Energy balance closure at global eddy covariance research sites is ...
On the use of eddy-covariance and optical remote sensing data for biogeochemical modelling
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Transcript of On the use of eddy-covariance and optical remote sensing data for biogeochemical modelling
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On the use of eddy-covariance and optical remote sensing data for biogeochemical
modellingMarkus Reichstein, Dario Papale
Biogeochemical Model-Data-Integration Group, Max-Planck-Institute Jena
Laboratory of Forest Ecology, University of Tuscia
Carbon Fusion International Workshop Edinburgh, May 2006
BEAM-DIG
MPI-BGC
BEAM-DIG
MPI-BGC
Biogeochemical
Model-Data Integration Group
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Ecosystem models
+ provide system understanding+ promise inter-/extrapolation capacity+ may include historical effects
– are simplifications of the world– can’t predict stochastic events
Remote sensing
+ objective/consistent observations+ spatially and temporally dense
– data quality lower– processes not directly observable,
no history, no prediction
Ecosystem data
+ Potentially high quality+ often high temporal resolution
– data compatibility ? – ‘point’ observations
BGC-Model-Data Integration Overview
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Outline
• Introduction to eddy covariance data
• Bottom-up perspective of an ‘ideal’ data integration-validation process
• Problems and obstacles in this process
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Observing ecosystem gas exchange: eddy covariance
Flux = speed x concentration
Pho
to:
Bal
docc
hi
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
+ Measures whole ecosystem exchange of CO2 and H2O, …+ Non-destructive & continuous+ time-scale hourly to interannual+ integrates over large area
- only on flat sites- relies on turbulent conditions ==> data gaps, stochastic data- source area varying (flux footprint)- only ‚point‘ measurements
Does not deliver compartment fluxes, but:NEP = GPP - Reco
CO2, H2O
Eddy covariance
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Half-hourly eddy covariance data
-25-20-15-10
-505
1015
5/2001 6/2001 7/2001 8/2001 9/2001 10/2001 10/2001 11/2001 12/2001 1/2002
Ca
rbo
n f
lux
[µm
ol m
-2 s
-1]
-20
30
80
130
180
230
280
5/2001 6/2001 7/2001 8/2001 9/2001 10/2001 10/2001 11/2001 12/2001 1/2002
Wate
r flu
x [W
m-2
]
0.00
0.20
0.40
0.60
0.80
1.00
1.20
5/2001 6/2001 7/2001 8/2001 9/2001 10/2001 10/2001 11/2001 12/2001 1/2002So
il w
ate
r co
nte
nt
[fra
ctio
n F
C]
Respiration
Carbon uptake
Evapotransp.
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Network of ecosystem-level observations
>1000 site-years 1012 raw measurements (1013 bytes)
• Network and intercomparison studies• Harmonised and documented data processing
• Aubinet et al. (2000), Falge et al. (2001), Foken et al. (2002), Göckede/Rebmann/Foken (2004) : general set-up and methodology, quality assurance, gap-filling
• Reichstein et al. (2005), Glob. Ch. Biol.: u*-correction, gap-filling, partitioning of NEE
• Papale et al. (in prep), Biogeosciences: Quality control, eval. uncertainties• Moffat et al. (in prep): Gap-filling inter-comparison• Online processing tool: http://gaia.agraria.unitus.it/lab/reichstein/
Raw data Knowledge
1013 108 106 102 bytesTurb stat. Synth./aggr. Model param.
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Ideal model-data integration cycle (bottom-up)
Model(re)formulation(Definition of model
structure)Model
characterization(Forward runs, consistency check,
sensitivity, uncert. analysis)
Model parameter estimation
(Multiple constraint)
Parameterinterpretation
(Thinking)
Generalization(‘up-scaling’)
Model validation(against indep. data, by scale or quantity)
Model application
DATA
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
The bottom-up model PROXEL
Canopy Layer 1Canopy Layer 2Canopy Layer 3
...
Canopy Layer n
Canopy
Solar radiation Air temperature [CO2] Relative humidity Wind speed
LAI, SAI
Leaf physiology
Phenology
CO2
H2O
Soil Layer 1Soil Layer 2Soil Layer 3
...
