Spatiotemporal variations of ozone (O3) and
nitrogen oxide (NOx) mixing ratios from 14 state-of-the-art
chemical transport models (CTMs) are intercompared and evaluated with
O3 observations in East Asia, within the framework of the Model
Inter-Comparison Study for Asia Phase III (MICS-Asia III). This study was designed to
evaluate the capabilities and uncertainties of current CTMs simulations for
Asia and to provide multi-model estimates of pollutant distributions. These
models were run by 14 independent groups working in China, Japan,
South Korea, the United States and other countries/regions. Compared with the
previous phase of MICS-Asia (MICS-Asia II), the evaluation with observations
was extended from 4 months to 1 full year across China and the western Pacific Rim. In general, model performance levels for O3 varied widely
by region and season. Most models captured the key patterns of monthly and
diurnal variation of surface O3 and its precursors in the North China Plain
and western Pacific Rim but failed to do so for the Pearl River Delta. A
significant overestimation of surface O3 was evident from
May to September/October and from January to May over the North China Plain,
the western Pacific Rim and the Pearl River Delta. Comparisons drawn from
observations show that the considerable diversity in O3 photochemical
production partly contributed to this overestimation and to high levels of
inter-model variability in O3 for North China. In terms of O3
soundings, the ensemble average of models reproduced the vertical structure
for the western Pacific, but overestimated O3 levels to below 800 hPa
in the summer. In the industrialized Pearl River Delta, the ensemble average
presented an overestimation for the lower troposphere and an underestimation
for the middle troposphere. The ensemble average of 13 models for O3 did not always exhibit superior performance compared with certain
individual models in contrast with its superior value for Europe. This finding
suggests that the spread of ensemble-model values does not represent all of the
uncertainties of O3 or that most MICS-Asia III models missed key
processes. This study improved the performance of modeling O3 in March
at Japanese sites compared with MICS-Asia II. However, it overpredicted surface
O3 concentrations for western Japan in July, which was not found by
MICS-Asia II. Major challenges still remain with regard to identifying the
sources of bias in surface O3 over East Asia in CTMs.
Introduction
Tropospheric ozone (O3) is a significant secondary air pollutant
produced via thousands of photochemical reactions and, as a strong
oxidant, it is
detrimental to human health, ecosystems and climate change (WHO, 2005; The Royal Society, 2008). Due to rapid industrialization
and urbanization over the last 2 decades, the O3 concentration is rising at
a higher rate in East Asia than in other regions, and on 30 % of days in
megacities (e.g., Beijing, Shanghai and Guangzhou in China) values exceed the air
quality standard of the World Health Organization (100 µg m-3) for the
8 h average surface O3 concentration (Wang et al., 2017). Thus, high
O3 concentrations have received more attention from the public and
policy makers in East Asia. The Ministry of Environment of Japan has imposed
stringent measures to reduce traffic emissions since the 1990s, and
non-methane volatile organic compounds (NMVOCs) and NOx mixing ratios
have decreased by 40 %–50 % and 51 %–54 %, respectively (Akimoto et al.,
2015). In 2012, China released a new ambient air quality standard under
which a limit on the 8 h O3 maximum was set for the first time.
However, these measures have not prevented the persistent increase of
ground-level O3 in East Asia. The averaged mixing ratio of O3 has
increased by 20 %–30 % in Japan over the last 20 years (Akimoto et al., 2015).
In Chinese megacities, 8 h O3 concentrations have increased by
10 %–30 % since 2013 (Wang et al., 2017).
The main method used for the detailed evaluation of the effects of air quality policies at the scale of East Asia is that of numerical air quality
modeling. Several global and regional scale CTMs (e.g., GEOS-Chem, CHASER,
CMAQ, CAMx, WRF-Chem and NAQPMS) have been developed over the past few
decades and have been widely used to simulate the O3 formation process
and to evaluate strategies for its control (Streets et al., 2007; Li et al.,
2007, 2008; Yamaji et al., 2006; Zhang et al., 2008; Liu et al., 2010; Wang
et al., 2013; He et al., 2017; Nagashima et al., 2010). Such simulations
have identified the key precursors of O3 formation in East Asia (Zhang
et al., 2008; Liu et al., 2010; Tang et al., 2010; He et al., 2017), have
assessed the contributions of international and regional transport (Streets
et al., 2008; Li et al., 2008) and have predicted O3 mixing ratios
under different future emission scenarios (Wang et al., 2013). However,
discrepancies remain between models and observations, indicating that model
simulations of O3 in East Asia still need to be improved (Han et al.,
2008). Modeling uncertainties related to emissions, chemistry, wet and dry
deposition, and transport can hardly be addressed using a single model.
Thus, model intercomparison has been recognized as an effective way to
address problems and has been successfully applied in Europe and North
America in phase 2 of the Air Quality Model Evaluation International
Initiative (AQMEII; Rao et al., 2011). Limited model intercomparison
related to air quality in East Asia has been conducted. Phases I and II of
the Model Inter-Comparison Study for Asia (MICS-Asia) were initiated in 1998
and 2003, respectively, to explore the potential sources of model uncertainties regarding
sulfur, O3, nitrogen compounds and aerosols (Carmichael et al., 2002,
2008). The study has shown that the predicted temporal variations of surface
O3 in eight regional CTMs generally tended to be lower than those
observed in 2001 with poor correlations in the western Pacific in March and
December (Han et al., 2008). Model performance levels for O3 were found
to vary greatly in southern China. Inconsistencies in the horizontal grids,
emissions and meteorological inputs used among models have rendered
explaining inter-model variability in MICS-Asia II results more difficult.
More importantly, model evaluations for industrialized China have not been
conducted due to a lack of observations, which has been detrimental to
efforts made to improve O3 model performance levels.
Recently, regional CTMs have been greatly improved by coupling more
mechanisms (e.g., heterogeneous chemistry and online calculation of
photolysis rates) and accurate chemical reaction rates. For example,
gas-phase chemistry mechanisms of Models-3 Community Multiscale Air Quality
(CMAQ) have been evolved into CBM05 and SAPRC07 from CB04 and SAPRC99. It is
critical to evaluate the updated models' abilities to simulate current air
quality levels over East Asia. In 2010, MICS-Asia was expanded to Phase III; in this phase, 13 regional CTMs and 1 global CTM were run over 1 full year by 14
independent groups from East Asia and North America, using a common
reference model input dataset (namely, the emission inventory,
meteorological fields and horizontal grids). In addition to observations
made in Japan by the Acid Deposition Monitoring Network in East Asia (EANET)
that were used for MICS-Asia II, new observational data from China were made
available for MICS-Asia III and were obtained from the Chinese Ecosystem
Research Network (CERN) and the Pearl River Delta Regional Air Quality
Monitoring Network (PRD RAQMN). An intercomparison of CTMs in China, Japan
and the western Pacific for 1 full year had never before been performed,
creating a broader database to use for comparisons. Therefore, the completeness of
MICS-Asia III is unique.
