We evaluate global tropospheric nitrogen dioxide (NO2) simulations
using the CHASER V4.0 global chemical transport model (CTM) at horizontal
resolutions of 0.56, 1.1, and 2.8∘. Model evaluation was conducted
using satellite tropospheric NO2 retrievals from the Ozone Monitoring
Instrument (OMI) and the Global Ozone Monitoring Experiment-2 (GOME-2) and
aircraft observations from the 2014 Front Range Air Pollution and
Photochemistry Experiment (FRAPPÉ). Agreement against satellite retrievals
improved greatly at 1.1 and 0.56∘ resolutions (compared to
2.8∘ resolution) over polluted and biomass burning regions. The
1.1∘ simulation generally captured the regional distribution of the
tropospheric NO2 column well, whereas 0.56∘ resolution was
necessary to improve the model performance over areas with strong local
sources, with mean bias reductions of 67 % over Beijing and 73 % over
San Francisco in summer. Validation using aircraft observations indicated
that high-resolution simulations reduced negative NO2 biases below
700 hPa over the Denver metropolitan area. These improvements in
high-resolution simulations were attributable to (1) closer spatial
representativeness between simulations and observations and (2) better
representation of large-scale concentration fields (i.e., at 2.8∘)
through the consideration of small-scale processes. Model evaluations
conducted at 0.5 and 2.8∘ bin grids indicated that the contributions
of both these processes were comparable over most polluted regions, whereas
the latter effect (2) made a larger contribution over eastern China and
biomass burning areas. The evaluations presented in this paper demonstrate
the potential of using a high-resolution global CTM for studying
megacity-scale air pollutants across the entire globe, potentially also
contributing to global satellite retrievals and chemical data assimilation.
Introduction
Nitrogen oxides (NOx≅NO+NO2) play a key
role in air quality, tropospheric chemistry, ecosystem, and climate change.
NOx is one of the main precursors of tropospheric ozone, a major pollutant
and greenhouse gas . Oxidation products from NOx,
including nitric acid (HNO3), alkyl nitrates (RONO2), and
peroxynitrates (RO2NO2), are partitioned to particulate nitrates,
which cause respiratory problems, degrade visibility, and affect the
radiative budget by scattering solar radiation. The
wet and dry deposition of nitrogen
compounds affects the productivities and diversities of terrestrial and
marine ecosystems on a global scale e.g.,.
Increasing NOx also reduces quantities of long-lived greenhouse gases,
such as methane, due to chemical destruction via hydroxyl radicals
(OH) through O3–HOx–NOx chemistry
e.g.,.
Major anthropogenic sources of NOx are ground transport and power
generation, with these accounting for more than half of total global
anthropogenic emissions . NOx is also emitted from
natural sources: biomass burning, microbial activity in soil, and lightning.
Main NOx sinks are oxidation with OH during daytime and
the hydrolysis of dinitrogen
pentoxide (N2O5) on aerosols during nighttime
.
The lifetime of NOx, which is a
function of OH concentration and NO2 photolysis during daytime
, is of the order of hours to days. It also depends on
aerosol surface area and composition during nighttime
e.g.,. Because of this short lifetime and heterogeneous
source distribution, tropospheric NOx is highly variable in space and time
over the globe.
Satellite observations of tropospheric NO2 columns from the Global
Ozone Monitoring Experiment (GOME), the SCanning Imaging Absorption
SpectroMeter for Atmospheric CHartographY (SCIAMACHY), the Ozone Monitoring
Instrument (OMI) e.g.,, and GOME-2
e.g., have been used for evaluations of
chemical transport models (CTMs)
e.g.,. Previous
model validation studies have revealed a general underestimation of simulated
tropospheric NO2 columns over polluted areas in global CTMs
. Global CTMs
typically have a horizontal resolution of 2–5∘. Meanwhile,
high-resolution simulations have been conducted using regional models, which
have shown the ability to simulate observed high tropospheric NO2
columns over major polluted regions such as East Asia, North America, and
Europe
e.g.,.
High-resolution simulations can lead to improvements in two ways: (1) through
reduced spatial representation gaps between observed and simulated fields and
(2) via improved representation of large-scale concentration fields through
a consideration of small-scale
processes. Using a regional CTM, suggested that
insufficient model resolution leads to enhanced OH, shortened
NO2 lifetime, and too-low NO2 over strong local emissions.
The authors also suggested that 4 and 12 km resolutions are sufficient to
accurately simulate the nonlinear effects of O3–HOx–NOx
chemistry on NO2 lifetime over power plants in Four Corners–San Juan
and Los Angeles. estimated up to 60 % error reduction
in simulated tropospheric NO2 columns at 20 km resolution over East Asia
compared to 80 km resolution. Using a global CTM, reported
that NOx lifetime over East Asia increased by 22 % when increasing model resolution from
5.6∘× 5.6∘ to 1.1∘× 1.1∘.
conducted a comprehensive evaluation of NO2,
SO2, and CH2O simulated in TM5-MP, revealing that increasing
horizontal model resolution from 3∘× 2∘ to
1∘× 1∘ reduced negative NO2 bias by up to
99 % against 35 % of the surface measurement sites (33 stations) in
Europe. Horizontal model resolution could also be a crucial factor even for
biomass burning areas because of highly varying emission sources and
nonlinear chemical processes. However, previous studies have mostly focused
on urban regions. Further investigations are required for both urban and
biomass burning regions. Vertical model resolution could also be important
through, for instance, vertical mixing between planetary boundary layers and
the free troposphere e.g.,.
Simulated global NO2 fields provide important information on
satellite retrieval and data assimilation, as well as contributing to a
better understanding of the atmospheric environment
e.g.,. The
quality of a priori fields is important for retrieval of the tropospheric
NO2 column . For instance, low-resolution global
CTMs poorly represent NO2 variations across urban and rural regions,
degrading the spatial variation of retrieved concentrations at high
resolution. Several retrieval studies
have employed high-resolution a priori fields from regional CTM simulations.
The authors demonstrated improvements in these regional retrievals using
high-resolution a priori fields in comparison to the ARCTRAS aircraft
observation and ground-based remote sensing MAX-DOAS through, for instance,
clearer separation of NO2 profiles between urban, rural, and ocean
regions and improved representations of altitude-dependent sensitivities
(i.e., averaging kernels).
Global chemical data assimilation e.g.,
and emission inversion e.g., would also
benefit from high-resolution global CTMs through improvements in model
performance e.g., and reduced spatial representation
gaps between observed and simulated fields. Several previous studies
demonstrated the importance of
high-resolution modeling in detecting small-scale NOx emission sources
such as urban, new power plant, and ship emissions. A systematic evaluation
of high-resolution models enables us to discuss the application potentials of global high-resolution models to
satellite retrievals and data assimilation.
In this study, we conduct a systematic evaluation of global high-resolution
simulations of tropospheric NO2 and related chemistry using CHASER
V4.0. We focus on the impacts of
horizontal model resolution on global tropospheric NO2 simulations.
Three horizontal resolutions of 2.8, 1.1, and 0.56∘ are evaluated
using satellite and aircraft measurements. The remainder of this paper is
structured as follows. Section describes the model
configuration and simulation settings and optimizes the simulated
meteorological fields at different horizontal resolutions.
Section describes the observations used for validations.
Section presents the model evaluation results of tropospheric
NO2 using satellite-derived retrievals for the year 2008 and aircraft
campaign observations from the 2014 Front Range Air Pollution and
Photochemistry Experiment (FRAPPÉ). Section discusses the
resolution dependence of tropospheric chemistry. In Sect. , we
then discuss the implications of this evaluation and the potential benefits
of applying global high-resolution CTMs. Finally, Sect.
provides concluding remarks.
MethodologyCHASER V4.0 model and simulations
CHASER V4.0 is a global chemical
transport model developed in the framework of the MIROC-ESM Earth system
model , which is coupled online with the MIROC-AGCM
atmospheric general circulation model (AGCM) and the
SPRINTARS aerosol transport model . Several
updates were made from CHASER V3.0 to CHASER V4.0, which
includes the consideration of aerosol species (sulfate, nitrate, ammonium,
black and organic carbon, soil dust, and sea salt) and the implementation of
related chemistry, radiation, and cloud processes. AGCM was also updated from
the NIES/CCSR AGCM 5.7b to the MIROC-AGCM. Detailed information on the AGCM
updates are provided by .
CHASER calculates gaseous, aqueous, and heterogeneous chemical reactions (93
species and 263 reactions), including the
O3–HOx–NOx–CH4–CO system with
the oxidation of non-methane
volatile organic compounds (NMVOCs). Major chemical reactions related to
NO2 are considered, including (1) the photochemical cycle of NO and NO2, (2) oxidation of
NO2 with OH, (3) heterogeneous hydrolysis of N2O5, (4) the
formation, thermal decomposition, and photolysis of peroxyacetyl nitrates
(PANs), and (5) the formation of
isoprene nitrates. CHASER also calculates stratospheric O3 chemistry
including Chapman mechanisms and catalytic reactions related to HOx,
NOx, ClOx, and BrOx below 50 hPa. Above 50 hPa, prescribed
concentrations of O3, nitrogen, and halogen species are used. Monthly
ozone climatology is obtained from UGAMP , whereas monthly
climatologies of nitrogen and halogen species are taken from the
Chemistry–Climate Model Initiative (CCMI) REF-C1SD simulation using NIES CCM
. Dry and wet (rain out and
washout) deposition is calculated based on the resistance-based
parameterization and cumulus convection and large-scale
condensation parameterizations, respectively. Advective tracer transport is
calculated using the piecewise parabolic method and the
flux-form semi-Lagrangian scheme . The model also incorporates
tracer transport on a sub-grid scale in the framework of the prognostic
Arakawa–Schubert cumulus convection scheme and the
vertical diffusion scheme .
We evaluated two 1-year global simulations for tropospheric NO2 in
2008 and 2014 with a 1-year spin-up calculation for each simulation. In each
case, three model calculations were conducted at different horizontal
resolutions: T42 (i.e., 2.8∘× 2.8∘; hereinafter
referred to as the 2.8∘ simulation), T106 (i.e.,
1.1∘× 1.1∘; hereinafter the 1.1∘
simulation), and T213 (i.e., 0.56∘× 0.56∘;
hereinafter the 0.56∘ simulation); 32 vertical layers from the
surface to approximately 40 km altitude were used across the three
simulations. To meet the Courant–Friedrich–Levy (CFL) condition, different
maximal time steps were used for each resolution: i.e., 20 min at
2.8∘ resolution, 8 min at 1.1∘ resolution, and 4 min at
0.56∘ resolution. Sea-surface temperatures (SSTs) and sea-ice
concentrations (SICs) were prescribed by HadISST for the corresponding year
. Simulated air temperature and horizontal wind were nudged
to 12-hourly ERA-Interim reanalysis data . ERA-Interim
reanalysis data at 0.75∘× 0.75∘ horizontal
resolution with 37 pressure levels were linearly interpolated to each model
grid, possibly degrading simulated meteorological fields at finer resolution
(i.e., 0.56∘). We specified 5 and 0.7 days of nudging time for
temperature and horizontal wind, respectively.
NOx emissions from anthropogenic, biomass burning, lightning, and soil
sources were considered. Anthropogenic emissions from the HTAP_v2.2
inventory for the year 2008 were employed for the 2008
simulations, with these originally having
0.1∘× 0.1∘ resolution. For the 2014 simulation,
anthropogenic emissions for the latest available year 2010 of the HTAP_v2.2
inventory were used. Biomass burning emissions were taken from the Global
Fire Emissions Database (GFED) version 4.1
(0.25∘× 0.25∘ resolution) for
the two study years. Soil emissions were obtained from the Global Emission
InitiAtive (GEIA) database (1∘× 1∘)
. Model of Emissions of Gases and Aerosols from Nature
(MEGAN) version 2 data (0.5∘× 0.5∘) were used for
biogenic NMVOCs emissions . Annual mean total global
NOx emissions from the surface were 45.3 and 45.9 Tg N yr-1 in
2008 and 2014, respectively. Lightning NOx sources were calculated as a
function of cloud top height in the cumulus convection parameterization
(prognostic Arakawa–Schubert scheme) at each time step of CHASER, following
.
We considered diurnal cycles of surface NOx emissions following
. Different diurnal cycles were assumed depending on
the dominant source category of each region: anthropogenic-type diurnal
cycles (with maxima in the morning and evening, with a factor of about 1.4)
over Europe, eastern China, Japan, and North America; biomass-burning-type
diurnal cycles (with a rapid increase in the morning and maximum midday,
with a factor of about 3) over Central Africa and South America; and
soil-type diurnal cycles (with maxima in the afternoon, with a factor of
about 1.2) in the grasslands or sparsely vegetated areas of Australia, Sahara,
and western China. confirmed that the application of
this scheme leads to improvements in global tropospheric NO2
simulations at 2.8∘ resolution. Improvements were commonly found in
the 1.1∘ resolution simulation, whereas we did not evaluate the
impact at 0.56∘ resolution. Over biomass burning regions, the emission
diurnal variability applied in this study is generally similar to variability
from 3-hourly GFED4.1 data, while distinct differences in relative magnitude
around the GOME-2 overpass time suggest that model performance could differ
in comparison to the GOME-2 retrievals when using the 3-hourly GFED4.1
data.
The CTM–AGCM online coupling framework used in this study has advantages
over the off-line CTM framework driven by meteorological analysis
or reanalysis data. First, the
online framework is able to simulate short-term nonlinear variations in
chemical and transport processes at every time step of the model (1–20 min
in this study) in contrast to off-line CTMs driven by meteorological data,
typically with 6-hourly intervals. Second, grid-scale and sub-grid-scale
transport processes (e.g., convection, turbulent mixing) are represented in a
consistent manner based on AGCM physics (e.g., mass balance) at short time
intervals. Third, the online framework allows for a flexible choice of CTM
resolution, whereas the off-line framework requires matching (or
interpolations without physically meaningful variations) between the CTM and
meteorological data resolutions.
ObservationsSatellite tropospheric NO2 retrievals
We used tropospheric NO2 column retrievals from OMI and GOME-2. OMI,
onboard the Aura satellite, is an ultraviolet–visible nadir-scanning
solar-backscatter spectrometer covering the spectral range of 270–500 nm
. The Aura satellite, launched in 2004, is in a
Sun-synchronous polar orbit at 705 km of altitude with a local Equator
crossing time of approximately 13:40 LT. The ground pixel size of OMI ranges
from 13 × 24 km2 to 26 × 128 km2 depending on the
satellite viewing angle. OMI tropospheric NO2 column retrievals have
daily global coverage. We used the DOMINO version 2.0 data product
obtained from the TEMIS website
(http://www.temis.nl/). Observations with cloud radiance fraction <
0.5, surface albedo < 0.3, and quality flag 0 were used. Retrievals from
2014 affected by row anomalies were screened using a quality flag.
