1. What determines column CO2?
An investigation using model and SCIAMACHY columns over North America during 2003
Paul Palmer, Michael Barkley, Paul Monks
Poster: Feng et al, EGU2008-A-03484, AS1.20-1FR2P-0131

2. SCIAMACHY CO2 data GEOS-Chem 3-D model
Driven by GEOS assimilated met. data
- 2ox2.5o horizontal resolution
- Emissions: BB (GFED2), Fuel (CDIAC), land BS (CASA), ocean BS (Takahashi)
Initialization using GLOBALVIEW CO2 data over the Pacific
Relatively broad tagged regions that distinguish between Fuel, BB and BS
Aboard Envisat, launched in 2002
CO2 columns retrieved in nm wavelength region
Mean fitting uncertainty: 1-4%
Typical nadir pixel size: 60 x 30 km2
10am local across-equator time
Cloudy scenes removed using PMDs
Exclude backscans and data with solar zenith angles >75o

3. a = tTA is the column averaging kernel
GEOS-Chem is sampled at the time and location of SCIAMACHY overpass during 2003
Model evalution using surface concentration data
Model columns W account for instrument averaging kernel A A
Model performance varies on a site-by-site basis
a = tTA is the column averaging kernel

4. Y2003 Apr May Red spots denote data lat > 50oN and lon > 100oW
Jun
GEOS-Chem CO2 Column [1021 molec cm-2]
Jul
Aug
Sep
SCIAMACHY CO2 Column [1021 molec cm-2]

5. Column CO2 VMR CO2 CVMR = CO2 VCD x p/ps
To remove the effect of orography, column CO2 [molec cm-2] is normalized by surface air pressure
Pressure exerted by 1 molecule is
CO2 CVMR = CO2 VCD x p/ps

6. Y2003 Apr May Red spots denote data lat > 50oN and lon > 100oW
Jun
GEOS-Chem CO2 CVMR [ppmv]
Jul
Aug
Sep
SCIAMACHY CO2 CVMR [ppmv]

7. CVMR due to Fuel CVMR due to BB CVMR due to BS
FL NA FL EU FL BA FL AS
FL NA FL EU FL BA FL AS
APR
AUG
MAY
SEP
JUN
[ppmv]
JUL

8. CVMR due to Fuel CVMR due to BB CVMR due to BS
BB NA BB EU BB BA BB AS
BB NA BB EU BB BA BB AS
APR
AUG
MAY
SEP
JUN
[ppmv]
JUL

9. CVMR due to Fuel CVMR due to BB CVMR due to BS
BS NA BS EU BS BA BS AS
BS NA BS EU BS BA BS AS
APR
AUG
MAY
SEP
JUN
[ppmv]
JUL

10. SCIAMACHY SCIAMACHY (smoothed) GEOS-Chem CO2 CVMR [ppmv]
Over Wisconsin, seasonal cycle in CO2 columns is dominated by local and Asian and European BS signals
Over UTAH, weak seasonal cycle in CO2 columns is dominated by BS signals from Boreal and mainland Asia
SCIAMACHY
SCIAMACHY (smoothed)
GEOS-Chem
CO2 CVMR [ppmv]
CO2 CVMR [ppmv]

11. Concluding Remarks Model-data comparison
1) Model has reasonable skill in reproducing North American surface concentration data BUT has little skill in reproducing CO2 column spatial patterns shown by SCIAMACHY over regions of active biosphere

12. Concluding Remarks Model-data comparison
1) Model has reasonable skill in reproducing North American surface concentration data BUT has little skill in reproducing CO2 column spatial patterns shown by SCIAMACHY over regions of active biosphere
Model calculations
3) Column CO2 is a complicated (often non-intuitive) superposition of many local/international source/sink signatures
4) Column CO2 is most sensitive to local surface fluxes over North America during summer when the land biosphere can represent more than 1% of the column. [% values likely much larger at native resolution of data]

13. Concluding Remarks Model-data comparison
1) Model has reasonable skill in reproducing North American surface concentration data BUT has little skill in reproducing CO2 column spatial patterns shown by SCIAMACHY over regions of active biosphere
Model calculations
3) Column CO2 is a complicated (often non-intuitive) superposition of many local/international source/sink signatures
4) Column CO2 is most sensitive to local surface fluxes over North America during summer when the land biosphere can represent more than 1% of the column. [% values likely much larger at native resolution of data]
Implications for inverse modelling
5) State vector will need to account for this (e.g., individual flux signatures are almost impossible to distinguish after 1-2 months)
6) Inversion calculations show that CO2 observations over the ocean will be powerful constraints for estimating continental fluxes [Feng poster]

14. SPARE SLIDES

15. Zonal distribution show 1) model/obs bias and 2) that CVMRs reflect surface VMR at peak growing season

16. Implications for source and sink estimation
Continental signatures in column space only distinguishable from the background within 3-4 months