In this paper, we mainly evaluate the capacities of models participating in
MICS-Asia III to simulate concentrations of O3 and its related species
within the MICS-Asia III framework. The following questions are addressed:
(1) How well do various air quality models perform in simulating O3
levels in East Asia? (2) How consistent or discrepant are the models? (3) How do multi-model ensembles improve O3 simulation accuracy?
This paper is expected to provide valuable insights into the capacities and limitations of CTMs when applied to East Asia.
Models and dataExperimental setup
In this study, all participating models were run for the year 2010 and
provided gridded monthly mean diurnal mixing ratios of O3 and its
precursors in the lowest model layer. For O3, monthly three-dimensional
data were also submitted.
Basic structures, schemes and relevant parameters of the 14
participating models.
a Ref represents the domain referenced by the MICS-Asia III project.
b Standard represents the reference meteorological field provided by the
MICS-Asia III project; WRF/NCEP and WRF/MERRA represent the meteorological
field of the participating model itself, which was run by WRF and driven by the
NCEP and Modern Era Retrospective-analysis for Research and Applications
(MERRA) reanalysis dataset.
c Boundary conditions of M10 are from MOZART and GOCART (Chin et al.,
2002; Horowitz et al., 2003), which provided results for gaseous pollutants
and aerosols, respectively.
Participating models and input data
Table 1 summarizes the specifications of participating CTMs. These models
include two versions of CMAQ (v4.7.1 and 5.0.2; Byun and Schere, 2006), the
Weather Research and Forecasting model coupled with Chemistry (WRF-Chem;
http://www.acd.ucar.edu/wrf-chem, last access: 8 October 2019), the Nested Air Quality Prediction Modeling
System (NAQPMS; Li et al., 2007), the Japan Meteorological Agency's (JMA)
non-hydrostatic meteorology–chemistry model (NHM-Chem; Kajino et al., 2012),
the NASA-Unified Weather Research and Forecasting model (NU-WRF; Tao et al., 2013) and GEOS-Chem (http://acmg.seas.harvard.edu/geos/, last access: 8 October 2019). They have been
documented in the scientific literature and have been widely applied in
modeling studies of East Asia. Table 1 does not list model names to maintain
each model's anonymity. Similar behavior was observed to MICS-Asia II and
other model intercomparison projects (e.g., AQME II).
Model domain of models (except M13 and M14) showing the locations of
three subregions marked in this study. Also shown are the locations of
surface monitoring stations used in this study. The meteorological model
used to provide meteorological fields for most models also uses this domain.
Note that the domains of M13 and M14 are shown in Fig. 10.
MICS-Asia III participants were provided with a reference meteorological
field for the year 2010, generated with the Weather Research and Forecasting
model (WRF; version 3.4.1). The domain of the meteorological fields is
shown in Fig. 1. WRF v3.4.1 is driven by the final analysis dataset
(ds083.2) from the National Centers for Environmental Prediction (NCEP),
with a 1∘×1∘ resolution and a temporal
resolution of 6 h. A four-dimensional data assimilation nudging toward the
NCEP dataset was performed to increase the accuracy of the WRF. The
horizontal model domain of 182×172 grids on a Lambert conformal
map projection with a 45 km horizontal resolution is shown in Fig. 1.
Vertically, the WRF grid structure consists of 40 layers from the surface to
the top of model (10 hPa). Standard meteorological fields were applied by
the majority of groups. Several other models were employed to perform
simulations using their own meteorological models (e.g., RAMS-CMAQ and
GEOS-Chem). WRF-Chem utilized the same model (WRF) as the standard
meteorological simulation but considered the feedback of pollutants to the
meteorological fields. Consequently, the meteorological fields from this model may be
slightly different from the standard. GEOS-Chem is driven by the GEOS-5
assimilated meteorological fields taken from the Goddard Earth Observing
System of the NASA Global Modeling Assimilation Office. The
meteorological data and CTMs couples varied for each group, likely resulting in a
diversified set of model outputs.
MICS-Asia III provided a set of monthly anthropogenic emission inventories
for the year 2010 called MIX (Li et al., 2016). MIX is a mosaic of
up-to-date regional and national emission inventories that includes Regional
Emission inventory in ASia (REAS) version 2.1 for the whole Asian region
(Kurokawa et al., 2013), the Multi-resolution Emission Inventory for China
(MEIC) developed by Tsinghua University, a high-resolution NH3 emission
inventory by Peking University (Huang et al., 2012), an Indian emission
inventory developed by Argonne National Laboratory (ANL India, Lu et al.,
2011; Lu and Streets, 2012) and the official Korean emission inventory from
the Clean Air Policy Support System (CAPSS; Lee et al., 2011). Biogenic
emissions were taken from the Model of Emissions of Gases and Aerosols from
Nature (MEGAN); hourly biogenic emissions were obtained for the entire year
of 2010 using version 2.04 (Guenther et al., 2006). Biomass burning
emissions were processed by regridding the Global Fire Emissions Database
version 3 (GFEDv3; 0.5∘ by 0.5∘). Volcano SO2 emissions were
provided with a daily temporal resolution by the Asia Center for Air
Pollution Research (ACAP). The MICS-Asia III emission group directly
prepared a gridded inventory according to the configuration of each CTM.
NMVOC emissions were spectated into model-ready inputs for three chemical
mechanisms: CBMZ, CB05 and SAPRC-99. Weekly and diurnal profiles were also
provided. The standard emission inventory was applied by all models. The
majority of models employed the official suggested vertical and time profiles of pollutants from each sector by emission group. M13 and M14 made the
projections themselves. More information can be found in Li et al. (2017)
and Gao et al. (2018).
MICS-Asia III also provided two sets of chemical concentrations for the top
and lateral boundaries of the model domain, which were derived from 3-hourly
global model outputs for the year 2010. The global models were run by the
University of Tennessee (http://acmg.seas.harvard.edu/geos/, last access: 8 October 2019) and Nagoya
University (Sudo et al., 2002). GEOS-Chem was run with a
2.5∘×2∘ horizontal resolution and
47 vertical layers by the University of Tennessee, and the Chemical AGCM for Study
of Atmospheric Environment and Radiative Forcing (CHASER) was run with a
2.8∘×2.8∘ horizontal resolution
with 32 vertical layers by Nagoya University. Some models applied boundary
conditions depending on their own past experiences.