Tropospheric NO2 retrievals from GOME-2 on MetOP-A and MetOP-B were
used to compare the years 2008 and 2014, respectively. GOME-2 is a
nadir-scanning ultraviolet–visible spectrometer covering the spectral range
of 240–790 nm. MetOp-A, launched in 2007, and MetOp-B, launched in 2013,
are on a Sun-synchronous polar orbit at 817 km with a local Equator crossing
time of 09:30 LT. The ground pixel size is 80 × 40 km2. We used
the TM4NO2A version 2.3 product obtained from the TEMIS website
. The GOME-2 retrievals were derived with the same basic
algorithm as in DOMINO version 2 .
For model–retrieval comparisons, we first sampled simulated NO2
profiles at the closest times to measurement using 2-hourly model outputs;
these were then linearly interpolated to the center of each measurement from
the four surrounding model grids. Second, averaging kernels (AKs) were
applied to the interpolated model profiles in order to consider the
altitude-dependent sensitivity of retrievals. Third, retrieved and simulated
NO2 columns were averaged on 0.5 and 2.8∘ bin grids for model
evaluation. In order to identify the drivers of model–retrieval differences
and causes of NO2 error reductions in high-resolution simulations, we
conducted model evaluations at 0.5 and 2.8∘ bin grids (i.e., the
model and retrieval fields were interpolated to 0.5 and 2.8∘ bin
grids). Improved agreement in high-resolution simulations can be attributed
to two factors: (1) closer spatial representativeness between simulations and
satellite retrievals (up to approximately 0.5∘) and (2) improvements
in mean concentration fields on a large scale (i.e., at 2.8∘) through
the consideration of small-scale
processes. The error reductions evaluated at the 0.5∘ bin grid should
reflect both effects, whereas error reductions evaluated at the 2.8∘
bin grid should mainly be attributed to the latter effect (2). When error
reductions evaluated at the 2.8∘ bin grid are about half the error
reductions evaluated at the 0.5∘ bin grid, the contributions of the
two effects should be identical. When error reductions evaluated at 2.8 and
0.5∘ bin grids are comparable, the latter effect (2) should be
dominant.
It should be noted that tropospheric NO2 retrievals from SCIAMACHY
were also available for 2008. The model evaluation results are generally
similar between GOME-2 and SCIAMACHY. Results using SCIAMACHY are not
discussed in this paper.
Aircraft observation data
Vertical profiles of NO, NO2, OH, HO2,
O3, H2O, and the photolysis rate of O3 to
O(1D) were obtained from the 2014 Front Range Air Pollution and
Photochemistry Experiment (FRAPPÉ) campaign . The FRAPPÉ
campaign was conducted using the NSF/NCAR C130 aircraft during the period from 16 July through 18 August
2014. The C130 flight track covered the northern Colorado plains and
foothills and the area west of the Continental Divide. NO,
NO2, and O3 concentrations were measured by two-channel (for
NO and NO2) and one-channel (for O3) chemiluminescence
instruments . OH and HO2 were analyzed using
a CIMS-based instrument that is part of the Mauldin–Cantrell HOx CIMS
instrument e.g.,. Water vapor was measured by a
wavelength-scanned cavity ring-down spectroscopy (WS-CRDS) analyzer. The
photolysis rate of O3 to O(1D) data, calculated from NCAR
HARP–CFAS (CCD-based actinic flux spectroradiometer), were used. We used
1 min merged data obtained from the NASA LaRC Airborne Science Data for
Atmospheric Composition (http://www-air.larc.nasa.gov/). For comparison
purposes, we sampled simulated profiles at the closest time to measurement
using 2-hourly model outputs; these were then linearly interpolated to
measurement from the four surrounding model grids in the horizontal. The
observed and simulated vertical profiles were compared by averaging data
within each vertical pressure bin: 850 hPa (using data between the surface
and 825 hPa), 800 hPa (825–775 hPa), 750 hPa (775–725 hPa), 700 hPa
(725–675 hPa), 650 hPa (675–625 hPa), 600 hPa (625–575 hPa), 550 hPa
(575–525 hPa), and 500 hPa (525–475 hPa).
Ozonesonde
Simulated vertical profiles of tropospheric ozone were also evaluated using
ozonesonde observations. The observed vertical profiles of ozone were
obtained from the World Ozone and Ultraviolet Data Center (WOUDC,
https://woudc.org/), the Southern Hemisphere ADditional OZonesondes
(SHADOZ, https://tropo.gsfc.nasa.gov/shadoz/)
, and the NOAA Earth System Research
Laboratory (ESRL) Global Monitoring Division (GMD,
https://www.esrl.noaa.gov/gmd/). All available data from these sources
were used. The observed and simulated ozone profiles were compared at
ozonesonde locations by averaging data within each vertical pressure bin:
850 hPa (875–825 hPa), 500 hPa (550–450 hPa), 300 hPa (350–275 hPa),
and 100 hPa (112.5–92.5 hPa).
Validations of meteorological fields
In the CTM-AGCM online framework, meteorological fields vary among different
model resolutions. From sensitivity calculations, the strength and
distribution of the cumulus convection were found to be sensitive to model
resolution. The cumulus convection parameterization for 2.8∘
resolution was optimized following . We then attempted to
optimize the relevant model parameters (critical relative humidity for
cumulus convection and ice-fall speed) for 1.1 and 0.56∘ resolutions.
The criterion was to minimize the root mean square error (RMSE) of annual
global total flash against the Lightning Imaging Sensor (LIS), outgoing
longwave radiation (OLR) against the NOAA 18 satellite observations
, and precipitation against the Global Precipitation
Climatology Project (GPCP) for the year 2008.
The obtained minimum values of RMSE for annual mean flash rate were 0.010,
0.011, and 0.011 flashes km-2 day-1 at 2.8, 1.1, and
0.56∘ resolutions, respectively. Optimizing the cumulus convection
setting reduced the positive bias of the annual global mean OLR by 80 %
at 1.1∘ resolution and by 50 % at 0.56∘ resolution.
Simulated global flash frequency and annual global lightning NOx sources
(in brackets) varied slightly: 43 flashes s-1 (5.4 Tg N yr-1)
at 2.8∘ resolution, 47 flashes s-1 (5.6 Tg N yr-1) at
1.1∘ resolution, and 46 flashes s-1 (5.5 Tg N yr-1) at
0.56∘ resolution.
Annual mean precipitation rate (mm day-1) from
GPCP (a) and outgoing longwave radiation (OLR; W m-2) from
NOAA 18 satellite (e) for 2008. The second, third, and fourth
columns show differences in precipitation (b–d) and
OLR (f–h) between the observations and the model simulations at
2.8∘(b, f), 1.1∘(c, g), and
0.56∘(d, h) resolutions, respectively. The observations and
model results are mapped onto 2.5 and 1∘ bin grids for precipitation
and OLR, respectively.
Annual mean tropospheric NO2 column
(× 1015 molecules cm-2) from satellite retrievals (first
column; a, e) and differences between the model simulation
at 2.8∘ (second column; b, f), 1.1∘ (third
column; c,g), and 0.56∘ (fourth column;
d, h) resolutions and satellite retrievals from OMI (upper
row; a–d) and GOME-2 (lower row; e–h) for 2008. The
observed and simulated fields are mapped onto a 0.5∘ bin grid. The
white square line in (a) represents the region used for the model
evaluation.
We also evaluated relevant meteorological fields (i.e., precipitation and
cloud) that have large impacts on chemistry simulations in the online CTM
framework e.g.,. In comparison to the GPCP
precipitation data, all simulations showed similar spatial error patterns
after optimization (Fig. a–d), having positive biases
(typically by a factor of 2) north and south of the intertropical convergence
zone (ITCZ) over the Pacific, Indian subcontinent, and Central Africa and
negative biases in the South Pacific convergence zone (SPCZ), west of the
Maritime Continent, over the Amazon, and over the southeastern United
States because of the use of the same physical package (e.g., cumulus
convection scheme). Meanwhile, increasing model resolution led to large error
reductions by up to 70 % at 1.1 and 0.56∘ resolutions over the
northwest Pacific and Atlantic oceans (negative biases) and over the
northern part of China and the western part of the North American continent
(positive biases). All simulations also showed reasonable agreement with OLR
derived from the NOAA 18 satellite. The global mean positive bias was 80 and
50 % lower at 1.1 and 0.56∘ resolutions, respectively, than at
2.8∘ resolution (Fig. e–h), suggesting improved
photolysis calculations in the high-resolution simulations. Among different
regions, the positive model bias at 2.8∘ resolution was largest over
the Maritime Continent, which was reduced by 86 % at 1.1∘
resolution and by 75 % at 0.56∘ resolution. Over northern South
America, in contrast, most of the positive biases remained at 1.1 and
0.56∘ resolutions. The model simulations were thus appropriately set
up at all resolutions, while various features of the high-resolution
framework were improved. Further validation at various spatial–temporal
scales for different meteorological parameters will be helpful to evaluate
detailed AGCM performance, even if it is beyond the scope of the current
study.
Comparisons of annual mean tropospheric NO2 column between
satellite retrievals (OMI and GOME-2) and the model simulation at 2.8, 1.1,
and 0.56∘ resolutions. MB is the mean bias. RMSE is the
root mean square error. S-Corr. signifies the spatial correlation coefficient.
Units of MB and RMSE are × 1015 molecules cm-2. The
definition of the regions is the same as in Fig. .
Figure compares the simulated annual mean tropospheric
NO2 column with satellite retrievals. Both OMI and GOME-2 retrievals
showed high tropospheric NO2 columns over eastern China, the United
States, Europe, India, Southeast Asia, Central and South Africa, and South
America. For most of these regions, observed concentrations were higher in
GOME-2 than OMI, reflecting the difference in overpass time and diurnal
variations in tropospheric chemistry . All model
simulations captured the observed global spatial variation well, with r>0.9 in comparison to both OMI and GOME-2 for annual mean concentration
fields. In terms of global averages, the 2.8∘ simulations were biased
on the low side by 40 % compared to OMI and by 47 % compared to
GOME-2. This negative global mean bias has commonly been reported using other
global CTMs . As summarized in
Table , the negative annual global mean bias compared to OMI
(GOME-2) was slightly reduced by 5 % (3 %) at 1.1∘ resolution
and by 2 % (1 %) at 0.56∘ resolution compared to the
2.8∘ resolution. Global RMSE was reduced by 15 % compared to OMI
and GOME-2 by increasing model resolution from 2.8 to 1.1∘. The
improvement when increasing resolution from 1.1 to 0.56∘ was limited.
Monthly time series of tropospheric NO2 column
(× 1015 molecules cm-2) averaged in E-China
(110–123∘ E, 30–40∘ N), E-USA (95–71∘ W,
32–43∘ N), W-USA (125–100∘ W, 32–43∘ N), Europe
(10∘ W–30∘ E, 35–60∘ N), India
(68–88∘ E, 8–35∘ N), Mexico (115–90∘ W,
15–25∘ N), N-Africa (20∘ W–40∘ E,
0–20∘ N), C-Africa (10–40∘ E, 20∘ S–0),
S-Africa (26–31∘ E, 28–23∘ S), S-America
(70–50∘ W, 20∘ S–0), and SE-Asia (96–105∘ E,
10–20∘ N). The black dots are OMI retrievals, the red dashed line
is the model simulation at 2.8∘ resolution, the yellow dashed-dotted
line is the model simulation at 1.1∘ resolution, and the blue dotted
line is the model simulation at 0.56∘ resolution. The vertical bars
indicate mean OMI retrieval errors.
Same as Fig. , but for GOME-2.
Same as Fig. , but for root mean square error (RMSE)
of tropospheric NO2 column in comparison to OMI.
Same as Fig. , but for RMSE of tropospheric
NO2 column in comparison to GOME-2.
Figures and
( and ) compare seasonal variations in the regional
and monthly mean tropospheric NO2 column (regional RMSEs) against OMI
and GOME-2 in 2008 using data incorporated at a 0.5∘ bin grid. Because
the validation results are similar for OMI and GOME-2 for most cases, the
results using OMI are discussed below.
Over eastern China, negative model biases at 2.8∘ resolution were
reduced at 1.1 and 0.56∘ resolutions from February to July. In
December, model bias varied with model resolution: -14 % at
2.8∘ resolution, +23 % at 1.1∘ resolution, and
-7 % at 0.56∘ resolution. Negative annual mean bias was reduced
by 90 % from 2.8 to 1.1∘ resolution and by 64 % from 2.8 to
0.56∘ resolution, with increasing spatial correlations (from r=0.80 at 2.8∘ resolution to r=0.86 at 1.1∘ resolution and
0.91 at 0.56∘ resolution). Annual mean RMSE was also reduced by
32 % from 2.8 to 1.1∘ resolution and by 9 % from 1.1 to
0.56∘ resolution.
Over the eastern United States, negative annual mean bias was reduced by
87 % at 1.1∘ resolution and by 65 % at 0.56∘
resolution compared to 2.8∘ resolution. The seasonal bias reduction
reached 95 % at 0.56∘ resolution in summer. Annual RMSE was
reduced by 37 % at 1.1∘ resolution and by 40 % at
0.56∘ resolution compared to 2.8∘ resolution. The larger
monthly RMSE at 1.1∘ than at 2.8∘ resolution during December
is attributed to large positive biases over New Jersey and Ohio, although the
reason for these is unclear. The spatial correlation for annual mean
concentration fields increased from r=0.83 at 2.8∘ resolution to
r=0.93 at 1.1∘ resolution and 0.96 at 0.56∘ resolution.
Over the western United States, negative annual mean bias was 13 % lower
at 1.1∘ resolution and 14 % lower at 0.56∘ resolution
compared to 2.8∘ resolution. In summer, the negative seasonal mean
bias at 0.56∘ resolution was slightly larger, reflecting negative
biases over rural areas. RMSE for annual mean fields was reduced by 20 %
from 2.8 to 1.1∘ resolution and by 23 % from 1.1 to 0.56∘
resolution. The spatial correlation for annual mean fields increased from r=0.65 at 2.8∘ resolution to r=0.82 at 1.1∘ resolution and
0.91 at 0.56∘ resolution.
Over Europe, negative model bias for annual mean concentrations was reduced
by 23 % from 2.8 to 1.1∘ resolution, but was 46 % larger at
0.56∘ resolution than at 1.1∘ resolution. Large negative bias
over the Po Valley at 2.8∘ resolution was reduced by 13 % at
1.1∘ resolution and further reduced by 10 % from 1.1 to
0.56∘ resolution. In contrast, negative bias over London was larger
at 0.56∘ resolution than at 1.1∘ resolution by a factor of 4,
leading to larger negative regional mean bias at 0.56∘ resolution.
Simulated planetary boundary layer (PBL) height in the 0.56∘
simulation was substantially higher (by 20 %) than ERA-Interim over
London, which may partially contribute to the large NO2 bias. Annual
RMSE was also lower by 16 % at 1.1∘ resolution and by 9 % at
0.56∘ resolution than at 2.8∘ resolution. The spatial
correlation for annual mean fields increased from 0.87 at 2.8∘
resolution to 0.91 at 0.56∘ resolution.
