
220
240
Soil Temperature:
An Objective Indicator of Phenology??
Soroe, Denmark
Beech Forest
1997
20
NEE, gC m-2 d-1
Tair, recursive filter, oC
Tsoil, oC
15
10
5
0
-5
-10
0
50
100
150
200
day
Data of Pilegaard et al.
250
300
350
Soil Temperature:
An Objective Measure of Phenology, part 2
Temperate Deciduous Forests
160
150
140
Day NEE=0
130
Denmark
Tennessee
Indiana
Michigan
Ontario
California
France
Massachusetts
Germany
Italy
Japan
120
110
100
90
80
70
70
80
90
100
110
120
Day, Tsoil >Tair
Baldocchi et al. Int J. Biomet, 2005
130
140
150
160
Spatialize Phenology with Transformation Using Climate Map
160
Day of NEE = 0
140
120
100
Coefficients:
b[0]: 169.3
b[1]: -4.84
r ²: 0.691
80
60
Baldocchi, White, Schwartz, unpublished
4
6
8
10
12
Mean Air Temperature, C
14
16
18
Flux Based Phenology
Patterns with Match
well with data from
Phenology Network
White, Baldocchi and Schwartz, unpublished
NEE, 2001-2006:
Upscaling Tower Fluxes with Remote Sensing, Climate and Regression Tree analysis
J. Xiao
Effects of Functional Types and Rsfc on Normalized Evaporation
2.00
1.75
Wheat
Corn
1.50
Boreal jackpine forest
Temperate deciduous forest
Mediterranean oak-grass savanna
LE/LEeq
1.25
1.00
0.75
0.50
0.25
0.00
10
100
1000
-1
Rcanopy (s m )
Rc is a f(LAI, N, soil moisture, Ps Pathway)
10000
Gc ~ f ( LAI , Gs max , N , v )
Boreal Forest
1.3
k=10
1.2
k=8.0
k=7.0
QE/QE,eq
1.1
1.0
0.9
0.8
0.7
0.6
0
20
40
60
80
100 120 140 160 180 200
Vcmax*LAI
ESPM 129 Biometeorology
Stand Age also affects differences between ET of forest vs grassland
Plynlimon, Wales
900
grassland
conifer forest
Evaporation (mm y-1)
800
700
600
500
400
300
200
1970
1975
1980
1985
1990
Year
Marc and Robinson, 2007 HESS
1995
2000
2005
2010
Use Models and Data to ask Science Questions
Global Convergence of Leaf Temperature (????)
Helliker and Richer 2008, Nature
ESPM 129 Biometeorology
35
Probability density function of mean leaf temperature of a
broadleaved forest in Tennessee
Temperate Broadleaved Forest
Days 100 to 273
0.12
0.10
1993
1981
1982
1984
1994
1997
1995
0.08
pdf
0.06
0.04
0.02
0.00
0
10
20
T leaf
ESPM 129 Biometeorology
30
40
36
Ponderosa Pine, Metolius, OR
0.14
0.12
Tleaf
Tair
0.10
pdf
0.08
0.06
0.04
Transpiration-weigthed
Tleaf = 23.6 C
0.02
0.00
-20
-15
-10
-5
0
5
10
Tleaf (C)
15
20
25
30
35