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

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 1561.03-1585.39 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

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

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]

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

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]

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

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

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

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]

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

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]

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]

SPARE SLIDES

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

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