Over India, negative model biases were smaller at 1.1 and 0.56∘
resolutions than at 2.8∘ resolution during January–May, but were
larger during June–September. RMSE for annual mean fields was reduced at 1.1
and 0.56∘ resolutions (except during summer), with 16 and 6 %
reductions, respectively. When comparing against OLR and precipitation
observations, we found an increased error at 0.56∘ resolution over
India during summer. This suggests the need to further optimize model
parameters relevant to tropical convection (see Sect. ) in
order to improve high-resolution NO2 simulations.
Over Mexico, the spatial correlation for annual mean fields increased
substantially at 1.1∘ (r=0.82) and 0.56∘ resolutions (r=0.93) compared to 2.8∘ resolution (r=0.61). Increasing model
resolution was important to reduce negative biases around Mexico City,
reducing annual RMSE by 17 % at 1.1∘ resolution and by 38 %
at 0.56∘ resolution compared to 2.8∘ resolution.
Over South Africa, the negative annual mean bias was reduced by 37 % at
1.1∘ resolution and by 43 % at 0.56∘ resolution compared
to 2.8∘ resolution, while annual RMSE was reduced by 46 and 56 %
at 1.1 and 0.56∘ resolutions, respectively. The spatial correlation
was 0.93 and 0.97 at 0.56∘ resolution in contrast to 0.61 at
2.8∘ resolution. Model resolution higher than 1.1∘ was thus
important for reproducing megacity-scale air pollution over the Highveld
region of South Africa, which is a complex source area of coal mining,
thermal power generation, metal mining, and metallurgical industry as
discussed by .
Over the selected biomass burning regions (South America, North Africa,
Central Africa, and Southeast Asia), all model simulations showed negative
biases throughout the year. In most cases, bias reduction with increasing
model resolution was limited because most forest fires burn over large
extents. Over South America, negative bias for the annual mean concentration
was 15 % lower at 1.1∘ resolution and 12 % lower at
0.56∘ resolution than at 2.8∘ resolution. Annual RMSE was
reduced by 15 % at 1.1∘ resolution and by 12 % at
0.56∘ resolution. The smaller spatial correlation at high resolutions
reflects an increased positive bias over a major biomass burning hot spot
(12∘ S, 50∘ W). Over North Africa, annual RMSE was smaller
by 9 % at 1.1∘ resolution and by 3 % at 0.56∘
resolution (compared to 2.8∘ resolution), whereas changes in mean
bias and spatial correlation were small. Over Central Africa, negative annual
mean bias was reduced by 24 % at 1.1∘ resolution and by 30 %
at 0.56∘ resolution, while RMSE increased during the biomass burning
season (by 11 % at 1.1∘ resolution and by 24 % at
0.56∘ resolution). The increased RMSE is associated with increased
positive biases around 10–20∘ S. Over Southeast Asia, RMSE for the
annual mean fields was reduced by 7 % at 1.1∘ resolution and by
5 % at 0.56∘ resolution compared to 2.8∘ resolution. The
increased errors over strong biomass burning hot spots in high-resolution
simulations could be a result of more pronounced influences of largely
uncertain inventories for individual burning points
e.g.,.
Negative biases with respect to GOME-2 were larger than to OMI in all
simulations over most regions. The differences suggest that all model
simulations underestimated high NO2 concentrations in the morning.
The underestimations could be associated with insufficient vertical model
resolution for capturing thin nocturnal boundary layers and uncertainties in
HOx–NOx–CO–VOCs chemistry, NO2 photolysis
rates, and emission diurnal cycles. The different model biases between OMI
and GOME-2 could also be attributed to the bias between these retrievals.
concluded that the bias between these retrievals is small
and insignificant for East Asia, whereas the bias between these retrievals is
unclear for other regions. For the most anthropogenically polluted regions,
bias reductions at 0.56∘ (compared to 2.8∘ resolution) were
similarly found for OMI and GOME-2. For South America and Central Africa,
reductions of the negative bias at 0.56∘ resolution were larger in
the comparison against OMI than GOME-2 during the biomass burning season,
suggesting that the high-resolution simulation improves
the representation of daytime
photochemistry in the presence of enhanced biomass burning emissions.
For the evaluations, we used simulated and observed concentrations
interpolated to a 0.5∘ bin grid. To identify the main drivers of
improvements in the high-resolution simulation, we conducted further
comparisons using two concentration fields interpolated to 2.8 and
0.5∘ bin grids. The drivers consist of (1) closer spatial
representativeness between observations and simulations (up to approximately
0.5∘ resolution) and (2) better representation of large-scale (i.e.,
at 2.8∘) concentration fields through the consideration of small-scale processes. Error reductions at
the 0.5∘ bin grid include the effects of both drivers. In contrast, error reductions at the
2.8∘ bin grid are mainly attributed to the latter effect (2). When
error reductions at the 2.8∘ bin grid are about half those at the
0.5∘ bin grid, the contributions of the two effects should be
identical. For annual RMSE reductions, the contributions of the two effects
were almost identical over the eastern United States, the western United
States, and South Africa (by up to -1.9 and
-0.9 × 1015 molecules cm-2 at 0.5 and 2.8∘ bin
grids, respectively). In contrast, over eastern China, improved
representations on the large scale (2) contributed up to 90 % (i.e.,
reductions by 1.1 and 1.0 × 1015 molecules cm-2 at 0.5
and 2.8∘ bin grids, respectively, at 1.1∘ resolution). In
this region, the large contribution of the second effect reflected spatially
homogeneous error reductions over Hebei and Henan provinces. Over most
biomass burning areas, improved representations on the large scale
(2) dominated improvements in high-resolution modeling, with RMSE reductions
of up to 0.072 × 1015 molecules cm-2 for the
2.8∘ bin grid and 0.071 × 1015 molecules cm-2
for the 0.5∘ bin grid. These results imply that, even for areas with
homogeneous concentration and emission fields, high-resolution modeling can
have significant impacts through a better representation of large-scale
fields.
Tropospheric NO2 over strong local sources
Figure compares the detailed spatial distribution of the
tropospheric NO2 column in summer, as represented by OMI measurements
and model simulations over four selected polluted areas: East Asia, South
Asia, the western United States, and South Africa. Over East Asia, high
concentrations were observed over the North China Plain, the Yangtze River
Delta, the Pearl River Delta, Seoul, and Tokyo, which could mainly be
attributed to emissions from traffic and large coal-fired
power plants in the North China Plain . The 2.8∘
simulation underestimated these high concentrations and overestimated low
concentrations over surrounding areas, probably associated with artificial
mixing at coarse model resolution. The 1.1 and 0.56∘ simulations
reduced negative biases over central eastern China, the Pearl River Delta,
Seoul, Tokyo, and the western part of Japan. Over the Yellow Sea, the East
China Sea, and off the Pacific coast of Japan, the positive biases at
2.8∘ resolution were mostly removed at 1.1 and 0.56∘
resolutions. Consequently, regional RMSE was 32 % lower at 0.56∘
resolution. In contrast, high-resolution simulations led to overestimation
over Beijing and the Yangtze River Delta.
Tropospheric NO2 column
(× 1015 molecules cm-2) from OMI retrievals (first
column; a, e, i, m) and differences between
the model simulation at 2.8∘ (second column;
b, f, j, n), 1.1∘ (third column;
c, g, k, o), and 0.56∘ (fourth
column; d, h, l, p) resolutions and OMI
retrievals over East Asia (first row; a–d), South Asia (second row;
e–h), and the western United States (third row; i–l) during
JJA and over South Africa (forth row; m–p) during DJF 2008.
Observed and simulated fields are mapped onto a 0.5∘ bin grid.
Regional mean bias (MB) and RMSE are also shown.
Over South Asia, high concentrations were observed over large cities such as
New Delhi, Chennai, Mumbai, and Kolkata in India, over Lahore and Multan in
Pakistan, and around reported coal-based thermal power plants at
24∘ N, 83∘ E and 22∘ N, 83∘ E in India
. The 2.8∘ simulation was biased on the low
side by up to 50 % over these areas, except westward of New Delhi, as
commonly reported using another coarse-resolution model at 2.8∘
resolution . These negative biases were reduced by up to
50 % at 1.1 and 0.56∘ resolutions, whereas high-resolution
simulations reveal excessively high concentrations over New Delhi. Over rural
areas, negative biases were larger at 1.1 and 0.56∘ resolutions,
resulting in larger regional RMSE than at 2.8∘ resolution (see
Sect. ).
Over the western United States, high concentrations were observed around Los
Angeles, San Francisco, Seattle, Phoenix, Salt Lake City, Denver, and the
Four Corners and San Juan power plants. Negative biases were reduced with
increasing model resolution over most of these regions. In contrast, negative
biases remained at 0.56∘ resolution around strong local sources. Over
rural areas, negative biases increased with model resolution, partly
reflecting suppressed artificial dilution from strong local sources. As a
result, regional RMSE was reduced by 18 and 27 % at 1.1 and 0.56∘
resolutions compared to 2.8∘ resolution. Errors, for instance, in
soil NOx emissions in summer e.g., could
contribute to underestimations over rural areas.
Scatter plots of observed and simulated tropospheric NO2
column (× 1015 molecules cm-2) over strong local sources
in East Asia (left column; a, c) and the western United
States (right column; b, d) during JJA 2008 for the OMI
retrievals (upper row; a–b) and GOME-2 retrievals (lower row;
c–d). The red marks are the model simulation at 2.8∘
resolution, the yellow marks are the model simulation at 1.1∘
resolution, and the blue marks are the model simulation at 0.56∘
resolution. The horizontal bars indicate mean retrieval errors in OMI and
GOME-2. For East Asia, the results are shown for Beijing (116.38∘ E,
39.92∘ N), Tianjin (117.18∘ E, 39.13∘ N), Shanghai
(121.47∘ E, 31.23∘ N), Nanjing (118.77∘ E,
32.05∘ N), Guangzhou (113.27∘ E, 23.13∘ N),
Shenzhen (114.10∘ E, 22.55∘ N), Seoul (126.96∘ E,
37.57∘ N), and Tokyo (139.68∘ E, 35.68∘ N). For
the western United States, the results are shown for Los Angeles
(118.25∘ W, 34.05∘ N), San Francisco (122.42∘ W,
37.78∘ N), Seattle (122.33∘ W, 47.61∘ N), Salt
Lake City (111.88∘ E, 40.75∘ N), Phoenix
(112.07∘ W, 33.45∘ N), Denver (104.88∘ W,
39.76∘ N), and the Four Corners and San Juan power plants
(108.48∘ W, 36.69∘ N). The values and mean retrieval errors
are averages within a 50 km distance from each strong source, with weighting
function application based on the inverse of the distance from each
location.
Over South Africa, high concentrations were observed over the Highveld region
of South Africa, a complex source area, as noted in Sect. .
The large negative bias (92 %
at 2.8∘ resolution) in peak concentration over a power plant region
in Mpumalanga Province (29.5∘ E, 26.2∘ S) was reduced to
69 % at 1.1∘ resolution and 53 % at 0.56∘ resolution.
Negative bias (75 % at 2.8∘ resolution) in the
Johannesburg–Pretoria megacity area (28∘ E, 25.7–26.2∘ S)
was also reduced to 54 % at 1.1∘ resolution and 50 % at
0.56∘ resolution. High-resolution simulations are thus important for
regions with complex and strong local sources. At the same time, the
remaining negative bias at 0.56∘ suggests that power plant and
industrial emissions are underestimated, as suggested by
, or that a model resolution higher than 0.56∘ is
essential.
Regional distributions of tropospheric NO2 column
(× 1015 molecules cm-2) from satellite retrievals (first
column; a, e) and differences between the model simulation
at 2.8∘ (second column; b, f), 1.1∘ (third
column; c, g), and 0.56∘ (fourth column;
d, h) resolutions and satellite retrievals from OMI (upper
row; a–d) and GOME-2 (lower row; e–h) over Colorado state
during July–August 2014. The observed and simulated fields are mapped onto a
0.5∘ bin grid. The DMA area is shown by the blue square
in (a).
Figure compares simulated high NO2 concentrations with
satellite retrievals at selected megacities. Eight strong source points were
selected from East Asia and seven points from the western United States
during June–July–August (JJA). We consider the summertime to be suitable for
evaluating local NO2 pollution because of the short NO2
lifetime. For the comparisons, retrieved and simulated tropospheric
NO2 columns were averaged within a 50 km distance from the selected
points while applying a distance-based weighting function (i.e., the inverse
of the distance was applied to each retrieval).
In comparison to OMI retrievals, with increasing model resolution, the
slope for East Asia became closer to 1 (0.67 at 0.56∘ resolution and
-0.19 at 2.8∘ resolution), and the intercept number became smaller (2.8
at 0.56∘ resolution and 6.4 at 2.8∘ resolution). The correlation
coefficient also increased (r=0.36 at 0.56∘ resolution in
contrast to r=-0.31 at 2.8∘ resolution). Large negative biases
were reduced at 0.56∘ resolution by 67 % over Beijing, by
73 % over Tianjin, by 18 % Shanghai, by 90 % over Nanjing, by
62 % over Guangzhou, by 48 % over Shenzhen, by 47 % over Seoul,
and by 62 % over Tokyo (compared to 2.8∘ resolution). The
estimated biases at 0.56∘ resolution are within mean OMI retrieval
errors. Reductions in negative biases at 0.56∘ resolution against
GOME-2 were also observed: by 91 % over Beijing, by 70 % over
Tianjin, by 76 % over Shanghai, by 67 % over Nanjing, by 32 %
over Guangzhou, by 50 % over Shenzhen, by 40 % over Seoul, and by
58 % over Tokyo. However, there is more degradation of slope and
intercept against GOME-2 than against OMI, reflecting large negative biases
over Guangzhou, Shenzhen, Seoul, and Tokyo.
Over the western United States, NO2 columns in all model simulations
were in agreement with OMI retrievals (r>0.9). The 0.56∘ model
reduced negative biases with respect to OMI by 30 % over Los Angeles, by
74 % over San Francisco, by 98 % over Seattle, by 58 % over Salt
Lake City, by 83 % over Phoenix, by 44 % over Denver, and by 78 %
over the Four Corners and San Juan power plants (compared to the 2.8∘
model). These bias reductions resulted in an improved slope number at
0.56∘ resolution (0.31) compared to 2.8∘ resolution (0.15).
In this region, comparison results were generally similar between OMI and
GOME-2. These validation results demonstrate the capability of the
0.56∘ simulation to represent high concentrations over strong local
sources.
