the big picture to assess the global carbon budget we need information that is ‘everywhere, all of...
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The Big Picture
• To assess the Global Carbon Budget we need information that is ‘Everywhere, All of the Time’
• Many Complementary Methods exist, Each with specific, scale-dependent Pros and Cons, so the Overall Premise is Impossible for any stand alone method
• Multi-Faceted, Integrative C Flux program is Needed• Statistics can be our ‘Friend’ and May Relieve us from Being ‘Everywhere’• To Address Phenology, Seasonaly Dynamics, Intermittency and Trends we still
nee to measure ‘All the Time’.
Dennis Baldocchi, University of California, Berkeley
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Methods To Assess Terrestrial Carbon Budgets 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.
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remote sensingof CO2
Tem
pora
l sca
le
Spatial scale [km]
hour
day
week
month
year
decade
century
local 0.1 1 10 100 1000 10 000 global
forestinventory
plot
Countries EUplot/site
talltowerobser-
vatories
Forest/soil inventories
Eddycovariance
towers
Landsurface remote sensing
From point to globe via integration with remote sensing (and gridded metorology)
From: Markus Reichstein, MPI
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FLUXNET: From Sea to Shining Sea500+ Sites, circa 2009
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How many Towers are needed to estimate mean NEE, GPPand assess Interannual Variability, at the Global Scale?
Green Plants Abhor a Vacuum, Most Use C3 Photosynthesis, so we May Not need to be Everywhere, All of the Time
We Need about 75 towers to produce Robust and Invariant Statistics
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Year
1992 1994 1996 1998 2000 2002 2004 2006 2008
Num
ber
of s
ites
1
10
100
1000
FLUXNET
Year
1992 1994 1996 1998 2000 2002 2004 2006 2008C
Flu
x, g
C m
-2 y
-10
200
400
600
800
1000
1200
1400
GPPReco
Year
1992 1994 1996 1998 2000 2002 2004 2006 2008
C F
lux,
gC
m-2
y-1
-500
-400
-300
-200
-100
0
Ne
Statistical Samping of Interannual variability of C fluxes with a Network
Baldocchi et al., in prep
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Flux-Derived Global GPP: 123 +/- 8 PgC/y
Beer et al, Science, 2010
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Upscale NEP, Globally, Explicitly
1. Compute GPP = f(T, ppt)2. Compute Reco = f(GPP,
Disturbance)3. Compute NEP = GPP-Reco
Reco = 101 + 0.7468 * GPP
Reco, disturbed= 434.99 + 0.922 * GPP
FA (gC m-2 y-1)
0 500 1000 1500 2000 2500 3000 3500 4000
FR (
gC m
-2 y
-1)
0
500
1000
1500
2000
2500
3000
3500
4000
UndisturbedDisturbed by Logging, Fire, Drainage, Mowing FLUXNET Synthesis
Baldocchi, 2008, Aust J Botany
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<NEE> = -129 gC m-2 y-1
S NEE = -17.5 PgC/y!!
Implies too Large NEE (|-700 gC m-2 y-1| Fluxes in TropicsIgnores C losses from Disturbance and Land Use Change
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Disturbance GPP,PgC y-1
Reco NBP,PgC y-1
0% 113.6 96.1 -17
1% - 101.05 -13.1
2% - 103.6 -10.4
4% - 109.7 +/1 0.17
-4.02 +/- 0.16
Carbon Balance if Randomly Disturb C pools
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Remote Sensing of NPP:Up and down PAR, LED, Pyranometer, 4 band Net Radiometer
Phenology, fpar, LAI, APAR, N, Vcmax
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Hemispherical Camera Upward Looking Camera
Web Camera
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2009.2 2009.4 2009.6 2009.8 2010.0 2010.2 2010.4
LA
I
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
DCP1DCP2DCP3
Year
2009.2 2009.4 2009.6 2009.8 2010.0 2010.2 2010.4
Lan
ds
cap
e L
AI
0.0
0.2
0.4
0.6
0.8
1.0
1.2
DCPLAI-2000
Phenology with Upward-Facing Digital Camera
Ryu et al, in prep
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ESPM 111 Ecosystem Ecology
Annual Grassland, 2004-2005
Wavelength (nm)
400 500 600 700 800 900 1000
Ref
lect
ance
0.0
0.2
0.4
0.6
0.8
1.0
Oct 13, 2004Oct 27, 2004Nov 11, 2004Jan 5, 2005Feb 2, 2005Apr 1, 2005Mar 9, 2005May 11, 2005Dec 29, 2005
Falk, Ma and Baldocchi, unpublished
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Ground Based, Time Series of Hyper-Spectral Reflectance Measurements, in Conjunction with Flux Measurements Can be Used to Design Future Satellites
Ryu et al. Agricultural and Forest Meteorology, in review
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Spectrally-Selective Vegetation Indices Track Seasonality of C Fluxes Well
Ryu et al. Agricultural and Forest Meteorology, in review
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Ryu et al. Agricultural and Forest Meteorology, in review
Vegetation Indices can be Used to Predict GPP with Light Use Efficiency Models
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UpScaling of FluxNetworks
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Xiao et al 2010, Global Change Biology
What We can Do:Is Precision Good Enough for Treaties?
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Xiao et al 2010, Global Change Biology
Map of Gross Primary Productivity Derived from Regression Tree AlgorithmsDerived from Flux Network, Satellite Remote Sensing and Climate Data
Youngryel Ryu and D. Baldocchi, unpublished
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Xiao et al 2010, Global Change Biology
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Net Ecosystem C Exchange
Xiao et al. 2008, AgForMet
springsummer
autumn winter
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Jingfeng Xiao and D Baldocchi
area-averaged fluxes of NEE and GPP were -150 and 932 gC m-2 y-1
net and gross carbon fluxes equal -8.6 and 53.8 TgC y-1
Upscale GPP and NEE to the Biome Scale
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Youngryel Ryu and D. Baldocchi, unpublished
Coupled Energy Balance-Photosynthesis Sun/Shade Model driven by MODIS,Implemented with Cloud-Computing System
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Youngryel Ryu and D. Baldocchi, unpublished
Coupled Energy Balance-Photosynthesis Sun/Shade Model driven by MODIS
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Conclusion
• Much Promise in Coupling Remote Sensing and Flux Network Information to produce State, Continental and Global Maps of Net and Gross Carbon Fluxes
• How Good is Good Enough?– Errors are still on the Order of +/- 8 PgC/y so we may
not have the resolution to detect inter-annual variability and may need Decades to Detect Trends in this Noisy System
– Perspective, Drawdown during Glacial-Inter-Glacial was 20 TgC/y of the Land-Ocean System