1. Prabir K. Patra Acknowledgments: S. Maksyutov, K. Gurney and TransCom-3 modellers TransCom Meeting, Paris; 13-16 June 2005 Sensitivity CO2 sources and sinks to ocean versus land-dominated observational networks.

2. Yet another sensitivity study! Plan of the talk –Why network sensitivity (using IAVs in flux anomalies) –Experimental setup (based on T3-L1 & L2) –Some results (may be useful for synthesis) –Conclusions

3. 64-Regions Inverse Model (using 15 years of interannually varying NCEP/NCAR winds) Patra et al., Global Biogeochem. Cycles., revised, 2005a Inv. Setup Chi2 22 reg 2.15 64 reg 1.11 64+IAV 0.99 C S = c s1 + c s2 …

4. Flux anomaly (6-month running averages) and initial conditions Flux anomaly = TDI Flux – avg. sea. cyc

5. Comparison of land flux anomalies

6. Comparison of ocean flux anomaly Source: C. Lequere

7. Sensitivity to networks and inversion methods(!) Thanks to: Philippe Bousquet Christian Rodenbeck

8. Validation…

10. Conclusions: IAV in fluxes (and fluxes indirectly) is controlled mainly by network selection Assumption: Biases in flux estimation are linked mainly to transport model errors

11. Inverse model framework and present day network (70% real data for the period 1999-2001)

12. Land Fluxes – Network and model Dependency

13. Ocean Fluxes – Network Dependency

14. Signal gradients at optimal stations - tropical

15. Signal gradients within regions – high/midlats

16. Global & hemispheric Scale Fluxes – Network Dependency

17. Land and Ocean Fluxes (70% real) – ocean versus all networks

19. Land Seasonal Cycle

20. Ocean Seasonal Cycle

21. Conclusions 1.The IAV is controlled mainly by observational network selection, less on techniques 2.Biases in fluxes estimation are linked to transport model errors 3.For synthesis of CO2 sources and sinks, we need to revisit the estimations Different networks Separate time period for inversion 4.Finally, any suggestions are welcome

22. Do not reject the land stations, but be careful …