steady state impacts in inverse model parameter optimization

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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,
doi10.1029/2007GB003033.
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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

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the CASA model
Potter et al., 1993
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approach to relax the steady state approach

• inclusion of a parameter that relaxed the steady
  state approach ?

Css
?
Cns
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experiment design

• significance of each parameter
• removing one parameter at a time
• alternatives to ?
• replacing by
• soil C turnover rates
• extra parameters on NPP and Rh temperature
  sensitivity.
• Levenberg-Marquardt least squares optimization

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site selection and data

• CARBOEUROPE-IP
• 10 Sites
• optimization constraints NEP
• model drivers
• site meteorological data
• remotely sensed fAPAR and LAI
• different temporal resolutions

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effect of ? in optimization
IT-Non sink 542gC m-2 yr-1
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determinants of parameter variability ANOVA
site
parameter vector
temporal resolution
site x parameter vector
site x temporal resolution
parameter vector x temporal resolution
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what drives ??
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model performance improvements
model performance in relaxed gt fixed steady state
assumptions.
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differences in parameter estimates and constraints
P/P
SE/SE
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total soil C pools
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steady state approach impacts

• model performance
• relaxed gt fixed
• parameter estimates
• biases
• parameter uncertainties
• relaxed lt fixed
• soil C pools estimates
• relaxed closer to measurements

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propagating parameters / uncertainties
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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

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spatial impacts NPP 1991
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seasonality NPP IP
relaxed versus fixed
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iav NEP IP
relaxed versus fixed
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seasonality and iav IP
(relax fix) / fix
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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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