CO budget and variability over the U.S. using the WRF-Chem regional model

1. CO budget and variability over the U.S. using the WRF-Chem regional model Anne Boynard, Gabriele Pfister, David Edwards National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA NAQC – 9 March 2011

2. Motivation Can we distinguish the different factors that are driving the variations of pollutants at the scale of interest to AQ? => Essential to understand how the surface and tropospheric variability is driven by 3 processes : emissions-chemistry-transport • Tropospheric CO is a key species in tropospheric chemistry (tracer of pollution and precursor of O3) • Air pollution monitoring is based on surface networks butlittle spatial coverage and no vertical information • Satellite observations : good spatial coverage and some vertical sensitivity but little information at the surface • Aircraft observations : vertical extension but little spatial coverage • Regional chemistry-transport model

3. Approach 1 [Wiedinmyer et al., 2006, 2010] 2 [Emmons et al., 2010] Emissions Anthropogenic: US EPA NEI 2005 Biogenic: MEGAN Wildfire: Fire INventory from NCAR1 Meteorological boundary conditions NCEP/GFS Regional CTM WRF-Chem Chemical boundary conditions MOZART-42 Model Evaluation CO tracers • Anthropogenic • Chemical • Fire • Inflow • Surface observations (EPA) • Satellite data (MOPITT) • Aircraft data (ARCTAS campaign) Period simulation: 10 June – 10 July 2008 (2 weeks spin up) Horizontal resolution: 24km x 24 km over the U.S. Allows to separate out the different CO source contributions

4. Model performance: comparison with surface data • Magnitude and variability well reproduced • On average good agreement: R=70% • Slightly low bias: 28 ppbv

5. Model performance: Case studies Good agreement but some discrepancies… Rural site (Washington state) Urban site (California state) Surface CO Surface CO Increase in the model but not as much as in the obs Surface CO tracers Surface CO tracers • First peak period: fire probably underestimated • Second peak period: mismatch probably due to an underestimate of fire emissions and a timing and magnitude problem in anthropogenic emissions • Decrease in relative contribution from transported pollution • Increase due to anthropogenic and fire emissions underestimated in the model • CO inflow is dominant

6. Model performance: comparison with satellite data Average over the period 24 June - 10 July 2008 (1e16 molecules cm-2) MOPITT (V4) Total CO Column WRF-Chem AK Total CO Column • Globally, similar patterns observed by both WRF-Chem and MOPITT • On average, good agreement : R=83% & bias of 1±8% • Fire emissions underestimated by the model (California) • Boundary conditions overestimated by the model (South and West of U.S.)

7. Model performance: comparison with aircraft data ARCTAS mission: NASA’s Arctic Research of the Composition of the Troposphere from Aircraft and Satellitesmission (Spring and Summer 2008) DC-8 Flight, 26 June 2008 (1-minute merged data) Altitude Aircraft CO WRF-chem DC-8 CO Underestimate by a factor of 3-4 WRF CO Fire tracer WRF-chem CO Fire DC-8 Acetonitrile Good agreement but fire emissions underestimated by the model Acknowledgments: ARCTAS science team (Glen Diskin for CO data and Armin Wisthalerfor CH3CN data)

8. Surface CO tracer contributions over the U.S. Average over the period 24 June - 10 July 2008 (ppbv) Total CO • Over the Eastern U.S.: high CO concentrations due to anthropogenic emissions and CO produced chemically • In California: high CO concentrations due to anthropogenic and fire emissions • CO is coming from the West and the North ! Note the different color scale for CO inflow 500 150 Anthropogenic Chemical Fire Inflow 18±14% 2±5% 63±19% 14±8% 0 70

9. Can satellite observations be used for AQ monitoring? Anthropogenic CO (ppbv) Thermal IR are sensitive in the lower FT (2-3km) • How much of the surface CO variability is reflected in the FT? • Is CO brought by long distance transport or produced locally? CO (ppbv) CO Inflow (ppbv) • Variability in CO inflow at the surface ≈ FT • At higher altitude, variability in inflow dominates the variability in anthropogenic CO • => A sounder will observe most of the variability in boundary conditions Surface finest scale variability not captured in the FT but average behavior captured

10. Summary • Model performance : • good agreement with surface, aircraft and satellite data • CO source contributions: • Anthropogenic and CO produced chemically dominant over the Eastern coast • CO inflow dominant over the Western and Northern U.S. • AQ monitoring from satellite : • Finest scale variability seen at the surface is not reflected in the FT but the average behavior is captured • Real need of sensitivity down towards the surface • Multispectral retrieval has a real sensitivity down towards the surface as recently demonstrated by MOPITT V5 [Worden et al., 2010] • Plan to use multispectral techniques for future geostationary AQ observations (e.gGEO-CAPE*) for CO and O3 • *GEO-CAPE: Geostationary Coastal and Air Pollution Events

11. Thank you for your attention! 