Observational data for O3
In this study, East Asia was divided into three subregions as shown in Fig. 1. The selection of subregions was based on emission, climate and
observation data coverage. The North China Plain (EA1) and the Pearl River Delta
(EA2) represent highly industrialized regions of the midlatitudes. EA1 is
characterized by a temperate and tropical continental monsoon climate with
marked seasonality. EA2 is located in southern China and is less affected by
continental air masses. EA3 covers the northwest Pacific and the Sea of
Japan and represents the downwind regions of the Asian continent with a
marine climate.
Hourly O3 and NOx observations for the year 2010 in East Asia were
obtained from the CERN, PRD-RAQMN and EANET. The CERN was built by the
Institute of Atmospheric Physics, Chinese Academy of Sciences and includes
19 surface stations covering an area of 500km× 500km across the
North China Plain (EA1 subregion; Ji et al., 2012). The stations are set up
according to United States Environmental Protection Agency method
designations. Half of them are remote, rural, suburban and clear urban
sites. Nine sites are located within meteorological stations or on campuses
of universities in urban regions, with little influence from local sources
and sinks. The PRD RAQMN was jointly established by the governments of
Guangdong Province and the Hong Kong Special Administrative Region and
consists of 16 automatic air quality monitoring stations located across the
EA2 subregion (Zhong et al., 2013). Thirteen of these stations are operated
by the Environmental Monitoring Centers in Guangdong Province and the other
three are located in Hong Kong and are managed by the Hong Kong
Environmental Pollution Department. The PRD RAQMN was designed to probe
regional air quality, in order to assess the effectiveness of emission reduction
measures and to enhance the roles of monitoring networks in characterizing
regional air quality and in supporting air quality management. Thus, the
sites are rarely influenced by local sources and sinks. The EANET was
launched in 1998 to address acid deposition problems in East Asia, following
the model of the Cooperative Program for Monitoring and Evaluation of the
Long-Range Transmission of Air Pollutants in Europe. In this study, eight
remote stations in the northwestern Pacific and Japan (EA3 subregion) were
selected to evaluate model performance levels for the downwind regions of
the Asian continent (Ban et al., 2016). More information on the EANET can be
found at http://www.eanet.asia/ (last access: 8 October 2019). Note that only stations with at least
75 % data validity were chosen. Table S1 in the Supplement
provides a detailed site description. Our comparisons of NOx and VOC
emission rates conducted on grids for these stations using 45 and 3 km
resolution emission inventories suggest that our selected stations
rarely received local emissions.
O3 was measured by a Thermo Scientific 49i ozone analyzer with UV photometric technology
in the CERN network and by a Thermo Scientific 49C ozone analyzer in the PRD-RAQMN and EANET networks.
NOx was measured by a Thermo Scientific 42C NO--NO2--NOx analyzer
with chemiluminescence technology at 40 sites in all three networks (CERN,
PRD-RAQMN and EANET). NOx measurements sometimes exhibited biases
(especially for stations located far from sources) when using molybdenum
converter devices as all nitrogen oxides were measured. This bias was
found to be dependent on the chemical conditions. A 1-month continuous
measurement with a chemiluminescence analyzer and
Aerodyne cavity-attenuated phase shift spectrometer (CAPS) undertaken in August at an urban
site in Beijing shows that this bias from the chemiluminescence analyzer is
minor when NO2 concentrations exceed 10–15 ppbv (parts per billion by
volume), ranging from 10 % to 30 % under low-NO2 conditions
(<10 ppbv) (Ge et al., 2013). Measurements collected from a rural
site in South Korea revealed a similar pattern across all seasons (Jung et
al., 2017). These comparisons suggest that observations made using
molybdenum converters may overestimate NO2 by 10 %–20 % for EA1 and EA2
and 30 % for EA3, introducing uncertainties into the NO2 model
evaluation in this study.
Statistical analysis for surface O3 in three subregions over
East Asia. R denotes the correlation coefficient, NMB is the normalized mean bias and RMSE is the root-mean-square error (units are ppbv).
Box plots of the observed and simulated annual NO2(a, d, g),
NO (b, e, h) and O3(c, f, i) frequency distributions
determined from 13 models and averaged for stations in EA1, EA2 and EA3 for 2010. n denotes the number of stations. The rectangle represents
the interquartile range (25th to 75th percentiles). The asterisk
identifies the mean, the continuous horizontal line within the rectangle
identifies the median, and whiskers extend between the minimum and maximum
values.
Model validation and general statisticsAnnual concentrations of surface O3, nitric oxide (NO) and nitrogen
dioxide (NO2)
Figure 2 provides a concise comparison of model performances for annual
O3, NO and NO2 for three sub regions of East Asia. A
box-and-whisker representation was used to show the frequency distribution
of monthly concentrations measured from stations in each subregion. The
O3 normalized mean bias (NMB) and root-mean-square error (RMSE) of the
ensemble mean were found to be significantly less than the ensemble median
in most cases (Table 2). Therefore, we only present multi-model mean
ensemble results (Ense). In general, the majority of the models significantly
overestimated annual surface O3 relative to observations in EA1, EA2
and EA3. Ense overestimated surface O3 by 10–30 ppbv for these
subregions. Ense NO2 levels closely reflected observations to within
±20 % across all subregions. In EA1 and EA2, Ense NO levels were
found to be 5–10 ppbv lower than observations while exhibiting reasonable
levels for EA3.
Of the models, M11 (for subregions EA1 and EA2) and M7 (for EA2 and EA3) more
closely reflected O3 observations. M11 simulated O3 with RMSE values of
9.5 and 13.3 ppbv for EA1 and EA2, respectively (Table 2). The models'
performance with respect to simulating O3 was found to be closely related to their
performance for NO2 and NO. In highly polluted regions (EA1 and EA2), a
persistent underestimation of NO was evident across most models. As an
interesting phenomenon, we found the models' performance regarding O3
measurements to vary greatly for EA3, although M8 exhibited consistent
performance with respect to NO and NO2. This finding suggests that
O3 was significantly affected by other factors in addition to local
chemistry in EA3. M8 underestimated O3 and overestimated NO in all
subregions by 40 %–50 %. The highest O3 titration level observed in M8
may have generated lower O3 levels than those indicated by other models
and observations.
Time series of monthly NO2, NO and O3 levels simulated by
all models and their ensembles (Ense) in parts per billion by volume (ppbv), averaged over all observed
stations across three subregions of East Asia: EA1 (a–c), EA2 (d–f) and EA3 (g–i). Observations are denoted by the solid black line. n
represents the number of stations. The gray lines represent NO2, NO and
O3 levels simulated by models except M1, M2, M4, M6, M11, M12, M13 and
M14.
