ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-3729-2017Interannual variation, decadal trend, and future change in ozone outflow
from East AsiaZhuJiaLiaoHonghongliao@nuist.edu.cnMaoYuhaoYangYanghttps://orcid.org/0000-0002-9008-5137JiangHuiState Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese
Academy of Sciences, Beijing, ChinaUniversity of Chinese Academy of Sciences, Beijing, ChinaSchool of Environmental Science and Engineering, Nanjing University of
Information Science & Technology, Nanjing, ChinaInternational Joint Laboratory on Climate and Environmental
Change, Nanjing University of Information Science & Technology, Nanjing,
ChinaAtmospheric Sciences & Global Change, Pacific Northwest
National Laboratory, Richland, Washington, USANational Meteorological Information Center, China Meteorological
Administration, Beijing, ChinaHong Liao (hongliao@nuist.edu.cn)17March20171753729374720October20161November201626February20172March2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/3729/2017/acp-17-3729-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/3729/2017/acp-17-3729-2017.pdf
We examine the past and future changes in the O3 outflow from
East Asia using a global 3-D chemical transport model,
GEOS-Chem. The simulations of Asian O3 outflow for 1986–2006 are
driven by the assimilated GEOS-4 meteorological fields, and those for
2000–2050 are driven by the meteorological fields archived by the NASA Goddard
Institute for Space Studies (GISS) general circulation model (GCM) 3 under
the IPCC SRES A1B scenario. The evaluation of the model results against
measurements shows that the GEOS-Chem model captures the
seasonal cycles and interannual variations of tropospheric O3
concentrations fairly well with high correlation coefficients of 0.82–0.93 at four
ground-based sites and 0.55–0.88 at two ozonesonde sites where
observations are available. The increasing trends in surface-layer O3
concentrations in East Asia over the past 2 decades are captured by the
model, although the modeled O3 trends have low biases. Sensitivity
studies are conducted to examine the respective impacts of meteorological
parameters and emissions on the variations in the outflow flux of O3.
When both meteorological parameters and anthropogenic emissions varied
from 1986–2006, the simulated Asian O3 outflow fluxes exhibited a
statistically insignificant decadal trend; however, they showed large interannual variations
(IAVs) with seasonal values of 4–9 % for the absolute percent departure from the mean (APDM)
and an annual APDM value of 3.3 %. The sensitivity simulations
indicated that the large IAVs in O3 outflow fluxes were mainly caused
by variations in the meteorological conditions. The variations in meteorological
parameters drove the IAVs in O3 outflow fluxes by altering the O3
concentrations over East Asia and by altering the zonal winds; the latter
was identified to be the key factor, since the O3 outflow was highly
correlated with zonal winds from 1986–2006. The simulations of the
2000–2050 changes show that the annual outflow flux of O3 will
increase by 2.0, 7.9, and 12.2 % owing to
climate change alone, emissions change alone, and changes in both climate
and emissions, respectively. Therefore, climate change will aggravate the effects of the
increases in anthropogenic emissions on future changes in the Asian O3
outflow. Future climate change is predicted to greatly increase the Asian
O3 outflow in the spring and summer seasons as a result of the
projected increases in zonal winds. The findings from the present study help us to
understand the variations in tropospheric O3 in the downwind regions of
East Asia on different timescales and have important implications for
long-term air quality planning in the regions downwind of China, such as
Japan and the US.
Introduction
Tropospheric ozone (O3) is an important air pollutant that has a
detrimental effect on human health (Fann et al., 2012; Jhun et al., 2014),
crops (Wilkinson et al., 2011; Tai et al., 2014), and ecosystems
(Fuentes et al., 2013; Yue and Unger, 2014). It is also an important
greenhouse gas that directly contributes to global warming (IPCC, 2013).
O3 has a relatively long lifetime of weeks in the free troposphere
(Young et al., 2013; Monks et al., 2015), which makes the intercontinental
transport of O3 an important issue for understanding O3
concentrations and planning emission control measures.
A number of previous studies have shown that Asian continental outflow
impacts the global O3 budget (Liu et al., 2002) and influences O3
air quality in the downwind regions from the western North Pacific
to western North America (Jacob et al., 1999; Tanimoto et al.,
2005; Kim et al., 2006; Li et al., 2008; Zhang et al., 2008; Chiang et al.,
2009; Kurokawa et al., 2009; Huang et al., 2010; Nagashima et al., 2010;
Walker et al., 2010; Ambrose et al., 2011; Lin et al., 2012; Ou-Yang et al.,
2013; Han et al., 2015; Pochanart et al., 2015). Liu et al. (2002) reported
that boundary-layer O3 pollution was lifted into the upper troposphere
by deep convection over the Asian maritime continent. From there, it was
transported northward along the upper branch of the local Hadley circulation
and into the mid-latitude westerlies, influencing the global O3 budget.
Using a global 3-D chemical transport model, GEOS-Chem, Zhang et al. (2008)
estimated that Asian pollution enhanced surface-layer O3 concentrations
by 5–7 ppbv over western North America in spring 2006. Walker et al. (2010)
used the same model (GEOS-Chem) to evaluate the sensitivities of tropospheric
O3 over Canada to Asian anthropogenic emissions and reported that the
contribution from Asian emissions to O3 profiles above Whistler,
Canada was 6–8 ppbv in spring 2006. Through an integrated analysis of in
situ and satellite measurements in May–June 2010 with a global
chemistry–climate model, GFDL AM3, Lin et al. (2012) reported that Asian
emissions could contribute 8–15 ppbv of O3 over the western United States
on days when the observed daily maximum 8 h average of O3 (MDA8
O3) exceeded 60 ppbv. They also reported that 20 % of MDA8 O3 exceedances of
60 ppbv would not have occurred in the southwestern United States in the
absence of Asian anthropogenic emissions.
Asian O3 outflow exhibits seasonal variations (Liu et al., 2002; Han et
al., 2015). Using a global 3-D chemical transport model, GEOS-Chem, Liu et al. (2002) simulated the seasonal variations in the Asian outflow flux
of
O3 over the Pacific, which was defined as the eastward flux integrated
for the tropospheric column through a wall located at 150∘ E
between 10 and 60∘ N. They found that the Asian
O3 outflow flux reached a maximum in early spring (March) and a
minimum in summer (July). Han et al. (2015) examined O3 measurements at
the
Ieodo Ocean Research Station located in the East China Sea, which is
regarded as an ideal place to observe Asian outflow without local effects.
They reported that the seasonal variation in O3 was distinct, with a
minimum in August and two peaks in April and October, and was greatly
affected by the seasonal wind pattern over East Asia.
