Why do Models Overestimate Surface Ozone in the Southeastern United States?

Why do Models Overestimate Surface Ozone in the Southeastern

United States?

Katherine R. Travis

Daniel J. Jacob

1,2

Jenny A. Fisher

3,4

Patrick S. Kim

Eloise A.

Marais

Lei Zhu

Karen Yu

Christopher C. Miller

Robert M. Yantosca

Melissa P.

Sulprizio

Anne M. Thompson

Paul O. Wennberg

6,7

John D. Crounse

Jason M. St

Clair

Ronald C. Cohen

Joshua L. Laughner

Jack E. Dibb

Samuel R. Hall

Kirk

Ullmann

Glenn M. Wolfe

11,12

Illana B. Pollack

Jeff Peischl

14,15

Jonathan A.

Neuman

14,15

, and

Xianliang Zhou

16,17

Department of Earth and Planetary Sciences and School of Engineering and Applied Sciences,

Harvard University, Cambridge, Massachusetts, USA

Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA

Centre for Atmospheric Chemistry, School of Chemistry, University of Wollongong, Wollongong,

NSW, Australia

School of Earth and Environmental Sciences, University of Wollongong, Wollongong, NSW,

Australia

NASA Goddard Space Flight Center, Greenbelt, Maryland, USA

Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA,

USA

Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA,

USA

Department of Chemistry, University of California, Berkeley, CA, USA

Earth System Research Center, University of New Hampshire, Durham, NH, USA

Atmospheric Chemistry Division, National Center for Atmospheric Research, Boulder, CO, USA

Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center,

Greenbelt, MD, USA

Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore,

MD, USA

Atmospheric Science Department, Colorado State University, Fort Collins, Colorado, USA

University of Colorado, Cooperative Institute for Research in Environmental Sciences, Boulder,

CO, USA

NOAA, Division of Chemical Science, Earth Systems Research Lab, Boulder, CO USA

Correspondence to

: Katherine R. Travis (ktravis@fas.harvard.edu).

NASA Public Access

Author manuscript

Atmos Chem Phys

. Author manuscript; available in PMC 2018 April 02.

Published in final edited form as:

Atmos Chem Phys

. 2016 ; 16(21): 13561–13577. doi:10.5194/acp-16-13561-2016.

NASA Author Manuscript

Department of Environmental Health and Toxicology, School of Public Health, State University

of New York at Albany, Albany, New York, USA

Wadsworth Center, New York State Department of Health, Albany, New York, USA

Abstract

Ozone pollution in the Southeast US involves complex chemistry driven by emissions of

anthropogenic nitrogen oxide radicals (NO

≡

NO + NO

) and biogenic isoprene. Model estimates

of surface ozone concentrations tend to be biased high in the region and this is of concern for

designing effective emission control strategies to meet air quality standards. We use detailed

chemical observations from the SEAC

RS aircraft campaign in August and September 2013,

interpreted with the GEOS-Chem chemical transport model at 0.25°×0.3125° horizontal

resolution, to better understand the factors controlling surface ozone in the Southeast US. We find

that the National Emission Inventory (NEI) for NO

from the US Environmental Protection

Agency (EPA) is too high. This finding is based on SEAC

RS observations of NO

and its

oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI

satellite observations of tropospheric NO

columns. Our results indicate that NEI NO

emissions

from mobile and industrial sources must be reduced by 30–60%, dependent on the assumption of

the contribution by soil NO

emissions. Upper tropospheric NO

from lightning makes a large

contribution to satellite observations of tropospheric NO

that must be accounted for when using

these data to estimate surface NO

emissions. We find that only half of isoprene oxidation

proceeds by the high-NO

pathway to produce ozone; this fraction is only moderately sensitive to

changes in NO

emissions because isoprene and NO

emissions are spatially segregated. GEOS-

Chem with reduced NO

emissions provides an unbiased simulation of ozone observations from

the aircraft, and reproduces the observed ozone production efficiency in the boundary layer as

derived from a regression of ozone and NO

oxidation products. However, the model is still biased

high by 8±13 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched

during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem

does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone

production in the model boundary layer.

1 Introduction

Ozone in surface air is harmful to human health and vegetation. Ozone is produced when

volatile organic compounds (VOCs) and carbon monoxide (CO) are photochemically

oxidized in the presence of nitrogen oxide radicals (NO

≡

NO+NO

). The mechanism for

producing ozone is complicated, involving hundreds of chemical species interacting with

transport on all scales. In October 2015, the US Environmental Protection Agency (EPA) set

a new National Ambient Air Quality Standard (NAAQS) for surface ozone as a maximum

daily 8-h average (MDA8) of 0.070 ppm not to be exceeded more than three times per year.

This is the latest in a succession of gradual tightening of the NAAQS from 0.12 ppm (1-h

average) to 0.08 ppm in 1997, and to 0.075 ppm in 2008, responding to accumulating

evidence that ozone is detrimental to public health even at low concentrations (

EPA, 2013

Chemical transport models (CTMs) tend to significantly overestimate surface ozone in the

Southeast US (

Lin et al., 2008

;

Fiore et al., 2009

;

Reidmiller et al., 2009

;

Brown-Steiner et

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al., 2015

;

Canty et al., 2015

), and this is an issue for the design of pollution control

strategies (

McDonald-Buller et al., 2011

). Here we examine the causes of this overestimate

by using the GEOS-Chem CTM to simulate NASA SEAC

RS aircraft observations of ozone

and its precursors over the region in August–September 2013 (

Toon et al., 2016

), together

with additional observations from surface networks and satellite.

