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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. Motivation. - PowerPoint PPT Presentation
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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
Motivation
Tropospheric CO is a key species in tropospheric chemistry (tracer of pollution and precursor of O3)
Air pollution monitoring is based on surface networks but little 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
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
Approach
Chemical boundary conditions
MOZART-42
Meteorological boundary conditionsNCEP/GFS
Anthropogenic: US EPA NEI 2005Biogenic: MEGANWildfire: Fire INventory from NCAR1
Emissions
1 [Wiedinmyer et al., 2006, 2010]
Regional CTM WRF-
Chem
CO tracers
Anthropogenic Chemical Fire Inflow
2 [Emmons et al., 2010]
Allows to separate out the different CO source contributions
Period simulation: 10 June – 10 July 2008(2 weeks spin up)Horizontal resolution: 24km x 24 km over the U.S.
Surface observations (EPA) Satellite data (MOPITT) Aircraft data (ARCTAS
campaign)
Model Evaluation
Model performance: comparison with surface data
Magnitude and variability well reproduced
On average good agreement: R=70% Slightly low bias: 28 ppbv
Rural site (Washington state)
Urban site (California state)
Model performance: Case studies
Surface CO
Surface CO tracers
Surface CO
Surface CO tracers
• Increase due to anthropogenic and fire emissions underestimated in the model
• CO inflow is dominant
• 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
Good agreement but some discrepancies…
Increase in the model but not as much as in the obs
Model performance: comparison with satellite data
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.)
Average over the period 24 June - 10 July 2008 (1e16 molecules cm-2)
Model performance: comparison with aircraft data
Aircraft CO
WRF CO
DC-8 Flight, 26 June 2008 (1-minute merged data) Altitude
CO
WRF-chem CO FireDC-8 Acetonitrile
WRF-chem DC-8
Underestimate by a factor of 3-4
Acknowledgments: ARCTAS science team (Glen Diskin for CO data and Armin Wisthaler for CH3CN data)
Good agreement but fire emissions underestimated by the model
ARCTAS mission: NASA’s Arctic Research of the Composition of the Troposphere from Aircraft and Satellites mission (Spring and Summer 2008)
Fire tracer
Surface CO tracer contributions over the U.S.
18±14%
14±8%
2±5% 63±19%
Average over the period 24 June - 10 July 2008 (ppbv)
Anthropogenic
Chemical Fire Inflow
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
70
150
0
Can satellite observations be used for AQ monitoring?
Surface finest scale variability not captured in the FT but average behavior captured
• 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
CO (ppbv)CO Inflow (ppbv)
Thermal IR are sensitive in the lower FT (2-3km)
How much of the surface CO variability is reflected in the FT?
Anthropogenic CO (ppbv)
Is CO brought by long distance transport or produced locally?
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.g GEO-CAPE*) for CO and O3*GEO-CAPE: Geostationary Coastal and Air Pollution Events
Thank you for your attention!
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