The continental outflow of O3 is expected to vary on interannual to decadal
timescales because tropospheric O3 concentrations and meteorological
parameters have variations on these timescales. Large interannual variations
(IAVs) in tropospheric O3 concentrations have been reported in previous
observational studies (Kurokawa et al., 2009; Zhou et al., 2013). After analyzing
11 years of ozonesonde data over Hong Kong, Zhou et al. (2013) reported that
observed tropospheric O3 levels from 2000–2010 exhibited high IAVs
with an annual averaged amplitude [defined as (maximum + 2nd maximum -
minimum - 2nd minimum) × 0.5/the average from 2000–2010] of up
to 30 % of the averaged concentrations at 3–8 km of altitude. Kurokawa et al. (2009) analyzed observed springtime O3 over Japan
from
1985–2005 and found that the observed O3 showed greater year-to-year
variations than the annual rate of the long-term trend. The decadal trends in
tropospheric O3 concentrations have been reported for different
locations on the basis of observations (Ding et al., 2008a; Xu et al., 2008;
Tang et al., 2009; Tanimoto, 2009; Wang et al., 2009b; Cooper et al., 2010;
Wang et al., 2012; Lin et al., 2014, 2015; Zhang et al., 2014), such as
-0.56 ppbv yr-1 over Lin'an in eastern China (Xu et al., 2008),
+0.58 ppbv yr-1 over Hong Kong in southern China (Wang et al.,
2009b), +1.0 ppbv yr-1 at Mount Happo in Japan (for springtime O3;
Tanimoto, 2009), and +0.35 ppbv yr-1 over Hawaii in the North Pacific
(for autumn O3; Lin et al., 2014). Asian NOx emissions almost
doubled over the past 20 years (Yang et al., 2015), which contributed to the
increased O3 observed over the regions downwind of Asia (Lin et al.,
2017).
Future changes in tropospheric O3 concentrations have also been
predicted by modeling studies (Racherla and Adams, 2006, 2009; Lin et al.,
2008; Wu et al., 2008a; Lam et al., 2011; Wild et al., 2012; Gao et al.,
2013; Liu et al., 2013; Wang et al., 2013; Lee et al., 2015; Val Martin et
al., 2015; Schnell et al., 2016; Zhu and Liao, 2016). Wang et al. (2013),
using the NASA GISS GCM and GEOS-Chem model combination, reported that the
summer surface-layer O3 levels averaged over China would increase by
11.9 ppbv due to the combined changes in climate and emissions from
2000–2050 under the SRES A1B scenario.
Meteorological parameters, especially winds that are important for O3
outflow, also exhibit variations on different timescales (Chang et al.,
2000; Ding et al., 2008b; Sun et al., 2009; Zhang and Guo, 2010; Hirahara et
al., 2012). Large IAVs in the East Asian summer monsoon (EASM) have been
reported in previous studies (Zhu et al., 2012; Yang et al., 2014). The
decadal-scale weakening of the EASM since the 1950s has also been reported
by many previous studies, and anomalous northeasterlies during the weak
monsoon years were found over the western North Pacific near 40∘ N, which did not favor the outflow of pollutants from northern China (Chang
et al., 2000; Ding et al., 2008b; Zhu et al., 2012). On the basis of
NCEP–NCAR reanalysis data, Sun et al. (2009) showed that the axis location
of the East Asian subtropical westerly jet (EASWJ) had been displaced southward
since the end of the 1970s, intensifying the westerly wind over
25–35∘ N and weakening it over 42–50∘ N, therefore
influencing the outflow of pollutants. Lin et al. (2014) reported that
the interannual variability in springtime Asian O3 transport, as inferred
by the East Asian COt (carbon monoxide-like tracer), was strongly influenced
by ENSO-related shifts in the subtropical jet stream. They also reported that the decrease
in ozone-rich Eurasian airflow reaching the eastern North Pacific in
spring in the 2000s was attributed to more frequent La Niña events. Most of
the models in the Coupled Model Intercomparison Project Phase 3 (CMIP3)
predicted
that the Asian jet would be intensified on its equatorward side by the end
of the 21st century (Zhang and Guo, 2010; Hirahara et al., 2012).
Few previous studies have examined the IAVs, decadal trends, and future
changes in O3 outflow. In this work, we examine the historical
(1986–2006) and future (2000–2050) changes in the O3 outflow from East
Asia and systematically quantify the roles of meteorological parameters
and/or anthropogenic emissions in the changes. The descriptions of the
model, emissions, and numerical simulations are presented in Sect. 2.
Section 3 evaluates the model performance for tropospheric O3. Section
4 discusses the IAVs and the decadal trends in the O3 outflow from East
Asia from 1986–2006. Future changes in O3 outflow from East Asia for
2000–2050 are presented in Sect. 5.
MethodsModel description
We apply the global 3-D chemical transport model GEOS-Chem to simulate
O3 outflow fluxes. The GEOS-Chem model includes a detailed simulation
of O3–NOx–hydrocarbon (∼ 80 species, ∼ 300 chemical reactions; Bey et al., 2001) and aerosol chemistry. Aerosol
species include sulfate (SO42-), nitrate (NO3-),
ammonium (NH4+; Park et al., 2004; Pye et al., 2009), black
carbon (BC) and organic carbon (OC; Park et al., 2003), mineral dust
(Fairlie et al., 2007), and sea salt (Alexander et al., 2005). The
simulations account for the impacts of aerosols on the distributions and
concentrations of O3 through heterogeneous reactions and changes in the
photolysis rates (Lou et al., 2014).
To simulate historical changes in the Asian outflow of O3, the
GEOS-Chem model is driven by the assimilated GEOS-4 meteorological fields
from the Goddard Earth Observing System (GEOS) of the NASA Global Modeling and
Assimilation Office (GMAO). We perform simulations for 1986–2006, which are
the years with available GEOS-4 meteorological datasets. The version of the
model used here has a horizontal resolution of 2∘ latitude by 2.5∘ longitude with 30 vertical layers.
To simulate future changes in O3 outflow fluxes from 2000–2050, the
GEOS-Chem simulation is driven by meteorological data from the National
Aeronautics and Space Administration Goddard Institute for Space Studies
(NASA GISS) general circulation model (GCM) 3 (Rind et al., 2007) for both
the “present day” (represented by data from 1996–2005) and the future (2046–2055), following Wu et al. (2008b), Pye et al. (2009), Wang et al. (2013), and Jiang et al. (2013). Both
the GISS and GEOS-Chem models used here have a horizontal resolution of
4∘ latitude by 5∘ longitude with 23 vertical layers.