A number of explanations have been proposed for the ozone model overestimates in the

Southeast US.

Fiore et al. (2003)

suggested excessive modeled ozone inflow from the Gulf

of Mexico.

Lin et al. (2008)

proposed that the ozone dry deposition velocity could be

underestimated.

McDonald-Buller et al. (2011)

pointed out the potential role of halogen

chemistry as a sink of ozone. Isoprene emitted from vegetation is the principal VOC

precursor of ozone in the Southeast US in summer, and

Fiore et al. (2005)

found that

uncertainties in isoprene emissions and in the loss of NO

from formation of isoprene

nitrates could also affect the ozone simulation.

Horowitz et al. (2007)

found a large

sensitivity of ozone to the fate of isoprene nitrates and the extent to which they release NO

when oxidized.

Squire et al. (2015)

found that the choice of isoprene oxidation mechanism

can alter both the sign and magnitude of the response of ozone to isoprene and NO

emissions.

The SEAC

RS aircraft campaign in August–September 2013 provides an outstanding

opportunity to improve our understanding of ozone chemistry over the Southeast US. The

SEAC

RS DC-8 aircraft hosted an unprecedented chemical payload including isoprene and

its oxidation products, NO

and its oxidation products, and ozone. The flights featured

extensive boundary layer mapping of the Southeast as well as vertical profiling to the free

troposphere (

Toon et al., 2016

). We use the GEOS-Chem global CTM with high horizontal

resolution over North America (0.25°×0.3125°) to simulate and interpret the SEAC

observations. We integrate into our analysis additional Southeast US observations during the

summer of 2013 including from the NOMADSS aircraft campaign, the SOAS surface site in

Alabama, the SEACIONS ozonesonde network, the CASTNET ozone network, the NADP

nitrate wet deposition network, and NO

satellite data from the OMI instrument. Several

companion papers apply GEOS-Chem to simulate other aspects of SEAC

RS and concurrent

data for the Southeast US including aerosol sources and optical depth (

Kim et al., 2015

isoprene organic aerosol (

Marais et al., 2016

), organic nitrates (

Fisher et al., 2016

formaldehyde and its relation to satellite observations (

Zhu et al., 2016

), and sensitivity to

model resolution (

Yu et al., 2016

2 GEOS-Chem Model Description

We use the GEOS-Chem global 3-D CTM (

Bey et al., 2001

) in version 9.02 (

www.geos-

chem.org

) with modifications described below. GEOS-Chem is driven with assimilated

meteorological data from the Goddard Earth Observing System (GEOS-5.11.0) of the

NASA Global Modeling and Assimilation Office (GMAO). The GEOS-5.11.0 data have a

native horizontal resolution of 0.25° latitude by 0.3125° longitude and a temporal resolution

of 3 h (1 h for surface variables and mixing depths). We use a nested version of GEOS-

Chem (

Chen et al., 2009

) with native 0.25° × 0.3125° horizontal resolution over North

America and adjacent oceans (130° − 60°W, 9.75° − 60°N) and dynamic boundary

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conditions from a global simulation with 4° × 5° horizontal resolution. Turbulent boundary

layer mixing follows a non-local parameterization based on K-theory (

Holtslag and Boville,

1993

) implemented in GEOS-Chem by

Lin and McElroy (2010)

. Daytime mixing depths are

reduced by 40% from the GEOS-5.11.0 data as described by

Kim et al. (2015)

and

Zhu et al.

(2016)

to match aircraft lidar observations. The GEOS-Chem nested model simulation is

conducted for August–September 2013, following six months of initialization at 4° × 5°

resolution.

2.1 Chemistry

The chemical mechanism in GEOS-Chem version 9.02 is described by

Mao et al, (2010

2013

). We modified aerosol reactive uptake of HO

to produce H

instead of H

O in

order to better match H

observations in SEAC

RS. We also include a number of updates

to isoprene chemistry, listed comprehensively in the Supplementary Material (Tables S1 and

S2) and describe here more specifically for the low-NO

pathways. Companion papers

describe the isoprene chemistry updates relevant to isoprene nitrates (

Fisher et al., 2016

) and

organic aerosol formation (

Marais et al., 2016

). Oxidation of biogenic monoterpenes also is

added to the GEOS-Chem mechanism (

Fisher et al., 2016

) but does not significantly affect

ozone.

A critical issue in isoprene chemistry is the fate of the isoprene peroxy radicals (ISOPO

)

produced from the oxidation of isoprene by OH (the dominant isoprene sink). When NO

sufficiently high, ISOPO

reacts mainly with NO to produce ozone (high-NO

pathway). At

lower NO

levels, ISOPO

may instead react with HO

or other organic peroxy radicals, or

isomerize, in which case ozone is not produced (low-NO

pathways). Here we increase the

molar yield of isoprene hydroperoxide (ISOPOOH) from the ISOPO

+ HO

reaction to

94% based on observations of the minor channels of this reaction (

Liu et al., 2013

Oxidation of ISOPOOH by OH produces isoprene epoxides (IEPOX) that subsequently react

with OH or are taken up by aerosol (

Paulot et al., 2009b

;

Marais et al., 2016

). We use

updated rates and products from

Bates et al. (2014)

for the reaction of IEPOX with OH.

