1. A global Carbon Cycle Data Assimilation System (CCDAS) to infer atmosphere- biosphere CO 2 exchanges and their uncertainties Marko Scholze 1, Peter Rayner 2, Wolfgang Knorr 1, Thomas Kaminski 3, Ralf Giering 3 & Heinrich Widmann 1 TransCom Tsukuba, 2004 FastOpt 12 3

2. Overview CCDAS set-up Calculation and propagation of uncertainties Data fit Global results New developments Conclusions and outlook

3. Combined ‘top-down’/’bottom-up’ Method 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

4. CCDAS set-up 2-stage-assimilation: 1.AVHRR data (Knorr, 2000) 2.Atm. CO 2 data 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)

5. Station network 41 stations from Globalview (2001), no gap-filling, monthly values 1979-1999. Annual uncertainty values from Globalview (2001).

6. Terminology GPPGross primary productivity (photosynthesis) NPPNet primary productivity (plant growth) NEPNet ecosystem productivity (undisturbed C storage) NBPNet biome productivity (C storage)

7. 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 dependent Carbon balance: average NPP =  average soil resp. (at each grid point)  <1: source  >1: sink  t=1h  t=1day  lat,  lon = 2 deg

8. Calibration Step Flow of information in CCDAS. Oval boxes represent the various quantities. Rectangular boxes denote mappings between these fields.

9. Prognostic Step Oval boxes represent the various quantities. Rectangular boxes denote mappings between these fields.

10. 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)

11. Calculation of uncertainties Error covariance of parameters = inverse Hessian Covariance (uncertainties) of prognostic quantities Adjoint, Hessian, and Jacobian code generated automatically from model code by TAF

12. Figure from Tarantola, 1987 Gradient Method 1 st derivative (gradient) of J (p) to model parameters p: yields direction of steepest descent. cost function J (p) Model parameter space (p) 2 nd derivative (Hessian) of J (p): yields curvature of J. Approximates covariance of parameters.

13. Data fit

14. Seasonal cycle Barrow Niwot Ridge observed seasonal cycle optimised modeled seasonal cycle

15. Global Growth Rate Calculated as: observed growth rate optimised modeled growth rate Atmospheric CO 2 growth rate

16. Parameters I 3 PFT specific parameters (J max, J max /V max and  ) 18 global parameters 57 parameters in all plus 1 initial value (offset) ParamInitialPredictedPrior unc. (%)Unc. Reduction (%) fautleaf c-cost Q 10 (slow)  (fast) 0.4 1.25 1.5 0.24 1.27 1.35 1.62 2.5 0.5 70 75 39 1 72 78  (TrEv)  (TrDec)  (TmpDec)  (EvCn)  (DecCn)  (C4Gr)  (Crop) 1.0 1.44 0.35 2.48 0.92 0.73 1.56 3.36 25 78 95 62 95 91 90 1

17. Parameters II Relative Error Reduction

18. Some values of global fluxes 1980-2000 (prior)1980-20001980-19901990-2000 GPP Growth resp. Maint. resp. NPP 135.7 23.5 44.04 68.18 134.8 22.35 72.7 40.55 134.3 22.31 72.13 40.63 135.3 22.39 73.28 40.46 Fast soil resp. Slow soil resp. NEP 53.83 14.46 -0.11 27.4 10.69 2.453 27.6 10.71 2.318 27.21 10.67 2.587 Value Gt C/yr

19. Carbon Balance latitude N *from Valentini et al. (2000) and others Euroflux (1-26) and other eddy covariance sites* net carbon flux 1980-2000 gC / (m 2 year)

20. Uncertainty in net flux Uncertainty in net carbon flux 1980-200 gC / (m 2 year)

21. Uncertainty in prior net flux Uncertainty in net carbon flux from prior values 1980-2000 gC / (m 2 year)

22. NEP anomalies: global and tropical global flux anomalies tropical (20S to 20N) flux anomalies

23. IAV and processes Major El Niño events Major La Niña event Post Pinatubo period

24. Interannual Variability I Normalized CO 2 flux and ENSO Lag correlation (low-pass filtered) ENSO and terr. biosph. CO 2 : Correlations seems strong with a maximum at ~4 months lag, for both El Niño and La Niña states.

25. Interannual Variabiliy II Lagged correlation on grid-cell basis at 99% significance correlation coefficient

26. Low-resolution CCDAS A fully functional low resolution version of CCDAS, BETHY runs on the TM2 grid (appr. 10° x 7.8°) 506 vegetation points compared to 8776 (high-res.) About a factor of 20 faster than high-res. Version -> ideal for developing, testing and debugging On a global scale results are comparable (can be used for pre- optimising)

27. Including the ocean A 1 GtC/month pulse lasting for three months is used as a basis function for the optimisation Oceans are divided into the 11 TransCom-3 regions That means: 11 regions * 12 months * 21 yr / 3 months = 924 additional parameters Test case:  all 924 parameters have a prior of 0. (assuming that our background ocean flux is correct)  each pulse has an uncertainty of 0.1 GtC/month giving an annual uncertainty of ~2 GtC for the total ocean flux

28. Including the ocean Seasonality at MLO Global land flux Observations Low-res incl. ocean basis functions Low resolution model High resolution standard model

29. Conclusions CCDAS with 58 parameters can fit 20 years of CO 2 concentration data; ~15 directions can be resolved Terr. biosphere response to climate fluctuations dominated by El Nino. A tool to test model with uncertain parameters and to deliver a posterior uncertainties on parameters and prognostics. With the ability of including ocean basis functions in the optimisation procedure CCDAS comprises a ‘normal’ atmospheric inversion.

30. Future Explore more parameter configurations. Include missing processes (e.g. fire). Upgrade transport model and extend data. Include more data constraints (eddy fluxes, isotopes, high frequency data, satellites) -> scaling issue. Projections of prognostics and uncertainties into future. Extend approach to a prognostic ocean carbon cycle model.

31. Visit: http://www.ccdas.org