Emissions
For simulations from 1986–2006, the global anthropogenic emissions of
reactive nitrogen oxides (NOx), carbon monoxide (CO), and sulfur
dioxide (SO2) in the model are from the Emission Database for Global
Atmospheric Research (EDGAR) (Olivier and Berdowski, 2001). The
global emissions of non-methane volatile organic compounds (NMVOCs) are from
the Global Emissions Inventory Activity (GEIA) (Piccot et al.,
1992). The global emissions of carbonaceous aerosols (BC and OC) follow Bond et al. (2007). Anthropogenic emissions of reactive NOx, CO, SO2,
NH3, and NMVOCs over East Asia are overwritten by the emissions
inventory of Streets et al. (2003) and Zhang et al. (2009). The IAVs in
anthropogenic emissions are represented by globally gridded annual scaling
factors as described by van Donkelaar et al. (2008) for NOx, CO, and
NMVOCs. The biomass burning emissions are taken from the Global Fire Emissions
Database (GFEDv3) (van der Werf et al., 2010) for 1997–2006.
The biomass burning emission data before 1997 are unavailable due to
the lack of datasets.
The anthropogenic emissionsa of O3 precursors for the present
day (the year 2000) and the future (the year 2050; under the SRES A1B scenario).
Global AsiabSpecies20002050Change (%)20002050Change (%)NOx33.459.6+78.410.928.3+159.6(Tg N yr-1)CO1054.21332.0+26.4393.7487.2+23.7(Tg CO yr-1)NMVOCs70.8134.1+89.428.562.3+118.6(Tg C yr-1)CH417502400+37.117502400+37.1(ppbv)
a Biomass burning emissions are included.
b Asia covers the domain of 60–150∘ E,
10∘ S–55∘ N.
The evolution of annual anthropogenic and biomass burning emissions
summed globally and for Asia (60–150∘ E, 10∘ S–55∘ N) for NOx (Tg N yr-1), CO (Tg CO yr-1), and
NMVOCs (Tg C yr-1) from 1986–2006. The blue squares represent
anthropogenic emissions, and the red circles represent the sum of anthropogenic
emissions and biomass burning emissions. The last panel shows the evolution
of global CH4 abundance (ppbv) from 1986–2006.
Figure 1 shows the evolution of anthropogenic and biomass burning emissions
of O3 precursors (NOx, CO, NMVOCs) summed globally and for Asia
(60–150∘ E, 10∘ S–55∘ N) from 1986–2006.
The global anthropogenic emissions of these precursors exhibited no significant
trends, while the Asian anthropogenic emissions showed large increases over
the past 2 decades. Relative to 1986, the Asian anthropogenic
emissions of NOx, CO, and NMVOCs in 2006 had increased by 70.0, 42.1, and 50.9 %, respectively. Compared with anthropogenic emissions,
biomass burning emissions had greater IAVs from 1997–2006. Figure 1 also
shows the pathway for the global CH4 abundance used in our simulations
of O3. The CH4 mixing ratio in 1986 was 1672 ppb, which had increased
by 6.3 % in 2006. Note that from 1996–2006 when NOx emissions and
satellite NO2 columns were simultaneously available, the trend in
NOx emissions over East Central China (ECC; 110–123∘ E,
30–40∘ N) was +8.2 % yr-1 on the basis of the emission
inventory used in this study; this is close to the trend of +9.0% yr-1 in
NO2 columns averaged for ECC on the basis of tropospheric NO2
vertical column density (VCD) data retrieved from GOME (1996–2002) and
SCIAMACHY (2003–2006), which are available from www.temis.nl.
Natural emissions of O3 precursors for the present day (the year
2000) and the future (the year 2050; under the SRES A1B scenario).
Global Asia*Species20002050Change (%)20002050Change (%)Lightning NOx4.85.7+18.81.21.4+16.7(Tg N yr-1)Soil NOx6.77.7+14.91.41.7+21.4(Tg N yr-1)Biogenic VOCs614.5750.2+22.1106.1126.2+18.9(Tg C yr-1)
* Asia covers the domain of 60–150∘ E, 10∘ S–55∘ N.
For future simulations from 2000–2050, the anthropogenic emissions of O3
precursors, including NOx, CO, and NMVOCs, are taken from Wu et al. (2008b). Those of NH3 and SO2 follow
Pye et al. (2009). The future anthropogenic emissions of O3 precursors, aerosol
precursors, and aerosols under the SRES A1B scenario are generated by the
Integrated Model to Assess the Greenhouse Effect (IMAGE) socioeconomic model
using growth factors for different species and countries (Streets et al.,
2004). Table 1 shows the present-day (the year 2000) and the future (the year 2050)
anthropogenic emissions of O3 precursors under the SRES A1B scenario.
The global emissions of NOx, CO, and NMVOCs are projected to increase
by 78.4, 26.4, and 89.4 % from 2000–2050; Asian
emissions are projected to increase by 159.6, 23.7, and 118.6 %, respectively. Present-day methane mixing ratios are specified as
1750 ppbv on the basis of observations (Wu et al., 2008b). The future methane
concentrations are set to 2400 ppbv, following the SRES A1B scenario (Pye et
al., 2009).
The natural emissions of O3 precursors, including NOx from
lighting and soil and NMVOCs from vegetation, are calculated on the basis
of the assimilated GEOS-4 meteorological fields and the GISS CGM 3
meteorological parameters. The lightning NOx emissions follow Price and
Rind (1992) with the NOx vertical profile proposed by Pickering et al. (1998). The soil NOx emission scheme in the GEOS-Chem model is based
on the work of Yienger and Levy (1995) and Wang et al. (1998). The biogenic
emissions of NMVOCs are calculated according to the Model of Emissions of
Gases and Aerosols from Nature (MEGAN; Guenther et al., 2006). Figure 2
shows the evolution of natural emissions summed globally and for Asia from
1986–2006. Both global and Asian NOx emissions from lightning
exhibited large IAVs and significant increasing trends. It has been shown
that a warming climate leads to increased lightning NOx (IPCC, 2013).
Compared with lightning NOx emissions, NOx emissions from soil
showed smaller IAVs and no significant decadal trend. Both global and Asian
biogenic emissions of NMVOCs have been shown to have large IAVs as a result
of the changes in both vegetation and meteorological parameters (Fu and
Liao, 2012). For future simulations from 2000–2050, the natural emissions
of O3 precursors are listed in Table 2. The simulated emissions of
lightning NOx, soil NOx, and biogenic VOCs are projected to
increase by 18.8, 14.9, and 22.1 % globally and by 16.7, 21.4, and 18.9 % for Asia, respectively.
The experimental design of the simulations for 1986–2006.
SimulationSimulated yearsMeteorologicalAnthropogenicCH4 abundanceBiomass burningparametersemissionsemissionsMet1986–2006Vary fromFixed at 2006Fixed at 2006Turned off1986–2006Emis1986–2006Fixed at 2006Vary fromVary fromTurned off1986–20061986–2006MetEmis1986–2006Vary fromVary fromVary fromTurned off1986–20061986–20061986–2006MetEmisB*1997–2006Vary fromVary fromVary fromVary from1997–20061997–20061997–20061997–2006
* The MetEmisB simulation is conducted for 1997–2006 owing to the
unavailability of biomass burning emission data before 1997.