Soil Layer n
Soil
Air temperature Wind speed
Soil hydraulicparameters
Soil thermalparameters
Soil respirationparameters
PrecipitationWater
extraction
Vapour pressure
Root distri-bution
CO2
H2Oeffective soil
{Quantum use efficiency,electron transport and carboxylation capacities, stomatal conductance}
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
I. Model charaterization / forward model run
Rei
chst
ein,
Ten
hune
n et
al.,
Glo
bal C
hang
e B
iolo
gy, 2
002
CO
2 flu
x of
GP
P [
µm
ol m
-2 s
-1]
00.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
-2
0
2
4
6
8
10
12
0 4 8 12 16 20 24Local time [hr]
H2O
flux
[m
m/h
]
(a)
(c)
Eddy cov.Sap flowModelled
Eddy cov.Modelled
Well watered conditions
0 4 8 12 16 20 24Local time [hr]
(b)
(d)
Drought stressed conditions
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
II. Dual-constraint parameter estimation
Reic
hst
ein
et
al. 2
00
3, JG
R
Target region
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
IIa. Inferred parameter timeseries
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
150 200 250 300
Estimated course of photosynthetic capacity (Vcmax)
Rel
ativ
e ca
paci
ty
Rain event
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
150 200 250 300
Rel
ativ
e
Rain event
Julian day
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
150 200 250 300
Estimated course of photosynthetic capacity (Vcmax)
Rel
ativ
e ca
paci
ty
Rain event
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
150 200 250 300
Rel
ativ
e
Rain event
Julian day
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 0.2 0.4 0.6 0.8 1 1.2
Rel
ativ
e va
lue
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 0.2 0.4 0.6 0.8 1 1.2
Rel
ativ
e va
lue
Reichstein et al. 2003, JGR
III. Interpretation & Generalization
Relative soil water content
Rel
ativ
e le
af a
ctiv
ity
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
ENF EBF DBF MF Sav Oshrub Crop
RU
E [
gC
/ M
J A
PA
R]
III. Interpretation and Generalization:Keyp. RUEmax
PFTs color coded
0 5 10
GPP_MOD17_MET [gC m-2 day-1]
0
5
10G
PP
[gC
m-2
day
-1]
0 5 10
0
5
10
Bi5/1
Bi7/1
Bi8/1Bi6/2Bi7/2Bi8/2
Bi9/2
Bi10/2
0 5 10
0
5
10
Bi1/1Bi2/1
Bi3/1Bi4/1
Bi5/1
Bi6/1Bi7/1
Bi8/1
Bi9/1Bi10/1
Bi11/1Bi12/1
0 5 10
0
5
10
Br1/1
Br2/1
Br3/1
Br4/1
Br5/1
Br6/1
Br7/1
Br8/1
Br9/1Br10/1Br11/1
Br12/1
0 5 10
0
5
10
El2/1
El3/1
El4/1
El5/1
El6/1
El8/1
El12/1El1/2
El6/2
El7/2
El8/2
El10/2El11/2El12/2
0 5 10
0
5
10
Ha1/1Ha2/1Ha3/1
Ha4/1
Ha5/1
Ha6/1
Ha7/1
Ha8/1
Ha9/1
Ha10/1
Ha11/1Ha12/1
0 5 10
0
5
10
He1/1 He2/1He3/1
He4/1
He5/1
He6/1He7/1
He8/1
He9/1
He10/1
He11/1
He12/1He1/2 He2/2
He3/2
He4/2
He5/2
He6/2He7/2
He8/2
He9/2
He10/2
He11/2He12/2
0 5 10
0
5
10
Hy1/1Hy3/1
Hy4/1
Hy5/1
Hy6/1
Hy7/1
Hy8/1
Hy9/1
Hy10/1
Hy11/1
0 5 10
0
5
10
Jo1/1Jo2/1Jo3/1Jo4/1Jo5/1
Jo6/1