ISOPO

isomerization produces hydroperoxyaldehydes (HPALDs) (

Peeters et al., 2009

;

Crounse et al., 2011

;

Wolfe et al., 2012

), and we explicitly include this in the GEOS-Chem

mechanism. HPALDs go on to react with OH or photolyze at roughly equal rates over the

Southeast US. We use the HPALD+OH reaction rate constant from

Wolfe et al. (2012)

and

the products of the reaction from

Squire et al. (2015)

. The HPALD photolysis rate is

calculated using the absorption cross-section of MACR, with a quantum yield of 1, as

recommended by

Peeters and Müller (2010)

. The photolysis products are taken from

Stavrakou et al. (2010)

. Self-reaction of ISOPO

is updated following

Xie et al. (2013)

A number of studies have suggested that conversion of NO

to nitrous acid (HONO) by gas-

phase or aerosol-phase pathways could provide a source of HO

radicals following HONO

photolysis (

Li et al., 2014

;

Zhou et al., 2014

). This mechanism would also provide a

catalytic sink for ozone when NO

is produced by the NO + ozone reaction, viz.,

NO+ O

+ O

(1)

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HONO

by various pathways

(2)

HONO+hυ

NO + OH

(3)

Observations of HONO from the NOMADSS campaign (

https://www2.acom.ucar.edu/

campaigns/nomadss

) indicate a mean daytime HONO concentration of 10 ppt in the

Southeast US boundary layer (

Zhou et al., 2014

), whereas the standard gas-phase

mechanism in GEOS-Chem version 9.02 yields less than 1 ppt. We add the pathway

proposed by

Li et al. (2014)

, in which HONO is produced by the reaction of the HO

•H

complex with NO

, but with a slower rate constant (

HO2•H2O+NO2

= 2×10

−12

molecule

−1

) to match the observed ~10 ppt daytime HONO in the Southeast US boundary layer.

The resulting impact on boundary layer ozone concentrations is negligible.

2.2 Dry Deposition

The GEOS-Chem dry deposition scheme uses a resistance-in-series model based on

Wesely

(1989)

as implemented by

Wang et al. (1998)

. Underestimate of dry deposition has been

invoked as a cause for model overestimates of ozone in the eastern US (

Lin et al., 2008

;

Walker, 2014

). Daytime ozone deposition is determined principally by stomatal uptake.

Here, we decrease the stomatal resistance from 200 s m

−1

for both coniferous and deciduous

forests (

Wesely, 1989

) by 20% to match summertime measurements of the ozone dry

deposition velocity for a pine forest in North Carolina (

Finkelstein et al., 2000

) and for the

Ozarks oak forest in southeast Missouri (

Wolfe et al., 2015

), both averaging 0.8 cm s

−1

the daytime. The mean ozone deposition velocity in GEOS-Chem along the SEAC

boundary layer flight tracks in the Southeast US averages0.7±0.3 cm s

−1

for the daytime (9–

16 local) surface layer. Deposition is suppressed in the model at night due to both stomatal

closure and near-surface stratification, consistent with the

Finkelstein et al. (2000)

observations.

Deposition flux measurements for isoprene oxidation products at the Alabama SOAS site

(

http://soas2013.rutgers.edu

) indicate higher deposition velocities than simulated by the

standard GEOS-Chem model (

Nguyen et al., 2015

). The diurnal cycle of dry deposition in

GEOS-Chem compares well with the observations from SOAS (

Nguyen et al., 2015

). As an

expedient,

Nguyen et al. (2015)

scaled the Henry’s law coefficients for these species in

GEOS-Chem to match their observed deposition velocities and we follow their approach

here. Other important depositing species include HNO

and peroxyacetyl nitrate (PAN),

with mean deposition velocities along the SEAC

RS Southeast US flight tracks in daytime

of 3.9 cm s

−1

and 0.6 cm s

−1

, respectively.

2.3 Emissions

We use hourly US anthropogenic emissions from the 2011 EPA national emissions inventory

(NEI11v1) at a horizontal resolution of 0.1° × 0.1° and adjusted to 2013 using national

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annual scaling factors (EPA, 2015). The scaling factor for NO

emissions is 0.89, for a 2013

US NEI total of 3.5 Tg N a

−1

. Further information on the use of the NEI11v1 in GEOS-

Chem can be found here:

http://wiki.seas.harvard.edu/geos-chem/index.php/EPA/

NEI11_North_American_emissions/

. Soil NO

emissions, including emissions from

fertilizer application, are computed according to

Hudman et al. (2012)

, with a 50% reduction

in the Midwest US based on a previous comparison with OMI NO

observations (

Vinken et

al., 2014

). Open fire emissions are from the daily Quick Fire Emissions Database (QFED)

(

Darmenov and da Silva, 2014

) with diurnal variability from the Western Regional Air

Partnership (

Air Sciences, 2005

). We emit 40% of open fire NO

emissions as PAN and 20%

as HNO

to account for fast oxidation taking place in the fresh plume (

Alvarado et al.,

2010

). Following

Fischer et al. (2014)

, we inject 35% of fire emissions above the boundary

layer, evenly between 3.5 and 5.5 km altitude. Lightning is an additional source of NO

but

is mainly released in the upper troposphere, as described below.