The evolution of annual natural emissions summed globally and for
Asia (60–150∘ E, 10∘ S–55∘ N) for lightning
NOx (Tg N yr-1), soil NOx (Tg N yr-1), and biogenic VOCs
(Tg C yr-1) from 1986–2006.
The effects of changes in the stratosphere–troposphere exchange (STE) of
O3 are not included in this study for past or future simulations.
The cross-tropopause O3 flux is represented by the synthetic O3
(Synoz) method (McLinden et al., 2000), which imposes a global annual mean
cross-tropopause O3 flux of 500 Tg yr-1.
Numerical experiments
To examine the respective and combined impacts of meteorological parameters,
anthropogenic emissions, and biomass burning emissions on the IAVs and
decadal trends in O3 outflow from East Asia from 1986–2006, we
perform simulations for four cases (Table 3):
Met: the simulation of O3 outflow for 1986–2006 to examine the
effect of variations in meteorological parameters alone. The meteorological
parameters vary from 1986 to 2006, and the anthropogenic emissions are fixed
at 2006 levels. Biomass burning emissions are turned off.
Emis: the simulation of O3 outflow for 1986–2006 to examine the
effect of variations in anthropogenic emissions alone. The anthropogenic
emissions vary from 1986 to 2006, and the meteorological parameters are
fixed at 2006 levels. Biomass burning emissions are turned off.
MetEmis: the simulation of O3 outflow for 1986–2006 with
variations in both meteorological parameters and anthropogenic emissions
from 1986–2006. Biomass burning emissions are turned off.
MetEmisB: the simulation of O3 outflow for 1997–2006 with
variations in meteorological parameters, anthropogenic emissions, and
biomass burning emissions from 1997–2006. Note that biomass burning
emission data in the model are not available before 1997.
To identify the relative roles of future changes in meteorological
parameters and emissions in 2000–2050 changes in the Asian O3 outflow
flux, another four simulations are carried out: (a) Met2000Emis2000,
present-day climate and emissions; (b) Met2050Emis2000, future climate and
present-day anthropogenic emissions; (c) Met2000Emis2050, present-day
climate and future anthropogenic emissions; and (d) Met2050Emis2050, future
climate and emissions. Both the future climate and anthropogenic emissions
follow the IPCC SRES A1B scenario.
The mass flux of O3 through the vertical plane along 135∘ E
from 20 to 55∘ N from the surface to 100 hPa is used
to quantify the Asian O3 outflow. The metric of mass flux through a
vertical plane was also used by Liu et al. (2002) to represent the Asian O3
outflow and by Jiang et al. (2013) and Yang et al. (2015) to represent the
Asian aerosol outflow. It should be noted that the O3 outflow flux from
East Asia includes the effects of emissions on different regions of the
world owing to the relatively long lifetime (∼ 3 weeks) of
O3 (Fiore et al., 2002; Liao et al., 2006). However, Liu et al. (2002)
found that anthropogenic sources in Asia made the largest contribution to
the Asian outflow flux of O3.
Information for the sites with O3 measurements used in the model
evaluation.
SiteLocationDatabaseHeightRaNMBb (%)Minami-Tori-shima24.3∘ N, 154.0∘ EWDCGGsurface0.92+12.7Yonaguni-jima24.5∘ N, 123.0∘ EWDCGGsurface0.93+12.6Rishiri Island45.1∘ N, 141.2∘ EEANETsurface0.82+2.4Ogasawara27.1∘ N, 142.2∘ EEANETsurface0.90+29.6Naha26.2∘ N, 127.7∘ EWOUDC500–300 hPa0.68-2.61700–500 hPa0.77+16.4850–700 hPa0.85+24.31000–850 hPa0.88+39.5Tsukuba36.1∘ N, 140.1∘ EWOUDC500–300 hPa0.55+15.8700–500 hPa0.76+12.3850–700 hPa0.76+8.611000–850 hPa0.60+8.5
a The correlation coefficient (R) between the observed and simulated
monthly O3 mixing ratios.
b The normalized mean bias (NMB, %) between the observed and simulated
monthly O3 mixing ratios.
Model evaluation
The GEOS-Chem simulations of O3 have been evaluated extensively for the
US (Liu et al., 2006; Wu et al., 2008b; Zhang et al., 2008), Europe
(Auvray and Bey, 2005; Liu et al., 2006; Kim et al., 2015), and China (Wang
et al., 2013; Lou et al., 2014; Yang et al., 2014; Zhu and Liao, 2016).
These studies showed that the GEOS-Chem model captured the magnitude and
distribution of the surface-layer concentration and column burden of
tropospheric O3 fairly well. The vertical distributions of O3 have
also been evaluated by aircraft campaigns and ozonesonde measurements (Zhang
et al., 2008; Walker et al., 2010; Wang et al., 2011), showing that the
GEOS-Chem model closely reproduced the observed O3 profiles.
The time series of monthly surface-layer O3 mixing ratios
measured by WDCGG and EANET (the blue line) and simulated by MetEmisB (the red
line). (a) Minami-Tori-shima and (b) Yonaguni-jima are WDCGG sites, and
(c) Rishiri Island and (d) Ogasawara are EANET sites.
Here, we conduct comparisons with measurements to evaluate whether the
version of the GEOS-Chem model used in this study can capture the temporal
variations in tropospheric O3. We use the observations of tropospheric
O3 available in East Asia as summarized in Table 4. Observations at two
sites (Minami-Tori-shima and Yonaguni-jima) are from the World Data Centre for
Greenhouse Gases (WDCGG; http://ds.data.jma.go.jp/gmd/wdcgg/), and those at
another two sites (Rishiri Island and Ogasawara) are from the Acid Deposition
Monitoring Network in East Asia (EANET,
www.eanet.asia/product/index.html).
These are used to evaluate the simulated surface-layer O3
concentrations. The four Japanese sites are “remote” sites in the downwind
regions of China. Figure 3 compares the time series of monthly surface-layer
O3 mixing ratios simulated by MetEmisB with those measured by WDCGG and
EANET. The simulated surface-layer O3 levels agree well with observations
at all four stations. The model captures the seasonal cycles
and interannual variations of surface O3 fairly well with high correlation
coefficients of 0.82–0.93 (Table 4). Generally, the GEOS-Chem model can
capture the high values in early spring or winter when the Asian O3
outflow flux is the highest, but it overestimates the low values in summer
when the Asian O3 outflow is at a minimum.
The time series of monthly O3 mixing ratios measured by
ozonesonde (the blue line) and simulated by MetEmisB (the red line). (a) Naha and
(b) Tsukuba are ozonesonde sites from the WOUDC. The comparisons are shown for four
altitude levels in the troposphere.