Monthly variations of surface O3, NO and NO2
Figure 3 presents monthly mean concentrations of O3, NO and NO2 for
the three subregions across East Asia. When two or more observation sites
are located in the same model grid, their mean values are used to evaluate
model performance. All models captured the observed seasonal cycles of
O3, NO and NO2 for EA1. From May to September, Ense O3 was 10–30 ppbv higher than observed values (30 %–70 % of observed values), while Ense
NO and NO2 levels appeared to be consistent with observations with mean
biases of <3 ppbv. This finding suggests that an intercomparison of
O3 production efficiency levels per NOx with observations is
needed. For EA2, Ense O3 values agreed well with observed high autumn
O3 levels but were overestimated by 5–15 ppbv (15 %–60 % of observations) from January to September. This overestimation reached a maximum in March–April (15 ppbv) and led to a spring peak in simulated
O3 values not found in the observations. This overestimation is partly
related to the underestimation of NO in the same months, which decreased the
titration effect. For NO2, Ense values agreed well with observed values
for June–December, and slightly underestimated observations for January–May.
For EA3, the ensemble NO2 was generally close to observed values
(within ±0.5 ppbv). Significant overestimations of O3 and
underestimations of NO were observed from June to October. Similar results were found in MICS-Asia II and in another model intercomparison
project of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP), suggesting that such results may stem from differences in the
representation of the dispersion of southwesterly clean marine air masses observed
across different metrological fields used in CTMs (Han et al., 2008; Fiore
et al., 2009).
For individual models, M11 achieved the highest degree of model
reproductivity for monthly mean O3 levels in EA1. Most of the other
models overestimated O3 by 100 %–200 % for May–October. The largest
levels of model bias and inter-model variability for NO and NO2 appeared
in the winter, and likely came from the NOx vertical diffusion and
heterogeneous chemistry (Akimoto et al., 2019). In EA2, M7 seems to have
achieved the highest levels of O3 reproducibility. Most of the models
(except for M11 and M12) showed high O3 concentrations for March–May
and September–November. Observed O3 values show that the highest
concentrations appeared from October to November. M11 captured the observed
January–May O3 value due to relatively high NO concentrations. However,
NO was overestimated by M11 from May to September, leading to an
underestimation of O3 levels. In EA3, spatially averaged O3
concentrations often differed by more than 20 ppbv in individual models. The
highest levels of inter-model variability in O3 values appeared from
May to October, overestimating O3 levels relative to observations by 10–40 ppbv. Interestingly, although M8, M9 and M14 exhibited similar magnitudes
with observations for June–September, they significantly underestimated
observations in other months by 200 %–300 %. A detailed investigation is
required in future studies.
Seasonal mean diurnal cycle of surface O3, in parts per billion by volume (ppbv), as a
function of hours, for all models and their ensembles, averaged across all
observed stations in three subregions of East Asia: EA1 (top row), EA2
(middle row) and EA3 (bottom row). Observations are denoted by the thick black line. n
represents the number of stations. The gray lines represent O3 levels
simulated by models except M1, M2, M4, M11, M12 and M14. Spring, summer,
autumn and winter were defined as March–April–May,
June–July–August, September–October–November and December–January–February,
respectively.
Diurnal concentrations of surface O3
Subregional O3 diurnal variations are shown in Fig. 4. In general,
model results for three subregions exhibited a larger spread with a
magnitude of 10–50 ppbv across the diurnal cycle than those observed in
Europe and North America (Solazzo et al., 2012). Summer Ense O3 levels
exhibited a systematic pattern of overestimation (20 ppbv) throughout the
diurnal cycle in EA1. This indicates that the models had difficulty in
estimating summer O3 levels for the North China Plain. Compared with
summer conditions, only a slightly systematic overestimation of Ense O3 levels was observed for the other seasons (3–5 ppbv). In EA2, Ense O3
levels generally agreed with summer, autumn and winter observations. In
particular, the O3 maximum occurring at around noon was reasonably
reproduced. Only a 3–5 ppbv overestimation was observed from 16:00 to 23:00 LT (local time) and
in the early morning (06:00 to 10:00 LT). In the spring, a systematic overestimation of
Ense O3 values was observed across the whole diurnal cycle (5–10 ppbv). In EA3, Ense captured the minor diurnal variations in O3 across
all four seasons, but significantly overestimated observations for the
summer and autumn (5–20 ppbv). In the spring and winter, differences between
Ense and observations fell within 5 ppbv.
Of all of the models, M11 exhibited the best model performance level with respect to
measuring peak daily O3 concentrations of 60 ppbv from 14:00 to 16:00 LT in
EA1, but it still overestimated nighttime O3 levels by 10 ppbv.
Compared with their performance in simulating summer patterns, the models
performed significantly better in simulating winter conditions due to the
weak intensity of photochemical reactions except in the case of M2, M10 and
M8. Differences between observations and most simulations for both the
nighttime and daytime fell within 5 ppbv. These differences in the models'
performances between the summer and winter imply that the variety of
chemistry parameterizations applied to different models partly explain the
inter-model variability of simulated O3 levels in EA1 (North China
Plain). For EA2, the majority of models agreed well with diurnal variations
occurring in the summer and autumn. However, most models exhibited a
tendency to overestimate the O3 concentrations for both the daytime and
nighttime in the spring. The overestimated magnitudes exceeded 10 ppbv and
25 ppbv (of observed values of 20–35 ppbv) for the nighttime and daytime,
respectively. M11 reproduced observed O3 levels for the spring but
underestimated O3 levels for the summer and autumn. For EA3,
significant levels of inter-model variability persisted throughout the year.
Amplitudes of inter-model variability except for those of M8 and M14 reached
approximately 20 ppbv and 10 ppbv in the spring–summer and autumn–winter,
respectively. M8 and M14 generated the lowest O3 values of the models
for the whole year.
Error statistics on surface concentrations
In this section, we present statistics on the models' performance based on
monthly values. Values are calculated using the equations shown in Appendix A. On
a yearly basis, all models showed the highest (0.8–0.9) and lowest (0.1–0.6)
correlation coefficients for O3 for EA1 and EA2, respectively (Table 2). High correlations were found in EA1 mainly because the summer-maximum
and winter-minimum seasonal cycles are typical of polluted regions
represented in all of the participating models. In general, Ense performed
better than the individual models in representing NO2 for East Asia,
reproducing the observed seasonal cycles and magnitudes. However, Ense did
not always exhibit superior performance in simulating O3 levels over
individual models for East Asia, which stands in contrast with its performance
for Europe (Table 2). M7 and M11 agreed well with observations for EA1 and
EA2, whereas Ense tended to overestimate O3 concentrations for
May–September in EA1 and for January–September in EA2. Loon et al. (2007)
indicated that Ense exhibits superior performance only when the
spread of ensemble-model values is representative of O3 uncertainty.
