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Sensitivity CO2 sources and sinks to ocean versus land-dominated observational networks.

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. Yet another sensitivity study!. Plan of the talk

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Sensitivity CO2 sources and sinks to ocean versus land-dominated observational networks.

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  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) CS = cs1 + cs2… Inv. Setup Chi2 22 reg 2.15 64 reg 1.11 64+IAV 0.99 Patra et al., Global Biogeochem. Cycles., revised, 2005a

  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…

  9. 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 ScaleFluxes – Network Dependency

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

  18. Land Seasonal Cycle

  19. Ocean Seasonal Cycle

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

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

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