Diapositive 1

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Laboratoire des Sciences du Climat et de
l'Environnement
Flux data to highlight model deficiencies The
use of satellite data and flux data to optimize
ecosystem model parameters
P. Peylin, C. Bacour, P. Ciais, H. Verbeek, P.
Rayner
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objectives
Optimization of the ORCHIDEE vegetation model

• Variational assimilation scheme to improve
  ORCHIDEE model
• Data at the site level
• NEE, H, and LE, fluxes
• fAPAR time series (SPOT 40m and MERIS 1 km)?

Scientific issues

• What do we learn from the optimisation process ?
• Can we combine flux data and satellite fAPAR at
  the site level ?

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The ORCHIDEE vegetation model
Atmosphere
LMDZ-GCM on-line
Climate data  off line 
sensible and latent heat fluxes, CO2 flux,
albedo, roughness, surface and soil temperature
precipitation, temperature, radiation, ...
Biosphere
phenology, roughness, albedo
STOMATE
SECHIBA
Energy balance Water balance Photosynthesis
Carbon balance Nutrient balances
stomatal conductance, soil temperature and
water profiles
½ h
daily
NPP, biomass, litter, ...
LAI, Vegetation type, biomass
anthropogenic effects
Vegetation structure
yearly
prescribed
Dynamic (LPJ)?
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Variational assimilation system
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Few technical aspects
Bayesian misfit function
J(X) (Yfluxdaily-M(X))T Rseason-1
(Yfluxdaily-M(X)) (Yfluxdiurnal-M(X))T
Rdiurnal-1 (Yfluxdiurnal-M(X))
(YfAPAR-M(X))T RfAPAR-1 (YfAPAR-M(X))
(X-X0)T P-1 (X-X0)?
daily means diurnal cycle fAPAR prior information
Technical difficulties

• Gradient of J(X) computed by finite differences
  ! (adjoint under completion)
• How to account for ½ hourly data/model error
  correlations ?
• Relative weight between H, LE, FCO2, Rn ?
• How to treat thresholds linked to phenology ?
  (i.e. GDD,)

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• Model data fit for several forest ecosystems
• ? Highlight of model deficiencies !

• Temperate deciduous forest
• HE (96-99), HV (92-96), VI (96-98), WB (95-98)
• Temperate conifers forest
• AB (97-98), BX (97-98), TH (96-00), WE (96-99)
• Boreal conifers forest
• FL (96-98), HY (96-00), NB (94-98), NO (96-98)

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Seasonal cycle fit temperate conifers
FCO2 (gC/m2/Jour)
FH2O (W/m2)
AB (97-98)
a priori model
BX (97-98)
Optimized model
TH (98-99)
Observations
WE (98-99)
1 year
1 year
1 year
1 year
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Diurnal cycle fit temperate conifers
FH2O
FCO2
FSENS
(µmol/m2/s)
(W/m2)
(W/m2)
a priori model
AB (97-98)
Optimized model
BX (97-98)
Observations
TH (98-99)
WE (98-99)
Diurnal Cycle
Diurnal Cycle
Diurnal Cycle
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Diurnal cycle fit temperate conifers
FH2O
FCO2
FSENS
(µmol/m2/s)
(W/m2)
(W/m2)
a priori model
AB (97-98)
Optimized model
BX (97-98)
Observations
Delay between model and observed FCO2
TH (98-99)
WE (98-99)
Overestimation of the sensible heat flux during
the night
Diurnal Cycle
Diurnal Cycle
Diurnal Cycle
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Seasonal cycle fit temperate deciduous
FCO2 (gC/m2/Jour)
FH2O (W/m2)
a priori model
HE (97-98)
Optimized model
HV (94-95)
Observations
VI (97-98)
Onset of the growingseason not fully captured !
WB (95-96)
1 year
1 year
1 year
1 year
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Seasonal cycle fit boreal conifers
FCO2 (gC/m2/Jour)
FH2O (W/m2)
FL (97-98)
a priori model
Optimized model
HY (98-99)
Observations
NB (96-97)
Instabilities because of snow falls
NO (96-97)
1 year
1 year
1 year
1 year
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• Complementarity between fAPAR and flux data ?
• ?First test for the Fontainebleau OAK forest

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Data at the Fontainebleau forest site
Deciduous Broadleaf forest (Oak )?
Flux tower measurements

• gap-filled half-hourly measurements (LE, H,
  FCO2)
• year 2006

Remotely sensed fAPAR

• Neural Network estimation algorithm
• SPOT- 40m temporal interpolation with a
  2-sigmoid model
• MERIS - 1km

SPOT MERIS
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Data at the Fontainebleau forest site
ORCHIDEE simulations

• 80 Temperate Broadleaf Summergreen
• 20 C3G
• local meteorological (30 time step)
• previous spinup of the soil carbon pools

obs prior
RMSE 0.054
RMSE 64.96
RMSE 33.66
SPOT MERIS
RMSE 0.17 RMSE 0.31
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Assimilation of flux data only
diurnal cycles (July)?
daily data
obs prior posterior
? improvement of the seasonal fit
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Assimilation of fAPAR data only
SPOT-fAPAR
obs prior posterior
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Assimilation of flux data fAPAR data
SPOT-fAPAR only
fluxes SPOT-fAPAR
obs prior posterior
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Estimated ORCHIDEE parameters
flux only flux SPOT flux MERIS

• Are the differences on the retrieved parameters
  induced by the use of SPOT or MERIS fAPARs
  significant?
• Still need to quantify the uncertainties on the
  parameters!

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Conclusion
Results

• ORCHIDEE simulates quite well the seasonal,
  synoptic, and diurnal flux variations at
  Fontainebleau this is even better after
  assimilation!
• Lesser agreement with remotely sensed fAPAR
• We learned on deficiencies of the model
• spatial heterogeneity leads to smooth increase
  of observed fAPAR
• unconsistency between NEE and fAPAR timing ?
• ? need for high temporal resolution / high
  resolution fAPAR data to conclude on
  potential deficiencies of ORCHIDEE

Perspectives

• Technical improvements
• improve the convergence performances thanks to
  ORCHIDEE adjoint model
• analyze the posterior on the estimated
  parameters
• Application to other sites!

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Experimental Validation Kvmax
Dependency of the carboxylation rates wrt leaves
age
Observations (Porté et al., 98)
Vcmax (µmol m-2 s-1)
Vc,jmax a priori
Vc,jmax optimized
Vjmax (µmol m-2 s-1)
Leaves Age
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Optimized values variabilities
Temperate deciduous
Boreal conifers
Temperate conifers
Parameters optimizedevery year Optimized Values
strongly variable amongst 1) the different
years of a same site.
2) between sites of a same PFT
Kvmax
ß
KHR
Constant parameters Optimized values follow the
same trends amongst the different sites and PFT.
KCsol
AB
BX
TH
WE
HE
HV
VI
WB
FL
HY
NB
NO
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a posteriori uncertainties
Temperate conifers
Temperate deciduous
Mean uncertainties
Boreal conifers
Kvmax
ß
KTopt
KTmin
KTmax
KMR
QMR
FRc
KHR
Q10
Kra
Kz0
Kalb
KCsol
SLA
Agef