A comparison of the simulated trends in seasonal or annual mean
surface-layer O3 concentrations from the MetEmis experiment with
observations for Hong Kong (22.2∘ N, 114.3∘ E;
1994–2007; Wang et al., 2009b), Waliguan (36.3∘ N, 100.9∘ E; 1994–2013;
Xu et al., 2016), Beijing (40.0∘ N, 116.5∘ E; 2001–2006; Tang et al., 2009), and Taiwan
(23.5∘ N, 121.0∘ E; 1994–2007; Lin et al., 2010). The
simulated trend at the Waliguan site for the winter is statistically insignificant.
The trends in the seasonal mean O3 concentrations at the Taiwan station are
unavailable.
To evaluate the simulated O3 concentrations for the boundary layer and
the
middle and upper troposphere, we use the ozonesonde data at two Japanese
sites from World Ozone and Ultraviolet Radiation Data Centre (WOUDC,
http://www.woudc.org/). The information for the two sites (Naha and Tsukuba) is
listed in Table 4. Figure 4 compares the time series of monthly O3
mixing ratios simulated by MetEmisB with those measured by ozonesonde.
Comparisons are shown for four altitudes in the troposphere. The GEOS-Chem
model captures the seasonal cycles and interannual variations of
tropospheric O3 at all altitudes with correlation coefficients ranging
from 0.68 to 0.88 for the Naha site and from 0.55 to 0.76 for the Tsukuba site.
However, the agreement with ozonesonde in the lowermost layer (1000–850 hPa) seems to be poorer than that with WDCGG or EANET. It is noted that the
ground-based measurements (WDCGG or EANET) and simulation results are
calculated from continuous data, while the ozonesondes are regularly
launched at a fixed local time with a typical frequency of 1–2 weeks
(Tanimoto et al., 2015). The inconsistency in sampling time may be
responsible for the poorer agreement with ozonesonde.
The increasing trend in surface-layer O3 in East Asia over the past 2
decades was reported by previous studies (Ding et al., 2008a; Wang et al.,
2009b; Xu et al., 2016). Figure 5 compares the simulated trends in seasonal
or annual mean surface-layer O3 concentrations from the MetEmis
experiment with the observed trends collected from previous studies.
The simulated O3 concentrations exhibit statistically significant
increasing trends at all sites except for Waliguan in winter, although the
model underestimates the trends for some stations and seasons. The modeled
O3 trends were also reported to have low biases in previous studies
(Tanimoto et al., 2009; Parrish et al., 2014; Strode et al., 2015). Parrish
et al. (2014) compared O3 trends simulated by three chemistry–climate
models with observations at Asian sites and reported that one model
captured less than one-third of the observed increasing trend, whereas the
other two models suggested no significant increasing trends.
In general, the GEOS-Chem model can capture the seasonal cycles
and interannual variations in tropospheric O3 fairly well, although the model
overestimates the low values in summer, indicating an overestimation of
the Asian O3 outflow in summer. The increasing trends in surface-layer
O3 in China over the past 2 decades can also be captured by the GEOS-Chem
model, although the modeled O3 trends have low biases.
Simulated Asian O3 outflow from 1986–2006Seasonal patterns of Asian O3 outflow
The pressure–latitude cross sections along 135∘ E of
the simulated seasonal O3 outflow fluxes and zonal winds in four
seasons averaged from 1997–2006 in the MetEmisB simulation. The O3
mass fluxes are shown by the colored shading (kg season-1 m-2), and
the winds are represented by the contours (m s-1). The positive
values
represent eastward fluxes, and the negative values represent westward fluxes.
Figure 6 shows the pressure–latitude cross sections along 135∘ E
of the seasonal O3 outflow fluxes averaged from 1997–2006 in the
MetEmisB simulation. The maximum O3 fluxes were found in the
middle to upper troposphere, which is consistent with Liu et al. (2002) and Wang et al. (2009a). This could be attributed to the vertical distributions of
both zonal winds and O3 concentrations. The westerlies strengthen with
altitude with the strongest winds occurring around 200 hPa (known as the
East Asian subtropical westerly jet; Ren et al., 2011). Concentrations of
O3 are high in the upper troposphere over the mid-latitudes (Wang,
1999).
The seasonal mass fluxes through the meridional plane (along 135∘ E from 20 to 55∘ N and from the surface to 100 hPa)
were calculated to be 509.6, 437.6, 126.6, and 268.7 Tg season-1 for
December–January–February (DJF), March–April–May (MAM),
June–July–August (JJA), and September–October–November (SON),
respectively. Although the seasonal flux was the highest in DJF, the monthly
O3 flux through the panel peaked in March and reached the nadir in
July (not shown in Fig. 6). Such monthly variations in the Asian O3
outflow flux agreed with those in Liu et al. (2002). The maximum O3
outflow in March was caused by the combined effects of meteorological
conditions, biomass burning emissions, and stratospheric O3 intrusion.
The “warm conveyor belt” (WCB) airstreams, which export pollution from the
Asian boundary layer to the free troposphere, and the mid-latitude
prevailing westerly winds in the free troposphere that transport pollution
from Asia to the northwestern Pacific were strongest during the early spring
period (Eckhardt et al., 2004; Pochanart et al., 2004). The contribution
from Asian biomass burning emissions to the O3 outflow was at a maximum in the
spring and insignificant in other seasons (Liu et al., 2002). The
stratospheric O3 intrusion was also found to be the most effective in late
winter and early spring (Danielsen and Mohnen, 1977; Mahlman and Moxim,
1978).
The evolution of the following: (a) the annual O3 outflow fluxes (Tg yr-1)
across the meridional plane along 135∘ E from 20 to
55∘ N and from the surface to 100 hPa from 1986–2006 in the
Met, Emis, and MetEmis simulations; (b) the associated deviations from the
mean (%); and (c) the deviations from the mean (%) of the detrended
O3 outflow fluxes. The deviation from the mean (DEV) is defined in
Sect. 4.2.
IAVs and decadal trends in Asian O3 outflow
Figure 7a shows the simulated annual O3 outflow fluxes through the
meridional plane along 135∘ E from 20 to
55∘ N from the surface to 100 hPa from 1986–2006 in the Met,
Emis, and MetEmis simulations. Figure 7b shows the associated deviation
from the mean (DEV). The simulations of the O3 outflow in Met, Emis,
and MetEmis examined the effects of variations in the
meteorological parameters alone, anthropogenic emissions alone, and both
meteorological parameters and anthropogenic emissions, respectively. The outflow fluxes of
O3 with changes in anthropogenic emissions alone (the Emis simulation)
exhibited a statistically significant (P < 0.001) increasing trend.
