1. Inferring terrestrial CO 2 fluxes from a global-scale Carbon Cycle Data Assimilation System (CCDAS) Marko Scholze 1, Peter Rayner 2, Wolfgang Knorr 1, Thomas Kaminski 3, Ralf Giering 3 & Heinrich Widmann 1 Atmospheric Science Seminars, Harvard University, 16 th January 2004 FastOpt 12 3

2. Overview Motivation Top-down vs. bottom-up approach CCDAS set-up Calculation and propagation of uncertainties Data fit Global results Conclusions and outlook

3. Motivation after Joos, 1996

4. Motivation Where are the sources/sinks? Which are the important processes? How do they evolve? Sketch of the global carbon cycle Fluxes in Gt C yr -1, pools in Gt C, after Prentice et al., 2001.

5. „top-down“ vs. „bottom-up“ net CO 2 flux at the surface Process Model climate and other driving data atmospheric inversion (Transport Model) atm. CO 2 data Advantages: Fluxes consistent with atm. data Estimation of uncertainties Disadvantages: No process information Coarse resolution Advantages: Process understanding -> prognostic modeling High resolution Disadvantages: Global validation difficult Parameter validity

6. Combined 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

7. 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)

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

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

10. 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

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

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

13. 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)

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

15. 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.

16. Data fit

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

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

19. 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

20. Parameters II Relative Error Reduction

21. 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

22. 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)

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

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

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

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

27. 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.

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

29. Regional Net Carbon Balance and Uncertainties

30. Conclusions CCDAS with 58 parameters can fit 20 years of CO 2 concentration data. Significant reduction of uncertainty for ~15 parameters. 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.

31. 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 ocean carbon cycle.