Optimising ORCHIDEE simulations at tropical sites Hans Verbeeck LSM/FLUXNET meeting June 2008,...
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Transcript of Optimising ORCHIDEE simulations at tropical sites Hans Verbeeck LSM/FLUXNET meeting June 2008,...
Optimising ORCHIDEE simulations
at tropical sites
Hans Verbeeck
LSM/FLUXNET meeting June 2008, Edinburgh
LSCE, Laboratoire des Sciences du Climat et de l'Environnement - FRANCE
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
Outline
Introduction Model: ORCHIDEE model Assimilation system: ORCHIS Temperate sites: results from
Santaren et al. Tropical sites: first results Conclusions
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
POLICE
Marie Curie project:
Parameter Optimisation of a terrestrial biosphere model to
Link processes to Inter annual variability of Carbon fluxes in
European forest Ecosystems
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
POLICE: 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
...
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
ORCHIDEE
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
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
ORCHIDEE
Meteorological forcing
PhotosynthesisTranspiration
Surface Energy budget
AutotrophicRespiration
Soil Moisture budget
Biophysical moduletime step: (half)hourly
Heterotrophic respiration
Allocation
Decomposition
Phenology
Mortality
Carbon dynamics moduletime step: daily
Output variables
Model Parameters
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
ORCHIDEE
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?
Obs.+Errors
Y, R
Parametersand uncertainties
X, P
ModelORCHIDEE
M
Forward approach Modeled flux
M(X)
E(X) = M(X) - YInverse approach« minimize E »
Meteorological driversInitial
conditions
FCO2 (μmol/m2/s)
1 DAY 1 DAY
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
Orchidee Inversion System
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
Orchidee Inversion System
Bayesian optimisation approach
Prior info on parameters (standard values
+ uncertainties PDF)
Data + uncertainties
Cost function
BFGS algorithm
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
Data•Fluxes:
• Carbon
• Latent Heat
• Sensible Heat
• Net Radation
• Only real data
• Errors on the data (PDF)
• Gaussian
• σ=15% (day),
30% (night)
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
Cost Function
)()()()(2
1)( 11
bbt
bt xxPxxxHyRxHyxJ
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
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
BFGS 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
1 year 1 year 1 year 1 year
AB
(97
-98)
BX
(97
-98)
TH
(98
-99)
WE
(98
-99)
FCO2 (gC/m2/Day) FH2O (W/m2)
A priori Model
Optimised Model
Observations
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
Santaren et al. GBC 2007
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
Results & 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, ...
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
Results & 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?
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
Santarem km 67
Parameter optimisation vs. Model structure improvement?
Saleska et al. Science, 2003
Wet Dry
Drought response
GPP: weak
R: strong
Unexpected seasonality dominated by moisture effects on respiration
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
Santarem km 67
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
Santarem km 67: GPP and Reco
Should we only use “real measured fluxes” or also GPP and Reco? Equifinality?
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
Santarem km 67: soil water stress
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
Conclusions
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?
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions
Conclusions
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?