However, the magnitude of the increasing trend was very small; the decadal
trend in the Asian O3 outflow flux in the Emis simulation was
calculated to be +16.7 Tg decade-1 (i.e., +1.2 % decade-1)
using a linear fit with the least-squares method. The DEV, defined as
DEV=100%×Ci-1n∑i=1nCi/1n∑i=1nCi,
where n is the number of years examined (n= 21 for 1986–2006) and Ci is the simulated O3 outflow flux in year i, changed from -1.3 %
(in 1986) to +1.4 % (in 2006), also indicating a small increasing trend
in the O3 outflow flux. With variations in the meteorological parameters
alone (the Met simulation), the simulated O3 outflow fluxes exhibited large
IAVs but a statistically insignificant (P > 0.05) decadal trend
of -3.4 % decade-1. The DEV values in the Met simulation ranged
from -8 to +16.5 %. With variations in both anthropogenic
emissions and meteorological parameters (the MetEmis simulation), the
simulated O3 outflow showed large IAVs but a statistically
insignificant (P > 0.05) decadal trend of -2.2 % decade-1.
To analyze the IAVs in O3 outflow fluxes, the decadal trend obtained
from the linear fit was removed from the time series of simulated O3
outflow fluxes, following the approach used in previous studies that
examined the IAVs in aerosol outflow fluxes (Yang et al., 2015) and O3
concentrations (Camp et al., 2003). The deviations from the mean of the
detrended O3 outflow fluxes from the Met, Emis, and MetEmis simulations
from 1986–2006 are shown in Fig. 7c. While the detrended outflow fluxes of
O3 in the Met and MetEmis simulations showed large IAVs with DEV values in
the range of -7.5 to +13.5 %, the DEV values in the Emis
simulation were very small (in the range of ±0.3 %). The two
deviation curves from the Met and MetEmis simulations almost coincided with
each other, indicating the dominant role of variations in the meteorological
parameters in the IAVs in the Asian O3 outflow flux.
The IAVs in the O3 outflow fluxes were further quantified with
statistical variables of the mean absolute deviation (MAD) and the absolute percent
departure from the mean (APDM), which have been used in previous IAV
studies, such as Mu and Liao (2014), Lou et al. (2015), and Yang et al. (2015). The absolute IAVs in the O3 outflow fluxes can be quantified by
the MAD, defined as
MAD=1n∑i=1n|Ci-1n∑i=1nCi|,
while the IAVs relative to the multiyear average outflow flux can be
quantified by the APDM, defined as
APDM=100%×MAD/1n∑i=1nCi,
where n is the number of years examined (n= 21 for years 1986–2006) and
Cii s the detrended O3 outflow flux in year i. The MAD and APDM
values of the detrended seasonal and annual O3 outflow fluxes across
the meridional plane along 135∘ E from 20 to
55∘ N from the surface to 100 hPa are shown in Fig. 8. The
seasonal MAD and APDM values in the Emis simulation were close to zero,
while those in the Met and MetEmis simulations were relatively large. The
APDM values in the Met and MetEmis simulations were at a maximum in JJA and
at a
minimum in SON. The MAD and APDM values in the Met simulation were almost
identical to those in the MetEmis simulation, which indicated again that the
IAVs in the O3 outflow fluxes were mainly dependent on the variations
in meteorological conditions, rather than the variations in anthropogenic
emissions. With variations in both the meteorological parameters and
anthropogenic emissions, the seasonal APDM values were in the range of 4–9 % and the annual APDM value was 3.3 %.
The MAD and APDM values of the detrended seasonal and annual
O3 outflow fluxes across the meridional plane along 135∘ E
from 20 to 55∘ N and from the surface to 100 hPa from 1986–2006 in the Met, Emis, and MetEmis simulations. Both MAD and APDM
are defined in Sect. 4.2. The units of MAD are Tg season-1 for seasonal
fluxes and Tg yr-1 for annual fluxes.
The pressure–latitude cross sections along 135∘ E of the
MAD values for the detrended annual O3 outflow fluxes and zonal winds
from
1986–2006 in the Met, Emis, and MetEmis simulations. The MAD values for
O3 mass fluxes are shown by the colored shading (kg yr-1 m-2), and
the MAD values for the winds are represented by the contours (m s-1).
Figure 9 shows the pressure–latitude cross sections of the MAD values along
135∘ E for detrended annual O3 outflow fluxes from the Met,
Emis, and MetEmis simulations. The O3 outflow in the Met simulation
exhibited large IAVs throughout the whole troposphere with MAD values
greater than 0.2 kg yr-1 m-2. The MAD values increased with
altitude, which could be attributed to the vertical distributions of the
IAVs in the westerly winds (see the MAD values of the winds in Fig. 9a). The variations
in anthropogenic emissions led to very small IAVs with MAD values less than
0.2 kg yr-1 m-2 (Fig. 9b) throughout the troposphere. With
variations in both the meteorological parameters and anthropogenic emissions, the MAD
values (Fig. 9c) showed almost identical magnitudes and spatial
distributions to those in the Met simulation (Fig. 9a), indicating the
dominant role of variations in meteorological conditions in the IAVs in the
O3 outflow.
Variations in meteorological conditions can influence the IAVs in the
O3 outflow fluxes by changing the O3 concentrations over East Asia
(Yang et al., 2014; Lou et al., 2015) and by altering the zonal winds (Kurokawa
et al., 2009). The O3 outflow flux is simulated to correlate positively
with the zonal winds averaged over 20–55∘ N along
135∘ E with a high correlation coefficient of +0.71 for annual
fluxes and zonal winds. The correlation coefficient between O3 fluxes
and zonal winds is calculated to be +0.96 in summer when the APDM
values of O3 outflow fluxes are at a maximum. The high correlation
coefficients indicate that the variation in zonal winds is the key factor
that leads to the large IAVs in O3 outflow fluxes.
Simulated present-day O3 fluxes and projected changes from the
present day (1996–2005) to the future (2046–2055) through the vertical
plane along 135∘ E from 20 to 55∘ N and
from the surface to 100 hPa due to future climate change alone, changes in
anthropogenic emissions alone, and changes in both climate and emissions.
* The units are Tg season-1 for seasonal fluxes and Tg yr-1
for annual fluxes. The values in parentheses are percentage changes relative to
the present-day fluxes.
Effect of variations in biomass burning emissions
The biomass burning emissions of the O3 precursors exhibited large IAVs
from 1997–2006 (Fig. 1). To analyze the impacts of biomass burning
emissions on the IAVs in O3 outflow fluxes, we compare the MAD and APDM
values of detrended O3 outflow fluxes from 1997–2006 in the
MetEmis and MetEmisB simulations. The MAD (APDM) was calculated to be 31.17 Tg yr-1 (2.35 %) in the MetEmis simulation and 31.82 Tg yr-1
(2.36 %) in the MetEmisB simulation. The minor influence of biomass
burning emissions on the IAVs in the O3 outflow fluxes from East Asia
was also supported by Voulgarakis et al. (2015). Furthermore, Lin et al. (2014) reported that meteorological variability, compared with the
variability in biomass burning, was much more important for driving the IAVs
in springtime O3 at the Mauna Loa Observatory, a remote North Pacific
site sensitive to Asian pollution outflow.
