1. FastOpt CAMELS A prototype Global Carbon Cycle Data Assimilation System (CCDAS) Wolfgang Knorr 1, Marko Scholze 2, Peter Rayner 3,Thomas Kaminski 4, Ralf Giering 4 1 234

2. FastOpt CAMELS Overview Top-down vs. bottom-up Gradient method and optimisation Results: Optimal fluxes Uncertainties in parameters + results Uncertainties in fluxes + results Possible assimilation of flux data Conclusions and Outlook

3. FastOpt CAMELS Top-down / Bottom-up atm. CO 2 data inverse atmospheric transport modelling net CO2 fluxes at the surface process model climate and other driving data

4. FastOpt CAMELS Carbon Cycle Data Assimilation Biosphere Model: BETHY Atmospheric Transport Model: TM2 Misfit to Observations Parameters: 58 Fluxes: 800,000 Station Conc. 6,500 Misfit 1 Forward modelling: Parameters –> Misfit Adjoint or Tangent linear model:  Misfit / ∂ Parameters parameter optimization

5. FastOpt CAMELS Figure taken from Tarantola '87 Space of m (model parameters) First derivative (Gradient) of J(m) w.r.t. m (model parameters) : –∂J(m)/∂m yields direction of steepest descent Gradient Method Cost Function J(m)

6. FastOpt CAMELS Carbon Cycle Data Assimilation System (CCDAS) CCDAS Step 2 BETHY+TM2 only Photosynthesis, Energy&Carbon Balance CO 2 + Uncert. Optimized Params + Uncert. Diagnostics + Uncert. veg. index satellite + Uncert. CCDAS Step 1 full BETHY Phenology Hydrology Assimilated Prescribed Assimilated Background CO 2 fluxes* * * ocean: Takahashi et al. (1999), LeQuere et al. (2000); emissions: Marland et al. (2001), Andres et al. (1996); land use: Houghton et al. (1990)

7. FastOpt CAMELS BETHY (Biosphere Energy-Transfer-Hydrology Scheme) GPP: C3 photosynthesis – Farquhar et al. (1980) C4 photosynthesis – Collatz et al. (1992) stomata – Knorr (1997) R aut : maintenance respiration = f(N leaf, T) – Farquhar, Ryan (1991) growth respiration ~ NPP – Ryan (1991) R het : fast/slow pool resp. = w   Q 10 T/10 C fast/slow /  fast/slow  slow –> infin. average NPP =  average R het (at each grid point)  <1: source  >1: sink  t=1h  t=1day  lat,  lon = 2 deg

8. FastOpt CAMELS Optimisation

9. FastOpt CAMELS Optimised fluxes (1) global fluxes Major El Niño events Major La Niña event Post Pinatubo Period

10. FastOpt CAMELS Optimised fluxes (2) normalized CO 2 flux and ENSO ENSO and terr. biosph. CO 2 : correlation seems strong lag correlation (low-pass filtered) correlation between Niño-3 SST anomaly and net CO 2 flux shows maximum at 4 months lag, for both El Niño and La Niña states

11. FastOpt CAMELS net CO 2 flux to atm. gC / (m 2 month) during El Niño (>+1  ) Optimised fluxes (3) lagged correlation at 99% significance -0.8-0.400.40.8 flux sites?

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

13. FastOpt CAMELS Error Covariances in Parameters examples: Cost function (misift): model diagnostics error covariance matrix of measurements measurements assumed model parameters a priori covariance matrix of parameter + model error a priori parameter values Error covariance of parameters after optimisation: = inverse Hessian

14. FastOpt CAMELS Relative Error Reduction

15. FastOpt CAMELS Error Covariances in Diagnostics Error covariance of diagnostics, y, after optimisation (e.g. CO 2 fluxes): error covariance of parameters adjoint or tangent linear model

16. FastOpt CAMELS Regional Net Carbon Balance and Uncertainties

17. FastOpt CAMELS Comparison shows impact of a (pseudo) flux measurement in the broadleaf evergreen biome on Q10 estimated by an inversion of SDBM: Upper panel: only concentration data Lower panel: concentration data + pseudo flux measurement (mean: as predicted sigma: 10gC/m^2/year) Details: Kaminski et al., GBC, 2001 a posteriori mean/uncertainties a priori mean/uncertainties

18. FastOpt CAMELS CCDAS with 58 parameters can already fit 20 years of CO 2 concentration data Sizeable reduction of uncertainty for ~13 parameters terr. biosphere response to climate fluctuations dominated by ENSO System can test model with uncertain parameters, and deliver a posteriori uncertainties on parameters, fluxes Conclusions

19. FastOpt CAMELS explore more parameter configurations include fire as a process with uncertainties need more constraints, e.g. eddy fluxes –> reduce uncertainties however: needs to solve scaling problem (satellites?) approach can be regionalized easily extend approach to ocean carbon cycle projection of uncertainties into future Outlook