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Optimising ORCHIDEE simulations at tropical sites. Hans Verbeeck. LSCE, Laboratoire des Sciences du Climat et de l'Environnement - FRANCE. LSM/FLUXNET meeting June 2008, Edinburgh. Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites Conclusions Outline. Introduction

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optimising orchidee simulations at tropical sites

Optimising ORCHIDEE simulations at tropical sites

Hans Verbeeck

LSCE, Laboratoire des Sciences du Climat et de l'Environnement - FRANCE

LSM/FLUXNET meeting June 2008, Edinburgh

introduction orchidee orchis temperate sites tropical sites conclusions outline
Introduction ORCHIDEEORCHIS Temperate sites Tropical sites ConclusionsOutline
  • 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
Introduction ORCHIDEEORCHIS Temperate sites Tropical sites ConclusionsPOLICE

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
Introduction ORCHIDEEORCHIS Temperate sites Tropical sites ConclusionsPOLICE: 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
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites ConclusionsORCHIDEE
  • 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 orchidee1

Biophysical module

time step: (half)hourly

Surface Energy

budget

Photosynthesis

Transpiration

Autotrophic

Respiration

Soil Moisture

budget

Carbon dynamics module

time step: daily

Phenology

Allocation

Decomposition

Mortality

Heterotrophic respiration

Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites ConclusionsORCHIDEE

Model Parameters

Output variables

Meteorological forcing

introduction orchidee orchis temperate sites tropical sites conclusions orchidee2
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites ConclusionsORCHIDEE
  • 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?

slide8

Inverse approach

« minimize E »

E(X) = M(X) - Y

Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites ConclusionsOrchidee Inversion System

Forward approach

Modeled flux

M(X)

Obs.+Errors

Y, R

Meteorological

drivers

Initial

conditions

FCO2 (μmol/m2/s)

Model

ORCHIDEE

M

Parameters

and uncertainties

X, P

1 DAY

1 DAY

introduction orchidee orchis temperate sites tropical sites conclusions orchidee inversion system
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites ConclusionsOrchidee 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
Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites ConclusionsData
  • Fluxes:
    • Carbon
    • Latent Heat
    • Sensible Heat
    • Net Radation
  • Only real data
  • Errors on the data (PDF)
    • Gaussian
    • σ=15% (day),
    • 30% (night)
slide11

Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites ConclusionsCost Function

  • 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
slide12

Introduction ORCHIDEE ORCHIS Temperate sites Tropical sites ConclusionsBFGS 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
slide13

Introduction ORCHIDEEORCHIS Temperate sites Tropical sites ConclusionsSantaren et al. GBC 2007

FCO2 (gC/m2/Day)

FH2O (W/m2)

AB (97-98)

A priori Model

Optimised Model

BX (97-98)

Observations

TH (98-99)

WE (98-99)

1 year

1 year

1 year

1 year

slide14

Introduction ORCHIDEEORCHIS Temperate sites Tropical sites ConclusionsResults & 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, ...

slide15

Introduction ORCHIDEEORCHIS Temperate sites Tropical sites ConclusionsResults & 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
Introduction ORCHIDEEORCHISTemperate sites Tropical sites ConclusionsSantarem km 67

Parameter optimisation vs. Model structure improvement?

saleska et al science 2003
Saleska et al. Science, 2003

Introduction ORCHIDEEORCHISTemperate sites Tropical sites ConclusionsSantarem km 67

Unexpected seasonality dominated by moisture effects on respiration

Drought response

GPP: weak

R: strong

Wet Dry

introduction orchidee orchis temperate sites tropical sites conclusions santarem km 67 gpp and reco
Introduction ORCHIDEEORCHISTemperate sites Tropical sites ConclusionsSantarem km 67: GPP and Reco

Should we only use “real measured fluxes” or also GPP and Reco? Equifinality?

slide21
Introduction ORCHIDEEORCHISTemperate sites Tropical sites ConclusionsSantarem km 67: soil water stress
introduction orchidee orchis temperate sites tropical sites conclusions conclusions
Introduction ORCHIDEEORCHISTemperate sites Tropical sites ConclusionsConclusions
  • 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 conclusions1
Introduction ORCHIDEEORCHISTemperate sites Tropical sites ConclusionsConclusions
  • 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?
slide24

Thank you!

  • Thanks to:
    • Philippe Peylin, Diego Santaren, Cédric Bacour, Philippe Ciais
    • Data at tropical sites: PIs from Guyana and Brazilian sites