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

2. 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 model Scientific issues What do we learn from the optimisation process ? Can we combine flux data and satellite fAPAR at the site level ? objectives

3. LMDZ-GCM «on-line» anthropogenic effects STOMATE SECHIBA Energy balance Water balance Photosynthesis Carbon balance Nutrient balances phenology, roughness, albedo stomatal conductance, soil temperature and water profiles precipitation, temperature, radiation,... sensible and latent heat fluxes, CO 2 flux, albedo, roughness, surface and soil temperature NPP, biomass, litter,... Biosphere Atmosphere daily ½ h yearly Vegetation structure LAI, Vegetation type, biomass prescribedDynamic (LPJ)‏ Climate data « off line » The ORCHIDEE vegetation model

4. Optimizer BFGS J(X) and dJ(X)/X Variational assimilation system flux tower measurements PFT composition ecosystem parameters initial conditions parameters ( X )‏  J(X)‏ M(X)‏M(X)‏ Y flux satellite fAPAR Y fAPAR J(X)‏ climate NEE, H, LE Governing 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

5. J(X)= (Y flux daily -M(X)) T R season -1 (Y flux daily -M(X)) + (Y flux diurnal -M(X)) T R diurnal -1 (Y flux diurnal -M(X)) + (Y fAPAR -M(X)) T R fAPAR -1 (Y fAPAR -M(X)) + (X-X 0 ) T P -1 (X-X 0 )‏ Bayesian misfit function Few 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 difficulties daily means diurnal cycle fAPAR prior information

6. 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)

7. 1 year AB (97-98) BX (97-98) TH (98-99) WE (98-99) F CO2 (gC/m 2 /Jour) F H2O (W/m 2 ) a priori model Optimized model Observations Seasonal cycle fit: temperate conifers

8. Diurnal Cycle a priori model Optimized model Observations AB (97-98) BX (97-98) TH (98-99) WE (98-99) F CO2 F SENS (μmol/m 2 /s) (W/m 2 ) F H2O Diurnal cycle fit: temperate conifers Diurnal Cycle

9. AB (97-98) BX (97-98) TH (98-99) WE (98-99) F CO2 F SENS (μmol/m 2 /s) (W/m 2 ) Overestimation of the sensible heat flux during the night Delay between model and observed F CO2 F H2O Diurnal Cycle a priori model Optimized model Observations Diurnal cycle fit: temperate conifers

10. 1 year HE (97-98) HV (94-95) VI (97-98) WB (95-96) F CO2 (gC/m 2 /Jour) F H2O (W/m 2 ) Onset of the growing season not fully captured ! a priori model Optimized model Observations Seasonal cycle fit: temperate deciduous

11. 1 year FL (97-98) HY (98-99) NB (96-97) NO (96-97) F CO2 (gC/m 2 /Jour) F H2O (W/m 2 ) a priori model Optimized model Observations Instabilities because of snow falls Seasonal cycle fit: boreal conifers

12. Complementarity between fAPAR and flux data ?  First test for the Fontainebleau “OAK” forest

13. Data at the Fontainebleau forest site gap-filled half-hourly measurements (LE, H, FCO2) year 2006 Flux tower measurements Neural Network estimation algorithm SPOT- 40m: temporal interpolation with a 2- sigmoid model MERIS - 1km: Remotely sensed fAPAR Deciduous Broadleaf forest (Oak )‏ SPOTMERIS

14. RMSE = 0.17 RMSE = 0.31 RMSE = 0.054 RMSE = 64.96 RMSE = 33.66 ORCHIDEE simulations 80% Temperate Broadleaf Summergreen 20% C3G local meteorological (30’ time step) previous spinup of the soil carbon pools SPOTMERIS obs prior Data at the Fontainebleau forest site

15. diurnal cycles (July)‏ daily data improvement of the seasonal fit obs prior posterior Assimilation of flux data only

16. SPOT-fAPAR Assimilation of fAPAR data only potential unconsistency of the phasing between NEE flux and fAPAR observations obs prior posterior

17. SPOT-fAPAR only fluxes & SPOT-fAPAR Assimilation of flux data + fAPAR data obs prior posterior

18. 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!

19. 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!

20. Experimental Validation K vmax Leaves Age Observations (Porté et al., 98) Vc,jmax optimized Vc,jmax a priori V cmax ( μmol m -2 s -1 ) V jmax ( μmol m -2 s -1 ) Dependency of the carboxylation rates wrt leaves age

21. Optimized values: variabilities K vmax β K HR K Csol ABBXTHWEHEHVVIWBFLHYNBNO Temperate conifers Temperate deciduous Boreal conifers Parameters optimized every 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.

22. Mean uncertainties a posteriori uncertainties β K vmax K Topt K Tmin K Tmax K MR Q MR F Rc K HR Q 10 K ra K z0 K alb K Csol SLA Age f Temperate conifers Temperate deciduous Boreal conifers