Vertical profiles of NO (pptv) (a), NO2
(pptv) (b), NO2 during mornings
(09:00–12:00 LT) (c) and afternoons
(13:00–16:00 LT) (d), OH (pptv) (e), HO2
(pptv) (f), O3 (ppbv) (g), specific humidity
(g kg-1) (h), the photolysis rate of O3
(s-1) (i), and the OH chemical production rate
(molecules cm-3 s-1) from O1D and
H2O(j) over the Denver metropolitan area
(39–41∘ N and 103–105.5∘ W) during the FRAPPÉ period
(from 16 July to 18 August 2014). The black dots represent the measurements,
the red dashed line is the model simulation at 2.8∘ resolution, the
yellow dashed-dotted line is the model simulation at 1.1∘ resolution,
and the blue dotted line is the model simulation at 0.56∘ resolution.
The horizontal bars represent the standard deviation of the measurements.
Validations using FRAPPÉ aircraft measurements
In this section, we evaluated model performance in relation to
O3–HOx–NOx chemistry over the Denver metropolitan area (DMA;
defined as 39–41∘ N and 103–105.5∘ W) in the western
United States using the FRAPPÉ campaign observation data and satellite
retrievals from July–August 2014. Figure compares the
spatial distribution of the tropospheric NO2 columns between
simulations and satellite retrievals around the FRAPPÉ locations. OMI and
GOME-2 observed high tropospheric NO2 columns over the DMA at around
40∘ N, 105∘ W. All models underestimated high
concentrations by about 50 % at 2.8∘ resolution compared to OMI,
with this declining by 37 % at 1.1∘ resolution and by 56 % at
0.56∘ resolution. The negative bias over the DMA was larger for
GOME-2 than OMI, suggesting larger underestimations in simulated fields
during mornings compared to afternoons. Outside the DMA, negative biases
increased by 16 % for OMI and by 11 % for GOME-2 at 0.56∘
resolution compared to 2.8∘ resolution. As a result, RMSE against
OMI and GOME-2 for the entire domain area was almost constant with varying
model resolution.
(a) Probability distribution functions of NO,
(b)OH, and (c)HO2 as a function of NO
(pptv). The black dots represent measurements, the red dashed line is the
model simulation at 2.8∘ resolution, the yellow dashed-dotted line is
the model simulation at 1.1∘ resolution, and the blue dotted line is
the model simulation at 0.56∘ resolution. The vertical bars represent
the standard deviation of the measurements.
Figure compares mean vertical profiles of trace gases and
reaction rates over the DMA. Large negative biases of NO and
NO2 at 2.8∘ resolution were mostly removed at 1.1 and
0.56∘ resolutions below 650 hPa (by up to 88 %), except at
800 hPa during daytime (09:00–16:00 LT). All simulations revealed large
negative biases at 800 hPa during mornings (09:00–12:00 LT), but the bias
was greater by 30 % at 2.8∘ resolution than at 1.1 and
0.56∘ resolutions. Afternoon lower tropospheric high concentrations
(13:00–16:00 LT) were captured well in high-resolution simulations. Strong
morning–afternoon variations in the lower troposphere were underestimated by
32 % at 0.56∘ resolution, by 48 % at 1.1∘ resolution,
and by 62 % at 2.8∘ resolution. The remaining large bias in the
morning at 0.56∘ resolution could be associated with insufficient
vertical model resolution to represent mixing within nocturnal thin boundary
layers.
Large negative OH biases at 2.8 and 1.1∘ resolutions at
850 hPa were reduced by 81 % at 0.56∘ resolution. From 800 to
750 hPa, the 1.1∘ simulation showed the closest agreement with
observations (0.5–7 %), whereas the 2.8 and 0.56∘ simulations
underestimated OH by 7–21 % and overestimated
OH by up to 27 %, respectively. Above 700 hPa, all simulations
overestimated OH with a factor of up to 2. All simulations also
underestimated HO2 by 10–32 % below 650 hPa, except at
800 hPa.
OH and HO2 concentrations depend greatly on NOx
concentrations through O3–HOx–NOx chemistry, as well as
HOx production and OH conversion reactions to peroxy radicals
(HO2 and RO2) with CO and VOCs. Figure a shows
the probability distribution function of NO from the FRAPPÉ aircraft
observation and the model simulations at 800 hPa over the DMA. The
observation revealed a wide range of NO concentrations from
10–10 000 pptv. The 2.8∘ simulation overestimated the occurrence
of concentrations < 100 pptv and underestimated the occurrence of
concentrations > 100 pptv. The 1.1 and 0.56∘ simulations captured
the observed probability distribution function, although they slightly
overestimated the peak frequency concentration and underestimated the
occurrence of low (< 100 pptv) and high (> 1000 pptv) concentrations.
Figure b shows the OH–NO relationship used to
validate O3–HOx–NOx chemistry. The observation showed
OH increase with increasing NO to 350 pptv and a decrease with
increasing NO from 350 pptv; all simulations captured the lower part
(NO< 800 pptv) of the observed NO–OH
relationship, suggesting that the model realistically simulates nonlinear
O3–HOx–NOx chemistry. The lack of high NO
(> 800 pptv) with low OH resulted in an overestimation of mean
OH concentrations at 0.56∘ resolution.
Figure c compares the HO2–NO relationship. All
simulations underestimated the occurrence of high HO2 (> 25 pptv)
at low NO (< 100 pptv). This implies an underestimation of HOx
chemical production in the simulations. We evaluated HOx production from
the chemical reaction of O(1D) with H2O using temperature,
specific humidity, O3 photolysis rate to O(1D) (JO3→O(1D)), and O3 concentration with
the assumption of O(1D)
equilibrium (Fig. g–j). All simulations underestimated HOx
production, with the underestimation being smaller by 13 % at
0.56∘ resolution at 800 hPa. The underestimation of HOx
production was primarily attributable to a negative bias in O3 by
11 % and JO3→O(1D) by 2.5 % at 0.56∘
resolution at 800 hPa. The negative biases of O3 and JO3→O(1D) were reduced by 39 and 58 %, respectively, at
0.56∘ (compared to 2.8∘) resolution. Biases in specific
humidity also had small impacts on calculated HOx production. Positive
biases of specific humidity at 2.8∘ resolution above 750 hPa were
reduced by up to 83 % at 0.56∘ resolution. The lack of nitrous
acid (HONO) in the model could explain a component of the HOx
production underestimation, especially during mornings
e.g.,. The underestimation of OH conversion to
peroxy radicals could also explain simulated errors in OH and
HO2. attributed OH overprediction and
HO2 underprediction in a box model simulation to underestimation of
total OH reactivity (i.e., missing OH sink) over the United
States.
Latitude–pressure distribution of zonal mean
(a–c)O3 (ppbv) and (d–f)OH
(× 105 molecules cm-3) in the model simulation at
2.8∘ (left column) during JJA in 2008 and differences between the
model simulation at 1.1∘ (middle column) and 0.56∘ (right
column) resolutions and the model at 2.8∘ resolution.
The 2014 simulations used the anthropogenic emission inventory for the year
2010 (see Sect. ). The optimized NOx emissions from an
assimilation of multiple species satellite measurements
suggest that surface NOx emissions over the DMA in July–August increased
by 7 % from 2010 to 2014. The temporal variation, together with large
uncertainties in the emission inventories, could explain part of the negative
biases of NO and NO2 at 800 hPa, which also affects
OH, HO2, and O3 through nonlinear chemistry
processes.
Tropospheric NO2-related chemistry
We analyzed the simulated global
distribution of O3, OH, and NOx in the year 2008 to
characterize the resolution dependence of NO2-related chemistry.
Figure compares zonal mean concentrations of O3 and
OH during JJA. O3 mixing ratios in the middle to high
latitudes were 10–60 % larger at 1.1 and 0.56∘ than at
2.8∘ resolution. As shown in Table , at 1.1 and
0.56∘ resolutions, negative biases against ozonesonde observations
were reduced by up to 8 ppbv at 850 hPa from middle to high latitudes in
both hemispheres and by up to 13 ppbv at 500 hPa in the Southern Hemisphere
(SH) and Northern Hemisphere (NH) middle and high latitudes. In contrast,
positive model biases in the upper troposphere and lower stratosphere (UTLS)
mostly increased with model resolution by up to 46 ppbv at 300 hPa in the
SH and NH high latitudes. The increased positive bias at high latitudes in
the UTLS was associated with strengthened downwelling, as will be discussed
below. RMSE against ozonesonde was reduced by up to 8 ppbv at 850 and
500 hPa in middle and high latitudes, except at 500 hPa in the NH high
latitudes.
Comparisons of seasonal mean tropospheric O3 concentration
during JJA in 2008 between ozonesonde and the model simulation at 2.8, 1.1,
and 0.56∘ resolutions. Units are ppbv.
Pressure levelModel resolutionGlobal 90–60∘ S 60–30∘ S 30∘ S–30∘ N 30–60∘ N 60–90∘ N MBRMSEMBRMSEMBRMSEMBRMSEMBRMSEMBRMSE2.8∘× 2.8∘-2.017.5-13.514.6-9.711.416.426.6-5.715.2-4.48.5850 hPa1.1∘× 1.1∘-3.012.6-12.513.4-7.79.30.412.6-2.213.2-3.79.70.56∘× 0.56∘-0.79.4-4.96.7-3.84.81.010.0-0.610.20.17.02.8∘× 2.8∘-7.419.3-7.310.8-4.88.22.722.5-10.320.1-13.217.5500 hPa1.1∘× 1.1∘-8.418.8-5.29.8-3.711.2-9.720.9-8.919.8-8.316.90.56∘× 0.56∘-7.417.21.17.3-2.37.9-9.619.4-9.417.90.0818.32.8∘× 2.8∘9.749.315.432.130.553.90.425.312.448.2-5.691.6300 hPa1.1∘× 1.1∘4.858.725.741.255.4113.6-10.125.3-4.446.947.1116.20.56∘× 0.56∘7.450.541.256.015.142.2-9.721.21.245.851.6102.12.8∘× 2.8∘399.5496.9556.3703.5709.2860.5172.4204.1410.4478.6498.9524.8100 hPa1.1∘× 1.1∘393.7536.5964.41054.1854.71091.680.6145.4356.1409.6519.8559.00.56∘× 0.56∘355.1438.2848.2901.6511.0589.0122.1155.6356.0395.6319.6352.5
In the tropics and subtropics, in contrast, O3 concentrations were
5–20 % lower at 1.1 and 0.56∘ than at 2.8∘ resolution,
reducing positive biases against ozonesonde observations from 2.8∘
resolution by 15 ppbv at 850 hPa in the tropics
(30∘ S–30∘ N) and by up to 15 ppbv at 300 hPa in the
midlatitudes of both hemispheres. In contrast, negative biases increased by
7 ppbv at 500 hPa and by 9 ppbv at 300 hPa in the tropics. RMSE was
smaller by 10 ppbv at 0.56∘ than at 2.8∘ resolution at
300 hPa in the SH midlatitudes. Substantial improvements were achieved from
the tropopause to lower stratosphere (i.e., at 100 hPa) by using
high-resolution simulations. Overall, RMSE with respect to the globally
available ozonesondes was reduced with increasing resolution (by up to
8.1 ppbv) at 850 and 500 hPa. In contrast, at 300 hPa, RMSE increased at
0.56∘ (by 1.2 ppbv) and 1.1∘ (by 9.4 ppbv) resolutions,
reflecting larger RMSE at 0.56 and 1.1∘ resolutions in the
high latitudes of both hemispheres.
Increased concentrations in the extratropics and decreased concentrations in
the tropics resulted in only small differences in the global tropospheric
ozone burden: -5.4 % at 1.1∘ resolution and -2.3 % at
0.56∘ resolution (compared to 2.8∘). Meanwhile, the budget
terms of global tropospheric O3 differ significantly between
simulations. High-resolution models simulated an enhanced
stratosphere–troposphere exchange (STE) of O3 (510 Tg yr-1 at
1.1∘ resolution and 548 Tg yr-1 at 0.56∘ resolution in
contrast to 500 Tg yr-1 at 2.8∘ resolution) and smaller
O3 chemical production (4647 Tg yr-1 at 1.1∘
resolution and 4565 Tg yr-1 at 0.56∘ resolution in contrast to
4809 Tg yr-1 at 2.8∘ resolution). Less O3 chemical
production was attributed to decelerating HO2+NO,
CH3O2+NO, and RO2+NO. The
estimated global mean O3 chemical lifetime was longer in
high-resolution simulations (26.1 days at 1.1∘ resolution and
26.3 days at 0.56∘ resolution in contrast to 25.3 days at
2.8∘ resolution) because of decreased water vapor in the middle and
upper troposphere. Model resolution dependence on global STE and ozone
chemical production has been similarly reported by ,
, , and . The latitudinal
distributions of O3 differences between simulations were determined
by both chemical (e.g., weakened chemical ozone production in the tropics)
and transport (e.g., strengthened downwelling from extratropical stratosphere
and upper tropospheric poleward motions from the tropics to the extratropics)
processes.
OH was smaller by 5–30 % at 1.1 and 0.56∘ than at
2.8∘ resolution in the tropics and subtropics during JJA, resulting
in smaller global burdens of tropospheric OH by 13.5 % at
1.1∘ resolution and by 12.4 % at 0.56∘ resolution. These
changes were associated with decreased HOx chemical production (i.e.,
O(1D)+H2O→2OH) and HO2
to OH conversion reaction (i.e.,
HO2+ NO →OH+NO2) by 5 %
at 1.1 and 0.56∘ resolutions (compared to 2.8∘ resolution). A
large relative OH increment was found over the Antarctic because weak
ultraviolet radiation led to small OH concentrations during a polar
night.
Global distributions of (a–c)NO2 partial column
(× 1015 molecules cm-2) and (d–f)OH
partial column (× 1011 molecules cm-2) integrated in the
lowermost five model layers in the model simulation at 2.8∘
resolution (first column) during JJA in 2008 and differences between the
model simulation at 1.1∘ (second column) and 0.56∘ (third
column) resolutions and the model at 2.8∘ resolution.
Regional net chemical production of NOx via all reactions and
HNO3 formation (Tg yr-1), NO2 burden (Gg), NO2
lifetime via HNO3 formation reaction (hours) in the lowermost five
model layers, and planetary boundary layer (PBL) height (m) in the model
simulations and ERA-Interim. The definition of the regions is the same as in
Fig. .
Figure compares the spatial distribution of NO2 and
OH in the lower troposphere between model simulations. Lower
tropospheric NO2 partial columns were larger around strong source
areas and smaller over rural and coastal areas around polluted regions at 1.1
and 0.56∘ resolutions, primarily resulting from suppressed artificial
dilution near strong sources and chemical feedback through the
O3–HOx–NOx system, as discussed in Sect. . The
lower tropospheric OH partial column integrated in the lowermost five
model layers (approximately below 800 hPa) was smaller at 1.1 and
0.56∘ resolutions over most of the continents. The differences in
OH and NO2 exhibited similar spatial patterns over polluted
and biomass burning regions: e.g., r=0.53 over the western United States,
r=0.61 over India, and r=0.57 over South America. NO2 and
OH thus interact with each other through O3–HOx–NOx
chemical reactions. Differences in simulated meteorological fields, such as
cumulus convection, water vapor, and cloud cover, could also cause OH
differences.
