1. Optimising ORCHIDEE simulations at tropical sites Hans Verbeeck LSM/FLUXNET meeting June 2008, Edinburgh LSCE, Laboratoire des Sciences du Climat et de l'Environnement - FRANCE

2. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions Outline  Introduction  Model: ORCHIDEE model  Assimilation system: ORCHIS  Temperate sites: results from Santaren et al.  Tropical sites: first results  Conclusions

3. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions POLICE Marie Curie project: P arameter O ptimisation of a terrestrial biosphere model to L ink processes to I nter annual variability of C arbon fluxes in European forest E cosystems

4. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites 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 ORCHIDEE ORCHIS Temperate sites Tropical sites 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

6. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions ORCHIDEE Meteorological forcing Photosynthesis Transpiration Surface Energy budget Autotrophic Respiration Soil Moisture budget Biophysical module time step: (half)hourly Heterotrophic respiration Allocation Decomposition Phenology Mortality Carbon dynamics module time step: daily Output variables Model Parameters

7. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions ORCHIDEE  13 Plant Functional Types (PFT’s) Standard parameterisation Specific phenology  Initial carbon pools Spinup runs (e.g. 500 years), until pools and fluxes are at equilibrium How to deal with spinup runs when optimising a model? New spinup run for each new parameter combinantion? Using forest inventory data to optimise spinup runs?

8. Obs.+Errors Y, R Parameters and uncertainties X, P Model ORCHIDEE M Forward approach Modeled flux M(X) E(X) = M(X) - Y Inverse approach « minimize E » Meteorological drivers Initial conditions F CO2 (μmol/m 2 /s) 1 DAY Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions Orchidee Inversion System

9. Bayesian optimisation approach Prior info on parameters (standard values + uncertainties PDF) Data + uncertainties Cost function BFGS algorithm

10. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions Data Fluxes: Carbon Latent Heat Sensible Heat Net Radation Only real data Errors on the data (PDF) Gaussian σ=15% (day), 30% (night)

11. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites 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

12. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions BFGS algorithm  Gradient based: calculates gradient at each time step (method of finite differences)  Takes into account lower and upper bound of each parameter  Minimum reached: curvature, sensitivity, uncertainties and correlations between parameters are calculated

13. 1 year AB (97-98) BX (97-98) TH (98-99) WE (98-99) F CO2 (gC/m 2 /Day) F H2O (W/m 2 ) A priori Model Optimised Model Observations Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions Santaren et al. GBC 2007

14. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions Results & problems  Preliminary results show that this is a promising aproach  Assimilating 3 weeks of summer data: Improves diurnal fit Diurnal fit for rest of growing season is not so good  seasonality Should we vary parameters with time? Yearly, monthly,...

15. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions Results & problems  Same results could be obtained when only NEE and λE observations were included  Photosynthesis parameters are well constrained  Respiration parameters can not be robustly determined. High dependence on initial carbon pools. Assimilate NEE, λE, GPP, Reco,...? How to constrain the pools?

16. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions Guyana

17. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions Santarem km 67 Parameter optimisation vs. Model structure improvement?

18. Saleska et al. Science, 2003 Wet Dry Drought response GPP: weak R: strong Unexpected seasonality dominated by moisture effects on respiration Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions Santarem km 67

19. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions Santarem km 67: GPP and Reco Should we only use “real measured fluxes” or also GPP and Reco? Equifinality?

20. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions Santarem km 67: soil depth

21. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions Santarem km 67: soil water stress

22. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions Conclusions  Possibilities to include forest inventory data: multiple constraint approach? (C pools, spinup runs,...)  How to modify the cost function to assimilate data on different time scales?  How much data are needed?

23. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions Conclusions  Temporal variation of parameters?  Optimal parameter value vs. biological significance? Model structure?  How to deal with uncertainty on the measured fluxes? Should we take correlation between uncertainties into account?  Use of GPP and Reco?

24.  Thanks to: Philippe Peylin, Diego Santaren, Cédric Bacour, Philippe Ciais Data at tropical sites: PIs from Guyana and Brazilian sites Thank you!