The Carbon Cycle Data Assimilation System (CCDAS)

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The Carbon Cycle Data Assimilation System (CCDAS)
CarboEurope IP Integration Meeting, 2224 August
2005

• Wolfgang Knorr
• QUEST/U Bristol, formerly Max-Planck Institute
  for Biogeochemistry, Jena
• with contributions from Marko Scholze (QUEST),
  Jens Kattge (MPI Jena),
• Nadine Gobron (JRC/IES, Ispra), Thomas Kaminski,
  Ralf Giering (FastOpt) and Peter Rayner (LSCE)

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Overview

• Carbon Cycle Observations
• Assimilation of Eddy Covariance Data
• Assimilation of Satellite "Greenness"
• Assimilation of Atmospheric CO2 Data
• Outlook

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Fluxnet Eddy Covariance Network
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Key Remotely Sensed Variables
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Atmospheric CO2 Measurements
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Carbon Cycle Data Assimilation System (CCDAS)
atm. CO2
eddy flux CO2 H2O
soil water LAI veg. distr.
CCDAS Step 2 BETHYTM2 energy balance/ photosynt.
params error cov.
Monte Carlo Param. Inversion full BETHY
CO2 and water fluxes uncert. 2x2
collaborators T. Kaminski, R. Giering (FastOpt)
P. Rayner (CSIRO) B. Pinty, N. Gobron, M.
Verstraete (JRC, Ispra)
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Overview

• Carbon Cycle Observations
• Assimilation of Eddy Covariance Data
• Assimilation of Satellite "Greenness"
• Assimilation of Atmospheric CO2 Data
• Outlook

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Carbon Cycle Data Assimilation System (CCDAS)
atm. CO2
eddy flux CO2 H2O
soil water LAI veg. distr.
CCDAS Step 2 BETHYTM2 energy balance/ photosynt.
params error cov.
Monte Carlo Param. Inversion full BETHY
CO2 and water fluxes uncert. 2x2
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The Cost Function
Measure of the mismatch (cost function)
aim sample expJ(m) probability density
function
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Convergence of parameters (BETHY model)
Convergence of Cost Function, diagnostic vs.
parameter (Bayes) space
Fig. 1, Knorr Kattge, GCB 2005
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Fig. 4, Knorr Kattge, GCB 2005
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C4 grassland FIFE
conifer forest Loobos
photosynth.
respiration
1sopt/sprior
energy balance
Fig. 3, Knorr Kattge 2005
stomata
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Fig. 5, Knorr Kattge, GCB 2005
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Overview

• Carbon Cycle Observations
• Assimilation of Eddy Covariance Data
• Assimilation of Satellite "Greenness"
• Assimilation of Atmospheric CO2 Data
• Outlook

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Carbon Cycle Data Assimilation System (CCDAS)
atm. CO2
satellite FAPAR
eddy flux CO2 H2O
soil water LAI veg. distr.
CCDAS Step 2 BETHYTM2 energy balance/ photosynt.
params error cov..
Monte Carlo Param. Inversion full BETHY
CCDAS Step 1 full BETHY
CO2 and water fluxes uncert. 2x2
collaborators B. Pinty, N. Gobron, M.
Verstraete (JRC, Ispra)
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The Cost Function
Measure of the mismatch (cost function)
aim minimize J(m) at each grid cell m relative
contributions of vegetation types
met. data
BETHY
FAPAR
J
parameters
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Step 1 FAPAR Assimilation
prior
optimized
cover fraction of PFT evergreen coniferous tree
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Step 1 FAPAR Assimilation
relative cover fraction tropical evergreen trees
prior
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Overview

• Carbon Cycle Observations
• Assimilation of Eddy Covariance Data
• Assimilation of Satellite "Greenness"
• Assimilation of Atmospheric CO2 Data
• Outlook

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Carbon Cycle Data Assimilation System (CCDAS)
atm. CO2
eddy flux CO2 H2O
soil water LAI veg. distr.
CCDAS Step 2 reduced BETHY TM2
params error cov.
Monte Carlo Param. Inversion full BETHY
Background CO2 fluxes
CO2 and water fluxes uncert. 2x2
Uses adjoint and Hessian generated by TAF of T.
Kaminski, R. Giering (FastOpt)
ocean Takahashi et al. (1999), LeQuere et al.
(2000) emissions Marland et al. (2001), Andres
et al. (1996) land use Houghton et al. (1990)
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The Cost Function
Measure of the mismatch (cost function)
aim minimize J(m) m 58 BETHY parameters
met. data
BETHYTM2
atm. CO2
J
parameters
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Prior/Optimized Fluxes
Table 4, Rayner et al., GBC 2005
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Error Covariances in Parameters
Figure taken from Tarantola '87
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relative error reduction
1sopt/sprior
CCDAS
photosynth.
plant resp.
soil resp.
from Table 1, Rayner et al., GBC 2005
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Error Covariances in Diagnostics
Error covariance of diagnostics, y, after
optimisation (e.g. CO2 fluxes)
adjoint or tangent linear model
error covariance of parameters
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Fig. 9/10, Rayner et al., GBC 2005
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Outlook

• More data inventories, regional inversions and
  budgets, satellite CO2 columns, isotopes, O2/N2
• More components ocean (free optimization
  indicates no big changes)
• More processes fire (under construction)
• Prognostic step...