Initial implementation of the above inventory in GEOS-Chem resulted in an 60–70%

overestimate of NO

and HNO

measured from the SEAC

RS DC-8 aircraft, and a 70%

overestimate of nitrate (NO

−

) wet deposition fluxes measured by the National Acid

Deposition Program (NADP) across the Southeast US. Correcting this bias required a ~40%

decrease in surface NO

emissions. Assuming strongly reduced soil and fertilizer NO

emissions (18% of total NO

emissions in the Southeast) and open fires (2%), also

considering the large uncertainty in these emissions, would be insufficient to correct this

bias. Emissions from power plant stacks are directly measured but account for only 12% of

NEI NO

emissions on an annual basis (EPA, 2015). Several local studies in recent years

have found that NEI NO

emissions for mobile sources may be too high by a factor of two

or more (

Castellanos et al, 2011

;

Fujita et al., 2012

;

Brioude et al., 2013

;

Anderson et al.,

2014

). We can achieve the required 40% decrease in total NO

emissions by reducing NEI

emissions from mobile and industrial sources (all sources except power plants) by 60%, or

alternatively by reducing these sources by 30% and zeroing out soil and fertilizer NO

emissions. Since it is apparent that there is some minimum contribution by soil NO

emissions we assessed the impact of the approach of reducing the NEI emissions by 60%.

The spatial overlap between anthropogenic and soil NO

emissions is such that we cannot

readily arbitrate between these two scenarios. Comparisons with observations will be

presented in the next Section.

We constrain the lightning NO

source with satellite data as described by

Murray et al.

(2012)

. Lightning NO

is mainly released at the top of convective updrafts following

Ott et

al. (2010)

. The standard GEOS-Chem model uses higher NO

yields for mid-latitudes

lightning (500 mol/flash) than for tropical (260 mol/flash) (

Huntrieser et al., 2007

2008

;

Hudman et al., 2007

;

Ott et al., 2010

) with a fairly arbitrary boundary between the two at

23°N in North America and 35°N in Eurasia.

Zhang et al. (2014)

previously found that this

leads GEOS-Chem to overestimate background ozone in the southwestern US and we find

the same here for the eastern US and the Gulf of Mexico. We treat here all lightning in the

35°S–35°N band as tropical and thus remove the distinction between North America and

Eurasia.

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Figure 1 gives the resulting surface NO

emissions for the Southeast US for August and

September 2013. With the original NEI inventory, fuel combustion accounted for 81% of

total surface NO

emissions in the Southeast US (not including lightning). If the required

reduction of non-power plant NEI emissions is 60%, the contribution from fuel combustion

would be 68%.

Biogenic VOC emissions are from MEGAN v2.1, including isoprene, acetone, acetaldehyde,

monoterpenes, and >C2 alkenes. We reduce MEGAN v2.1 isoprene emissions by 15% to

better match SEAC

RS observations of isoprene fluxes from the Ozarks (

Wolfe et al., 2015

)

and observed formaldehyde (

Zhu et al., 2016

Yu et al. (2016)

show the resulting isoprene

emissions for the SEAC

RS period.

3 Overestimate of NO

emissions in the EPA NEI inventory

Figure 2 shows simulated and observed median vertical distributions of NO

, total inorganic

nitrate (gas-phase HNO

+aerosol NO

−

), and ozone concentrations along the SEAC

flight tracks over the Southeast US. Here and elsewhere the data exclude urban plumes as

diagnosed by [NO

] > 4 ppb, open fire plumes as diagnosed by [CH

CN] > 200 ppt, and

stratospheric air as diagnosed by [O

]/[CO] > 1.25 mol mol

−1

. These filters exclude <1%,

7%, and 6% of the data respectively. We would not expect the model to be able to capture

these features even at native resolution (

Yu et al., 2016

Model results in Figure 2 are shown both with the original NO

emissions (dashed line) and

with non-power plant NEI fuel emissions decreased by 60% (solid line). Decreasing

emissions corrects the model bias for NO

and also largely corrects the bias for inorganic

nitrate. Boundary layer ozone is overestimated by 12 ppb with the original NO

emissions

but this bias disappears after decreasing the NO

emissions. Results are very similar if we

decrease the non-power plant NEI fuel emissions by only 30% and zero out soil and

fertilizer emissions. Thus the required decrease of NO

emissions may involve an

overestimate of both anthropogenic and soil emissions.

Further support for decreasing NO

emissions is offered by observed nitrate wet deposition

fluxes from the NADP network (

NADP, 2007

). Figure 3 compares simulated and observed

fluxes for the model with decreased NO

emissions. Model values have been corrected for

precipitation bias following the method of

Paulot et al. (2014)

, in which the monthly

deposition flux is assumed to scale to the 0.6

power of the precipitation bias. We diagnose

precipitation bias in the GEOS-5.11.0 data relative to high-resolution PRISM observations

(

http://prism.oregonstate.edu

). For the Southeast US, the precipitation bias is −34% in

August and −21% in September 2013. We see from Figure 3 that the model with decreased

emissions reproduces the spatial variability in the observations with only +8% bias over

the Southeast US and +7% over the contiguous US. In comparison, the model with original

emissions had a 63% overestimate of the nitrate wet deposition flux nationally and a 71%

overestimate in the Southeast. The high deposition fluxes along the Gulf of Mexico in Figure

3, both in the model and in the observations, reflect particularly large precipitation.