This indicates that most models do not reflect this uncertainty or miss key
processes of MICS-Asia III.
Considerable overestimations made by most of the models for May–September
led to high NMB (0.25–1.25) and RMSE (10–33 ppbv) values for EA1. M11
generated the lowest NMB (0.09) and RMSE (9.46 ppbv) values of the examined
models. For EA2, M9 and M10 generated stronger correlations than the other
models. However, their corresponding NMB and RMSE values were also the
highest. These findings imply that systematic model biases are present in
these two models. M7 exhibited lower NMB and RMSE values than the other
models, but its correlation was measured as only 0.29. For EA3, correlations
exhibited the largest degree of inter-model variability across all
subregions, ranging from -0.13 to 0.65. M7 generated the lowest NMB and RMSE,
likely due to the canceling effect of its overestimation for the summer and
underestimation for other seasons (Fig. 3).
Statistical analysis for surface NO in three subregions over East
Asia. R denotes the correlation coefficient, NMB is the normalized mean bias and RMSE is the root-mean-square error (units are ppbv).
Statistical analysis for surface NO2 in three subregions over
East Asia. R denotes the correlation coefficient, NMB is the normalized mean bias and RMSE is the root-mean-square error (units are ppbv).
For NO, model correlations for EA1 ranged from 0.57 to 0.68, showing that all
of the models effectively reproduced the spatial variability in NO for this
subregion (Table 3). NMBs indicated that underestimations by the models
except in the case of M8, mostly occurred for the winter. This
underestimation can be partly attributed to the coarse model horizontal
resolution (45 km) used in MICS-Asia III, which hardly reproduced
concentrations of short-lived species (e.g., NO). In contrast with most of the
other models, M8 overestimated NO concentrations for all three subregions.
It is noted that NO observations for EA3 were too low (<0.3 ppbv)
to be discussed in this study.
Table 4 shows statistics regarding the models' performance in measuring NO2
levels. In general, most of the models performed better with respect to representing
NO2 than O3 and NO for EA1. NMBs ranged from -0.28 to 0.32, falling
far below those measured for O3 (0.48–1.25). Correlations of
0.54–0.66 were recorded, implying the models' reliable performance in
reproducing spatial and monthly variability of NO2 for EA1. Similar to
those for O3 and NO, correlation coefficients for NO2 in EA2
remained low. Thus, a dedicated investigation of O3, NO and NO2
levels in EA2 is urgently needed, but falls beyond the scope of this study.
In EA3, correlation coefficients ranged from 0.43- to 0.72. NMBs and RMSEs,
except for those of M8, ranged from -0.23 to 0.46 and 0.90 to 1.79 ppbv,
respectively.
Simulated O3 profiles for the summer (June–July–August) and
winter (December–January–February) of 2010, averaged over all observed
stations across three subregions of East Asia: EA1 (a, d), EA2 (b, e) and EA3: (c, f). Ozonesonde data for 2010 were taken from the World
Ozone and Ultraviolet Radiation Data Centre (WOUDC) database.
Vertical profiles of O3
Figure 5 shows the vertical profiles of the observed and simulated O3 levels
for East Asia for the summer and winter. Ensemble means (Ense) showed
underestimations and overestimations of EA2 O3 levels for the middle
(500–800 hPa) and lower (below 900 hPa) troposphere, respectively. In the
winter, underestimations extended to 200 hPa. Magnitudes of underestimations
and overestimations reached 10–40 and 10–20 ppbv, respectively. For
EA3, Ense reproduced the vertical structure of O3 for both the summer and
winter. An overestimation of the region below 800 hPa, with a magnitude of 10–20 ppbv, was observed for the summer.
High levels of inter-model variability in O3 exceeding 300 hPa was
evident across all subregions, which was attributable to the varied top
boundary conditions applied by the models. However, this considerable
variability was not transmitted to the middle troposphere (400–600 hPa), in
which O3 concentrations were consistent across the models. In the lower
troposphere, a minor level of inter-model variability below 900 hPa appeared
in the winter in three subregions, and slowly decreased with height. Mean
standard deviations (SD) of models below 900 hPa were recorded as 7.6
6.9 and 6.0 ppbv for EA1, EA2 and EA3, respectively, covering 18.3 %,
15.0 % and 15.4 % of mean O3 concentrations. In the 700–900 hPa region, SD
levels decreased to 5.4, 4.4 and 4.8 ppbv for EA1, EA2 and EA3, representing
12.2 %, 9.4 % and 10.8 % of mean O3 concentrations, respectively.
In the lower troposphere, inter-model variability in the summer was generally
higher than that in the winter. In polluted regions (EA1), SD levels reached
16.3 ppbv (20.8 % of mean concentrations) in the summer, greatly
exceeding those in winter (6.2 ppbv, 15.2 %). Various vertical structures
of O3 were found below 700 hPa in summer. O3 concentrations slowly
increased with height in M8 and M11, but they mixed well in the planetary
boundary layer (PBL) and decreased from 800 to 700 hPa in the other
models. Akimoto et al. (2019) found that the parameterization on downward
O3 transport from the upper boundary layer contributed considerably to
discrepancies between M1, M6 and M11. In EA2, vertical structures of O3
among models were found to be consistent, but concentrations differed more
than those in EA1. SD values covered 22 % of mean concentrations.
Ensemble mean seasonal surface O3 concentrations and CV values
for different seasons. CV is defined as the standard deviation of the
modeled fields divided by the average for different seasons.
Multi-model ensemble O3 and comparison with
MICS-Asia IISpatial distributions of single-model and multi-model ensemble O3
Figure 6 shows the spatial distributions of MICS-Asia III ensemble mean
surface O3 values (Ense) and the coefficient of variation (CV). The CV
is defined as the standard deviation of the modeled O3 divided by the
average. The larger the CV value, the lower the degree of consistency among
the models. For the summer, Ense predicted an elevated O3 concentration
belt in the midlatitudes (30–45∘ N). A region of O3 in
excess of 60 ppbv stretched across the North China Plain and East China Sea,
far exceeding MICS-Asia II (45–50 ppbv) values for 2001 (Han et al., 2008).
In other seasons, the O3 distribution showed higher O3 over the
ocean than in eastern China, reflecting O3 titration from high NOx
emissions. Due to the stratospheric injection, surface O3 over the
Tibetan Plateau remained at high levels throughout the year, ranging from 50
to 65 ppbv. The seasonal cycle of surface O3 levels determined from
Ense via MICS-Asia III agreed with that observed from MICS-Asia II, but
O3 levels in polluted regions were higher (Han et al., 2008).
