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Prabir K. Patra, Shamil Maksyutov, A. Ito and TransCom-3 modellers Jena; 13 May 2003

An evaluation of an ecosystem model for studying CO2 seasonal cycle TransCom-3 (Level-1) related activities at FRSGC. Prabir K. Patra, Shamil Maksyutov, A. Ito and TransCom-3 modellers Jena; 13 May 2003. Goals…. To configure optimal observation system

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Prabir K. Patra, Shamil Maksyutov, A. Ito and TransCom-3 modellers Jena; 13 May 2003

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  1. An evaluation of an ecosystem model for studying CO2 seasonal cycleTransCom-3 (Level-1) related activities at FRSGC Prabir K. Patra, Shamil Maksyutov, A. Ito and TransCom-3 modellers Jena; 13 May 2003

  2. Goals… To configure optimal observation system • Measurement network optimisation (surface) • Estimate benefits of satellite data in inversion • Evaluate of theirrelative performance

  3. Inverse Modelling Least squares fitting of observed data and model simulations Matrix multiplication and SVD TransCom-3 setup for 11 land and 11 ocean regions HiRes setup for 42 land and 11 ocean regions Forward Modelling 16 global transport models of TransCom-3 Advection, PBL, Convection etc. are treated differently ECMWF, NCEP, GCM meteorological fields Simulation of monthly-mean source/basis functions Tools

  4. Network Optimization CD=RSD2 Patra and Maksyutov, GRL, 29, 28 May 2002

  5. Incremental Optimization of Surface Network (Case 1) O basic [] Model Ensemble

  6. Average uncertainty for TransCom-3 models Total Source Covar C =  CS; Average Unc = C/ No. of Region

  7. Signal gradients at optimal stations

  8. Model Dependent Uncertainty Reduction 1:UCB 2:UCI 3:UCI:s 4:UCI:b 5:JMA 6:MATCH:b 7:MATCH:c 8:MATCH:l 9:NIES:FRSGC 10:NIRE-CTM 11:RPN:SEF 12:SKIHI 13:TM2 14:TM3 15:CSU Patra et al., Tellus, 55B(2), 2003

  9. Signal gradients within NH regions

  10. Occultation based satellite measurements (Case 2) CD=RSD2 +Inst. Err. 2

  11. Regional flux uncertainty at several satellite data precision

  12. Satellite vs Surface data inversion (inst err=0)

  13. Ecosystem production distribution: a justification for high resolution inverse model The fossil fuel emission do not have seasonality. Oceanic sources and sinks are weaker compared to the land and less heterogeneous.

  14. HiRes Inverse Model(42 Land and 11 Ocean Regions)

  15. Inverse Model Intercomparison

  16. Optimal Networks:TransCom-3 vs HiRes

  17. Comparison of average flux uncertainty C_D=RSD^2

  18. Satellite vs Surface Observations TransCom-3 HiRes setup C_D=RSD^2 + P^2

  19. Multimodel Inversion of SOFIS data Three model groups: 1. High, Low and Intermediate signal in the “global” middle-upper troposphere High C_Ds compared to the signal – flat flux unc. with precision

  20. Multimodel Inversion (no RSDs) Is the use of RSDs (derived from NIES model only) in satellite data inversion justified?

  21. Comparisons for different latitude belts

  22. Conclusions • Flux uncertainty reduction with surface network extension depends on vertical profiles near the surface • Diving the Tracom-3 region into four smaller regions do seem to pose a severe aggregation problem • The use to different ATM simulations effect the pseudo-satellite inversion results

  23. An evaluation of an ecosystem model for studying CO2 seasonal cycle

  24. Tests with an Ecosystem Model Outputs • Optimisation of SimCYCLE model parameters: • 1. Q10 for respiration change with temperature • 2. Leaf-level Photosynthetic Capacity (PC) • Both parameters were changed by -20%, -10%, -5%, -3%, -1%, +1%, +3%, +5%, +10%, and +20% SimCYCLE: SIMulation model of the Carbon cYCle in Land Ecosystem (Ito and Oikawa, Eco. Mod., 2002)

  25. Flowchart of SimCYCLE model Source: A. Ito

  26. Light-photosynthesis relationship with different maximum rate Source: A. Ito

  27. Temperature-respiration relationship with different Q10 Source: A. Ito

  28. Procedure • Monthly-mean SimCYCLE outputs are transported using NIES/FRSGC model • Signals are sampled at 8 background stations in NH high latitude: • Alert, Greenland 82.45 297.48 210. • Zeppelin St., Norway 78.90 11.88 474. • Mould Bay, Canada 76.25 240.65 58. • Barrow, Alaska 71.32 203.40 11. • Atlantic Ocean, Norway 66.00 2.00 7. • Storhofdi, Iceland 63.25 339.85 100. • Baltic Sea, Poland 55.50 16.67 7. • Cold Bay, Alaska 55.20 197.28 25. • Mace Head 53.33 350.10 26. • Shemya Island, Alaska 52.72 174.10 40. • The simulations are then fitted to the Observed seasonal cycles of CO2

  29. Fitting at Alert Q10 is not so sensitive Best fit at PSR=-10%

  30. (Bad)Fitting at Baltic Sea Best fit at PSR=-10%

  31. (Good)Fitting at Mace Head Good fit at PSR=-5%

  32. Summary Recommended: 5 to 10% Q10 & -5 to -10% PSR

  33. Thanks for your attention TransCom-3 Modellers: D. Baker (NCAR), P. Bousquet (LSCE), L. Bruhwiler (CMDL), Y-H. Chen (MIT), P. Ciais (LSCE), A. S. Denning (CSU), S. Fan (PU), I. Y. Fung (UCB), M. Gloor (MPI), K. R. Gurney (CSU), M. Heimann (MPI), K. Higuchi (MSC), J. John (UCB), R. M.Law (CSIRO), T. Maki (JMA), P. Peylin (LSCE), M. Prather (UCI), B. Pak (UCI), P. J. Rayner (CSIRO), J. L. Sarmiento (PU), S. Taguchi (NIAIST), T. Takahashi (LDEO), C-W. Yuen (MSC)

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