1. Earth Observation Data and Carbon Cycle Modelling Marko Scholze QUEST, Department of Earth Sciences University of Bristol GAIM/AIMES Task Force Meeting, Yokohama, 24-29 Oct. 2004 (an incomplete and subjective view…)

2. Overview Atmospheric CO 2 observations –TransCom Model-Data Synthesis –Oceanic DIC observations: Inverse Ocean Modelling Project –Terrestrial observations: Eddy-flux towers –Atmospheric observations: Carbon Cycle Data Assimilation system

3. TransCom 3 Linear atmospheric transport inversion to calculate CO 2 sources and sinks: 4 background "basis functions" for land, ocean, fossil fuels 1990 & 1995 11 land regions, spatial pattern proportional to terr. NPP 11 ocean regions, uniform spatial distribution þSolving for 4 (background) + 22 (regions) * 12 (month) basis functions!

4. TransCom 3 Seasonal Results (mean over 1992 to 1996) Guerney et al., 2004 response to background fluxes: ppm inversion results: -35 15 4 -5 Gt C/yr

5. TransCom 3 Interannual Results (1988 - 2003) red: land blue: ocean darker bands: within-model uncertainty lighter bands: between- model uncertainty larger land than ocean variability interannual changes more robust than seasonal... but atmosphere well mixed interannually... Baker et al. 2004 Gt C/yr

6. Model-Data Synthesis: The Inverse Ocean Modelling Project C* of Gruber, Sarmiento, and Stocker (1996) to estimate anthropogenic DIC. Innumerable data authors, but represented by Feely, Sabine, Lee, Key. Recent ocean carbon survey, ~ 60.000 observations

7. The Inverse Ocean Modelling Project Jacobson, TransCom3 Meeting, Jena, 2003

8. The Inverse Ocean Modelling Project Gloor et al. 2003 southward carbon transport of 0.37 Pg C/yr for pre-industrial times present-day transport -0.06 Pg C/yr (northwards)

9. Terrestrial observations: Fluxnet a global network of eddy covariance measurements Inversion of terrestrial ecosystem parameter values against eddy covariance measurements by Metropolis Monte Carlo sampling

10. A Posteriori parameter PDF for Loobos site g a,v : vegetation factor of atmospheric conductance E vm : activation energy of Vm Knorr & Kattge, 2004

11. Carbon sequestration at the Loobos site during 1997 and 1998 Knorr & Kattge, 2004

12. 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

13. 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)

14. 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) Uncertainties of parameters Uncertainties of prognostics X

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. Error Reduction in Parameters Relative Error Reduction

20. 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)

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

22. Interannual Variability Normalized CO 2 flux and ENSO Lag correlation (low-pass filtered) correlation coefficient

23. Outlook Data assimilation: problem better constrained without "artefacts" (e.g. spatial patterns created by station network) but: cannot resolve processes that are not included in the model (look at residuals and learn about the model) Simultaneous inversion of land and ocean fluxes Isotopes More data over tropical lands: satellites Model-Data-Synthesis: problem better constrained without "artefacts" (e.g. spatial patterns created by station network) but: cannot resolve processes that are not included in the model (look at residuals and learn about the model) Simultaneous inversion of land and ocean fluxes Further data constraints (e.g. Isotopes, Inventories) More data over tropical lands: satellites

24. Posterior Uncertainty in Net Flux Uncertainty in net carbon flux 1980-200 gC / (m 2 year)

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

26. Atm. Inversion on Grid-cell prior and posterior uncertainties sensitivities (colors) Rödenbeck et al. 2003

27. Atm. Inversion on Grid-cell prior/posterior fluxes and reduction in uncertainty Rödenbeck et al. 2003 Not really at model grid of TM3, but aggregated to TM2 grid, 8° x 10°, Underdetermined problem  correlation matrix (e.g. l=1275 km for NEE)

28. CO 2 Satellite Measurements Vertical weighting functions Sciamachy, OCO Airs (U) (=Upper limit) Airs (L) (=Lower limit) Houweling et al. 2003

29. Pseudo Satellite Data Inversion posterior/prior uncertainty Houweling et al. 2003