Optimising the ORCHIDEE model using eddy covariance data at tropical forest sites in the Amazon

1. Optimising the ORCHIDEE model using eddy covariance data at tropical forest sites in the Amazon Hans Verbeeck Philippe Peylin, Cédric Bacour, Philippe Ciais LSCE, Laboratoire des Sciences du Climat et de l'Environnement - FRANCE Amazon in perspective conference November 2008, Manaus

2. Introduction ORCHIDEEData assimilation Results ConclusionsOutline • Introduction • ORCHIDEE model • Data assimilation system • Results • Conclusions

3. Introduction ORCHIDEEData assimilation Results Conclusions POLICE Marie Curie project: Parameter Optimisation of a terrestrial biosphere model to Link processes to Inter annual variability of Carbon fluxes in European forest Ecosystems

4. Introduction ORCHIDEEData assimilation Results Conclusions POLICE: goals • Increase knowledge about parameters • Variation between and within species (PFT’s) • Spatio-temporal variability of parameters • Validation of the model, model deficiencies • Improve the model’s performance • ...

5. Introduction ORCHIDEEData assimilation Results Conclusions LBA sites...

6. Saleska et al. Science, 2003 Introduction ORCHIDEEData assimilation Results Conclusions Santarem km 67 Unexpected seasonality dominated by moisture effects on respiration • Deep rooting, deep soil columns with high water storage. • Hydraulic redistribution • More light druring dry season • Phenology Wet Dry

7. Introduction ORCHIDEE Data assimilation Results Conclusions ORCHIDEE • ORganizing Carbon and Hydrology In Dynamic EcosystEms • Process-driven global ecosystem model • Spatial: Developed for global applications  “grid point mode” • Time scales: 30 min – 1000’s years

8. Introduction ORCHIDEE Data assimilation Results Conclusions ORCHIDEE 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 13 PFT’s Spinup runs 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)‏

9. Introduction ORCHIDEE Data assimilation Results Conclusions Bayesian optimisation approach • Prior info on parameters (standard values + uncertainties PDF) • Data + uncertainties on the measured fluxes • NEE, LE, H (Energy balance closure problem) • Cost function • BFGS algorithm (gradient based)

10. Introduction ORCHIDEE Data assimilation Results Conclusions Cost function • Mismatch between model and observed fluxes • Mismatch between a priori and optimised parameters • Covariance matrices containing a priori uncertainties on parameters and fluxes and error correlations

11. Introduction ORCHIDEEData assimilation Results Conclusions 5 sites, 7 parameters Data (NEE, LE, H): Optimised parameters: Vcmax Fstress* Q10 Soil depth Gsslope Kalbedo KsoilC

12. Introduction ORCHIDEEData assimilation Results Conclusions K67 NEE

13. Introduction ORCHIDEEData assimilation Results Conclusions First results

14. Introduction ORCHIDEEData assimilation Results Conclusions K83 latent heat

15. Introduction ORCHIDEEData assimilation Results Conclusions Energy balance closure?

16. Introduction ORCHIDEEData assimilation Results Conclusions Partitioned C fluxes

17. Introduction ORCHIDEEData assimilation Results Conclusions Resulting parameters

18. Introduction ORCHIDEEData assimilationResults Conclusions Conclusions • First tests are promising. But what is the meaning of the resulting parameters? (equifinality, model structure uncertainty) • Possibilities to include other data into a multiple constraint approach? (forest inventory data, soil respiration,...) • Temporal variation of parameters • Energy balance closure • GPP, Reco

19. Obrigado • Thanks to: • Philippe Peylin, Diego Santaren, Cédric Bacour, Philippe Ciais • Data providers: PIs from Guyana and Brazilian sites: Damien Bonal, Steve Wofsy, Celso von Randow, Scott Milller...