1. The Carbon Cycle Data Assimilation System (CCDAS)
CarboEurope IP Integration Meeting, 22–24 August 2005
The Carbon Cycle Data Assimilation System (CCDAS)
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)

2. Overview Carbon Cycle Observations
Assimilation of Eddy Covariance Data
Assimilation of Satellite "Greenness"
Assimilation of Atmospheric CO2 Data
Outlook

3. Fluxnet Eddy Covariance Network

4. Key Remotely Sensed Variables
ITOC
ITOC
FAPAR:
[(ITOC+IS)–(ITOC+IS)]
/ ITOC
canopy
soil
IS
IS

5. Atmospheric CO2 Measurements
CCDAS inverse modelling period
... and more stations in CCDAS

6. Carbon Cycle Data Assimilation System (CCDAS)
satellite
FAPAR
CCDAS Step 1
full BETHY
atm. CO2
eddy flux
CO2 & H2O
soil water
LAI
veg. distr.
CCDAS Step 2
BETHY+TM2
energy balance/
photosynt.
params
& error cov.
Monte Carlo
Param. Inversion
full BETHY
CO2 and water
fluxes + uncert.
2°x2°
collaborators:
T. Kaminski, R. Giering (FastOpt); P. Rayner (CSIRO)
B. Pinty, N. Gobron, M. Verstraete
(JRC, Ispra)

7. Overview Carbon Cycle Observations
Assimilation of Eddy Covariance Data
Assimilation of Satellite "Greenness"
Assimilation of Atmospheric CO2 Data
Outlook

8. Carbon Cycle Data Assimilation System (CCDAS)
satellite
FAPAR
CCDAS Step 1
full BETHY
atm. CO2
eddy flux
CO2 & H2O
soil water
LAI
veg. distr.
CCDAS Step 2
BETHY+TM2
energy balance/
photosynt.
params
& error cov.
Monte Carlo
Param. Inversion
full BETHY
CO2 and water
fluxes + uncert.
2°x2°

9. a priori error covariance error covariance matrix
The Cost Function
Measure of the mismatch (cost function):
measurements
model diagnostics
assumed
model parameters
a priori
parameter values
a priori error covariance
matrix of parameters
error covariance matrix
of measurements
BETHY
met. data
eddy flux
CO2 & H2O
(7 selected days) J
parameters
aim: sample exp{–J(m)}
=probability density function

10. Convergence of parameters (BETHY model)
Convergence of Cost Function, diagnostic vs. parameter (=Bayes) space
Fig. 1, Knorr & Kattge, GCB 2005

11. Fig. 4, Knorr & Kattge, GCB 2005

12. 1–sopt/sprior Fig. 3, Knorr & Kattge 2005 C4 grassland [FIFE]
conifer forest [Loobos]
1–sopt/sprior
photosynth.
respiration
energy balance
Fig. 3, Knorr & Kattge 2005
stomata

13. Fig. 5, Knorr & Kattge, GCB 2005

14. Overview Carbon Cycle Observations
Assimilation of Eddy Covariance Data
Assimilation of Satellite "Greenness"
Assimilation of Atmospheric CO2 Data
Outlook

15. Carbon Cycle Data Assimilation System (CCDAS)
atm. CO2
satellite
FAPAR
eddy flux
CO2 & H2O
soil water
LAI
veg. distr.
CCDAS Step 2
BETHY+TM2
energy balance/
photosynt.
params
& error cov..
Monte Carlo
Param. Inversion
full BETHY
CCDAS Step 1
full BETHY
CO2 and water
fluxes + uncert.
2°x2°
collaborators:
B. Pinty, N. Gobron, M. Verstraete
(JRC, Ispra)

16. a priori error covariance error covariance matrix
The Cost Function
Measure of the mismatch (cost function):
measurements
model diagnostics
assumed
model parameters
a priori
parameter values
a priori error covariance
matrix of parameters
error covariance matrix
of measurements
aim: minimize J(m)
at each grid cell:
m: relative contributions of
vegetation types
met. data
BETHY J
FAPAR
parameters

17. Step 1: FAPAR Assimilation
prior
optimized
cover fraction of PFT:
evergreen coniferous tree

18. Step 1: FAPAR Assimilation
relative cover fraction: tropical evergreen trees
optimized
prior
deforestation?

19. Overview Carbon Cycle Observations
Assimilation of Eddy Covariance Data
Assimilation of Satellite "Greenness"
Assimilation of Atmospheric CO2 Data
Outlook

20. Carbon Cycle Data Assimilation System (CCDAS)
satellite
FAPAR
CCDAS Step 1
full BETHY
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.
2°x2°
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)

21. a priori error covariance error covariance matrix
The Cost Function
Measure of the mismatch (cost function):
measurements
model diagnostics
assumed
model parameters
a priori
parameter values
a priori error covariance
matrix of parameters
error covariance matrix
of measurements
aim: minimize J(m):
m: 58 BETHY parameters
met. data
BETHY+TM2 J
atm. CO2
parameters

22. Prior/Optimized Fluxes
Table 4, Rayner et al., GBC 2005

23. Error Covariances in Parameters
J(x)
Second Derivative (Hessian) of J(m):
∂2J(m)/∂m2
yields curvature of J,
provides estimated
uncertainty in mopt
Figure taken from Tarantola '87
Space of m (model parameters)

24. CCDAS relative error reduction 1–sopt/sprior
photosynth.
plant resp.
soil resp.
from Table 1, Rayner et al., GBC 2005

25. Error Covariances in Diagnostics
Error covariance of diagnostics, y,
after optimisation (e.g. CO2 fluxes):
adjoint or
tangent linear
model
error covariance
of parameters

26. gC m-2 yr -1
mean NEP 1980–2000, CCDAS
uncertainty in mean NEP 1980–2000, CCDAS
gC m-2 yr -1
Fig. 9/10, Rayner et al., GBC 2005

27. 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...