Table summarizes the chemical budget of NO2 in the
lowermost five model layers over eastern China, the western United States,
and South America during summertime in each hemisphere. Over the selected
regions, the NO2 burden increased with model resolution by 33 %
over eastern China, by 9 % over the western United States, and by
23 % over South America. Over eastern China and the western United
States, the conversion from NO2 to HNO3 with OH
(P–L(NOx)HNO3) dominated over the net chemical production of
NOx (P–L(NOx)). The estimated NO2 lifetime via HNO3
formation (1 /k[OH][M]) was 8 % longer at 0.56∘ than at
2.8∘ resolution. A longer NO2 lifetime with increasing model
resolution over East Asia is consistently reported by . Over
the western United States, the estimated NO2 lifetime was longer by
6 % at 1.1∘ than at 2.8∘ resolution, whereas it was
shorter by 6 % at 0.56∘ than at 1.1∘ resolution. Over
South America, the conversion of NO2 to HNO3 contributed
13–20 % of the total net chemical production of NOx, resulting from
competition against chemical conversion to peroxy acetyl nitrates (PANs) and
organic nitrates. The estimated NO2 lifetime via HNO3
formation was longer by 18 % at 0.56∘ than at 2.8∘
resolution. Over other regions, the regional NO2 burden increased
with model resolution, whereas changes in NO2 lifetime via OH
oxidation varied across locations (not shown), reflecting a nonlinear
chemical system involving NOx.
Global distributions of (a–c)NO2 partial column
(× 1015 molecules cm-2) integrated from the sixth layer
to the tropopause, (d–f) convective cloud updraft
(× 103 kg m-2 s-1) at 500 hPa, and
(g–i) vertically integrated lightning NOx production
(× 1012 kg N m-2 s-1) in the model simulation at
2.8∘ resolution (first column) during JJA in 2008 and differences
between the model simulation at 1.1∘ (second column) and
0.56∘ (third column) resolutions and the model at 2.8∘
resolution.
Differences in simulated meteorological fields between simulations could also
have effects on NO2 and related species. Improvements in PBL height
are especially expected to improve NO2 simulations in the lower
troposphere e.g.,. Table compares regional
mean PBL height over eastern China, the western United States, and South
America in summer between ERA-Interim reanalysis and the model simulations. The
2.8∘ simulation overestimated regional mean PBL height in
ERA-Interim; the positive bias was reduced at 0.56∘ resolution by
40 % over eastern China, by 62 % over the western United States, and
by 9 % over South America.
Figure shows the spatial distribution of the NO2
partial column in the free troposphere, convective cloud updraft mass flux at
500 hPa, and vertically integrated lightning NOx production. The
simulated NO2 partial column in the free troposphere was smaller by
17 % at 1.1∘ resolution and by 14 % at 0.56∘
resolution than at 2.8∘ resolution over the northern subtropics and
midlatitudes, primarily because of smaller NO2 concentrations above
400 hPa. These changes in the free tropospheric NO2 were in contrast
to the changes in the lower tropospheric NO2, which were associated
with suppressed convective cloud updraft over the continents by up to
76 % at 1.1 and 0.56∘ resolutions over the northern subtropics
and midlatitudes. In contrast, over the Maritime Continent, South America,
and Central Africa, the free tropospheric NO2 column was larger at
1.1∘ resolution by up to 18 % and at 0.56∘ resolution by
up to 20 % than at 2.8∘ resolution, primarily reflecting
increased NO2 concentration between 600 and 800 hPa. Lightning
NOx production is also largely different between the simulations in the
tropics. Over the tropics, although the mean convective cloud updraft was
weaker at 1.1 and 0.56∘ resolutions than at 2.8∘ resolution,
the high-resolution simulations revealed increased ice cloud in the upper
troposphere and stronger (but less frequent) convection, thus increasing
lightning NOx sources, especially over Asia. Meanwhile, given the same
amount of lightning NOx production (using a commonly prescribed lightning
NOx field in all the simulations), the high-resolution simulations
revealed slightly less ozone chemical production (by 1 %) through
the representation of local highly
concentrated NOx plumes in July 2008 (figure not shown).
The obtained evaluation results of multiple species and meteorological fields
suggest that changes in NO2 with increasing model resolution can be
due to complex chemical interactions and different representations of
meteorological fields. Further detailed validations of individual components
would therefore be helpful to identify causal mechanisms and to further
reduce uncertainty in high-resolution simulations.
DiscussionOther model error sources
Various factors other than horizontal model resolution can lead to errors in
tropospheric NO2 simulation. Insufficient vertical model resolution
could introduce additional errors in vertical mixing, atmospheric transport,
and subsequent chemistry processes, for instance, under stable boundary layer
conditions during nighttime . Such errors could also cause
large negative NO2 biases during mornings in the lower troposphere
(see Sect. ). More detailed validation of diurnal variations
is required using ground-based observations such as MAX-DOAS and lidar in
future work.
Chemical kinetics information could also have large uncertainties.
and suggested that the uptake of
HO2 on aerosols is the most important factor but remains largely
uncertain. CHASER includes simplified NOx–VOC chemistry related to
PANs and isoprene nitrates . The incorporation of more detailed NOx–VOC chemistry
would also be needed to improve simulated peroxy nitrates and organic
nitrates, as per and .
Surface emissions are another important error source. The total amounts of
anthropogenic NOx emissions in China in 2008 differ by 27 % between two
(highest and lowest) bottom-up inventories: EDGAR4.2 and MEIC
. also discussed large diversity in
emission inventories over East Asia. Biomass burning NOx emissions also
differ significantly between inventories: for example, the annual mean
emission is 2.293 Tg yr-1 in GFASv1.0 in contrast to
2.700 Tg yr-1 in GFEDv3.1 over the SH Africa, as reported by
.
Based on data assimilation of multiple species satellite measurements,
investigated large uncertainty in anthropogenic and
fire-related emission factors and a significant underestimation of soil
NOx sources in bottom-up emission inventories. Using a similar approach,
optimized lightning NOx sources and indicated that
the widely used lightning parameterization based on the C-shape assumption
has large uncertainty. Implementing these
optimized emissions could improve model performance, although optimal
emissions could also be dependent on model resolution.
Representations of meteorological parameters, such as cloud optical depth,
temperature, water vapor, PBL height, and relevant transport and chemical
processes, are also important in tropospheric NO2 simulations
. Because we employed an AGCM–CTM online coupling system,
meteorological fields are simulated explicitly at each model resolution. This
could help to improve the tropospheric chemistry simulation. For instance, we
found that simulated regional mean PBL height is sensitive to the choice of
model resolution, with the 0.56∘ simulation showing closer agreements
with ERA-Interim reanalysis, as discussed in Sect. .
Resolving small-scale cloud distributions may lead to improved photolysis and
convective transport calculations in high-resolution simulations.
Nevertheless, the AGCM meteorological fields still need to be carefully
validated and improved. For instance, cumulus convection and cloud
parameterization calculations were sensitive to model resolution. Although
the relevant model parameters have been optimized separately for each model
resolution, there are still some discrepancies against observed OLR and
precipitation distributions (see Sect. ).
Nonlinearity in model error reductions
Model performance was clearly better at 0.56 and 1.1∘ resolutions
than at 2.8∘ resolution in most cases. The 0.56∘ simulation
largely improved spatial variations over eastern China, the eastern and
western United States, Mexico, and South Africa, as confirmed by large RMSE
reductions, especially for megacities and regions with power plants (see
Sect. ). In most cases, the improvement was smaller from 1.1
to 0.56∘ resolution than from 2.8 to 1.1∘ resolution.
Meanwhile, regional RMSEs increased at 0.56∘ from 1.1∘
resolution for some cases over Europe, India, and the selected biomass
burning regions, possibly related to more pronounced errors in meteorological
fields for Europe and India and in biomass burning hot spot emissions.
Comparisons to aircraft measurements showed better performance of NOx
simulation at high resolutions. However, the representation of NO
variability (i.e., the probability distribution function) was insufficient
even at 0.56∘ resolution. Further improvements could be obtained
using a model with resolution finer than 0.56∘. For instance,
noted that 4 and 12 km resolutions are required for Four
Corners and Los Angeles, respectively, to accurately simulate
the nonlinear chemical feedback of
the O3–HOx–NOx system. reported that
errors in the simulated tropospheric NO2 column at 20 km
resolution did not yet approach
convergence over Tokyo.
Most previous high-resolution modeling studies have used regional models to
simulate NO2 concentration fields at high spatial resolution,
primarily focusing on urban regions, with reduced or equivalent computational
costs compared to global models. Several studies have demonstrated that a
better representation of the
long-range transport of NOx reservoir species such as peroxyacetyl nitrate
(PAN) are important on simulated NO2 in the free troposphere
in remote areas
e.g.,. A two-way
nesting between regional and coarse-resolution global models
e.g., is able to consider both small-scale processes
inside focusing regions and long-range transport over the globe, which has an
advantage over regional models. An important advantage of global
high-resolution models over regional models and two-way nesting systems is
the ability to simulate NO2 concentration fields at high resolutions
over the entire globe across urban, biomass burning, and remote regions in a
consistent framework. Even over remote regions, a high-resolution simulation
has the potential to improve model performance through considering the
effects of nonlinear chemistry in highly concentrated NOx plumes emitted
from ships and lightning . These
NOx emission sources in remote regions have significant impacts on climate
and air quality . It is
thus important to clarify the importance of resolving small-scale sources and
plumes within a global modeling framework for a better understanding of the
global atmospheric environment and chemistry–climate system.
also showed that the differences in NO2 profiles
between the TM5-MP model at 3∘× 2∘ and
1∘× 1∘ horizontal resolutions are within a few
percentage points below 850 hPa over the Pacific in boreal spring. They also
showed much larger differences with changing model resolution over Texas in
autumn. Model resolution impact thus varies significantly with location and
season. Further investigations using other aircraft measurements would be
helpful to evaluate model performance in different cases.
High-resolution chemical transport modeling requires huge computational
resources: e.g., compared to the simulation at 2.8∘ resolution
(approximately 480 s computer time for a 1-day simulation), the
computational cost increased by a factor of 67 at 0.56∘ resolution
(approximately 32 000 s computer time) and by a factor of 14 at
1.1∘ resolution (approximately 6700 s computer time).
High-performance computing (HPC) systems are thus essential for performing
high-resolution simulations. At the same time, because the size of a 3-D
array is large in the high-resolution model, computational efficiency is
important: e.g., efficient data throughputs in memory transfer, network
communication between multiple nodes, and file input–output. In the future,
further improvements in computational efficiency will be required, together
with the development of HPC systems.
Application for satellite retrieval and data assimilation
An important application of high-resolution tropospheric NO2
simulations is to provide a priori profile information on satellite
retrieval and chemical data assimilation . Here, we would like
to discuss the potentials of the obtained results for these applications.
Current satellite retrievals of the tropospheric NO2 column use a
priori NO2 profiles obtained from global model simulations at
relatively coarse resolutions: from TM5 at 3∘× 2∘
in DOMINO-2 and GEOS-Chem at
2.5∘× 2∘ in OMNO2 , whereas the TROPOMI retrieval product will employ
1∘× 1∘ resolution simulation fields from TM5
. To provide high-resolution (ranging from 4 to 50 km) a
priori information, several regional retrievals have employed regional models
, showing improvements in the retrieved
fields in comparison to independent observations. High-resolution a priori
fields from global CTMs are important in providing consistent global
datasets.
To avoid spatial representation gaps between satellite measurements and
coarse-resolution global models, super-observation techniques have been
employed to produce representative data before assimilation
e.g.,. The average of averaging kernels over a
number of retrievals within a super-observation grid does not hold any
physical meaning. This may inhibit effective improvement by assimilating over
regions with varying conditions. High-resolution CTMs allow for
the assimilation of satellite
measurements, with reduced representation gaps without any averages.
Because of the distinct
nonlinearity in chemical reactions, the high-resolution assimilation of
satellite measurements considering small-scale variations in background error
covariance would be essential in making the best use of observational
information. High-resolution chemical data assimilation could also benefit
air pollutant emission estimates
e.g.,, especially using
high-resolution measurements from future satellite missions such as TOROPOMI
and geostationary satellites (e.g., Sentinel-4, GEMS, TEMPO), even when model
resolution is still coarser than the measurement resolution through improved model processes and
spatial representativeness for mega-cities as demonstrated by this study.
Summary and conclusions
We evaluated the performance of high-resolution global NO2
simulations using CHASER based on comparisons against tropospheric
NO2 column retrievals from two satellite sensors, OMI and GOME-2, and
aircraft observations during the FRAPPÉ aircraft campaign. Three different
horizontal resolutions at 0.56, 1.1, and 2.8∘ were evaluated.
The high-resolution models at 1.1 and 0.56∘ resolutions showed
substantial improvements in simulating tropospheric NO2. With
increasing horizontal model resolution from 2.8 to 1.1∘, negative
regional mean model biases (RMSEs) for annual mean tropospheric NO2
column were reduced over polluted regions: e.g., by 90 % (32 %) over
eastern China, by 13 % (20 %) over the western United States, and by
37 % (45 %) over South Africa. RMSEs were further reduced by
increasing model resolution from 1.1 to 0.56∘ over most of the
polluted regions. We emphasize large error reductions from 1.1 to
0.56∘ resolutions by 23 % over the western United States, by
25 % over Mexico, and by 20 % over South Africa. The high-resolution
simulation at 0.56∘ was also essential to capture observed high
tropospheric NO2 columns over strong sources such as megacities and
power plants. In comparison to OMI, increasing model resolution from 2.8 to
0.56∘ reduced negative biases over strong local sources by 67 %
over Beijing, by 47 % over Seoul, by 62 % over Tokyo, by 30 %
over Los Angeles, by 74 % over San Francisco, and by 78 % over the
Four Corners and San Juan power plants in summer. Over biomass burning
regions, model performance also improved with increasing model resolution
from 2.8 to 1.1∘ and 0.56∘. For instance, RMSE was reduced by
15 % at 1.1∘ resolution (compared to 2.8∘ resolution)
over South America. We attempted to distinguish between two different effects
that led to improvements in high-resolution modeling: (1) closer spatial
representativeness between observations and simulations (up to approximately
0.5∘ resolution) and (2) better representation of large-scale (i.e.,
at 2.8∘) concentration fields through the consideration of small-scale processes, for instance,
associated with nonlinear O3–HOx–NOx chemistry. The relative
contributions of these two effects were mostly identical over the eastern and
western United States and South Africa, whereas the latter effect (2) was
dominant over eastern China and biomass burning regions.
