Optimising ORCHIDEE simulations at tropical sites

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

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

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

7. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites ConclusionsORCHIDEE • 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. Inverse approach « minimize E » E(X) = M(X) - Y Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites ConclusionsOrchidee Inversion System Forward approach Modeled flux M(X) Obs.+Errors Y, R Meteorological drivers Initial conditions FCO2 (μmol/m2/s) Model ORCHIDEE M Parameters and uncertainties X, P 1 DAY 1 DAY

9. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites ConclusionsOrchidee Inversion System 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 ConclusionsData • 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 ConclusionsCost 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 ConclusionsBFGS 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. Introduction ORCHIDEEORCHIS Temperate sites Tropical sites ConclusionsSantaren et al. GBC 2007 FCO2 (gC/m2/Day) FH2O (W/m2) AB (97-98) A priori Model Optimised Model BX (97-98) Observations TH (98-99) WE (98-99) 1 year 1 year 1 year 1 year

14. Introduction ORCHIDEEORCHIS Temperate sites Tropical sites ConclusionsResults & 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 ORCHIDEEORCHIS Temperate sites Tropical sites ConclusionsResults & 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 ORCHIDEEORCHISTemperate sites Tropical sites ConclusionsGuyana

17. Introduction ORCHIDEEORCHISTemperate sites Tropical sites ConclusionsSantarem km 67 Parameter optimisation vs. Model structure improvement?

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

19. Introduction ORCHIDEEORCHISTemperate sites Tropical sites ConclusionsSantarem km 67: GPP and Reco Should we only use “real measured fluxes” or also GPP and Reco? Equifinality?

20. Introduction ORCHIDEEORCHISTemperate sites Tropical sites ConclusionsSantarem km 67: soil depth

21. Introduction ORCHIDEEORCHISTemperate sites Tropical sites ConclusionsSantarem km 67: soil water stress

22. Introduction ORCHIDEEORCHISTemperate sites Tropical sites ConclusionsConclusions • 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 ORCHIDEEORCHISTemperate sites Tropical sites ConclusionsConclusions • 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. Thank you! • Thanks to: • Philippe Peylin, Diego Santaren, Cédric Bacour, Philippe Ciais • Data at tropical sites: PIs from Guyana and Brazilian sites