Future changes in Asian O3 outflow for 2000–2050
In this part of the study, we quantify the future decadal changes in the Asian
O3 outflow from 2000–2050 under the SRES A1B scenario and examine
the relative impacts of variations in climate and anthropogenic emissions on
the changes. We conduct each simulation for 10 years driven by 1996–2005
meteorology to represent the present-day (the year 2000) climate and by
2046–2055 meteorological fields to represent the future (the year 2050)
climate, following 1 year of model spin-up. All the results presented below
are 10-year averages. The simulated present-day and future changes in seasonal
and annual fluxes in O3 across the vertical plane along 135∘ E from 20 to 55∘ N are summarized in Table 5.
Present-day O3 outflow
The pressure–latitude cross sections along 135∘ E of the
simulated present-day (Met2000Emis2000) seasonal O3 outflow fluxes,
driven by the meteorological inputs provided by GISS GCM 3, are shown in
Fig. 10a. The magnitudes, spatial distributions, and seasonal variations
agree closely with those driven by the assimilated GEOS-4 meteorological
fields (Fig. 6). The O3 outflow flux through the vertical plane is
simulated to be 1877.1 Tg yr-1 with GISS GCM 3 meteorology and 1342.5 Tg yr-1 with the GEOS-4 assimilated meteorological fields, which
indicates the reliability of the simulated present-day O3 outflow
fluxes.
Effect of future climate change
Relative to the present-day value, the 2050 annual outflow of O3 is
estimated to increase by 2.0 % (Table 5) as a result of climate change
alone (Met2050Emis2000 minus Met2000Emis2000). The outflow of O3 shows
a slight decrease of 1.8 in DJF and of 3.8 % in SON, but a large
increase of 14.5 in JJA and of 7.3 % in MAM. The spatial
distributions of the projected changes in O3 fluxes are consistent
with those of the changes in zonal winds (Fig. 10b). The wind speed of the
westerlies in DJF and SON decreases across the troposphere over
30–45∘ N, leading to reductions in the O3 outflow
fluxes. In contrast, increases in the zonal winds in JJA and MAM lead to
increases in O3 outflow fluxes throughout the troposphere over
30–45∘ N. Our projected future changes in the zonal winds are
consistent with previous studies. By analyzing 18 CMIP5 models, Huang and
Wang (2016) assessed the future changes in atmospheric circulation in
spring over East Asia. They found that although different models projected
different changes (even in sign) in the zonal winds, the ensemble mean of 5
better-skill models among the 18 CMIP5 models exhibited overall increases in
the
zonal winds throughout the whole troposphere in spring, which agrees
with our simulation. Based on 31 (29)-model ensemble mean results, Jiang and
Tian (2013) showed that the westerlies along 135∘ E in winter
(summer) were projected to weaken (strengthen). Such projected patterns of
future changes in the westerlies in winter and summer are also captured by
our model. The changes in O3 concentrations also contribute to the changes
in O3 outflow; although the zonal winds are projected to increase north
of 40∘ N in the upper troposphere in SON, the O3 outflow
fluxes are simulated to decrease because of the significant decreases in
O3 levels north of 40∘ N in the upper troposphere (Fig. S1 in the Supplement).
(a) The pressure–latitude cross sections along 135∘ E
of the simulated present-day O3 mass fluxes and zonal winds
(Met2000Emis2000). The projected changes in O3 mass fluxes and zonal winds
from the present day (1996–2005) to the future (2046–2055) caused by
(b) climate change alone (Met2050Emis2000 minus Met2000Emis2000), (c) changes in
anthropogenic emissions alone (Met2000Emis2050 minus Met2000Emis2000), and
(d) changes in both climate and anthropogenic emissions (Met2050Emis2050
minus Met2000Emis2000). The O3 mass fluxes are shown by the colored shading (kg season-1 m-2), and the winds are represented by the contours
(m s-1). The dotted areas are statistically significant at the 95 %
level, as determined by a two-sample Student's t test.
Effect of future changes in anthropogenic emissions
The annual outflow of O3 through the vertical plane is simulated to
increase by 7.9 % relative to the present-day value (Table 5) as a result
of the changes in anthropogenic emissions alone (Met2000Emis2050 minus
Met2000Emis2000). Considering that the O3 outflow with changes in
anthropogenic emissions alone exhibits an increasing trend of 1.2 % decade-1 from 1986–2006 (Sect. 4.2), the increasing trend of 1.2 % decade-1 (i.e., 6.0 % half-century-1) is close to the
value of 7.9 % for the future half-century.
The projected future O3 fluxes show increases in all seasons, which
can be attributed to the increases in the O3 concentrations at all
altitudes over Asia and upwind regions (i.e., Europe and Central Asia; Fig. S1) as a result of the increases in the anthropogenic emissions of the O3
precursors (NOx and NMVOCs) and CH4 concentrations. NOx
emissions in 2050 are projected to increase by 159.6 % over Asia and by
78.4 % globally, while NMVOCs emissions are projected to increase by
118.6 % over Asia and by 89.4 % globally under the SRES A1B scenario
(Table 1). The CH4 mixing ratios are projected to increase by 37.1 %
relative to the present-day value. The largest increases in O3 outflow
fluxes are located in the middle to upper troposphere (Fig. 10c) owing to the
strong westerlies located here. It is noted that, in spite of the
significant increases in emissions, the simulated surface-layer O3
concentrations show slight decreases over the North China Plain in DJF,
which subsequently leads to small decreases in the O3 outflow fluxes at
the surface layer over 30–40∘ N. In DJF, biogenic VOC emissions
are especially low over the North China Plain, whereas anthropogenic
NOx emissions are fairly high due to residential heating, leading
to a low VOCs / NOx ratio in this region (Lou et al., 2010; Fu et al.,
2012). Therefore, increases in NOx emissions lead to decreases in
surface-layer O3 concentrations over the North China Plain.
Effect of future changes in both climate and anthropogenic
emissions
The annual outflow of O3 through the vertical plane is projected to
increase by 12.2 % (Table 5) from 2000–2050 as a consequence of
future changes in both climate and anthropogenic emissions (Met2050Emis2050
minus Met2000Emis2000). Climate change in DJF and SON slightly offsets the
effects of changes in the anthropogenic emissions, while climate change in MAM
and JJA enhances the effects of variations in anthropogenic emissions. When
considering future changes in both emissions and climate, the projected
O3 outflow fluxes show increases throughout almost the entire
troposphere along 135∘ E in all seasons (Fig. 10d).