The comparison to FRAPPÉ aircraft observations over the DMA indicated
that the 0.56∘ simulation greatly reduced negative biases of
NO2 by up to 88 % from the surface to 650 hPa, while improving
the representation of morning–afternoon differences below 800 hPa (with a
50 % reduction at 1.1∘ resolution). The high-resolution
simulations also improved the probability distribution of NO
concentration ranging from 100–1000 pptv. However, all simulations failed
to reproduce the observed low (< 100 pptv) and high (> 1000 pptv)
NO concentrations, resulting in positive biases of mean OH
through nonlinear NO–OH relationships.
Changes in NO2 across model simulations were associated with
different representations of the tropospheric chemical and transport system.
By increasing model resolution from 2.8 to 0.56∘, tropospheric ozone
increased by up to 60 % in middle to high latitudes during JJA, while ozone
decreased by up to 20 % in the tropics and subtropics. These changes
mostly led to improved agreements against the global ozonesonde measurements.
The high-resolution simulation also lowered OH concentrations
throughout the troposphere by up to 30 %. The regional NO2 burden
was larger at 0.56∘ than at 2.8∘ in the lower troposphere, by
33 % over eastern China, by 9 % over the western United States, and
by 23 % over South America. Changes in NO2 lifetime via oxidation
with OH varied between locations. These model resolution dependencies
suggest that NO2 and OH interact with each other through
nonlinear relationships between NO and OH (i.e.,
O3–HOx–NOx chemistry).
In conclusion, the 1.1∘ simulation generally captures the regional
distribution of the tropospheric NO2 column well, but the
0.56∘ resolution is essential for the simulation of high NO2 concentrations on a megacity scale.
Meanwhile, for Europe, India, and the selected biomass burning regions,
errors increased with model resolution from 1.1 to 0.56∘, possibly
related to more pronounced errors in meteorological fields over Europe and
India and to more pronounced influences of largely uncertain inventories for
individual burning points over the selected biomass burning regions. The
computational cost largely increases at 0.56∘ resolution, while
overall improvements were small at 0.56∘ resolution compared to
1.1∘ resolution except over megacities. Therefore, we consider the
horizontal resolution of approximately 1∘ to be a realistic option to
obtain improved overall performance of global tropospheric NO2
simulations.
The developed high-resolution CTM framework will be a powerful tool when
combined with future high-resolution satellite observations, providing
valuable information on the atmospheric environment and related long-term
changes on the megacity scale. We are developing a high-resolution global
chemical data assimilation system based on an ensemble Kalman filter data
assimilation technique and the developed high-resolution
CTM. A post-petascale supercomputer, also known as a post-K computer, is
being developed by Japan's FLAGSHIP 2020 project
e.g., and will facilitate future studies using the
high-resolution global chemical data assimilation system and satellite
observations from a new constellation of low Earth orbit sounders (e.g.,
IASI, AIRS, CrIS, TROPOMI, and Sentinel-5) and geostationary satellites
(e.g., Sentinel-4, GEMS, and TEMPO).
Code and data availability
The source code for CHASER V4.0 is not publicly
available because of license restrictions. The source code can be obtained
from Kengo Sudo (kengo@nagoya-u.jp) upon request. Most of the source code is
written in Fortran 77 and 90. The simulation data will be available upon
request to the corresponding author. Satellite retrievals of the tropospheric
NO2 column from OMI and GOME-2 were obtained from the TEMIS website
(http://www.temis.nl/). Measurement data from the 2014 FRAPPÉ
campaign were provided at the NASA LaRC Air-borne Science Data for
Atmospheric Composition (http://www-air.larc.nasa.gov/). GPCP combined
precipitation data were downloaded from the NASA GSFC global precipitation
analysis website (https://precip.gsfc.nasa.gov/). Ozonesonde data were
obtained from WOUDC (https://woudc.org/), SHADOZ
(https://tropo.gsfc.nasa.gov/shadoz/), and NOAA ESRL GMD
(https://www.esrl.noaa.gov/gmd/). Interpolated OLR from NOAA 18 was
provided at NOAA ESRL PSD (https://www.esrl.noaa.gov/psd/).
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was supported by the post-K computer project Priority Issue 4 (the
advancement of meteorological and global environmental predictions utilizing
observational Big Data) and by the Global Environment Research Fund (S-12) of
the Ministry of the Environment (MOE). We acknowledge the use of tropospheric
NO2 column data from GOME-2 and OMI sensors obtained from the
Tropospheric Emission Monitoring Internet Service
(http://www.temis.nl/index.php). We would also like to express our
thanks for the use of measurement data from the 2014 Front Range Air
Pollution and Photochemistry Experiment (FRAPPÉ) campaign through the NASA
LaRC Airborne Science Data for Atmospheric Composition
(https://www-air.larc.nasa.gov/). The GPCP combined precipitation data
were provided by the NASA Goddard Space Flight Center's Laboratory for
Atmospheres, which developed the data as a contribution to the GEWEX Global
Precipitation Climatology Project. We would like to acknowledge the use of
ozonesonde data obtained from the World Ozone and Ultraviolet Data Center
(WOUDC), the Southern Hemisphere ADditional Ozonesondes (SHADOZ), and the
NOAA Earth System Research Laboratory (ESRL) Global Monitoring Division
(GMD). Interpolated OLR data were provided by the NOAA/OAR/ESRL PSD from
their website (http://www.esrl.noaa.gov/psd/). The Earth
Simulator was used for simulations
as a “Strategic Project with Special Support” of the Japan Agency for
Marine-Earth Science and Technology. Some simulations were also conducted by
the K computer provided by the RIKEN Advanced Institute for Computational
Science through the HPCI System Research Project (project IDs: hp150288,
hp160231, hp170232). We would like to thank the two anonymous reviewers for
their useful comments. Edited by: Andrea
Stenke Reviewed by: two anonymous referees
ReferencesAdler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak,
J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind,
J., Arkin, P., and Nelkin, E.: The Version-2 Global Precipitation Climatology
Project (GPCP) Monthly Precipitation Analysis (1979–Present), J.
Hydrometeorol., 4, 1147–1167,
10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003.Akiyoshi, H., Zhou, L. B., Yamashita, Y., Sakamoto, K., Yoshiki, M.,
Nagashima, T., Takahashi, M., Kurokawa, J., Takigawa, M., and Imamura, T.: A
CCM simulation of the breakup of the Antarctic polar vortex in the years
1980–2004 under the CCMVal scenarios, J. Geophys. Res., 114, D03103,
10.1029/2007JD009261, 2009.Akiyoshi, H., Nakamura, T., Miyasaka, T., Shiotani, M., and Suzuki, M.: A
nudged chemistry-climate model simulation of chemical constituent
distribution at northern high-latitude stratosphere observed by SMILES and
MLS during the 2009/2010 stratospheric sudden warming, J. Geophys. Res., 121,
1361–1380, 10.1002/2015JD023334, 2015JD023334, 2016.Arellano Jr., A. F., Raeder, K., Anderson, J. L., Hess, P. G., Emmons, L. K.,
Edwards, D. P., Pfister, G. G., Campos, T. L., and Sachse, G. W.: Evaluating
model performance of an ensemble-based chemical data assimilation system
during INTEX-B field mission, Atmos. Chem. Phys., 7, 5695–5710,
10.5194/acp-7-5695-2007, 2007.Banerjee, A., Archibald, A. T., Maycock, A. C., Telford, P., Abraham, N. L.,
Yang, X., Braesicke, P., and Pyle, J. A.: Lightning NOx, a key
chemistry–climate interaction: impacts of future climate change and
consequences for tropospheric oxidising capacity, Atmos. Chem. Phys., 14,
9871–9881, 10.5194/acp-14-9871-2014, 2014.Boersma, K. F., Eskes, H. J., and Brinksma, E. J.: Error analysis for
tropospheric NO2 retrieval from space, J. Geophys. Res., 109, D04311,
10.1029/2003JD003962, 2004.Boersma, K. F., Jacob, D. J., Eskes, H. J., Pinder, R. W., Wang, J., and
van der A, R. J.: Intercomparison of SCIAMACHY and OMI tropospheric NO2
columns: Observing the diurnal evolution of chemistry and emissions from
space, J. Geophys. Res., 113, D16S26, 10.1029/2007JD008816, 2008.Boersma, K. F., Eskes, H. J., Dirksen, R. J., van der A, R. J., Veefkind, J.
P., Stammes, P., Huijnen, V., Kleipool, Q. L., Sneep, M., Claas, J.,
Leitão, J., Richter, A., Zhou, Y., and Brunner, D.: An improved
tropospheric NO2 column retrieval algorithm for the Ozone Monitoring
Instrument, Atmos. Meas. Tech., 4, 1905–1928,
10.5194/amt-4-1905-2011, 2011.Brown, S. S., Ryerson, T. B., Wollny, A. G., Brock, C. A., Peltier, R.,
Sullivan, A. P., Weber, R. J., Dubé, W. P., Trainer, M., Meagher, J. F.,
Fehsenfeld, F. C., and Ravishankara, A. R.: Variability in Nocturnal Nitrogen
Oxide Processing and Its Role in Regional Air Quality, Science, 311, 67–70,
10.1126/science.1120120, 2006.Bucsela, E. J., Celarier, E. A., Wenig, M. O., Gleason, J. F., Veefkind,
J. P., Boersma, K. F., and Brinksma, E. J.: Algorithm for NO2 vertical
column retrieval from the ozone monitoring instrument, IEEE Trans. Geosci.
Remote Sens., 44, 1245–1258, 10.1109/TGRS.2005.863715, 2006.Canty, T. P., Hembeck, L., Vinciguerra, T. P., Anderson, D. C., Goldberg, D.
L., Carpenter, S. F., Allen, D. J., Loughner, C. P., Salawitch, R. J., and
Dickerson, R. R.: Ozone and NOx chemistry in the eastern US: evaluation of
CMAQ/CB05 with satellite (OMI) data, Atmos. Chem. Phys., 15, 10965–10982,
10.5194/acp-15-10965-2015, 2015.Castellanos, P., Boersma, K. F., Torres, O., and de Haan, J. F.: OMI
tropospheric NO2 air mass factors over South America: effects of biomass
burning aerosols, Atmos. Meas. Tech., 8, 3831–3849,
10.5194/amt-8-3831-2015, 2015.Celarier, E. A., Brinksma, E. J., Gleason, J. F., Veefkind, J. P., Cede, A.,
Herman, J. R., Ionov, D., Goutail, F., Pommereau, J.-P., Lambert, J.-C., van
Roozendael, M., Pinardi, G., Wittrock, F., Schönhardt, A., Richter, A.,
Ibrahim, O. W., Wagner, T., Bojkov, B., Mount, G., Spinei, E., Chen, C. M.,
Pongetti, T. J., Sander, S. P., Bucsela, E. J., Wenig, M. O., Swart, D.
P. J., Volten, H., Kroon, M., and Levelt, P. F.: Validation of Ozone
Monitoring Instrument nitrogen dioxide columns, J. Geophys. Res., 113,
D15S15, 10.1029/2007JD008908, 2008.Charlton-Perez, C. L., Evans, M. J., Marsham, J. H., and Esler, J. G.: The
impact of resolution on ship plume simulations with NOx chemistry, Atmos.
Chem. Phys., 9, 7505–7518, 10.5194/acp-9-7505-2009, 2009.Colella, P. and Woodward, P. R.: The Piecewise Parabolic Method (PPM) for
gas-dynamical simulations, J. Comput. Phys., 54, 174–201,
10.1016/0021-9991(84)90143-8, 1984.Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, I., Biblot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Greer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M.,
Matricardi, M., McNally, A. P., Mong-Sanz, B. M., Morcette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart,
F.: The ERA-Interim reanalysis: Configuration and performance of the data
assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597,
10.1002/qj.828, 2011.Dentener, F. J. and Crutzen, P. J.: Reaction of N2O5 on tropospheric
aerosols: Impact on the global distributions of NOx, O3, and OH, J.
Geophys. Res., 98, 7149–7163, 10.1029/92JD02979, 1993.Ding, J., Miyazaki, K., van der A, R. J., Mijling, B., Kurokawa, J.-I., Cho,
S., Janssens-Maenhout, G., Zhang, Q., Liu, F., and Levelt, P. F.:
Intercomparison of NOx emission inventories over East Asia, Atmos. Chem.
Phys., 17, 10125–10141, 10.5194/acp-17-10125-2017, 2017a.Ding, J., van der A, R. J., Mijling, B., and Levelt, P. F.: Space-based
NOx emission estimates over remote regions improved in DECSO, Atmos.
Meas. Tech., 10, 925–938, 10.5194/amt-10-925-2017, 2017b.Duce, R. A., LaRoche, J., Altieri, K., Arrigo, K. R., Baker, A. R., Capone,
D. G., Cornell, S., Dentener, F., Galloway, J., Ganeshram, R. S., Geider,
R. J., Jickells, T., Kuypers, M. M., Langlois, R., Liss, P. S., Liu, S. M.,
Middelburg, J. J., Moore, C. M., Nickovic, S., Oschlies, A., Pedersen, T.,
Prospero, J., Schlitzer, R., Seitzinger, S., Sorensen, L. L., Uematsu, M.,
Ulloa, O., Voss, M., Ward, B., and Zamora, L.: Impacts of Atmospheric
Anthropogenic Nitrogen on the Open Ocean, Science, 320, 893–897,
10.1126/science.1150369, 2008.Duncan, B. N., Lamsal, L. N., Thompson, A. M., Yoshida, Y., Lu, Z., Streets,
D. G., Hurwitz, M. M., and Pickering, K. E.: A space-based, high-resolution
view of notable changes in urban NOx pollution around the world
(2005–2014), J. Geophys. Res., 121, 976–996, 10.1002/2015JD024121,
2016.Emori, S., Nozawa, T., Numaguti, A., and Uno, I.: Importance of Cumulus
Parameterization for Precipitation Simulation over East Asia in June, J.
Meteorol. Soc. Jpn., 79, 939–947, 10.2151/jmsj.79.939, 2001.Evans, M. J. and Jacob, D. J.: Impact of new laboratory studies of N2O5
hydrolysis on global model budgets of tropospheric nitrogen oxides, ozone,
and OH, Geophys. Res. Lett., 32, L09813, 10.1029/2005GL022469, 2005.Eyring, V., Isaksen, I. S., Berntsen, T., Collins, W. J., Corbett, J. J.,
Endresen, O., Grainger, R. G., Moldanova, J., Schlager, H., and Stevenson,
D. S.: Transport impacts on atmosphere and climate: Shipping, Atmos.
Environ., 44, 4735–4771, 10.1016/j.atmosenv.2009.04.059, 2010.Finney, D. L., Doherty, R. M., Wild, O., Young, P. J., and Butler, A.:
Response of lightning NOx emissions and ozone production to climate
change: Insights from the Atmospheric Chemistry and Climate Model
Intercomparison Project, Geophys. Res. Lett., 43, 5492–5500,
10.1002/2016GL068825, 2016.Fischer, E. V., Jaffe, D. A., Reidmiller, D. R., and Jaeglé, L.:
Meteorological controls on observed peroxyacetyl nitrate at Mount Bachelor
during the spring of 2008, J. Geophys. Res., 115, D03302,
10.1029/2009JD012776, 2010.Fischer, E. V., Jacob, D. J., Yantosca, R. M., Sulprizio, M. P., Millet, D.
