Richard T. McNider

Atmospheric Sciences Department

University of Alabama in Huntsville

mcnider@nsstc.uah.edu

The Role of the Physical Atmosphere in Air Quality Impacts

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

Physical Atmosphere Can Significantly Impact Atmospheric Chemistry and Resulting Air Quality

Most Importantly the Physical Atmosphere Can Impact Control Strategy Efficacy and Response

Temperature Mixing HeightsClouds Winds

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

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.

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

MoistureSoil 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

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.

Clouds

Insolation

Temperature

Mixing Heights

Emissions

Photolysis J (NO2)

Deep Vertical Mixing

Boundary Layer Venting

Aqueous Chemistry

Aerosol Formation and Aging

Traditional view is that high pollution potential would occur near the center of a high pressure system.

A. Subsidence due to conservation of mass and potential vorticity would decrease mixing heights.

B. Light horizontal winds would reduce dilution

C. Clear skies increase photochemical potential

D. Temperatures are hot due to low ventilation and clear skies

HLight winds

Subsidence

June 24,1988 Nashville

Charlotte

Atlanta/Montgomery

Trough Line

DFW Daily Maxim um OzoneAugust 1999

0

20

40

60

80

100

120

140

160

180

8/1 8/6 8/11 8/16 8/21 8/26 8/31

Ozo

ne (

ppb)

Background Concentration Local Contribution

Figure 1.1 Plot of daily ozone values for DFW after Breitenbach 2004

Aug 4-5

Aug 14-17 Aug 25

High ozone events during 1999 were associated with stationary front

August 4, 1999

August 14, 1999

August 25, 1999

Figure 1. Strawman flight plan – plan view.

VERTICAL CROSS SECTIONS at 96.4W, 31.5N, and 34.1N FOR AUGUST 22, 2006, 20:00 GMT

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

c

c

2

c

c

RiRi,0

RiRi0,Ri

RiRi)Ri(mf

Km= Kh = l2s)Ri(mf

Quadratic Form

Depicted for Rc=0.2

Typical Boundary Layer Stable Parameterization

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.

ndissipatioz

gK

z

VK

t

TKEhm

2)()(

shear generation buoyancy suppression

2)/(z

V

z

gRi

Ratio of buoyancy term and shear generation term is the Richardson Number

2)/(z

V

z

gRi

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)

2)/(z

V

z

gRi

zV

gRi

2)(

0 0.1 0.2 0.3 0.4 0.5 0.60

0.2

0.4

0.6

0.8

1

1.2

England-McNiderDuynkerkeBeljaars-HoltslagLouis

F h(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

APPENDIX Goal-Minimize numerical diffusion

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.

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

Only PBL Turbulence

X

X

X

X

X

X

X X X X XX

X

XX

Plume spread with PBL shear and inertial osciilltion.

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.

How Well Do Weather Models Predict CoBL Processes / Conditions?

Synoptic Diurnal Synoptic Diurnal

Observed wind spectra Model wind spectra