Jo7/1
Jo8/1
Jo9/1
Jo10/1Jo11/1Jo12/1Jo1/2Jo2/2Jo3/2
Jo4/2
Jo5/2
Jo6/2
Jo7/2
Jo8/2
Jo9/2
Jo10/2Jo11/2Jo12/2
0 5 10
0
5
10
Mi1/1
Mi4/1
Mi6/1
Mi11/1
Mi2/2
Mi4/2
Mi7/2Mi9/2
0 5 10
0
5
10
No2/1
No3/1
No5/1
No6/1
No7/1
No8/1
No9/1
No10/1
No11/1No12/1
0 5 10
0
5
10
Pi4/2
Pi5/2
Pi6/2
Pi7/2
Pi8/2
Pi9/2
0 5 10
0
5
10
Pu1/1
Pu2/1
Pu3/1
Pu4/1Pu5/1
Pu6/1
Pu7/1
Pu8/1Pu9/1
Pu10/1Pu11/1
Pu12/1
0 5 10
0
5
10
Sa3/1
Sa4/1
Sa5/1Sa6/1
Sa7/1
Sa8/1
Sa9/1Sa10/1
Sa12/1
0 5 10
0
5
10
TC1/1TC2/1TC3/1
TC4/1
TC5/1
TC6/1
TC7/1
TC8/1
TC9/1
TC10/1
TC11/1
TC12/1TC1/2TC2/2
TC3/2 TC4/2
TC6/2
TC8/2
TC9/2
TC10/2
TC11/2TC12/2
0 5 10
0
5
10
Th1/1Th2/1
Th3/1
Th4/1
Th5/1
Th6/1
Th7/1
Th8/1
Th9/1
Th10/1
Th11/1
Th12/1
0 5 10
0
5
10
Vi1/1
Vi2/1
Vi3/1
Vi4/1
Vi6/1 Vi7/1
Vi8/1
Vi9/1
Vi10/1
Vi11/1
Vi12/1
0 5 10
0
5
10
Ya1/1
Ya2/1
Ya3/1
Ya6/1Ya8/1Ya9/1Ya10/1
Ya1/2
Ya2/2Ya3/2
Ya4/2
Ya5/2
Ya7/2 Ya8/2Ya9/2
Ya12/2
PFTs color coded
0 5 10
GPP_MOD17epsmax_new [gC m-2 d-1]
0
5
10G
PP
[gC
m-2
day
-1]
0 5 10
0
5
10
Bi5/1
Bi7/1
Bi8/1Bi6/2Bi7/2Bi8/2
Bi9/2
Bi10/2
0 5 10
0
5
10
Bi1/1Bi2/1
Bi3/1Bi4/1
Bi5/1
Bi6/1Bi7/1
Bi8/1
Bi9/1Bi10/1
Bi11/1Bi12/1
0 5 10
0
5
10
Br1/1
Br2/1
Br3/1
Br4/1
Br5/1
Br6/1
Br7/1
Br8/1
Br9/1Br10/1Br11/1
Br12/1
0 5 10
0
5
10
El2/1
El3/1
El4/1
El5/1
El6/1
El8/1
El12/1El1/2
El6/2
El7/2
El8/2
El10/2El11/2El12/2
0 5 10
0
5
10
Ha1/1 Ha2/1Ha3/1
Ha4/1
Ha5/1
Ha6/1
Ha7/1
Ha8/1
Ha9/1
Ha10/1
Ha11/1Ha12/1
0 5 10
0
5
10
He1/1 He2/1He3/1
He4/1
He5/1
He6/1He7/1
He8/1
He9/1
He10/1
He11/1
He12/1He1/2 He2/2
He3/2
He4/2
He5/2
He6/2He7/2
He8/2
He9/2
He10/2
He11/2He12/2
0 5 10
0
5
10
Hy1/1Hy3/1
Hy4/1
Hy5/1
Hy6/1
Hy7/1
Hy8/1
Hy9/1
Hy10/1
Hy11/1
0 5 10
0
5
10
Jo1/1Jo2/1Jo3/1 Jo4/1Jo5/1
Jo6/1
Jo7/1
Jo8/1
Jo9/1
Jo10/1Jo11/1Jo12/1Jo1/2Jo2/2Jo3/2
Jo4/2
Jo5/2
Jo6/2
Jo7/2
Jo8/2
Jo9/2
Jo10/2Jo11/2Jo12/2
0 5 10
0
5
10
Mi1/1
Mi4/1
Mi6/1
Mi11/1
Mi2/2
Mi4/2
Mi7/2Mi9/2
0 5 10
0
5
10
No2/1
No3/1
No5/1
No6/1
No7/1
No8/1
No9/1
No10/1
No11/1No12/1
0 5 10
0
5
10
Pi4/2
Pi5/2
Pi6/2
Pi7/2
Pi8/2
Pi9/2
0 5 10
0
5
10
Pu1/1
Pu2/1
Pu3/1
Pu4/1Pu5/1
Pu6/1
Pu7/1
Pu8/1Pu9/1
Pu10/1Pu11/1
Pu12/1
0 5 10
0
5
10
Sa3/1
Sa4/1
Sa5/1Sa6/1
Sa7/1
Sa8/1
Sa9/1Sa10/1
Sa12/1
0 5 10
0
5
10
TC1/1TC2/1TC3/1
TC4/1
TC5/1
TC6/1
TC7/1
TC8/1
TC9/1
TC10/1
TC11/1
TC12/1TC1/2TC2/2
TC3/2 TC4/2
TC6/2
TC8/2
TC9/2
TC10/2
TC11/2TC12/2
0 5 10
0
5
10
Th1/1Th2/1
Th3/1
Th4/1
Th5/1
Th6/1
Th7/1
Th8/1
Th9/1
Th10/1
Th11/1
Th12/1
0 5 10
0
5
10
Vi1/1
Vi2/1
Vi3/1
Vi4/1
Vi6/1 Vi7/1
Vi8/1
Vi9/1
Vi10/1
Vi11/1
Vi12/1
0 5 10
0
5
10
Ya1/1
Ya2/1
Ya3/1
Ya6/1Ya8/1Ya9/1Ya10/1
Ya1/2
Ya2/2Ya3/2
Ya4/2
Ya5/2
Ya7/2Ya8/2Ya9/2
Ya12/2
250.0500.0750.01000.01250.01500.01750.02000.0
0.00
2000.00
250.0500.0750.01000.01250.01500.01750.02000.0
0.00
2000.00
250.0500.0750.01000.01250.01500.01750.02000.0
0.00
2000.00
• inter-PFT variability• intra-PFT variability• f(species, N, T???)
