1. Validating the GHG production chain at multiple levels Julia Marshall (MPI-BGC), Richard Engelen (ECMWF), Cyril Crevoisier (LMD), Peter Bergamaschi (JRC), Frédéric Chevallier (LSCE), Peter Rayner (LSCE), and various data providers (referenced throughout)

2. Data flow of the GHG system: CO 2 Assimilation into ECMWF system Independen t retrievals (ANN) 4D-fields Flux inversion system Gridded flux fields Biospher e models AIRS data AIRS & IASI data

3. Data flow of the GHG system: CO 2 Assimilation into ECMWF system Independen t retrievals (ANN) 4D-fields Flux inversion system Gridded flux fields Biospher e models AIRS data AIRS & IASI data

4. Data flow of the GHG system: CO 2 Assimilation into ECMWF system Independen t retrievals (ANN) 4D-fields Flux inversion system Gridded flux fields Other satellite data (e.g. SCIAMACHY ) Surface- based measurem ents Flux towers Biospher e models AIRS data AIRS & IASI data

5. 4D IFS CO 2 fields independent AIRS CO 2 retrievals gridded flux fields Flux towers biosphere model AIRS data IASI data IASI CO 2 retrievals SCIAMACH Y data SCIAMACHY CO 2 retrievals independent 4D CO 2 fields surface- informed gridded flux fields surface- based measuremen ts prior-informed 4D LMDZ CO 2 fields

6. 4D IFS CH 4 fields gridded flux fields IASI data IASI CH 4 retrievals SCIAMACH Y data independent SCIAMACHY CH 4 retrievals surface- based measuremen ts optimized 4D TM5 CH 4 fields

8. Data flow of the GHG system: CO 2 Assimilation into ECMWF system Independen t retrievals (ANN) 4D-fields Flux inversion system Gridded flux fields Surface- based measurem ents Flux towers Biospher e models Surface- based assimilation and inversion systems (e.g. CarbonTrac ker) Other satellite data (e.g. SCIAMACHY ) AIRS data AIRS & IASI data

9. Data flow of the GHG system: CO 2 Assimilation into ECMWF system Independen t retrievals (ANN) 4D-fields Flux inversion system Gridded flux fields Surface- based measurem ents Flux towers Biospher e models Surface- based assimilation and inversion systems (e.g. CarbonTrac ker) Other satellite data (e.g. SCIAMACHY ) AIRS data AIRS & IASI data

10. A note on the models considered here All data are matched to the gridbox matching the altitude of the measurement, and linearly interpolated in time Model nameGrid resolution Timestep IFS assimilated 1x1 degree6hr IFS free-run (CASA fluxes) 1x1 degree6hr TM3 4D fields 4x5 degree6hr CarbonTrack er 4x6 degree3hr

11. Surface-based measurement network For the purposes here, this includes surface stations, ship-based measurements, aircraft data, and ground-based remote sensing (i.e. FTIR) 179 datasets considered at present

12. Some metrics to be considered: Based on VAL scoring document: –Modified normalized mean bias: –Fractional gross error: –Correlation coefficient: Visualization with Taylor diagrams

13. A brief note on Taylor diagrams

19. Some results from validation with station data Most stations show reasonable agreement Standard deviation tends to be somewhat high, but scattered

20. Correlation coefficients Remote stations generally show good agreement Poor correlation over highly variable regions, such as Europe

21. Comparison for MNM Bias ( ) Similar pattern of disagreement, showing up to a 10% positive bias over Europe Southern hemisphere well-constrained, slightly positive tendency in northern hemisphere

22. Fractional Gross Error ( ) Similar pattern of disagreement, showing up to a 10% fractional error Again, low error in remote regions

23. A view of the errors in time and space

24. NH summer

25. A view of the errors in time and space NH summer Increasing with time

26. Comparison to surface-data constrained assimilation systems: CarbonTracker and TM3 are not always independent Correlation better, but standard deviation consistently low

27. Comparison with CarbonTracker: considering subset of 99 independent data sets More similar results when comparing only independent stations

28. Comparison with CarbonTracker: considering only independent flight data Yet more comparable when looking at only flight data

29. Comparison with free run, i.e. the effect of the satellite data (aircraft data only) Improvement in variability of the model, if not correlation coefficients

30. A look at the total column results: Northern hemisphere bias seen at Park Falls, but seasonal cycle reproduced well Poor agreement with Darwin R=-.33 RMSE=3.6 ppm R=0.91 RMSE=5.9 ppm

31. Some conclusions: IFS 4D fields compare well with remote observations Positive bias and higher error seen over highly populated regions with heterogeneous fluxes Slight northern-hemisphere high bias, seems related to too weak seasonal cycle Trend shows some divergence over time Performance when considering non- surface data is comparable to that of an inversion system using only surface- based data

32. Other activities: Further comparisons carried out with CO 2 flux output Comparison to independent satellite retrievals Similar work done for methane validation, which will be briefly discussed in the VAL session tomorrow morning

33. Data sources: WMO Global Atmosphere Watch data CarboEurope IP concentration measurements, including flights, tall towers, and flasks NOAA ESRL tall towers and routine flight data flight data over Siberia from Machida et al. Darwin FTIR:Deutscher et al., (in preparation) Park Falls FTIR: Washenfelder et al., 2006 CarbonTracker 2008 results provided by NOAA ESRL, Boulder, Colorado, USA from the website at http://carbontracker.noaa.gov. TM3 4D fields: from Christian Rödenbeck