1. Error correlation between CO 2 and CO as a constraint for CO 2 flux inversion using satellite data from different instrument configurations Helen Wang hwang@cfa.harvard.edu D.J. Jacob, M. Kopacz, D.B.A. Jones, P. Suntharalingam, J.A. Fisher, R. Nassar, S. Pawson, J.E. Nielsen, C. Clerbaux

2. GEOS-Chem simulated column for January 2006 Similarities between CO 2 and CO CO 2 50 ppb 150 CO 380 PPM 388 Both share common combustion sources & transport : Correlation CO has stronger CO gradient : more sensitive to transport error CO is relatively easier to observe from space: additional useful information TIR (mid/upper toposphere) NIR (down to PBL) Active CO,CO 2 am CO 2, CO?CO 2 CO,CO 2 COPm ASCENDSOCO?GOSATSCIAIASITESAIRSMOPITT

3. CO 2 – CO joint inversion Inverse model minimizes cost function: Observation vector CO 2 and CO concentration State vector CO 2 and CO surface flux Forward model: Jacobian Observation error covariance a priori error covariance

4. Coupling between CO 2 and CO occurs through off-diagonal elements in error covariance matrices S r : error correlation coefficient CO 2 :CO a priori error correlation is weak due to large uncertainties in CO emission factor. CO 2 :CO observational error correlation is potentially useful in joint inversion.

5. observational error in inverse flux modeling Instrument error Representation error Model error Observational error instrument noise retrieval error smoothing error resolution &timing mismatch among retrieval, forward & inverse model transport & chemistry Covariance between CO 2 and CO occurs through model error : Model error is dominant for satellite CO [Heald et al., 2004] important for satellite CO 2 [Baker et al., 2008]

6. r M =0.96 ΔCO 2 (kg/m 2 )‏ Δ CO (kg/m 2 )‏ Jan 2006 (65W, 54N)‏ CO 2 – CO model error correlation calculation: paired-model and paired forecast method  For each grid box and each month: Correlate time series of differences between CO and CO2 runs 1. Paired model: Perform runs with the same surface fluxes driven by different meteorology for CO2 and CO 2. Paired forecast: 48-hour – 24-hour chemical forecast that are valid at the same time

7. Jan 2006 July 2006 Positive correlation in winter due to common combustion and combustion & respiration region overlap Negative correlation in summer due to CO 2 uptake by biosphere Correlation pattern is robust w.r.t. methods, meteorology & CO 2 flux changes of NTE magnitudes Model error correlation coefficients from paired model method 1PM CO : 1PM CO 2 without averaging kernels

8. 14-region analytical inversion using pseudo data with & without CO 2 – CO model error correlation CO combustion 50% CO 2 combustion 30% CO 2 biosphere 80% CO chemistry 30% Rest of the World 30% Sate vector X a priori uncertainty Assume 90% of observational error is due to model error

9. 0.5 0.75 1.0 Jan 2006 biosphere 14-region analytical inversion using pseudo data with & without CO 2 – CO model error correlation CO combustion 50% CO 2 combustion 30% CO 2 biosphere 80% CO chemistry 25% Rest of the World 30% Sate vector X a priori uncertainty a posteriori error covariance matrix Smaller α implies greater improvement Assume 90% of observational error is due to model error

10. α CO combustion CO 2 combustion CO 2 biosphere Short bar implies big improvement relative to single species inversion Joint inversion show substantial improvement during Dec – Apr for mid – high latitudes α The Tropics show <20% improvements year round

11. NIR CO PM – NIR CO 2 PMNIR CO AM – NIR CO 2 PM TIR CO PM – TIR CO 2 PMTIR CO PM – NIR CO 2 PM Model error correlation for Jan 2006 with kernels Matching local time NIR CO and CO 2 have the best model error correlation

12. Jan 2006 results with pseudo data for various instruments Assume model error accounts for 90% of observational error for CO and CO 2 Best result is expected for matching sample time and flat averaging kernel Results are better when model error accounts for a larger fraction Improvement is still significant with relaxed requirements (75% CO, 62.5%CO 2 )‏ CO : CO 2 NIR_PM : NIR_PM TIR_PM : NIR_PM NIR_AM : NIR_PM TIR_AM : NIR_PM TIR_PM : TIR_PM