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)

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

Fluxnet Eddy Covariance Network

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

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

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)

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

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°

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

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

Fig. 4, Knorr & Kattge, GCB 2005

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

Fig. 5, Knorr & Kattge, GCB 2005

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

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)

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

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

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

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

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)

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

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

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)

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

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

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

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...