B., Mao, J., Paulot, F., Singh, H. B., Roiger, A., Ries, L., Talbot, R. W.,
Dzepina, K., and Pandey Deolal, S.: Atmospheric peroxyacetyl nitrate (PAN): a
global budget and source attribution, Atmos. Chem. Phys., 14, 2679–2698,
10.5194/acp-14-2679-2014, 2014.Giglio, L., Randerson, J. T., and van der Werf, G. R.: Analysis of daily,
monthly, and annual burned area using the fourth-generation global fire
emissions database (GFED4), J. Geophys. Res., 118, 317–328,
10.1002/jgrg.20042, 2013.Gressent, A., Sauvage, B., Cariolle, D., Evans, M., Leriche, M., Mari, C.,
and Thouret, V.: Modeling lightning-NOx chemistry on a sub-grid scale in a
global chemical transport model, Atmos. Chem. Phys., 16, 5867–5889,
10.5194/acp-16-5867-2016, 2016.Griffith, S. M., Hansen, R. F., Dusanter, S., Michoud, V., Gilman, J. B.,
Kuster, W. C., Veres, P. R., Graus, M., de Gouw, J. A., Roberts, J., Young,
C., Washenfelder, R., Brown, S. S., Thalman, R., Waxman, E., Volkamer, R.,
Tsai, C., Stutz, J., Flynn, J. H., Grossberg, N., Lefer, B., Alvarez, S. L.,
Rappenglueck, B., Mielke, L. H., Osthoff, H. D., and Stevens, P. S.:
Measurements of hydroxyl and hydroperoxy radicals during CalNex-LA: Model
comparisons and radical budgets, J. Geophys. Res., 121, 4211–4232,
10.1002/2015JD024358, 2016.Gruber, N. and Galloway, J. N.: An Earth-system perspective of the global
nitrogen cycle, Nature, 451, 293–296, 10.1038/nature06592, 2008.Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron,
C.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of
Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6,
3181–3210, 10.5194/acp-6-3181-2006, 2006.Han, K. M., Lee, S., Chang, L. S., and Song, C. H.: A comparison study
between CMAQ-simulated and OMI-retrieved NO2 columns over East Asia for
evaluation of NOx emission fluxes of INTEX-B, CAPSS, and REAS inventories,
Atmos. Chem. Phys., 15, 1913–1938, 10.5194/acp-15-1913-2015,
2015.Harkey, M., Holloway, T., Oberman, J., and Scotty, E.: An evaluation of CMAQ
NO2 using observed chemistry-meteorology correlations, J. Geophys. Res.,
120, 11775–11797, 10.1002/2015JD023316, 2015JD023316, 2015.Heckel, A., Kim, S.-W., Frost, G. J., Richter, A., Trainer, M., and Burrows,
J. P.: Influence of low spatial resolution a priori data on tropospheric
NO2 satellite retrievals, Atmos. Meas. Tech., 4, 1805–1820,
10.5194/amt-4-1805-2011, 2011.Hess, P. G. and Vukicevic, T.: Intercontinental transport, chemical
transformations, and baroclinic systems, J. Geophys. Res., 108, 4354,
10.1029/2002JD002798, 2003.Holmes, C. D., Prather, M. J., and Vinken, G. C. M.: The climate impact of
ship NOx emissions: an improved estimate accounting for plume chemistry,
Atmos. Chem. Phys., 14, 6801–6812, 10.5194/acp-14-6801-2014,
2014.Hudman, R. C., Jacob, D. J., Cooper, O. R., Evans, M. J., Heald, C. L., Park,
R. J., Fehsenfeld, F., Flocke, F., Holloway, J., Hübler, G., Kita, K.,
Koike, M., Kondo, Y., Neuman, A., Nowak, J., Oltmans, S., Parrish, D.,
Roberts, J. M., and Ryerson, T.: Ozone production in transpacific Asian
pollution plumes and implications for ozone air quality in California, J.
Geophys. Res., 109, D23S10, 10.1029/2004JD004974, 2004.Huffman, G. J., Adler, R. F., Bolvin, D. T., and Gu, G.: Improving the global
precipitation record: GPCP Version 2.1, Geophys. Res. Lett., 36,
10.1029/2009GL040000, 2009.Huijnen, V., Eskes, H. J., Poupkou, A., Elbern, H., Boersma, K. F., Foret,
G., Sofiev, M., Valdebenito, A., Flemming, J., Stein, O., Gross, A.,
Robertson, L., D'Isidoro, M., Kioutsioukis, I., Friese, E., Amstrup, B.,
Bergstrom, R., Strunk, A., Vira, J., Zyryanov, D., Maurizi, A., Melas, D.,
Peuch, V.-H., and Zerefos, C.: Comparison of OMI NO2 tropospheric columns
with an ensemble of global and European regional air quality models, Atmos.
Chem. Phys., 10, 3273–3296, 10.5194/acp-10-3273-2010, 2010a.Huijnen, V., Williams, J., van Weele, M., van Noije, T., Krol, M., Dentener,
F., Segers, A., Houweling, S., Peters, W., de Laat, J., Boersma, F.,
Bergamaschi, P., van Velthoven, P., Le Sager, P., Eskes, H., Alkemade, F.,
Scheele, R., Nédélec, P., and Pätz, H.-W.: The global chemistry
transport model TM5: description and evaluation of the tropospheric chemistry
version 3.0, Geosci. Model Dev., 3, 445–473,
10.5194/gmd-3-445-2010, 2010b.Inness, A., Blechschmidt, A.-M., Bouarar, I., Chabrillat, S., Crepulja, M.,
Engelen, R. J., Eskes, H., Flemming, J., Gaudel, A., Hendrick, F., Huijnen,
V., Jones, L., Kapsomenakis, J., Katragkou, E., Keppens, A., Langerock, B.,
de Mazière, M., Melas, D., Parrington, M., Peuch, V. H., Razinger, M.,
Richter, A., Schultz, M. G., Suttie, M., Thouret, V., Vrekoussis, M., Wagner,
A., and Zerefos, C.: Data assimilation of satellite-retrieved ozone, carbon
monoxide and nitrogen dioxide with ECMWF's Composition-IFS, Atmos. Chem.
Phys., 15, 5275–5303, 10.5194/acp-15-5275-2015, 2015.IPCC: Climate Change 2013: The Physical Science Basis, Contribution of
Working Group I to the Fifth Assessment Report of the Intergovernmental Panel
on Climate Change, Cambridge University Press, Cambridge, UK, New York, NY,
USA, 10.1017/CBO9781107415324, 2013.Irie, H., Boersma, K. F., Kanaya, Y., Takashima, H., Pan, X., and Wang, Z.
F.: Quantitative bias estimates for tropospheric NO2 columns retrieved
from SCIAMACHY, OMI, and GOME-2 using a common standard for East Asia, Atmos.
Meas. Tech., 5, 2403–2411, 10.5194/amt-5-2403-2012, 2012.Itahashi, S., Uno, I., Irie, H., Kurokawa, J.-I., and Ohara, T.: Regional
modeling of tropospheric NO2 vertical column density over East Asia during
the period 2000–2010: comparison with multisatellite observations, Atmos.
Chem. Phys., 14, 3623–3635, 10.5194/acp-14-3623-2014, 2014.Ito, A., Sillman, S., and Penner, J. E.: Effects of additional nonmethane
volatile organic compounds, organic nitrates, and direct emissions of
oxygenated organic species on global tropospheric chemistry, J. Geophys.
Res., 112, D06309, 10.1029/2005JD006556, 2007.Ito, A., Sillman, S., and Penner, J. E.: Global chemical transport model
study of ozone response to changes in chemical kinetics and biogenic volatile
organic compounds emissions due to increasing temperatures: Sensitivities to
isoprene nitrate chemistry and grid resolution, J. Geophys. Res., 114,
D09301, 10.1029/2008JD011254, 2009.Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Dentener, F., Muntean, M.,
Pouliot, G., Keating, T., Zhang, Q., Kurokawa, J., Wankmüller, R., Denier
van der Gon, H., Kuenen, J. J. P., Klimont, Z., Frost, G., Darras, S., Koffi,
B., and Li, M.: HTAP_v2.2: a mosaic of regional and global emission grid
maps for 2008 and 2010 to study hemispheric transport of air pollution,
Atmos. Chem. Phys., 15, 11411–11432,
10.5194/acp-15-11411-2015, 2015.Jiang, Z., Worden, J. R., Payne, V. H., Zhu, L., Fischer, E., Walker, T., and
Jones, D. B. A.: Ozone export from East Asia: The role of PAN, J. Geophys.
Res., 121, 6555–6563, 10.1002/2016JD024952, 2016.K-1 model developers: K-1 Coupled GCM (MIROC) Description, Tech. rep.,
Center for Climate System Research (Univ. of Tokyo), National Institute for
Environmental Studies, and Frontier Research Center for Global Change,
available at:
http://ccsr.aori.u-tokyo.ac.jp/~hasumi/miroc_description.pdf, 2004.Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones,
L., Morcrette, J.-J., Razinger, M., Schultz, M. G., Suttie, M., and van der
Werf, G. R.: Biomass burning emissions estimated with a global fire
assimilation system based on observed fire radiative power, Biogeosciences,
9, 527–554, 10.5194/bg-9-527-2012, 2012.Kanaya, Y., Matsumoto, J., Kato, S., and Akimoto, H.: Behavior of OH and
HO2 radicals during the Observations at a Remote Island of Okinawa
(ORION99) field campaign: 2. Comparison between observations and
calculations, J. Geophys. Res., 106, 24209–24223,
10.1029/2000JD000179, 2001.Kim, S.-W., Heckel, A., Frost, G. J., Richter, A., Gleason, J., Burrows,
J. P., McKeen, S., Hsie, E.-Y., Granier, C., and Trainer, M.: NO2 columns
in the western United States observed from space and simulated by a regional
chemistry model and their implications for NOx emissions, J. Geophys.
Res., 114, D11301, 10.1029/2008JD011343, 2009.Krotkov, N. A., McLinden, C. A., Li, C., Lamsal, L. N., Celarier, E. A.,
Marchenko, S. V., Swartz, W. H., Bucsela, E. J., Joiner, J., Duncan, B. N.,
Boersma, K. F., Veefkind, J. P., Levelt, P. F., Fioletov, V. E., Dickerson,
R. R., He, H., Lu, Z., and Streets, D. G.: Aura OMI observations of regional
SO2 and NO2 pollution changes from 2005 to 2015, Atmos. Chem. Phys.,
16, 4605–4629, 10.5194/acp-16-4605-2016, 2016.Levelt, P. F., van den Oord, G. H. J., Dobber, M. R., Malkki, A., Visser, H.,
de Vries, J., Stammes, P., Lundell, J. O. V., and Saari, H.: The ozone
monitoring instrument, IEEE Trans. Geosci. Remote Sens., 44, 1093–1101,
10.1109/TGRS.2006.872333, 2006.
Li, D. and Shine, K.: A 4-dimensional ozone climatology for UGAMP models,
UGAMP Internal Rep., 35, 35, 1995.
Liebmann, B.: Description of a complete (interpolated) outgoing longwave
radiation dataset, B. Am. Meteorol. Soc., 77, 1275–1277, 1996.Lin, J.-T. and McElroy, M. B.: Impacts of boundary layer mixing on pollutant
vertical profiles in the lower troposphere: Implications to satellite remote
sensing, Atmos. Environ., 44, 1726–1739,
10.1016/j.atmosenv.2010.02.009, 2010.Lin, J.-T., Liu, Z., Zhang, Q., Liu, H., Mao, J., and Zhuang, G.: Modeling
uncertainties for tropospheric nitrogen dioxide columns affecting
satellite-based inverse modeling of nitrogen oxides emissions, Atmos. Chem.
Phys., 12, 12255–12275, 10.5194/acp-12-12255-2012, 2012.Lin, J.-T., Martin, R. V., Boersma, K. F., Sneep, M., Stammes, P., Spurr, R.,
Wang, P., Van Roozendael, M., Clémer, K., and Irie, H.: Retrieving
tropospheric nitrogen dioxide from the Ozone Monitoring Instrument: effects
of aerosols, surface reflectance anisotropy, and vertical profile of nitrogen
dioxide, Atmos. Chem. Phys., 14, 1441–1461,
10.5194/acp-14-1441-2014, 2014.Lin, S.-J. and Rood, R. B.: Multidimensional Flux-Form Semi-Lagrangian
Transport Schemes, Mon. Weather Rev., 124, 2046–2070,
10.1175/1520-0493(1996)124<2046:MFFSLT>2.0.CO;2,
1996.Liu, F., Zhang, Q., Tong, D., Zheng, B., Li, M., Huo, H., and He, K. B.:
High-resolution inventory of technologies, activities, and emissions of
coal-fired power plants in China from 1990 to 2010, Atmos. Chem. Phys., 15,
13299–13317, 10.5194/acp-15-13299-2015, 2015.Liu, X., Mizzi, A. P., Anderson, J. L., Fung, I. Y., and Cohen, R. C.:
Assimilation of satellite NO2 observations at high spatial resolution
using OSSEs, Atmos. Chem. Phys., 17, 7067–7081,
10.5194/acp-17-7067-2017, 2017.Lu, Z. and Streets, D. G.: Increase in NOx Emissions from Indian Thermal
Power Plants during 1996–2010: Unit-Based Inventories and Multisatellite
Observations, Environ. Sci. Technol., 46, 7463–7470,
10.1021/es300831w, 2012.Mauldin, R. L., Cantrell, C. A., Zondlo, M., Kosciuch, E., Eisele, F. L.,
Chen, G., Davis, D., Weber, R., Crawford, J., Blake, D., Bandy, A., and
Thornton, D.: Highlights of OH, H2SO4, and methane sulfonic acid
measurements made aboard the NASA P-3B during Transport and Chemical
Evolution over the Pacific, J. Geophys. Res., 108, 8796,
10.1029/2003JD003410, 2003.Mellor, G. L. and Yamada, T.: A Hierarchy of Turbulence Closure Models for
Planetary Boundary Layers, J. Atmos. Sci., 31, 1791–1806,
10.1175/1520-0469(1974)031<1791:AHOTCM>2.0.CO;2, 1974.Menut, L., Bessagnet, B., Colette, A., and Khvorostiyanov, D.: On the impact
of the vertical resolution on chemistry-transport modelling, Atmos. Environ.,
67, 370–384, 10.1016/j.atmosenv.2012.11.026, 2013.Mijling, B. and van der A, R. J.: Using daily satellite observations to
estimate emissions of short-lived air pollutants on a mesoscopic scale, J.
Geophys. Res., 117, D17302, 10.1029/2012JD017817, 2012.Miyazaki, K., Eskes, H. J., and Sudo, K.: Global NOx emission estimates
derived from an assimilation of OMI tropospheric NO2 columns, Atmos. Chem.
