1. steady state impacts in inverse model parameter optimization Carvalhais, N., Reichstein, M., Seixas, J., Collatz, G.J., Pereira, J.S., Berbigier, P., Carrara, A., Granier, A., Montagnani, L., Papale, D., Rambal, S., Sanz, M.J., and Valentini, R.(2008), Implications of the carbon cycle steady state assumption for biogeochemical modeling performance and inverse parameter retrieval, Global Biogeochem. Cycles, 22, GB2007, doi:10.1029/2007GB003033.

2. motivation / goals CASA model parameter optimization spin-up routines force soil C pools estimates impacts of the steady state in: –model performance –parameter estimates / constraints propagation of C fluxes estimates uncertainties for the Iberian Peninsula

3. the CASA model Potter et al., 1993

4. =C ss ∙ η∙ ηC ns inclusion of a parameter that relaxed the steady state approach: η approach to relax the steady state approach Fix Steady State Relaxed Steady State

5. experiment design significance of each parameter: –removing one parameter at a time; alternatives to η : –replacing by : soil C turnover rates; extra parameters on NPP and Rh temperature sensitivity. Levenberg-Marquardt least squares optimization

6. site selection and data CARBOEUROPE-IP: –10 Sites optimization constraints: NEP model drivers: –site meteorological data; –remotely sensed f APAR and LAI; –different temporal resolutions

7. effect of η in optimization adding η IT-Non [sink: 542gC m -2 yr -1 ]

8. determinants of parameter variability: ANOVA site parameter vector temporal resolution site x parameter vector site x temporal resolution parameter vector x temporal resolution

9. what drives η ? r 2 : 0.76; α < 0.001

10. model performance improvements model performance in relaxed > fixed steady state assumptions.

11. differences in parameter estimates and constraints ε*ε* T opt BwεBwε Q 10 A ws relaxed fixed relaxed fixed ε*ε* T opt BwεBwε Q 10 A ws P/PP/P SE / SE ↑NPP ↓Rh

12. total soil C pools relaxedfixed measurements

13. steady state approach impacts model performance – relaxed > fixed parameter estimates – biases parameter uncertainties – relaxed < fixed soil C pools estimates – relaxed closer to measurements

14. propagating parameters / uncertainties

15. spatial simulations Iberian Peninsula optimized parameters per site: –optimization: naïve bootstrap approach no assumption on parameters distribution –GIMMS NDVIg : 8km, biweekly; parameter propagation per PFT: –estimating NEP / NPP / Rh

16. spatial impacts : NPP 1991 relaxedfixedrelaxed - fixed

17. seasonality : NPP : IP relaxed versus fixed

18. iav : NEP : IP relaxed versus fixed

19. seasonality and iav : IP var. inter annual variability seasonal amplitude uncertainties Minmaxminmaxminmax NPP-9%62%-11%53%-60%-2% Rh-15%74%-39%131%-60%-2% NEP-10%63%-10%91%-60%6% (relax – fix) / fix

20. remarks biases in optimized parameters lead to significant differences in flux estimates: seasonality and iav uncertainties propagation show significant reductions under relaxed steady state approaches impacts in data assimilation schemes

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