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The model with decreased NO

emissions also reproduces the spatial distribution of NO

the Southeast US boundary layer as observed in SEAC

RS. This is shown in Figure 4 with

simulated and observed concentrations of NO

along the flight tracks below 1.5 km altitude.

The spatial correlation coefficient is 0.71. There are no obvious spatial patterns of model

bias that would point to specific source sectors as responsible for the NO

emission

overestimate, beyond the blanket 30–60% decrease of non-power plant NEI emissions

needed to correct the regional emission total.

4 Using satellite NO

data to verify NO

emissions: sensitivity to upper

troposphere

Observations of tropospheric NO

columns by solar backscatter from the OMI satellite

instrument offer an additional constraint on NO

emissions (

Duncan et al., 2014

;

Lu et al.,

2015

). We compare the tropospheric columns simulated by GEOS-Chem with the NASA

operational retrieval (Level 2, v2.1) (

NASA, 2012

;

Bucsela et al., 2013

) and the Berkeley

High-Resolution (BEHR) retrieval (

Russell et al., 2011

). The NASA retrieval has been

validated to agree with surface measurements to within ± 20% (

Lamsal et al., 2014

). Both

retrievals fit the observed backscattered solar spectra to obtain a slant tropospheric NO

column,

, along the optical path of the backscattered radiation detected by the satellite.

The slant column is converted to the vertical column,

, by using an air mass factor (

AMF

)

that depends on the vertical profile of NO

and on the scattering properties of the surface

and the atmosphere (

Palmer et al., 2001

AMF

∫

(4)

In Equation 4,

AMF

is the geometric air mass factor that depends on the viewing geometry

of the satellite,

w(z)

is a scattering weight calculated by a radiative transfer model that

describes the sensitivity of the backscattered radiation to NO

as a function of altitude,

S(z)

is a shape factor describing the normalized vertical profile of NO

number density, and

the tropopause. Scattering weights for NO

retrievals typically increase by a factor of 3 from

the surface to the upper troposphere (

Martin et al., 2002

). Here we use our GEOS-Chem

shape factors to re-calculate the AMFs in the NASA and BEHR retrievals as recommended

Lamsal et al. (2014)

for comparing model and observations. We filter out cloudy scenes

(cloud radiance fraction > 0.5) and bright surfaces (surface reflectivity > 0.3).

Figure 5 shows the mean NO

tropospheric columns from BEHR, NASA, and GEOS-Chem

(with NO

emission reductions applied) over the Southeast US for August–September 2013.

The BEHR retrieval is on average 6% higher than the NASA retrieval. GEOS-Chem is on

average 11±19% lower than the NASA retrieval and 16±18% lower than the BEHR retrieval.

With the original NEI NO

emissions, GEOS-Chem would be biased high against both

retrievals by 26–31%. The low bias in the model with reduced NO

emissions does not

appear to be caused by an overcorrection of surface emissions but rather by the upper

troposphere. Figure 6 (top left panel) shows the mean vertical profile of NO

number density

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as measured from the aircraft by two independent instruments (NOAA and UC Berkeley)

and simulated by GEOS-Chem. At the surface, the median difference is 1.8×10

molecules

−3

which is within the NOAA and UC Berkeley measurement uncertainties of +/− 0.030

ppbv + 7% and +/− 5%, respectively. The observations show a secondary maximum in the

upper troposphere above 10 km, absent in GEOS-Chem. It has been suggested that aircraft

measurements of NO

in the upper troposphere could be biased high due to decomposition

in the instrument inlet of thermally unstable NO

reservoirs such as HNO

and

methylperoxynitrate (

Browne et al., 2011

;

Reed et al., 2016

). This would not affect the UC

Berkeley measurement (

Nault et al., 2015

) and could possibly account for the difference

with the NOAA a measurement in Figure 6.

The top right panel of Figure 6 shows the cumulative contributions from different altitudes

to the slant NO

column measured by the satellite, using the median vertical profiles from

the left panel and applying mean altitude-dependent scattering weights from the NASA and

BEHR retrievals. The boundary layer below 1.5 km contributes only 19–28% of the column.

The upper troposphere above 8 km contributes 32–49% in the aircraft observations and 23%

in GEOS-Chem. Much of the observed upper tropospheric NO

likely originates from

lightning and is broadly distributed across the Southeast because of the long lifetime of NO

at that altitude (

Li et al., 2005

;

Bertram et al., 2007

;

Hudman et al., 2007

). The NO

vertical

profile (shape factor) assumed in the BEHR retrieval does not include any lightning

influence, and the Global Modeling Initiative (GMI) model vertical profile assumed in the

NASA retrieval has little contribution from the upper troposphere (

Lamsal et al., 2014

These underestimates of upper tropospheric NO

in the retrieval shape factors will cause a

negative bias in the AMF and therefore a positive bias in the retrieved vertical columns.

