Laboratoire des Sciences du Climat et de l'Environnement

P. Peylin, C. Bacour, P. Ciais,

H. Verbeek, P. Rayner

Flux data to highlight model Flux data to highlight model

deficienciesdeficiencies

& &

The use of satellite data and flux data The use of satellite data and flux data

to optimize ecosystem model to optimize ecosystem model

parameters parameters

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)

Optimization of the ORCHIDEE vegetation modelOptimization of the ORCHIDEE vegetation model

Scientific issuesScientific issues

What do we learn from the optimisation process ?

Can we combine flux data and satellite fAPAR at the site level ?

objectivesobjectives

LMDZ-GCM«on-line»

anthropogeniceffects

STOMATESTOMATE SECHIBASECHIBAEnergy balanceWater balance

Photosynthesis

Carbon balanceNutrient balances

phenology, roughness, albedo

stomatal conductance, soil temperature and

water profiles

precipitation, temperature, radiation, ...

sensible and latent heat fluxes, CO2 flux,

albedo, roughness, surface and soil temperature

NPP, biomass, litter, ...

BiosphereBiosphere

AtmosphereAtmosphere

dailydaily ½ h½ h

year

lyye

arlyVegetation structure

LAI, Vegetation type,biomass

prescribed Dynamic (LPJ)

Climate data« off line »

The ORCHIDEE vegetation modelThe ORCHIDEE vegetation model

Optimizer BFGSJ(X) and dJ(X)/X

Variational assimilation systemVariational assimilation system

flux tower measurements

PFT compositionecosystem parameters

initial conditions

parameters(X)

J(X)M(X)

Yflux

satellitefAPARYfAPAR

J(X)J(X)

climate NEE, H, LE

Governing processes and parameters to optimizeGoverning processes and parameters to optimize

Carbon assimilation

Autotrophic respiration

Heterotrophic respiration

Plant phenology

Energy balance

Hydrology

Kvmax, Gsslope, LAIMAX, SLA, ThetaLeaf

frac_resp_growth, respm_T_slope, respm_T_ord

Q10, Hc, Kresph

Kgdd, Tsen, Leafage

albedo, capasoil, r_aero

depth_soil_res

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)

Bayesian misfit functionBayesian misfit function

Few technical aspectsFew technical aspects

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,…)

Technical difficultiesTechnical difficulties

daily means

diurnal cycle

fAPAR

prior information

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)

1 year 1 year 1 year 1 year

AB

(97

-98)

BX

(97

-98)

TH

(98

-99)

WE

(98

-99)

FCO2 (gC/m2/Jour) FH2O (W/m2)

a priori model

Optimized model

Observations

Seasonal cycle fit: temperate conifers

Diurnal Cycle

a priori model

Optimized model

Observations

AB

(97

-98)

BX

(97

-98)

TH

(98

-99)

WE

(98

-99)

FCO2 FSENS(μmol/m2/s) (W/m2) (W/m2)

FH2O

Diurnal cycle fit: temperate conifers

Diurnal Cycle Diurnal Cycle

AB

(97

-98)

BX

(97

-98)

TH

(98

-99)

WE

(98

-99)

FCO2 FSENS(μmol/m2/s) (W/m2) (W/m2)

Overestimation of the sensible heat flux during the night

Delay between model and observed FCO2

FH2O

Diurnal Cycle Diurnal Cycle Diurnal Cycle

a priori model

Optimized model

Observations

Diurnal cycle fit: temperate conifers

1 year 1 year 1 year 1 year

HE

(97

-98)

HV

(94

-95)

VI

(97-

98)

WB

(95

-96)

FCO2 (gC/m2/Jour) FH2O (W/m2)

Onset of the growingseason not fully captured !

a priori model

Optimized model

Observations

Seasonal cycle fit: temperate deciduous

1 year 1 year 1 year 1 year

FL

(97

-98)

HY

(98

-99)

NB

(96

-97)

NO

(96

-97

)FCO2 (gC/m2/Jour) FH2O (W/m2)

a priori model

Optimized model

Observations

Instabilities because of snow falls

Seasonal cycle fit: boreal conifers

Complementarity between fAPAR and flux data ?

First test for the Fontainebleau “OAK” forest

Data at the Fontainebleau forest site Data at the Fontainebleau forest site

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

year 2006

Flux tower measurementsFlux tower measurements

Neural Network estimation algorithm SPOT- 40m: temporal interpolation with a 2-

sigmoid model

MERIS - 1km:

Remotely sensed fAPAR Remotely sensed fAPAR

Deciduous Broadleaf forest (Oak )

SPOTSPOTMERISMERIS

RMSE = 0.17RMSE = 0.31

RMSE = 0.054

RMSE = 64.96

RMSE = 33.66

ORCHIDEE simulationsORCHIDEE simulations

80% Temperate Broadleaf Summergreen

20% C3G

local meteorological (30’ time step)

previous spinup of the soil carbon pools

SPOTSPOTMERISMERIS

obsprior

Data at the Fontainebleau forest site Data at the Fontainebleau forest site

diurnal cycles (July)diurnal cycles (July)daily datadaily data

improvement of the seasonal fit

obspriorposterior

Assimilation of flux data onlyAssimilation of flux data only

SPOT-fAPARSPOT-fAPAR

Assimilation of fAPAR data onlyAssimilation of fAPAR data only

potential unconsistency of the phasing between NEE flux and

fAPAR observations

obspriorposterior

SPOT-fAPAR onlySPOT-fAPAR only fluxes & SPOT-fAPARfluxes & SPOT-fAPAR

Assimilation of flux data + fAPAR data Assimilation of flux data + fAPAR data

obspriorposterior

Estimated ORCHIDEE parametersEstimated ORCHIDEE parameters

flux onlyflux + SPOTflux + 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!

Conclusion

ResultsResults

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

PerspectivesPerspectives Technical improvements:

improve the convergence performances thanks to ORCHIDEE adjoint model

analyze the posterior on the estimated parameters

Application to other sites!

Experimental Validation Kvmax

Leaves Age

Observations (Porté et al., 98)

Vc,jmax optimized

Vc,jmax a priori

Vcm

ax (μ

mol

m-2 s

-1)

Vjm

ax (μ

mol

m-2 s

-1)

Dependency of the carboxylation rates wrt leaves age

Optimized values: variabilitiesK

vmax

βK

HR

KC

sol

AB BX TH WE HE HV VI WB FL HY NB NO

Temperate conifers

Temperate deciduous

Boreal conifers

Parameters optimizedevery year

Optimized Values strongly variable amongst:

1) the different years of a same site.

2) between sites of a same PFT

Constant parameters :

Optimized values follow the same trends amongst the different sites and PFT.

Mea

n u

ncer

tain

ties

a posteriori uncertainties

β

Kvm

ax

KT

opt

KT

min

KT

max

KM

R

QM

R

FR

c

KH

R

Q10

Kra

Kz0

Kal

b

KC

sol

SL

AA

gef

Temperate conifers

Temperate deciduous

Boreal conifers