1. Results from the Carbon Cycle Data Assimilation System (CCDAS) 3 FastOpt 4 2 Marko Scholze 1, Peter Rayner 2, Wolfgang Knorr 1 Heinrich Widmann 3, Thomas Kaminski 4 & Ralf Giering 4 1

2. Methodology sketch CCDAS – Carbon Cycle Data Assimilation System CO 2 station concentration Biosphere Model: BETHY Atmospheric Transport Model: TM2 Misfit to observations Model parameterFluxes Misfit 1 Forward Modeling: Parameters –> Misfit Inverse Modeling: Parameter optimization

3. CCDAS set-up Background fluxes: 1.Fossil emissions (Marland et al., 2001 und Andres et al., 1996) 2.Ocean CO 2 (Takahashi et al., 1999 und Le Quéré et al., 2000) 3.Land-use (Houghton et al., 1990) Transport Model TM2 (Heimann, 1995)

4. BETHY (Biosphere Energy-Transfer-Hydrology Scheme) GPP: C3 photosynthesis – Farquhar et al. (1980) C4 photosynthesis – Collatz et al. (1992) stomata – Knorr (1997) Plant respiration: maintenance resp. = f(N leaf, T) – Farquhar, Ryan (1991) growth resp. ~ NPP – Ryan (1991) Soil respiration: fast/slow pool resp., temperature (Q 10 formulation) and soil moisture dependant Carbon balance: average NPP =  average soil resp. (at each grid point)  <1: source  >1: sink  t=1h  t=1day  lat,  lon = 2 deg

5. Methodology Minimize cost function such as (Bayesian form): where - is a model mapping parameters to observable quantities - is a set of observations - error covariance matrix  need of (adjoint of the model)

6. Calculation of uncertainties Error covariance of parameters = inverse Hessian Covariance (uncertainties) of prognostic quantities

7. Fate of terrestrial C under climate change Including biomass burning Uncertainties of prognostic (2000-2004) net fluxes (still calculating) Improvements and further applications since Rayner et al. 2005 Improved carbon balance Improved spin-up of fast soil pool Weaker prior constraint on parameters

8. Seasonal cycle of CO 2 at Barrow, Alaska The red line is the simulation of R05 while the green line Is the improved simulation. Observations are shown by diamonds.

9. Global atmospheric growth rate Weighted sum of Mauna Loa (0.75) and South Pole (0.25) concentrations

10. Parameters I 3 PFT specific parameters (J max, J max /V max and  ) 18 global parameters 56 parameters in all plus 1 initial value (offset)

11. Parameters II Relative Error Reduction

12. Some values of global fluxes 1980-2000 (prior) 1980-1999 R05 New GPP NPP Fast Resp. Slow Resp. 135.7 68.18 53.83 14.46 134.8 40.55 27.4 10.69 144.7 64.92 25.7 36.9 NEP-0.112.452.32 Value Gt C/yr

13. Carbon Balance net carbon flux 1980-2000 gC / (m 2 year) Uncertainty in net carbon flux 1980-2000 gC / (m 2 year)

14. Terrestrial C cycling under climate change

15. Off-line model for prognostic slow pool Some equations: P: slow pool, r F : fast resp., f S : allocation fast to slow pool  : soil moisture T a : air temperature Finding  : Assume P(t = 1979) Adjust  to yield NEP(t = 1979-200)  iterative process

16. Initial slow pool size

17. Decadal mean global NEP 1980-2090 Red lines indicate simulations with climate change and black lines with no climate change. Solid lines indicate simulations with optimized parameters and broken lines with a priori parameters.

18. Including biomass burning A biomass burning climatology (monthly resolved) based on the v. d. Werf data is used as a yearly basis function for the optimisation Land is divided into the 11 TransCom-3 regions That means: 11 regions * 21 yr = 231 additional parameters van der Werf et al., 2004, Continental-Scale Partitioning of fire emissions during the 1997 to 2001 El Niño/La Niña Period. Science, 303, 73-76.

19. Parameters revisited ParameterPriorNo fireInc. fire f R,leaf c cost f S  Q 10,f Q 10,s  f 0.4 1.25 0.2 1.0 1.5 0.22 1.09 0.32 0.63 2.06 1.31 8.7 0.3 1.23 0.78 0.34 2.08 1.46 7.35

20. Global fluxes revisited PriorNo fireInc. fire GPP NPP Fast Resp. Slow Resp. Fire 135.7 68.18 53.83 14.46 144.7 64.92 25.7 36.9 143.9 57.89 13.26 39.28 2.96 NEP-0.112.322.39 Mean value 1980-1999 Gt C/yr

21. Global growth rate revisited Calculated as: Atmospheric CO 2 growth rate observed no fire with fire

22. blue barsCCDAS red barsv. d. Werf et al. Interannual variability in biomass burning estimate year Gt C/yr

23. Conclusions & Outlook Prognostic future net carbon flux under climate change: more productive & more sensitive More processes: fire (‘weak constraint’ as a first step) More components: ocean (not-shown, but “free” optimization indicates no big changes, ideally also process-based) Prognostic uncertainties on net carbon flux for 2000- 2004: calculations finished by now.. More data: inventories, regional inversions and budgets, satellite CO 2 columns, isotopes, O 2 /N 2