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: /2007GB

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

the CASA model Potter et al., 1993

=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

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

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

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

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

what drives η ? r 2 : 0.76; α < 0.001

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

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

total soil C pools relaxedfixed measurements

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

propagating parameters / uncertainties

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

spatial impacts : NPP 1991 relaxedfixedrelaxed - fixed

seasonality : NPP : IP relaxed versus fixed

iav : NEP : IP relaxed versus fixed

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

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

…