Surface O3 spatial distribution derived from 13 models for
summer 2010 (units in ppbv).
The CV ranged from 0.1 to 0.6 in East Asia. The high values were found in EA1
in the winter. These high values in the low-latitude western Pacific
(10∘ S–15∘ N) and Indian oceans were likely caused by the treatment
of lateral boundaries in the models. For MICS-Asia III, M7, M8 and M9
employed the default model configurations, and the others employed outputs
of the GEOS-Chem/CHASER/MOZART-GOCART global model. Compared with those of
MICS-Asia II, CVs for the Asian continent except for the winter remained at
similar levels in this study (0.1–0.3) (Carmichael et al., 2008).
Although all of the models similarly predicted the emergence of an elevated
summer O3 concentration belt in the midlatitudes
(30–45∘ N), the magnitudes of enhanced O3 levels varied
between the models (Fig. 7). M5 predicted the highest O3 concentrations
of 60–90 ppbv for the North China Plain (EA1) and for its outflow pathways
including the Bohai Sea, East China Sea, Korea, Japan and the Sea of Japan
(locations are shown in Fig. S1 in the Supplement), whereas M8
predicted the lowest levels of 35–50 ppbv. Overhangs of 30 ppbv contour
lines extending into the northwestern Pacific along the Asian continent
outflow plume differed considerably between the models. A plume of 30 ppbv, or higher O3 levels, was simulated in M1–M6, M13 and M14, reaching
further south and east of Japan (135∘ E, 20∘ N), than those of M8,
M10 and M11 (120∘ E, 30∘ N). From MICS-Asia II and HTAP, differences
in the frequency of marine air masses from the western Pacific Ocean were
thought to be a possible cause of O3 discrepancies observed over oceans
between the models due to different meteorological drivers (Han et al.,
2008). For MICS-Asia III, wind fields employed by the models were similar
due to the use of the same or similar meteorological fields (Fig. S2). These inconsistencies between the models resulted
from the combined effects of a series of factors, including the diversity of the
condensed gas chemical mechanism and heterogeneous chemistry. Li et al.
(2016) found chemical production to be the dominant controlling factor of
O3 along outflow pathways near the North China Plain in the summer
rather than lateral and top boundary conditions. Impacts of aerosols on
O3 in these regions have frequently been reported (e.g., Olson et al., 1997 and
Li et al., 2018) to alter photolysis rates and heterogeneous chemistry
patterns. Detailed comparisons of parameterizations of these processes in
models are needed in future inter-model comparison projects focused on Asia.
In the winter, distribution patterns of O3 were quite similar between
the models with high concentrations observed over parts of western China,
northeastern India and the western Pacific from the East China Sea to
southern Japan (Fig. S3). In the spring and
autumn (Figs. S4 and S5), O3 concentrations were
generally higher than they were in the winter across the whole model domain
due to the enhancement of solar radiation or stratosphere–troposphere
exchange fluxes of O3. All of the models exhibited an enhancement of
O3 levels over southern Tibet, northeastern India and the western
Pacific, generally echoing patterns observed in the winter. Increases of
O3 further north in Japan were comparable with winter.
Modeled and observed monthly mean concentrations of O3 for EANET
sites in phases II (a–c) and III (d–f) of the MICS-Asia
project. The solid line represents the ensemble mean. Note that MICS-Asia II
and III data refer to March, July and December of 2001 and 2010,
respectively. IDs of the monitoring sites denote the following: 1 – Rishiri
(45.12∘ N, 141.23∘ E), 2 – Ogasawara (27.83∘ N, 142.22∘ E), 3 –
Sado-seki (38.23∘ N, 138.4∘ E), 4 – Oki (36.28∘ N,
133.18∘ E), 5 – Hedo (26.85∘ N, 128.25∘ E) and 6 – Banryu
(34.67∘ N, 131.80∘ E)
Comparison with MICS-Asia II
From MICS-Asia II, model evaluation of O3 was conducted on sites in
the western Pacific. Figure 8 presents the simulated and observed surface
O3 levels at these monitoring sites derived from phases II and III
of the MICS-Asia project. Note that different models were employed in the two
phases. In general, most of the models captured distributions of O3
mixing ratios at most sites in both MICS-Asia II and III. Ense results were
consistent for March and December of 2001 and 2010. Underestimations of
O3 levels in March at the Japanese sites (Site 4 – Oki, Site 5 – Hedo and Site
6 – Banryu) in Phase II were largely remedied in Phase III. However, surface
O3 observed in western Japan (Site 4 – Oki, Site 5 – Hedo and Site 6 – Banryu) were severely overestimated in July 2010 by 10–30 ppbv. This
overestimation was not found in Phase II, for which differences from
observations were valued at approximately 5 ppbv. Rural sites in western
Japan are located in the upwind regions of Japanese domestic emissions, and
are subjected to the impacts of Asian continent outflows. Overestimated
O3 values for the North China Plain (EA1) in Phase III contributed
considerably to enhanced concentrations simulated for western Japanese sites
in July 2010. This indicates that transboundary transport from the Asian
continent according to MICS-Asia III was likely overestimated relative to
that measured from MICS-Asia II.
Discussions
In reference to MICS-Asia II, Han et al. (2008) hypothesized that variations
in meteorological fields, dry deposition, PBL, model treatments of chemistry
and other physical processes contributed to model biases in relation to
observations and inter-model variability. Quantifying the contributions of
these processes can help explain model biases through sensitivity
simulations. However, this task comes with tremendous computational costs
when applied to 14 models. The qualitative analysis of potential causes of
these processes based on comparisons of models and observations is essential
to selecting sensitivity simulating scenarios for the next phase of
MICS-Asia. In MICS-Asia III, common input data (emission and meteorology)
were effectively used in this qualitative analysis based on model
parameterizations. We evaluated the models on dry deposition, PBL and
chemistry by collecting their observations (dry deposition velocity and PBL
height). This work was not conducted under MICS-Asia II and is intended to
help model developers improve model performance for East Asia.
Dry deposition
Previous studies show that dry deposition processes serve as the key net
sink of O3, accounting for roughly 25 % of the total O3 removed from the
troposphere (Lelieveld and Dentener, 2000). The uncertainty of dry
deposition in CTMs is still high because many processes are heavily
parameterized in models (Hardacre et al., 2015). In this study, the
simulated dry deposition velocities of O3 were compared. Simulated
deposition velocities were calculated from Eq. (1):
Vd=F/C,
where F and C represent the simulated dry deposition flux and surface
O3 concentrations, respectively. We determined spatial mean dry
deposition velocities from stations in each subregion.
