1. Using CO observations from space to track long-range transport of pollution Daniel J. Jacob with Patrick Kim, Peter Zoogman, Helen Wang and funding from NASA

2. MOPITT CO validation during NASA/TRACE-P (Mar-Apr 2001) DC-8 Asian outflow MOPITT Pressure, hPa CO, ppbv DC-8 w/MOPITT AK MOPITT DC-8 data MOPITT sample validation profile Validation statistics r 2 = 0.98 bias = +6% 0.3 km 12 km Jacob et al. [JGR 2003]

3. Application of the GEOS-Chem model adjoint to optimize CO sources using multi-sensor data Annual mean CO column May 2004- April2005 emission AIRS MOPITT TES SCIAMACHY (Bremen) bottom-up annual sources correction factors Adjoint inversion NEI EMEP GFED EDGAR Streets Kopacz et al. [2010]

4. hPa MOPITT multispectral retrieval can separate optimization of CO sources and sink MOPITT NIR+TIR averaging kernel [Worden et al., 2010] South America China Observations (2006) GEOS-Chem (GEOS-4) GEOS-Chem (GEOS-5) GEOS-Chem (-20% OH) Below 800 hPa Free troposphere CO, ppbv 150 330 150 80 160 60 110 50 200 500 800 Patrick Kim (Harvard) J J D

5. Interannual variability of Arctic spring pollution from AIRS CO ARCTAS demonstrated value of AIRS CO for tracking plumes over the Arctic 2003-2008 April mean AIRS COInterannual anomaly (ENSO Index) European sector most polluted, N American sector cleanest Fisher et al. [2010] Transport of Asian pollution to the Arctic is correlated with ENSO through strength of Aleutian Low 2003 + = 2004 = 2005 + = 2006 - = 2007 = 2008 - ARCTAS

6. AIRS CO and OMI tropospheric ozone (700-400 hPa, 2008) ozone, SON CO, MAM CO, SON ozone, MAM OMI AIRS NASA A-Train AIRS and OMI: 1.Provide daily coverage 2.Observe same scenes at same time of day 3.Have similar averaging kernels Patrick Kim (Harvard)

7. Ozone-CO correlations in the free troposphere Slopes of RMA regression lines for O 3 vs. CO at 700-400 hPa on 2 o x2.5 o grid (2008) OMI ozone and AIRS CO GEOS-Chem ozone pollution outflow is correct stratospheric influence is too low Patrick Kim (Harvard)

8. GEO-CAPE: geostationary satellite observation of air quality over North America Ozone CO UV +Vis +TIR NIR +TIR Payload to include ozone and CO measurements with sensitivity in lowest 2 km Achieving that sensitivity for ozone likely requires UV+Vis+TIR multispectral observation Can model error correlation between ozone and CO enable such sensitivity through data assimilation? Natraj et al. [2012]

9. How ozone data assimilation works – and how ozone-CO error correlation can help ozone CO ozone produce ozone model forecast for t o continuous 3-D field assimilate ozone observations Improved ozone 3-D field at t o produce forecast for t o +  t CO assimilate CO observations CO forecast Improved CO apply ozone-CO error correlations produce joint forecast for t o +  t

10. Ozone-CO model error correlations in surface air O3O3  CO Afternoon error correlations, Aug 2006 Negative error correlations over eastern US are driven by PBL height: PBL height   Ozone  CO  from GEOS-Chem simulations using GEOS-5 vs. GEOS-4 meteorological fields  ≡ GEOS-5 – GEOS4 Peter Zoogman, Harvard

11. 7.0 813 4.7 675 4.2 487 476 5.9 Observation system simulation experiment (OSSE) shows benefit of accounting for ozone-CO error correlations in a data assimilation system for surface ozone RMSE for daily max 8-h average surface ozone in US in August 2006, and number of misdiagnosed exceedences of air quality standard (75 ppb) Ozone-CO error correlation enables a UV-only ozone instrument to perform comparably to UV+Vis+TIR instrument if error correlations can be adequately characterized Root-mean square error (RMSE) in ppb Number of exceedence errors (false positives or negatives) Peter Zoogman, Harvard forecast

12. Using CO 2 -CO model error correlations to improve CO 2 surface flux inversions from satellite data GEOS-Chem CO 2 -CO column error correlations, GEOS-5 vs. GEOS-4 (2006) JanuaryJuly Correlations are positive in growing season, negative for growing season OSSE suggests that joint CO 2 -CO inversion can reduce errors in CO 2 surface flux inversions by up to 50% Joint CO 2 -CO inversion can also reduce the aggregation error from temporal and spatial averaging of fire emissions Wang et al. [2009] Helen Wang, Harvard-Smithsonian