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Laboratoire des Sciences du Climat et de l'Environnement

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. objectives.

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Laboratoire des Sciences du Climat et de l'Environnement

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  1. 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

  2. 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 ?

  3. The ORCHIDEE vegetation model Atmosphere Climate data « off line » LMDZ-GCM «on-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)‏

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

  5. 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,…)

  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. 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

  8. Diurnal cycle fit: temperate conifers FH2O FCO2 FSENS (W/m2) (μmol/m2/s) (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

  9. Diurnal cycle fit: temperate conifers FH2O FCO2 FSENS (W/m2) (μmol/m2/s) (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

  10. 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

  11. 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

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

  13. 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

  14. 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

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

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

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

  21. 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 β 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

  22. a posteriori uncertainties Temperate conifers Temperate deciduous Mean uncertainties Boreal conifers Kvmax QMR Agef Kra KMR KHR KCsol SLA FRc KTopt KTmin KTmax β Q10 Kalb Kz0

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