Simulated and observed monthly O3 dry deposition velocities
(Vd) for M1, M2, M4, M6, M11, M12, M13 and M14 for three subregions of
East Asia: EA1 (a), EA3 (b) and EA3 (c). TEX, STR, GGSEX
and AMMA denote observations for TexAQS06 (7 July–12 September 2006;
northwestern Gulf of Mexico), STRATUS06 (9–27 October 2006; the persistent
stratus cloud region off the coast of Chile in the eastern Pacific Ocean),
GasEx08 (29 February–11 April 2008; the Southern Ocean), and AMMA08 (27
April–18 May 2008; the southern and northern Atlantic Ocean). Observational
data were taken from Sorimachi et al. (2003), Pan et al. (2010) and Helmig
et al. (2012).
Figure 9 presents the simulated and observed monthly spatial mean dry
deposition velocities of O3. For EA1, ensemble mean values
overestimated observed dry deposition velocities of O3 (vd) for
August–September, but they still fell within the range of the observed standard
deviation. This shows that factors other than dry deposition could
play important roles in overestimations of August–September O3 values
in EA1. In October–November, simulated vd apparently underestimated
observations by 30 %–50 %. Among the models, the lower dry deposition
velocities in May–July for M1, M2, M4 and M6 than that of M11 partly
explained the higher May–July surface O3 from those simulations than the value from
in M11. However, M13 and M14 still produced high O3 concentrations in
May–September, although their dry deposition velocities were similar to that
of M11 (Fig. 3). Notably, our observations were made on grassland, which
covers ∼20 % of the land area in EA1. There are few vd
observations on agriculture crops (50 % of the land area) in North China.
Hardacre et al. (2015) reported O3 dry deposition measurements on crops
in Europe and simulated O3 dry deposition in 15 global models. Both
observations and simulations showed that O3 dry deposition velocities
on the agriculture crop class were quite similar to those of grassland, displaying that
uncertainties related to the representativeness of measurement sites used
in this study did not affect our conclusions.
For EA2, similar features to those of EA1 were found. M1, M2, M4 and M6 were
quite consistent with each other, with a seasonal cycle and a spring
minimum. M11, M12 and M14 show no obvious signs of seasonal variability with
a magnitude of 0.1–0.2 cm s-1. Seasonal patterns in M13 are considerably
different from those of the other models, exhibiting a maximum in
April–September with higher dry deposition velocities (0.5 cm s-1). The
performance of the models for dry deposition velocities was not always
consistent with O3 concentrations. For example, O3 concentrations
in M13 remained high under higher dry deposition velocities.
In EA3, most stations were located at remote oceanic sites, and few dry
deposition observations were made. Thus, we collected observations from
other oceanic sites to evaluate model performance (Helmig et al., 2012).
Ense values for vd agreed reasonably well with observations (Fig. 9).
Both observations and simulated vd values showed a July–September
maximum with a magnitude of 0.02–0.03 cm s-1. Park et al. (2014) found surface
O3 levels in EA3 to be more sensitive to dry deposition
parameterization schemes in CTMs. O3 measured from oceans differed by
5–15 ppbv in East Asia due to the use of various dry deposition
parameterization schemes. Thus, more observations are needed over oceans in
EA3 to mitigate O3 simulation uncertainties.
Scatterplots for monthly daytime (08:00–20:00 LT) surface NOx and
O3 for each station in EA1 (red), EA2 (green) and EA3 (blue) in
May–October, for observations (Obs) and models. Also shown are the linear
regression equations for NOx and O3 in EA1 (red) and EA2 (green).
Relationships between surface NOx and O3
In general, surface O3 mainly comes from photochemistry processes
involving NOx and VOCs in polluted regions. Examining O3-NOx
relationships is effectual to investigating sources of inter-model
variability and model errors concerning O3 chemistry in East Asia. Fig. 10 presents O3 concentrations as a function of NOx in
May–September based on the monthly daytime (08:00–20:00 LT) mean observed and
simulated results for the stations shown in Fig. 1.
For EA1 (North China Plain), observations clearly show that O3
concentrations decreased with an increase in the NOx concentrations.
O3 concentrations mostly remained at high levels (40–60 ppbv) when
NOx was less than 20 ppbv. The slope and intercept of the regression
line between observed O3 and NOx were measured as -0.77 ppbv ppbv-1 and 59.5 ppbv, respectively. Among the models, M11 results were in
relative agreement with observations. The slope and intercept (-1.01 ppbv ppbv-1, 63.23 ppbv) reflected the observations. The other models showed a
higher degree of model bias and inter-model variability in relationships
between O3 and NOx. Their slopes mostly ranged from -1.25 to -2.13 ppbv ppbv-1, amounting to 1.3–2.8 times the observed slope.
Their intercepts were 74.9–121.2 ppbv, far exceeding observations (59.5 ppbv). Akimoto et al. (2019) calculated the net photochemical production of
M1, M6 and M11 and found that weak net chemical production in M11 was
mostly responsible for low O3 compared with those in M1 and M6. This
finding is consistent with the low slope in M11. To reduce the impact of
O3 buildup and transport due to NOx consumption, the relationship between
Ox (NO2+O3) and NOx was compared (Fig. S7). Observed Ox increases with an increase in
NOx levels, with a coefficient of determination (R2) of 0.61. Most
of the models (except for M8, M11 and M13) failed to reproduced observed
positive correlations between Ox and NOx, and their R2 only
ranged from 0.01 to 0.08. The slope, intercept and R2 of M8 and M11 were in
relative agreement with observations.
For EA2, all models reproduced observed key patterns in which Ox
positively correlated with NOx. For O3-NOx relationships,
M1, M2, M4 and M6 reproduced observed O3 levels under low-NOx
conditions (<30 ppbv) but failed to capture low O3 under high-NOx conditions (30–40 ppbv), accounting for overestimations of these
models for O3 in May–September. By contrast, M8 and M11 produced
excessively high NOx values, resulting in their underestimations of
O3 values. For M13 and M14, O3 concentrations were nearly
constant at all levels of NOx. O3 was positively correlated with
NOx in M9 and M10, which stands in contrast with observations. This
finding suggests that more attention is required when using M9, M10, M13 and M14.
Stations in EA3 are mostly located over clean oceans or islands. NOx
concentrations were less than 3 ppbv, showing that local chemistry was not a
key factor shaping O3 formation. Thus, we did not examine the simulated
O3–NOx relationship further.