Phys., 12, 2263–2288, 10.5194/acp-12-2263-2012, 2012.Miyazaki, K., Eskes, H. J., Sudo, K., and Zhang, C.: Global lightning NOx
production estimated by an assimilation of multiple satellite data sets,
Atmos. Chem. Phys., 14, 3277–3305, 10.5194/acp-14-3277-2014,
2014.Miyazaki, K., Eskes, H. J., and Sudo, K.: A tropospheric chemistry reanalysis
for the years 2005–2012 based on an assimilation of OMI, MLS, TES, and
MOPITT satellite data, Atmos. Chem. Phys., 15, 8315–8348,
10.5194/acp-15-8315-2015, 2015.Miyazaki, K., Eskes, H., Sudo, K., Boersma, K. F., Bowman, K., and Kanaya,
Y.: Decadal changes in global surface NOx emissions from multi-constituent
satellite data assimilation, Atmos. Chem. Phys., 17, 807–837,
10.5194/acp-17-807-2017, 2017.Miyoshi, T., Kondo, K., and Terasaki, K.: Big Ensemble Data Assimilation in
Numerical Weather Prediction, Computer, 48, 15–21,
10.1109/MC.2015.332, 2015.Morgenstern, O., Hegglin, M. I., Rozanov, E., O'Connor, F. M., Abraham, N.
L., Akiyoshi, H., Archibald, A. T., Bekki, S., Butchart, N., Chipperfield, M.
P., Deushi, M., Dhomse, S. S., Garcia, R. R., Hardiman, S. C., Horowitz, L.
W., Jöckel, P., Josse, B., Kinnison, D., Lin, M., Mancini, E., Manyin, M.
E., Marchand, M., Marécal, V., Michou, M., Oman, L. D., Pitari, G.,
Plummer, D. A., Revell, L. E., Saint-Martin, D., Schofield, R., Stenke, A.,
Stone, K., Sudo, K., Tanaka, T. Y., Tilmes, S., Yamashita, Y., Yoshida, K.,
and Zeng, G.: Review of the global models used within phase 1 of the
Chemistry–Climate Model Initiative (CCMI), Geosci. Model Dev., 10, 639–671,
10.5194/gmd-10-639-2017, 2017.Oikawa, P. Y., Ge, C., Wang, J., Eberwein, J. R., Liang, L. L., Allsman,
L. A., Grantz, D. A., and Jenerette, G. D.: Unusually high soil nitrogen
oxide emissions influence air quality in a high-temperature agricultural
region, Nat. Commun., 6, 8753, 10.1038/ncomms9753, 2015.Pickering, K. E., Wang, Y., Tao, W.-K., Price, C., and Müller, J.-F.:
Vertical distributions of lightning NOx for use in regional and global
chemical transport models, J. Geophys. Res., 103, 31203–31216,
10.1029/98JD02651, 1998.Platt, U. F., Winer, A. M., Biermann, H. W., Atkinson, R., and Pitts, J. N.:
Measurement of nitrate radical concentrations in continental air, Environ.
Sci. Technol., 18, 365–369, 10.1021/es00123a015, 1984.Prasad, A. K., Singh, R. P., and Kafatos, M.: Influence of coal-based thermal
power plants on the spatial–temporal variability of tropospheric NO2
column over India, Environ. Monit. Assess., 184, 1891–1907,
10.1007/s10661-011-2087-6, 2012.
Prather, M. and Ehhalt, D.: Atmospheric Chemistry and Green house gases,
chap. 4, in: Contribution of working group 1 to the Third Assessment Report
of the IPCC, edited by: Houghton, J. T., Ding, Y., Griggs, D. J., Nouger, M.,
van der Linden, P. J., Dai, X., Maskell, K., and Johnson, C. A., Cambridge
University Press, 241–287, 2001.Price, C. and Rind, D.: A simple lightning parameterization for calculating
global lightning distributions, J. Geophys. Res., 97, 9919–9933,
10.1029/92JD00719, 1992.Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander,
L. V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea
surface temperature, sea ice, and night marine air temperature since the late
nineteenth century, J. Geophys. Res., 108, 4407, 10.1029/2002JD002670,
2003.Ridley, B., Ott, L., Pickering, K., Emmons, L., Montzka, D., Weinheimer, A.,
Knapp, D., Grahek, F., Li, L., Heymsfield, G., McGill, M., Kucera, P.,
Mahoney, M. J., Baumgardner, D., Schultz, M., and Brasseur, G.: Florida
thunderstorms: A faucet of reactive nitrogen to the upper troposphere, J.
Geophys. Res., 109, D17305, 10.1029/2004JD004769, 2004.Russell, A. R., Perring, A. E., Valin, L. C., Bucsela, E. J., Browne, E. C.,
Wooldridge, P. J., and Cohen, R. C.: A high spatial resolution retrieval of
NO2 column densities from OMI: method and evaluation, Atmos. Chem. Phys.,
11, 8543–8554, 10.5194/acp-11-8543-2011, 2011.Saikawa, E., Kim, H., Zhong, M., Avramov, A., Zhao, Y., Janssens-Maenhout,
G., Kurokawa, J.-I., Klimont, Z., Wagner, F., Naik, V., Horowitz, L. W., and
Zhang, Q.: Comparison of emissions inventories of anthropogenic air
pollutants and greenhouse gases in China, Atmos. Chem. Phys., 17, 6393–6421,
10.5194/acp-17-6393-2017, 2017.Sekiya, T. and Sudo, K.: Roles of transport and chemistry processes in global
ozone change on interannual and multidecadal time scales, J. Geophys. Res.,
119, 4903–4921, 10.1002/2013JD020838, 2014.Sheel, V., Lal, S., Richter, A., and Burrows, J. P.: Comparison of satellite
observed tropospheric {NO2} over India with model simulations, Atmos.
Environ., 44, 3314–3321, 10.1016/j.atmosenv.2010.05.043, 2010.Shindell, D. T., Faluvegi, G., Koch, D. M., Schmidt, G. A., Unger, N., and
Bauer, S. E.: Improved Attribution of Climate Forcing to Emissions, Science,
326, 716–718, 10.1126/science.1174760, 2009.Stavrakou, T., Müller, J.-F., Boersma, K. F., van der A, R. J., Kurokawa,
J., Ohara, T., and Zhang, Q.: Key chemical NOx sink uncertainties and how
they influence top-down emissions of nitrogen oxides, Atmos. Chem. Phys., 13,
9057–9082, 10.5194/acp-13-9057-2013, 2013.Stock, Z. S., Russo, M. R., and Pyle, J. A.: Representing ozone extremes in
European megacities: the importance of resolution in a global chemistry
climate model, Atmos. Chem. Phys., 14, 3899–3912,
10.5194/acp-14-3899-2014, 2014.Sudo, K. and Akimoto, H.: Global source attribution of tropospheric ozone:
Long-range transport from various source regions, J. Geophys. Res., 112,
D12302, 10.1029/2006JD007992, 2007.Sudo, K., Takahashi, M., Kurokawa, J., and Akimoto, H.: CHASER: A global
chemical model of the troposphere 1. Model description, J. Geophys. Res.,
107, 4339, 10.1029/2001JD001113, 2002.Takemura, T., Nozawa, T., Emori, S., Nakajima, T. Y., and Nakajima, T.:
Simulation of climate response to aerosol direct and indirect effects with
aerosol transport-radiation model, J. Geophys. Res., 110, D02202,
10.1029/2004JD005029, 2005.Takemura, T., Egashira, M., Matsuzawa, K., Ichijo, H., O'ishi, R., and
Abe-Ouchi, A.: A simulation of the global distribution and radiative forcing
of soil dust aerosols at the Last Glacial Maximum, Atmos. Chem. Phys., 9,
3061–3073, 10.5194/acp-9-3061-2009, 2009.Thompson, A. M., Witte, J. C., McPeters, R. D., Oltmans, S. J., Schmidlin,
F. J., Logan, J. A., Fujiwara, M., Kirchhoff, V. W. J. H., Posny, F.,
Coetzee, G. J. R., Hoegger, B., Kawakami, S., Ogawa, T., Johnson, B. J.,
Vömel, H., and Labow, G.: Southern Hemisphere Additional Ozonesondes
(SHADOZ) 1998–2000 tropical ozone climatology 1. Comparison with Total Ozone
Mapping Spectrometer (TOMS) and ground-based measurements, J. Geophys. Res.,
108, 8238, 10.1029/2001JD000967, 2003a.Thompson, A. M., Witte, J. C., Oltmans, S. J., Schmidlin, F. J., Logan,
J. A., Fujiwara, M., Kirchhoff, V. W. J. H., Posny, F., Coetzee, G. J. R.,
Hoegger, B., Kawakami, S., Ogawa, T., Fortuin, J. P. F., and Kelder, H. M.:
Southern Hemisphere Additional Ozonesondes (SHADOZ) 1998–2000 tropical ozone
climatology 2. Tropospheric variability and the zonal wave-one, J. Geophys.
Res., 108, 8241, 10.1029/2002JD002241, 2003b.Uno, I., He, Y., Ohara, T., Yamaji, K., Kurokawa, J.-I., Katayama, M., Wang,
Z., Noguchi, K., Hayashida, S., Richter, A., and Burrows, J. P.: Systematic
analysis of interannual and seasonal variations of model-simulated
tropospheric NO2 in Asia and comparison with GOME-satellite data, Atmos.
Chem. Phys., 7, 1671–1681, 10.5194/acp-7-1671-2007, 2007.Valin, L. C., Russell, A. R., Hudman, R. C., and Cohen, R. C.: Effects of
model resolution on the interpretation of satellite NO2 observations,
Atmos. Chem. Phys., 11, 11647–11655,
10.5194/acp-11-11647-2011, 2011.Valks, P., Pinardi, G., Richter, A., Lambert, J.-C., Hao, N., Loyola, D., Van
Roozendael, M., and Emmadi, S.: Operational total and tropospheric NO2
column retrieval for GOME-2, Atmos. Meas. Tech., 4, 1491–1514,
10.5194/amt-4-1491-2011, 2011.van Noije, T. P. C., Eskes, H. J., Dentener, F. J., Stevenson, D. S.,
Ellingsen, K., Schultz, M. G., Wild, O., Amann, M., Atherton, C. S.,
Bergmann, D. J., Bey, I., Boersma, K. F., Butler, T., Cofala, J., Drevet, J.,
Fiore, A. M., Gauss, M., Hauglustaine, D. A., Horowitz, L. W., Isaksen, I. S.
A., Krol, M. C., Lamarque, J.-F., Lawrence, M. G., Martin, R. V., Montanaro,
V., Müller, J.-F., Pitari, G., Prather, M. J., Pyle, J. A., Richter, A.,
Rodriguez, J. M., Savage, N. H., Strahan, S. E., Sudo, K., Szopa, S., and van
Roozendael, M.: Multi-model ensemble simulations of tropospheric NO2
compared with GOME retrievals for the year 2000, Atmos. Chem. Phys., 6,
2943–2979, 10.5194/acp-6-2943-2006, 2006.Vinken, G. C. M., Boersma, K. F., Jacob, D. J., and Meijer, E. W.: Accounting
for non-linear chemistry of ship plumes in the GEOS-Chem global chemistry
transport model, Atmos. Chem. Phys., 11, 11707–11722,
10.5194/acp-11-11707-2011, 2011.Vu, K. T., Dingle, J. H., Bahreini, R., Reddy, P. J., Apel, E. C., Campos, T.
L., DiGangi, J. P., Diskin, G. S., Fried, A., Herndon, S. C., Hills, A. J.,
Hornbrook, R. S., Huey, G., Kaser, L., Montzka, D. D., Nowak, J. B., Pusede,
S. E., Richter, D., Roscioli, J. R., Sachse, G. W., Shertz, S., Stell, M.,
Tanner, D., Tyndall, G. S., Walega, J., Weibring, P., Weinheimer, A. J.,
Pfister, G., and Flocke, F.: Impacts of the Denver Cyclone on regional air
quality and aerosol formation in the Colorado Front Range during FRAPPÉ
2014, Atmos. Chem. Phys., 16, 12039–12058,
10.5194/acp-16-12039-2016, 2016.Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H.,
Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E.,
Takata, K., Emori, S., and Kawamiya, M.: MIROC-ESM 2010: model description
and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4,
845–872, 10.5194/gmd-4-845-2011, 2011.Weber, B., Wu, D., Tamm, A., Ruckteschler, N., Rodríguez-Caballero, E.,
Steinkamp, J., Meusel, H., Elbert, W., Behrendt, T., Sörgel, M., Cheng, Y.,
Crutzen, P. J., Su, H., and Pöschl, U.: Biological soil crusts accelerate
the nitrogen cycle through large NO and HONO emissions in drylands, P. Natl.
Acad. Sci. USA, 112, 15384–15389, 10.1073/pnas.1515818112, 2015.Wesely, M.: Parameterization of surface resistances to gaseous dry
deposition in regional-scale numerical models, Atmos. Environ., 23,
1293–1304, 10.1016/0004-6981(89)90153-4, 1989.
Wild, O. and Prather, M. J.: Global tropospheric ozone modeling: Quantifying
errors due to grid resolution, J. Geophys. Res., 111, D11305,
10.1029/2005JD006605, 2006.Williams, J. E., Boersma, K. F., Le Sager, P., and Verstraeten, W. W.: The
high-resolution version of TM5-MP for optimized satellite retrievals:
description and validation, Geosci. Model Dev., 10, 721–750,
10.5194/gmd-10-721-2017, 2017.Yamaji, K., Ikeda, K., Irie, H., Kurokawa, J., and Ohara, T.: Influence of
model grid resolution on NO2 vertical column densities over East Asia, J.
Air Waste Manage. Assoc., 64, 436–444, 10.1080/10962247.2013.827603,
2014.Yan, Y., Lin, J., Chen, J., and Hu, L.: Improved simulation of tropospheric
ozone by a global-multi-regional two-way coupling model system, Atmos. Chem.
Phys., 16, 2381–2400, 10.5194/acp-16-2381-2016, 2016.Yienger, J. J. and Levy, H.: Empirical model of global soil-biogenic NOx
emissions, J. Geophys. Res., 100, 11447–11464, 10.1029/95JD00370,
1995.Zheng, B., Huo, H., Zhang, Q., Yao, Z. L., Wang, X. T., Yang, X. F., Liu, H.,
and He, K. B.: High-resolution mapping of vehicle emissions in China in 2008,
Atmos. Chem. Phys., 14, 9787–9805, 10.5194/acp-14-9787-2014,
2014.Zien, A. W., Richter, A., Hilboll, A., Blechschmidt, A.-M., and Burrows, J.
P.: Systematic analysis of tropospheric NO2 long-range transport events
detected in GOME-2 satellite data, Atmos. Chem. Phys., 14, 7367–7396,
10.5194/acp-14-7367-2014, 2014.