Richard T. McNider Atmospheric Sciences Department University of Alabama in Huntsville

1. The Role of the Physical Atmosphere in Air Quality Impacts Richard T. McNider Atmospheric Sciences Department University of Alabama in Huntsville mcnider@nsstc.uah.edu

2. Use of Satellite Data to Improve the Physical Atmosphere in Air Quality Decision Models NASA Air Quality Applied Science Team Project Physical Atmosphere Panel Meeting April 25-26, 2012 Atlanta, GA

3. Physical Atmosphere Can Significantly Impact Atmospheric Chemistry and Resulting Air Quality Most Importantly the Physical Atmosphere Can Impact Control Strategy Efficacy and Response Clouds Temperature Winds Mixing Heights

4. Temperature In most areas maximum temperature is most correlated with ozone. Thermal decomposition of nitrogen species – (Sillman and Samson 1995) Emissions – Biogenic and anthropogenic evaporative VOCs Emissions – Soil NO and electric demand

5. Impact of Physical Atmosphere on SIP Control Strategies Temperature – over prediction of temperature can bias ozone controls toward NOx controls as thermal decomposition of increases slope of ozone/NOy curves. Additionally, biogenic emissions will be overestimated.

6. Mixing Heights – Underestimate of mixing heights can cause an over-estimate of the sensitivity of controls. Emission reductions confined to a smaller volume cause a larger reduction in pollutants. A 30% error in mixing heights can produce 30% error in emission change impacts

7. Moisture Soil moisture impacts NOx emissions. Atmospheric moisture can impact dry chemistry and wet chemistry. Pollutant uptake by plants is directly related to photosynthesis and transpiration. Under-estimation of moisture and associated surface loss can overestimate the role of long range transport in local air pollution levels. Climatology Drought

8. Winds Winds can have a direct impact on precursor concentrations. Light winds increase the accumulation of pollutants as air parcels have longer resident times over emission areas. Underestimation of winds can increase control strategy sensitivity. Wind Direction can also be critical for emission loading.

9. Clouds Temperature Insolation Mixing Heights Emissions Photolysis J (NO2) Deep Vertical Mixing Boundary Layer Venting Aqueous Chemistry Aerosol Formation and Aging

10. Traditional view is that high pollution potential would occur near the center of a high pressure system. • Subsidence due to conservation of mass and potential vorticity would decrease mixing heights. • Light horizontal winds would reduce dilution • Clear skies increase photochemical potential • Temperatures are hot due to low ventilation and clear skies H Subsidence Light winds

11. June 24,1988 Nashville

12. Charlotte

13. Atlanta/Montgomery Trough Line

14. Aug 4-5 Aug 14-17 Aug 25 High ozone events during 1999 were associated with stationary front

15. August 4, 1999

16. August 14, 1999

17. August 25, 1999

25. Beginning of sea breeze produces dead zones. Parcels in this area accumulate emissions and then are advected away with high precursor concentrations

30. Typical Boundary Layer Stable Parameterization Km= Kh = l2s Quadratic Form Depicted for Rc=0.2

31. How well do models handle the stable boundary layer Higher resolution boundary layer models generally have a closure scheme dependent on turbulent kinetic energy (TKE) equations or Richardson Number analogues. shear generation buoyancy suppression Ratio of buoyancy term and shear generation term is the Richardson Number

32. The problem with implementing these closures in large scale models is that the closure may be grid dependent While the Richardson Number is dimensionless it is dependent on grid size Thus as the vertical grid size increases Ri becomes larger Modelers engineer around this by adding more mixing or using stability functions with more mixing (Louis profiles)

35. APPENDIX Goal-Minimize numerical diffusion Fh(Ri) Ri Figure 2A: Stability functions used in the present paper. Ri is the gradient Richardson Number. See England and McNider 1995, Duynkerke 1991, Beljaars and Holtslag 1991 and Louis 1979. Duynkerke, Beljaars and Holtslag and Louis represent curve fits to the original parameterization. See also Van de Weil et al. 2002a

36. Figure 11: Differential heating for the case with clear air radiational forcing added radiative energy minus base case versus wind speed for different stability functions.

37. ECMWF/GABLS workshop 7 - 10 November 2011 ( 34 ) Conclusions on wind and momentum issues • Diurnal cycle of wind is attenuated in the ECMWF model by the stable diffusion scheme • The momentum boundary layer is too deep resulting in a too weak low level jet

39. Only PBL Turbulence

40. X X X X X X X X X X X Plume spread with PBL shear and inertial osciilltion. X X X X

44. Initial urban plume The inertial oscillation distorts the plume but in the stable conditions little true diffusion occurs (i.e. concentrations are not changed) However, the next morning PBL turbulence acts on the distorted plume so that the effective diffusion over night is very large resulting in a wide but diluted urban plume McNider et al. 1993 . Atmos. Envir.

45. How Well Do Weather Models Predict CoBL Processes / Conditions? Model wind spectra Observed wind spectra Diurnal Synoptic Diurnal Synoptic