steady state impacts in inverse model parameter optimization

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.

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

ha RRGPPNEP

APARNPPPARfAPARAPAR

WT *

p

issii MTWkCRh )1(

Potter et al., 1993

OptT wB10QwsA *

= Css∙ ηCns

• inclusion of a parameter that relaxed the steady state approach: η

approach to relax the steady state approach

OptT wB10QwsA *

Fix Steady State

Relaxed Steady State

111

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 fAPAR and LAI;– different temporal resolutions

effect of η in optimizationadd

ing

η

IT-N

on [

sink:

542

gC

m-2 y

r-1]

determinants of parameter variability: ANOVA

40

165

33

42

*

39

7 1

33

172

Topt

17

9

2

27

39

6

Bw

9

28

056

42

Q10

464

55

19

12

Aws

83

116

80

FST PRM TMR FST*PRM FST*TMR PRM*TMR

site

parameter vector

temporal resolution

site xparameter vector

site xtemporal resolution

parameter vector x temporal resolution

what drives η?r2: 0.76; α < 0.001

model performance improvements

model performance in relaxed > fixed steady state assumptions.

differences in parameter estimates and constraints

ε*

Topt Bwε Q10 Aws

relaxed

fixed

relaxed

fixedε*

Topt Bwε Q10 Aws

P/P SE/SE

↑NPP ↓Rh

total soil C poolsrelaxed fixedmeasurements

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

relaxed fixed relaxed - fixed

seasonality : NPP : IPrelaxed versus fixed

iav : NEP : IPrelaxed versus fixed

seasonality and iav : IP

var.

inter annual variability

seasonal amplitude

uncertainties

Min max min max min max

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

…