The GEOS-Chem underestimate of observed upper tropospheric NO

in Figure 6 is partly

driven by NO/NO

partitioning. The bottom left panel of Figure 6 shows the [NO]/[NO

]

concentration ratio in GEOS-Chem and in the observations (NOAA for NO, UC Berkeley

for NO

). One would expect the [NO]/[NO

] concentration ratio in the daytime upper

troposphere to be controlled by photochemical steady-state:

(5)

(6)

hυ

→

(7)

If reaction (6) plays only a minor role then [NO]/[NO

]

≈

]), defining the NO-

-O

photochemical steady state (PSS). The PSS plotted in Figure 6 agrees closely with

GEOS-Chem. Such agreement has previously been found when comparing photochemical

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models with observed [NO]/[NO

] ratios from aircraft in the marine upper troposphere

(

Schultz et al., 1999

) and lower stratosphere (

Del Negro et al., 1999

). The SEAC

observations show large departure. The NO

photolysis frequencies

computed locally by

GEOS-Chem are on average within 10% of the values determined in SEAC

RS from

measured actinic fluxes (

Shetter and Muller, 1999

), so this is not the problem.

A possible explanation is that the model underestimates peroxy radical concentrations and

hence the contribution of reaction (6) in the upper troposphere.

Zhu et al. (2016)

found that

GEOS-Chem underestimates the observed HCHO concentrations in the upper troposphere

during SEAC

RS by a factor of 3, implying that the model underestimates the HO

source

from convective injection of HCHO and peroxides (

Jaeglé et al., 1997

;

Prather and Jacob,

1997

;

Müller and Brasseur, 1999

). HO

observations over the central US in summer during

the SUCCESS aircraft campaign suggest that this convective injection increases HO

concentrations in the upper troposphere by a factor of 2 (

Jaeglé et al., 1998

). The bottom

right panel of Figure 6 shows median modeled and observed vertical profiles of the HO

reservoir hydrogen peroxide (H

) during SEAC

RS over the Southeast US. GEOS-Chem

underestimates observed H

by a mean factor of 1.7 above 8km. The bottom left panel of

Figure 6 shows the [NO]/[NO

] ratio in GEOS-Chem with HO

and RO

doubled above 8

km. Such a change corrects significantly the bias relative to observations.

The PSS and GEOS-Chem simulation of the NO/NO

concentration ratio in Figure 6 use

= 3.0×10

−12

exp[−1500/T] cm

molecule

−1

and spectroscopic information for

from

Sander et al. (2011)

. It is possible that the strong thermal dependence of

has some error,

considering that only one direct measurement has been published for the cold temperatures

of the upper troposphere (

Borders and Birks, 1982

Cohen et al. (2000)

found that reducing

the activation energy of

by 15% improved model agreement in the lower stratosphere.

Correcting the discrepancy between simulated and observed [NO]/[NO

] ratios in the upper

troposphere in Figure 6 would require a similar reduction to the activation energy of

, but

this reduction would negatively impact the surface comparison. This inconsistency of the

observed [NO]/[NO

] ratio with basic theory needs to be resolved, as it affects the inference

of NO

emissions from satellite NO

column measurements. Notwithstanding this

inconsistency, we find that NO

in the upper troposphere makes a significant contribution to

the tropospheric NO

column observed from space.

5 Isoprene oxidation pathways

Measurements aboard the SEAC

RS aircraft included first-generation isoprene nitrates

(ISOPN), isoprene hydroperoxide (ISOPOOH), and hydroperoxyaldehydes (HPALDs)

(

Crounse et al., 2006

;

Paulot et al., 2009a

;

St. Clair et al., 2010

;

Crounse et al., 2011

;

Beaver

et al., 2012

;

Nguyen et al., 2015

). Although measurement uncertainties are large (30%, 40%,

and 50%, respectively (

Nguyen et al., 2015

)), these are unique products of the ISOPO

NO, ISOPO

+ HO

, and ISOPO

isomerization pathways and thus track whether oxidation

of isoprene proceeds by the high-NO

pathway (producing ozone) or the low-NO

pathways.

Figure 2 (bottom row) compares simulated and observed concentrations. All three gases are

restricted to the boundary layer because of their short lifetimes. Mean model concentrations

in the lowest altitude bin (Figure 2, approximately 400m above ground) differ from

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observations by +19% for ISOPN, +70% for ISOPOOH, and −50% for HPALDs. The

GEOS-Chem simulation of organic nitrates including ISOPN is further discussed in

Fisher et

al. (2016)

. Our HPALD source is based on the ISOPO

isomerization rate constant from

Crounse et al. (2011)

. A theoretical calculation by

Peeters et al. (2014)

suggests a rate

constant that is 1.8× higher, which would reduce the model bias for HPALDs and ISOPOOH

and increase boundary layer OH by 8%.

St. Clair et al. (2015)

found that the reaction rate of

ISOPOOH + OH to form IEPOX is approximately 10% faster than the rate given by

Paulot

et al. (2009b)

, which would further reduce the model overestimate. For both ISOPOOH and

HPALDs, GEOS-Chem captures much of the spatial variability (

= 0.80 and 0.79,

respectively).

Figure 7 shows the model branching ratios for the fate of the ISOPO

radical by tracking the

mass of ISOPO

reacting via the high-NO

pathway (ISOPO

+NO) and the low-NO

pathways over the Southeast US domain. The mean branching ratios for the Southeast US

are ISOPO

+NO 54%, ISOPO

+HO

26%, ISOPO

isomerization 15%, and ISOPO

+RO

5%. The lack of dominance of the high-NO

pathway is due in part to the spatial segregation

of isoprene and NO

emissions (

Yu et al., 2016

). This segregation also buffers the effect of

changing NO

emissions on the fate of isoprene. Our original simulation with higher total

emissions (unadjusted NEI11v1) had a branching ratio for the ISOPO

+NO reaction of

only 62%.

