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

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

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

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

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)

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)

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

FastOpt CAMELS Optimisation

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

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

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

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)

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

FastOpt CAMELS Relative Error Reduction

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

FastOpt CAMELS Regional Net Carbon Balance and Uncertainties

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

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

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