1. 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 CO 2, SF 6, Radon-222 for the period 2002-2003 Prescribed surface fluxes for CO 2 Fossil fuel emission for 1998 (EDGAR) Oceanic exchange (Takahashi-2002, updated) Terrestrial biosphere CASA 3-hourly, monthly mean SiB hourly, daily, monthly SF 6 emission from EDGAR 1995 with growth rate Radon-222; land: 1.66x10 -20 mol m -2 s -1 (60 o S-60 o N); Ocean: 8.3x20 -23 mol m -2 s -1 ; half-life: 3.8 days

4. List of models and model variants participating in TransCom continuous experiment (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 CO 2 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. Taylor diagrams: All data (a), Winter (b), Summer (c) Averaged over all stations are shown, i.e., one symbol per flux per model COMET REMO STAGN

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 Patra et al., ACPD, 2006 Chi2