6 Implications for ozone: aircraft and ozonesonde observations

Figure 2 compares simulated and observed median vertical profiles of ozone concentrations

over the Southeast US during SEAC

RS. There is no significant bias through the depth of

the tropospheric column. The median ozone concentration below 1.5 km is 49 ppb in the

observations and 51 ppb in the model. We also find excellent model agreement across the

US with the SEACIONS ozonesonde network (Figure 8). The successful simulation of

ozone is contingent on the decrease in NO

emissions. As shown in Figure 2, a simulation

with the original NEI emissions overestimates boundary layer ozone by 12 ppb.

The model also has success in reproducing the spatial variability of boundary layer ozone

seen from the aircraft, as shown in Figure 4. The correlation coefficient is

= 0.71 on the

0.25°×0.3125° model grid, and patterns of high and low ozone concentration are consistent.

The highest observed ozone (>75 ppb) was found in air influenced by agricultural burning

along the Mississippi River and by outflow from Houston over Louisiana. GEOS-Chem does

not capture the extreme values and this probably reflects a dilution effect (

Yu et al., 2016

A critical parameter for understanding ozone production is the ozone production efficiency

(OPE) (

Liu et al., 1987

), defined as the number of ozone molecules produced per molecule

of NO

emitted. This can be estimated from atmospheric observations by the relationship

between odd oxygen (O

≡

+NO

) and the sum of products of NO

oxidation, collectively

called NO

and including inorganic and organic nitrates (

Trainer et al., 1993

;

Zaveri, 2003

The O

vs. NO

linear relationship (as derived from a linear regression) provides an upper

estimate of the OPE because of rapid deposition of NO

, mainly HNO

(

Trainer et al., 2000

;

Rickard et al., 2002

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Figure 9 shows the observed and simulated daytime (9–16 local) O

vs. NO

relationship in

the SEAC

RS data below 1.5 km, where NO

is derived from the observations as NO

-NO

≡

HNO

+ aerosol nitrate + PAN + alkyl nitrates. The resulting OPE from the observations

(17.4±0.4 mol mol

−1

) agrees well with GEOS-Chem (16.7±0.3). Previous work during the

INTEX-NA aircraft campaign in summer 2004 found an OPE of 8 below 4 km (

Mena-

Carrasco et al., 2007

). By selecting INTEX-NA data only for the Southeast and below 1.5

km we find an OPE of 14.1±1.1 (Figure 9, right panel). The median NO

was 1.1 ppb during

SEAC

RS and 1.5 ppb during INTEX-NA, a decrease of approximately 40%. With the

original NEI11v1 NO

emissions (53% higher), the OPE from GEOS-Chem would be

14.7±0.3. Both the INTEX-NA data and the model are consistent with the expectation that

OPE increases with decreasing NO

emissions (

Liu et al., 1987

7 Implications for ozone: surface air

Figure 10 compares maximum daily 8-h average (MDA8) ozone values at the US EPA Clean

Air Status and Trends Network (CASTNET) sites in June-August 2013 to the corresponding

GEOS-Chem values. The model has a mean positive bias of 6±14 ppb with no significant

spatial pattern. The model is unable to match the low tail in the observations, including a

significant population with MDA8 ozone less than 20 ppb. The improvements to dry

deposition described in Section 2.2 minimally reduce (approximately 1 ppb) GEOS-Chem

ozone compared to SEAC

RS boundary layer and CASTNET surface MDA8 ozone

observations. The reduction of daytime mixing depths described in Section 2 results in a

small increase in mean MDA8 ozone (approximately 2 ppb).

The positive bias in the model for surface ozone is remarkable considering that the model

has little bias relative to aircraft observations below 1.5 km altitude (Figures 2 and 4). A

standard explanation for model overestimates of surface ozone over the Southeast US, first

proposed by

Fiore et al. (2003)

and echoed in the review by

McDonald-Buller et al. (2011)

is excessive ozone over the Gulf of Mexico, which is the prevailing low-altitude inflow. We

find that this is not the case. SEAC

RS included four flights over the Gulf of Mexico, and

Figure 11 compares simulated and observed vertical profiles of ozone and NO

concentrations that show no systematic bias. The median ozone concentration in the marine

boundary layer is 26 ppb in the observations and 29 ppb in the model. This successful

simulation is due to our adjustment of lightning NO

emission (Section 2.3); a sensitivity

test with the original (twice higher) GEOS-Chem lightning emissions in the southern US

increases surface ozone over the Gulf of Mexico by up to 6 ppb. The aircraft observations in

Figure 4 further show no indication of a coastal depletion that might be associated with

halogen chemistry. Remarkably, the median ozone over the Gulf of Mexico is higher than

approximately 8% of MDA8 values at sites in the Southeast.

It appears instead that there is a model bias in boundary layer vertical mixing and chemistry.

Figure 12 shows the median ozonesonde profile at a higher vertical resolution over the

Southeast US (Huntsville, Alabama and St. Louis, Missouri sites) during SEAC

RS as

compared to GEOS-Chem below 1.5 km. The ozonesondes indicate a decrease of 7 ppb

from 1.5 km to the surface, whereas GEOS-Chem features a reverse gradient of increasing

ozone from 1.5 to 1 km with flat concentrations below. This implies a combination of two

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model errors in the boundary layer: (1) excessive vertical mixing, (2) net ozone production

whereas observations indicate a net loss.