Uncertainty discussion
There are some uncertainties in our simulations. First, the influence of the
interannual variation in the stratosphere–troposphere exchange on tropospheric
O3 is not considered in this study. Terao et al. (2008) reported that
the stratosphere–troposphere exchange had large impacts on the interannual
variability in tropospheric O3 over Canada and Europe, but the impact
was much smaller over East Asia. The second is the uncertainty associated
with the selection of the longitudinal transect. We calculate the O3 flux
through the vertical plane along 135∘ E because 135∘ E
is the easternmost boundary of China (i.e., the Wusuli River in northeastern
China). We also calculate the O3 outflow flux along 120∘ E,
which is
closer to the ozone production region in central to eastern China, and find
that the variations in O3 fluxes calculated at 120∘ E are
similar to those calculated at 135∘ E. With variations in both
anthropogenic emissions and meteorological parameters (the MetEmis
simulation), the simulated O3 outflow shows large IAVs but a
statistically insignificant (P > 0.05) trend. The conclusion is
consistent with that drawn from the variations in O3 outflow calculated
at 135∘ E. Finally, projecting the future atmospheric circulation on
regional scales has a large amount of uncertainty, which is undergoing
continuous
improvement.
Conclusions
We quantify the past and future changes in the O3 outflow from East
Asia using the global 3-D chemical transport model GEOS-Chem. The historical
(1986–2006) simulations are driven by the assimilated GEOS-4 meteorological
fields, and the future (2000–2050) simulations under the IPCC SRES A1B
scenario are driven by the meteorological fields archived from GISS GCM 3.
Sensitivity studies are conducted to examine the respective impacts of
meteorological parameters and emissions on the variations in the outflow
flux.
The measurements from WDCGG and EANET are used to evaluate the simulated
surface-layer O3 concentrations; the ozonesonde data from WOUDC are
used to evaluate the simulated O3 concentrations for the boundary
layer and the middle and upper troposphere. Generally, the seasonal cycles and
interannual variations in tropospheric O3 concentrations are captured
fairly well by the GEOS-Chem model with high correlation coefficients of
0.82–0.93 at four ground-based sites and 0.55–0.88 at two ozonesonde
sites. The increasing trends in surface-layer O3 concentrations in East
Asia over the past 2 decades can also be captured by the GEOS-Chem model,
although the modeled O3 trends have low biases. The simulated Asian O3
outflow flux peaks in early spring and reaches the nadir in summer. The
maximum O3 fluxes are located in the middle to upper troposphere.
The IAVs and decadal trends in Asian O3 outflow are examined from
1986–2006. The simulated O3 outflow fluxes showed large IAVs but an
insignificant decadal trend; with variations in both meteorological
parameters and anthropogenic emissions, the seasonal APDM values were in the
range of 4–9 %. The sensitivity simulations showed that the large IAVs of
the O3 outflow fluxes were mainly caused by the variations in
meteorological conditions, rather than the variations in anthropogenic and
biomass burning emissions. Although variations in the meteorological parameters
could influence the IAVs in the O3 outflow fluxes by changing the O3 concentrations over East Asia and by altering the zonal winds, the latter was
identified to be the key factor because of the high correlation coefficient
of +0.71 between the annual fluxes and the zonal winds.
The decadal changes in the Asian O3 outflow are also examined from
2000–2050. The present-day annual O3 flux through the vertical plane
is calculated as 1877.1 Tg, which is projected to change from 2000–2050 by
+2.0, +7.9, and +12.2 % due to climate
change alone, emissions change alone, and changes in both climate and
emissions, respectively. In MAM and JJA, climate change plays a larger role in the
future changes in O3 outflow compared with emissions changes, owing to
the significant increases in the zonal winds in these two seasons. It is
noted that climate change will aggravate the impacts of increases in
anthropogenic emissions on the O3 outflow from East Asia from
2000–2050 under the SRES A1B scenario.
These findings are helpful for understanding the temporal evolution in the
tropospheric O3 on different timescales in the downwind regions of East
Asia. The observed IAVs in tropospheric O3 on a relatively short timescale
can be attributed to variations in the meteorological parameters. Furthermore,
the conclusions from this study will have important implications for long-term
air quality planning for the regions downwind of China, such as Japan and
the US. Since future climate change will increase the O3 outflow from East
Asia, extra efforts are needed to reduce the anthropogenic emissions of O3
precursors to offset the adverse effects caused by climate change.
GEOS-Chem is an open-access model developed collaboratively at Harvard
University and other institutes in North America, Europe, and Asia. The
source codes can be downloaded from http://acmg.seas.harvard.edu/geos/. The
tropospheric NO2 vertical column density (VCD) data are retrieved from
GOME (1996–2002) and SCIAMACHY (2003–2006), which are available from
www.temis.nl. The O3 measurements at Minami-Tori-shima and Yonaguni-jima
are available from the World Data Centre for Greenhouse Gases (WDCGG;
http://ds.data.jma.go.jp/gmd/wdcgg/). Those at Rishiri Island and Ogasawara are
available from the Acid Deposition Monitoring Network in East Asia (EANET;
www.eanet.asia/product/index.html). The ozonesonde data at Naha and Tsukuba
are available from the World Ozone and Ultraviolet Radiation Data Centre
(WOUDC; www.woudc.org). All data presented in this study are available upon
request from the corresponding author.
The Supplement related to this article is available online at doi:10.5194/acp-17-3729-2017-supplement.
Hong Liao and Jia Zhu conceived the study and designed the experiments. Jia Zhu
performed the simulations, carried out the data analysis, and prepared the
paper. Yuhao Mao provided useful comments on the paper. Yang Yang and
Hui Jiang helped with performing the experiments.
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by the National Basic Research Program of China
(973 Program, grant no. 2014CB441202) and the National Natural Science
Foundation of China under grants 91544219 and 41475137. We acknowledge the
free use of GOME and SCIAMACHY tropospheric NO2 vertical column density
(VCD); data available from www.temis.nl. The following data centers are also
acknowledged: the World Data Centre for Greenhouse Gases (WDCGG;
http://ds.data.jma.go.jp/gmd/wdcgg/) operated by the Japan Meteorological Agency
(JMA) in cooperation with the World Meteorological Organization (WMO); the World
Ozone and Ultraviolet Radiation Data Centre (WOUDC; www.woudc.org) operated
by Environment Canada for the Global Atmosphere Watch (GAW) program of the WMO.
The Rishiri Island and Ogasawara sites are operated by the Ministry of the Environment
of Japan as part of the Acid Deposition Monitoring Network in East Asia
(EANET; www.eanet.asia/product/index.html). We are also very
grateful to the reviewers for their helpful comments and thoughtful
suggestions.
Edited by: Y. Kanaya
Reviewed by: two anonymous referees
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