1. TransCom Continuous Experiment CSU NASA PCTM Scott Denning, John Kleist

2. PCTM Simulations 1° x 1.25° lat/lon grid, 55 levels (we use 25) NASA GEOS4 reanalyzed winds, turbulence, and convective mass fluxes for 2000-2002 Partial results only: –FF –Takahashi –SiB monthly –CASA monthly Doesn’t balance budget

3. Seasonal Cycles BRW MLO SMO LEF green = obs red = model

4. April, 2002 BRW MLO SMO LEF green = obs red = model

5. May, 2002 BRW MLO SMO LEF green = obs red = model

6. July, 2002 BRW MLO SMO LEF green = obs red = model

7. September, 2002 BRW MLO SMO LEF green = obs red = model

8. October, 2002 BRW MLO SMO LEF green = obs red = model

9. Source/Sink Inversions of Synthetic Satellite CO 2 with Errors Scott Denning Kevin Gurney Kathy Corbin Mick Christi TransCom3 Modelers

10. Inversions of Monthly Synthetic Data Generate global CO 2 from TransCom models Background fields plus G m post Interpolate all models onto common 4 x 5 grid Apply ISCCP cloud climatology to mask grid Invert for fluxes using all models’ response functions (standard T3 cyclostationary method) All results using flasks (with T3 error) plus satellite

11. Synthetic Satellite Retrievals TransCom models provide monthly mean 3D arrays of CO 2 mole fraction over 9 pressure-bounded layers To simulate satellite products from these fields, we need to treat –Vertical “weighting” of 9 layers corresponding to instrument retrieval (not the same as retrieval averaging kernel!) –An estimate of uncertainty in the retrieval –Averaging of many retrievals in each model grid column, with appropriate treatment of error reduction –Effects of clouds on number of retrievals in each model grid column, and therefore on aggregate uncertainty –Sampling biases (?) due to 1 PM Equatorial crossing time and measurement of only clearsky conditions

12. Vertical Weighting Sample synthetic data using two different vertical weighting: –Thermal IR (AIRS-like) sees mostly mid-to-upper troposphere –near-IR (OCO-like) sees column mean, with information all the way to surface

13. Monthly Column Uncertainty  =  0 exp(1.5 f c 3 ) Implemented by deweighting sat retrievals ppmv

14. Inversions of Flasks Plus AIRS Perfect transport on perfect data returns perfect fluxes Transport error makes satellite data pretty ineffective Even perfect model needs retrieval error about 1 ppm for significant improvement over flasks

15. Inversions of Flasks Plus OCO Significant improvements over flasks Fairly robust against terrible transport error! Caveats: monthly mean inversion! severe transport error!

16. Tropical America Flask constraint very weak Even poor transport beats flasks

17. Temperate North America Flask constraint already pretty good Need better transport to beat flask-only inversion aggregate uncertainty (ppmv)