8 Conclusions

We used aircraft (SEAC

RS), surface, satellite, and ozonesonde observations from August

and September 2013, interpreted with the GEOS-Chem chemical transport model, to better

understand the factors controlling surface ozone in the Southeast US. Models tend to

overestimate ozone in that region. Determining the reasons behind this overestimate is

critical to the design of efficient emission control strategies to meet the ozone NAAQS.

A major finding from this work is that the EPA National Emission Inventory (NEI11v1) for

(the limiting precursor for ozone formation) is biased high across the US by as much as

a factor of 2. Evidence for this comes from (1) SEAC

RS observations of NO

and its

oxidation products, (2) NADP network observations of nitrate wet deposition fluxes, and (3)

OMI satellite observations of NO

. Presuming no error in emissions from large power plants

with continuous emission monitors (14% of unadjusted NEI inventory), we find that

emissions from other industrial sources and mobile sources must be 30–60% lower than NEI

values, depending on the assumption of the contribution from soil NO

emissions. We thus

estimate that anthropogenic fuel NO

emissions in the US in 2013 were 1.7–2.6 Tg N a

−1

, as

compared to 3.5 Tg N a

−1

given in the NEI.

OMI NO

satellite data over the Southeast US are consistent with this downward correction

of NO

emissions but interpretation is complicated by the large contribution of the free

troposphere to the NO

tropospheric column retrieved from the satellite. Observed (aircraft)

and simulated vertical profiles indicate that NO

below 2 km contributes only 20–35% of the

tropospheric column detected from space while NO

above 8 km (mainly from lightning)

contributes 25–50%. Current retrievals of satellite NO

data do not properly account for this

elevated pool of upper tropospheric NO

, so that the reported tropospheric NO

columns are

biased high. More work is needed on the chemistry maintaining high levels of NO

in the

upper troposphere.

Isoprene emitted by vegetation is the main VOC precursor of ozone in the Southeast in

summer, but we find that only 50% reacts by the high-NO

pathway to produce ozone. This

is consistent with detailed aircraft observations of isoprene oxidation products from the

aircraft. The high-NO

fraction is only weakly sensitive to the magnitude of NO

emissions

because isoprene and NO

emissions are spatially segregated. The ability to properly

describe high- and low-NO

pathways for isoprene oxidation is critical for simulating ozone

and it appears that the GEOS-Chem mechanism is successful for this purpose.

Our updated GEOS-Chem simulation with decreased NO

emissions provides an unbiased

simulation of boundary layer and free tropospheric ozone measured from aircraft and

ozonesondes during SEAC

RS. Decreasing NO

emissions is critical to this success as the

original model with NEI emissions overestimated boundary layer ozone by 12 ppb. The

ozone production efficiency (OPE) inferred from O

vs. NO

aircraft correlations in the

mixed layer is also well reproduced. Comparison to the INTEX-NA aircraft observations

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over the Southeast in summer 2004 indicates a 14% increase in OPE associated with a 40%

reduction in NO

emissions.

Despite the successful simulation of boundary layer ozone (Figures 2 and 9), GEOS-Chem

overestimates MDA8 surface ozone observations in the Southeast US in summer by 6±14

ppb. Daytime ozonesonde data indicate a 7 ppb decrease from 1.5 km to the surface that

GEOS-Chem does not capture. This may be due to excessive boundary layer mixing and net

ozone production in the model. Excessive mixing in GEOS-Chem may be indicative of an

overestimate of sensible heat flux (

Holtslag and Boville, 1993

), and thus an investigation of

boundary layer meteorological variables is warranted. Such a bias may not be detected in the

comparison of GEOS-Chem with aircraft data, generally collected under fair-weather

conditions and with minimal sampling in the lower part of the boundary layer. An

investigation of relevant meteorological variables and boundary layer source and sink terms

in the ozone budget to determine the source of bias and its prevalence across models will be

the topic of a follow-up paper.

Supplementary Material

Refer to Web version on PubMed Central for supplementary material.

Acknowledgments

We are grateful to the entire NASA SEAC

RS team for their help in the field. We thank Tom Ryerson for his

measurements of NO and NO

from the NOAA NO

instrument. We thank L. Gregory Huey for the use of his

CIMS PAN measurements. We thank Fabien Paulot and Jingqiu Mao for their helpful discussions of isoprene

chemistry. We thank Christoph Keller for his help in implementing the NEI11v1 emissions into GEOS-Chem. We

acknowledge the EPA for providing the 2011 North American emission inventory, and in particular George Pouliot

for his help and advice. These emission inventories are intended for research purposes. A technical report

describing the 2011-modeling platform can be found at:

http://www.epa.gov/ttn/chief/net/2011nei/

2011_nei_tsdv1_draft2_june2014.pdf

. A description of the 2011 NEI can be found at:

http://www.epa.gov/

ttnchie1/net/2011inventory.html

. This work was supported by the NASA Earth Science Division and by STAR

Fellowship Assistance Agreement no. 91761601-0 awarded by the US Environmental Protection Agency (EPA). It

has not been formally reviewed by EPA. The views expressed in this publication are solely those of the authors. JAF

acknowledges support from a University of Wollongong Vice Chancellor’s Postdoctoral Fellowship. This research

was undertaken with the assistance of resources provided at the NCI National Facility systems at the Australian

National University through the National Computational Merit Allocation Scheme supported by the Australian

Government.

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