1. Atmospheric Research Inverting high temporal frequency data: latest results Rachel Law Peter Rayner, Ying-Ping Wang Law et al., GBC, 16(4), 1053, doi:10.1029/2001GB001593, 2002 Law et al., Tellus, 55B, 512-521, doi:10.1034/j.1600-0560.2003.29.x, 2003 Interesting but …... what if the sources have sub-monthly temporal variability?

2. Atmospheric Research Current inversions use monthly baseline concentration data CO 2 data at sites near Australia Resulting source estimates have large uncertainty Australian source estimate

3. Atmospheric Research ? Cape Grim continuous data How would our inversions be improved if we used continuous data?

4. Atmospheric Research Getting from where we are now to where we want to be Inversion method - does it work with continuous data? Step 1: pseudodata tests in cyclo-stationary mode with monthly mean sources Step 2: pseudodata tests in cyclo-stationary mode with time-varying sources Step 3: pseudodata tests in time-dependent mode How good does the data need to be? Transport modelling Need analysed rather than GCM winds What if the model simulation isn’t perfect? Put it all together with real continuous data

5. Atmospheric Research Pseudodata tests with 4 hourly data at 83 sites Estimating sources for 22 regions Australian source estimate Low uncertainty but large bias - the source estimate is wrong

6. Atmospheric Research Subdivide Australian region into grid-cells and solve for each grid-cell Australian source estimate Uncertainty is larger but bias is smaller than uncertainty - source estimate is ‘correct’ Annual mean OK 0.043 ± 0.063 GtC/yr (correct 0.078 GtC/yr)

7. Atmospheric Research Some useful information obtained for grid-cell sources (though uncertainties quite large)

8. Atmospheric Research Pseudodata tests using fluxes with sub-monthly variability Diurnal variations Synoptic variations Source (GtC/yr)

9. Atmospheric Research Source estimates for Australia using 4 hourly data Sites at all Australian grid pointsSites at offshore points Uncertainties small. Some estimates biased. Need to use ‘daytime’ basis functions when using data from land sites.

10. Atmospheric Research Annual mean bias for each region in Australia Australian grid-point cases blue: standard red: + daytime Offshore grid-point cases

11. Atmospheric Research Benefit of adding one site given Cape Grim (blue good, red bad) Variable source prior uncertaintyConstant source prior uncertainty Variable data uncertainty Constant data uncertainty

12. Atmospheric Research Network design - incremental optimisation Restricted to 1 site/region No restriction Average annual mean uncertainty

13. Atmospheric Research Fossil fuel emissions Biosphere (July) Ocean exchange (January) Forward modelling with CSIRO-CC model and simple CO 2 sources and sinks

14. Atmospheric Research Use CC model with NCEP winds to model synoptic variations at sites - simple CO 2 sources only Schauinsland, Germany Summer Winter Blue: hourly observations Red: 4 hourly model ‘Baseline’ removed Cape Grim - summer

15. Atmospheric Research How far have we got? Inversion method - does it work with continuous data? Step 1: pseudodata tests in cyclo-stationary mode with monthly mean sources (GBC Dec 2002)  Step 2: pseudodata tests in cyclo-stationary mode with time-varying sources  Step 3: pseudodata tests in time-dependent mode  How good does the data need to be? (Tellus 2003)  Transport modelling Need analysed rather than GCM winds  What if the model simulation isn’t perfect? (T03)  Put it all together with real continuous data 

16. Atmospheric Research