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TransCom continuous experiment: synoptic scale variations in atmospheric CO 2

TransCom continuous experiment: synoptic scale variations in atmospheric CO 2. P. K. Patra*, R. M. Law, W. Peters, C. Rodenbeck et al. *Frontier Research Center for Global Change/JAMSTEC Yokohama, Japan. Woulter Peters at Paris, 2005. 2003. Summer 2006: Prabir ‘suckered into’.

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TransCom continuous experiment: synoptic scale variations in atmospheric CO 2

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  1. TransCom continuous experiment: synoptic scale variations in atmospheric CO2 P. K. Patra*, R. M. Law, W. Peters, C. Rodenbeck et al. *Frontier Research Center for Global Change/JAMSTEC Yokohama, Japan

  2. Woulter Peters at Paris, 2005 • 2003 Summer 2006: Prabir ‘suckered into’

  3. TransCom Continuous Experiment • Transport model simulations of CO2, SF6, Radon-222 for the period 2002-2003 • Prescribed surface fluxes for CO2 • Fossil fuel emission for 1998 (EDGAR) • Oceanic exchange (Takahashi-2002, updated) • Terrestrial biosphere • CASA 3-hourly, monthly mean • SiB hourly, daily, monthly • SF6 emission from EDGAR 1995 with growth rate • Radon-222; land: 1.66x10-20 mol m-2 s-1 (60oS-60oN); Ocean: 8.3x20-23 mol m-2 s-1; half-life: 3.8 days

  4. List of modelsand model variantsparticipating in TransComcontinuousexperiment (20 global, 3 regional)

  5. Stations with hourly observations (approx. 37)

  6. Extraction of seasonal cycle and synoptic variations: anything between 0-~10 days is defined as Synoptic Low bias in seasonal cycle estimation using digital filtering, and thus Synoptic variations

  7. A station with large diurnal amplitude: effect of PM/ALL hourly data selection Seasonal cycle simulation depends on flux amplitudes and time resolution Synoptic variations are less so

  8. Vertical profiles: Park Falls, WI (LEF) observations (Source: NOAA/ESRL) Synoptic scale variations in nighttime CO2 are more consistent with meteorology!

  9. Vertical profiles: Afternoon vs. Nighttime selection at Park Falls (comparison with all/21 models)

  10. Synoptic variation correlations between observation and models at stations (in ascending order w.r.t. AM2) Worse seasonal cycle simulations at SH stations caused by error in flux

  11. Synoptic variation correlations: Effect of resolution and model versions

  12. Synoptic variation correlations: statistical significance and physically meaningful? Correlations >0.15 are statistically significant (N~300; P=0.01)

  13. Synoptic variation correlations: relation to representation/sampling error Correlations increases with closer model grids to station locations

  14. REMO STAGN COMET Taylor diagrams: All data (a), Winter (b), Summer (c) Averaged over all stations are shown, i.e., one symbol per flux per model

  15. Conclusions • Synoptic scale variations can be robustly estimated from hourly/daily data and mode simulations • The modeled variations are statistically significantly correlated with observed variations at most stations and aren’t random • Taylor diagrams suggest that SiB-hourly fluxes are better suited for sub-daily CO2 simulations (compared to CASA-3hr) • Spatial representation error is still a major problem in multi-model analysis

  16. Recommendations • Analysis using models at two spatial resolution indicates higher resolution is better!!! • A result of lesser spatial representation error and better meteorology • Interpolation to observation grid is an approximate solution (M. Krol)? • Plots using observations and model simulations at LEF suggest nighttime CO2 variability is reasonably well simulated • Should those be used in inversions?

  17. Woulter Peters at Paris, 2005 ? ~

  18. extras

  19. Seasonal cycle at Southern ocean stations Chi2 Patra et al., ACPD, 2006

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