The Carbon Cycle Data Assimilation System (CCDAS)

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

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

6. satellite FAPAR CCDAS Step 1 full BETHY Carbon Cycle Data Assimilation System (CCDAS) 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. satellite FAPAR CCDAS Step 1 full BETHY Carbon Cycle Data Assimilation System (CCDAS) 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. measurements model diagnostics error covariance matrix of measurements assumed model parameters a priori error covariance matrix of parameters a priori parameter values met. data eddy flux CO2 & H2O (7 selected days) BETHY J parameters The Cost Function Measure of the mismatch (cost function): 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. C4 grassland [FIFE] conifer forest [Loobos] photosynth. respiration 1–sopt/sprior 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. measurements model diagnostics error covariance matrix of measurements assumed model parameters a priori error covariance matrix of parameters a priori parameter values 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 J FAPAR parameters

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

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

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

20. satellite FAPAR CCDAS Step 1 full BETHY 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. 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. measurements model diagnostics error covariance matrix of measurements assumed model parameters a priori error covariance matrix of parameters a priori parameter values The Cost Function Measure of the mismatch (cost function): 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. Second Derivative (Hessian) of J(m): ∂2J(m)/∂m2 yields curvature of J, provides estimated uncertainty in mopt J(x) Space of m (model parameters) Error Covariances in Parameters Figure taken from Tarantola '87

24. relative error reduction 1–sopt/sprior CCDAS 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...