Transcript McNide_Physical_Atmos_UCDavisx

The Role of the Physical Atmosphere in Air Quality
Impacts
Richard T. McNider
Atmospheric Sciences Department
University of Alabama in Huntsville
[email protected]
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
Clouds
Temperature
Winds
Mixing Heights
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 overestimate 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
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.
Climatolog
y
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
Temperature
Insolation
Mixing
Heights
Emissions
Photolysis
Deep Vertical
Mixing
Aqueous
Chemistry
J (NO2)
Boundary
Layer
Venting
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
H
Light
winds
Subsidence
June 24,1988 Nashville
Charlotte
Atlanta/Montgomery
Trough Line
High ozone events during 1999 were associated with stationary front
D F W D aily Maxim u m O z o n e
Aug 4-5
August 1999
180
Aug 14-17
Aug 25
160
140
O zon e (pp b )
120
100
80
60
40
20
0
8/1
8/6
8/11
8/16
B ackground C oncentration
8/21
8/26
8/31
Local C ontribution
Figure 1.1 Plot of daily ozone values for DFW after Breitenbach 2004
August 4, 1999
August 14, 1999
August 25, 1999
Figure 1. Strawman flight plan – plan view.
VERTICAL CROSS SECTIO
Beginning of sea breeze produces dead zones. Parcels in this
area accumulate emissions and then are advected away with high
precursor concentrations
Typical Boundary Layer Stable Parameterization
Km= Kh = f m (Ri ) l2s
 Ri  Ri  2
 c

f m (Ri )   Ri  , 0  Ri  Ri c
c


0,
Ri  Ri c

Quadratic Form
Depicted for Rc=0.2
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.
(TKE )
V 2
g 
 Km (
)  Kh
 dissipatio n
t
 z
 z
shear generation
Ratio of buoyancy term
and shear generation term
is the Richardson Number
buoyancy suppression
g   V 2
Ri 
/(
)
 z  z
The problem with implementing these closures in large scale
models is that the closure may be grid dependent
g   V 2
Ri 
/(
)
 z  z
g   V 2
Ri 
/(
)
 z  z
g 
Ri 
z
2
 (V )
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)
While the
Richardson
Number
is dimensionless
it is dependent
on grid size
APPENDIX
Goal-Minimize numerical
diffusion
1.2
1
Fh(Ri)
0.8
England-McNider
Duynkerke
Beljaars-Holtslag
Louis
0.6
0.4
0.2
0
0
0.1
0.2
0.3
0.4
0.5
0.6
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
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 XX
X
X
X
X
X
X
X
X
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?
Observed wind spectra
Synoptic
Diurnal
Model wind spectra
Synoptic
Diurnal