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IV. Validation at larger scale
70°N29,2° W
11° W 23° E
58° E
60°N
50°N
40°N
"Les Landes"
0
5
10
15
20
Flight 'Upscaled' Schmittgen et al. (2004), JGR
NE
E in
teg
rate
d 1
2:30
-14:
30[µ
mol
m-2
s-1]
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
GCB, in press
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
The problems
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
To consider with DA of eddy covariance data:
• How is the error structure of the data itself?
• How to address mismatch of scales (‘point’ versus pixel)?– Remote sensing– Meteorological data
• How do perform up-scaling from tower sites?– Representativity– Generalization
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Errors in the data
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Error model influence on parameter estimates
Const. abs errors Const. rel. errors
Pa
ram
ete
r es
tima
te
Search strategy
I
II
Simplified after Trudinger et al. (OPTIC)
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Errors in eddy covariance data
• Random errors – ~ 30% for the half-hourly flux, (turbulences !)
• Systematic errors– can be largely controlled/avoided
• Selective systematic errors– Conditions where the theory does not apply:– Low turbulent conditions (night-time)– Advection→ good quality control necessary
→“Better few unbiased data, than a lot of biased data”
→Uncertainties: mean NEE > interannual variability
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Characterization of the random error
cf. Richardson et al. (2006)
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
NEE
0 6 12 18 24
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
0 6 12 18 24
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
NEE_sigma
0 6 12 18 24
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
0 6 12 18 24
Jan
Feb
Mar
Apr
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2468101214
0.00
15.00
-10-505101520
-13.0
20.0
NEE[µmol m-2 s-1]
NEE_sigma[µmol m-2 s-1]
Quantifying uncertaintie
s
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Error distribution of eddy covariance data
-20.0-16.9-13.8-10.8 -7.7 -4.6 -1.5 1.5 4.6 7.7 10.8 13.8 16.9 20.0
Error NEE [umol m-2 s-1]
0.00
0.10
0.20
0.30
0.40
1 0 3 6 3 11 10 20 31 39 75115
329
1401
3133
1286
371
16866 44 39 20 15 9 5 5 1 1 0
Skewness KurtosisGaussian: 0 0
Laplace: 0 6Empirical: -0.08 15
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Distribution of model error against eddy data
Chevalier et al. (in rev.)
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
PDF only 10am-3pm and Jun-Sep
-13.9-12.5-11.1 -9.7 -8.2 -6.8 -5.4 -4.0 -2.6 -1.2 0.2 1.6 3.1 4.5 5.9 7.3 8.7 10.1 11.5 12.9 14.4Variable
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-13.9-12.5-11.1 -9.7 -8.2 -6.8 -5.4 -4.0 -2.6 -1.2 0.2 1.6 3.1 4.5 5.9 7.3 8.7 10.1 11.5 12.9 14.4NEE error
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NEE error
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
More complicated error structures
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Maximizing the likelihood?
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Bayesian approachCost function:
Trust in data Trust in apriori model parameters
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Spatial representation problem I
• Does the tower site represent the grid cell of interest?
– 0.25-2km km for MODIS/SEAWIFS remote
sensing
– 30-100 km for meteorological fields
– 30-100 km for DGVMs, BGCs applied in
global context
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Aerial photo
Spatial heterogeneity...
Landsat
MODIS
1 km
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
It‘s not always so bad...
TM3 coeff. of variation
TM 3,4,7 MODIS 1,2,7
Dinh et al., subm.
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Spatial representation problem II
• Does the network of tower sites represent the spatial domain of interest or are there chances to generalize with scaling variables?
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Day of the year
fAP
AR
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T)
We have to have up-scaling strategies
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Conclusions
• Eddy covariance data contains a lot of interpretable information on both carbon and water cycle
• Inclusion of pools and fluxes for system understanding and for linking short and long time-scales necessary
• Major challenge within eddy data– Characterization of the error (random, bias)
– Scale and representativeness problem
– Interpret. & Generalization of site specific parameters
– Documentation of site dynamics, that may violate model structure (e.g., soil water, management)
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Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein
Conclusions