Other factors
Previous studies show that O3 precursors are mostly constrained within
the boundary layer (Quan et al., 2013). The planetary boundary layer height
(PBLH) model evaluation is essential for the interpretation of model biases
with observations. Unfortunately, this evaluation was not applied in
MICS-Asia II. In 2016, Guo et al. (2016) calculated the PBLH using the bulk
Richardson number (Ri) method from the radiosonde network of the L-band
sounding system of the China Meteorological Administration (Vogelezang and
Holtslag, 1996). The system provides fine-resolution profiles of temperature,
pressure, relative humidity, wind speed and direction. In MICS-Asia III, all
selected models exhibited the spring-maximum and winter-minimum seasonal cycle
for EA1 (Fig. S6), capturing the main
climatological pattern of PBLH observations (Guo et al., 2016). The Ense on the
PBLH only overestimated radiosonde measurements by 100–200 m
(∼10–15 %), likely due to sampling bias between the models
and measurements. The simulation was recorded as the mean value of 12 h
(08:00–20:00 LT), while the average of the measurements was calculated based on
a 3 h period (08:00, 14:00 and 20:00 LT). For EA2, the observed PBLH did not
vary as much as that for EA1, and differences between seasons ranged within
100 m. This pattern was captured by the models. As was observed from EA1,
the simulated PBLH for EA2 exceeded the measurements by 100–200 m. Few
measurements of remote oceanic sites in East Asia were collected. Thus, we
compared simulations with European Centre for Medium-Range Weather Forecasts
Reanalysis data (von Engeln et al., 2013). Both showed a winter-maximum
pattern for PBLH.
Summary
Under the MICS-Asia III framework, the evaluation and intercomparison of 13 CTMs
were conducted using a wide variety of observations covering two Chinese
industrialized regions and the western Pacific, using continuous simulations
for 2010 with a focus on O3, NO and NO2. In particular, surface
O3 levels in China, which were neglected in previous
model intercomparison projects, were evaluated. Considerable levels of
inter-model variability in O3 were observed across all subregions of
East Asia, with model concentrations varying by factors of 2 to 3 between
different models.
A model ensemble was produced and evaluated. In general, the model ensemble
captured key patterns of monthly and diurnal O3, NO and NO2 in the
North China Plain and on the western Pacific Rim. It failed to capture the observed
seasonal cycle of O3 for the Pearl River Delta. For the North
China Plain and the western Pacific Rim, the model ensemble severely
overestimated surface O3 levels for May–September by 10–30 ppbv. This
overestimation systematically appeared at both daytime and nighttime.
Similarly, the model ensemble tended to overestimate spring daytime and
nighttime O3 concentrations for the Pearl River Delta. Compared with
MICS-Asia II, MICS-Asia III was less prone to underestimating surface
O3 in March for Japanese sites. However, it predicted excessively high
surface O3 concentrations for western Japan in July, which was not the
case for MICS-Asia II. In term of O3 soundings, the ensemble model used
in this study reproduced the vertical structure in the western Pacific, but
overestimated O3 below 800 hPa in the summer. For the industrialized
Pearl River Delta, the ensemble average presented an overestimation of
O3 levels for the lower troposphere and underestimations in the middle
troposphere. We find that the ensemble average of 13 models for O3 did not always perform better than individual models for East Asia in
contrast with their performance for Europe. This suggest that the spread of
ensemble-model values does not represent all uncertainties in O3 levels
or that most MICS-Asia III models missed key processes. In contrast to
performance levels for O3, Ense performed better than individual models
for NO2 in East Asia.
MICS-Asia II outlines potential causes of variability among models.
Quantifying the contributions of these processes to O3 concentrations
serves as an effective way to explain model biases through sensitivity
simulations. However, this would incur tremendous computational costs when
applied to 14 models. In this study, we conducted a qualitative analysis of
potential causes by comparing models and observations for these processes to
identify sensitivity simulating scenarios for the next phase of MICS-Asia.
Our comparisons show that the ensemble model overestimated observed dry
deposition velocities of O3 for August–September in the North China Plain,
displaying that other factors rather than dry deposition may contribute to the
overestimation of the simulated O3 concentrations in the summer. For the
western Pacific, simulated vd values agreed reasonably well with observations. Photochemical treatment in models may contribute to O3
overestimations in the North China Plain. The models studied captured the major
climatological pattern of PBLH observations for three subregions of East
Asia. More evaluations of turbulent kinetic energy in the PBL are needed to
assess vertical mixing in future studies.
Data availability
Data in Figs. 2, 3, 4, 5 and 9 can be obtained in https://figshare.com/s/cf48231dcd9529fc3bc6 (Li, 2019, last access date: 8 October 2019). The outputs from the simulations in Figs. 6, 7, 8 and 10 are available at https://pan.baidu.com/s/1IaaCDhrAR-z2tO6yQNz2cg (Chen et al., 2019, last access date: 8 October 2019).
Statistical measures
Defining yij and Obsij (modeled and observed, respectively) of the ith monthly
concentrations of air pollutants at the jth station, with respective mean values of
y‾ and Obs‾. m and n represent the number of stations and
months, respectively.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-12993-2019-supplement.
Author contributions
JL, ZW and GC designed the study. JL, TN, BG, KY, JF, XW, QF, SI,
HL, CK, CL, MZ, ZT, MK, HL and ZW contributed to modeling data. ML, JW, JK and QW
provided the emission data. LK helped with data processing. HA, GC and ZW
were involved in the scientific interpretation and discussion. JL prepared
the paper with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Regional assessment of air pollution and climate change over East and Southeast Asia: results from MICS-Asia Phase III”. It is not associated with a conference.
Acknowledgements
This work was supported by the Natural Science Foundation of China
(grant nos. 41620104008, 41571130034, 91544227 and 91744203), and the National Key R&D
Program of China (grant nos. 2017YFC0212402 and 2018YFC0213205). This work was partly
supported by the Environment Research and Technology Development Fund (grant no. S-12)
of the Environmental Restoration and Conservation Agency of Japan and the
Ministry of Environment, Japan. We thank the Pearl River Delta Regional Air
Quality Monitoring Network for observations in the Pearl River Delta. Kengo
Sudo from Nagoya University and Rokjin J. Park provided us with CHASER and
GEOS-Chem outputs for boundary conditions. This paper was edited by
Wallace Academic Editing.
Financial support
This research has been supported by the National Key R&D Program of China (grant no. 2017YFC0212402), the Natural Science Foundation of China (grant nos. 41571130034, 91544227, 91744203, 41225019 and 41425020), and the Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency of Japan and the Ministry of Environment, Japan (grant no. S-12).
Review statement
This paper was edited by Yugo Kanaya and reviewed by two anonymous referees.
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