ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-14-12649-2014Tropospheric carbon monoxide over the Pacific during HIPPO: two-way coupled simulation of GEOS-Chem and its multiple nested
modelsYanY.-Y.https://orcid.org/0000-0001-6251-0899LinJ.-T.linjt@pku.edu.cnhttps://orcid.org/0000-0002-2362-2940KuangY.YangD.ZhangL.Laboratory for Climate and Ocean–Atmosphere Studies, Department of
Atmospheric and Oceanic Sciences, School of Physics, Peking University,
Beijing 100871, ChinaJ.-T. Lin (linjt@pku.edu.cn)2December20141423126491266317June201421July201427October201431October2014This 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://www.atmos-chem-phys.net/14/12649/2014/acp-14-12649-2014.htmlThe full text article is available as a PDF file from https://www.atmos-chem-phys.net/14/12649/2014/acp-14-12649-2014.pdf
Global chemical transport models (CTMs) are used extensively to study air
pollution and transport at a global scale. These models are limited by
coarse horizontal resolutions that do not allow for a detailed representation of
small-scale nonlinear processes over the pollutant source regions. Here we
couple the global GEOS-Chem CTM and its three high-resolution nested models
to simulate the tropospheric carbon monoxide (CO) over the Pacific Ocean
during five High-performance Instrumented Airborne Platform for
Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO) campaigns between 2009
and 2011. We develop a two-way coupler, the PeKing University CouPLer (PKUCPL), allowing for the exchange and
interaction of chemical constituents between the global model (at
2.5∘ long. × 2∘ lat.) and the three nested
models (at 0.667∘ long. × 0.5∘ lat.)
covering Asia, North America, and Europe. The coupler obtains
nested model results to modify the global model simulation within the
respective nested domains, and simultaneously acquires global model results
to provide lateral boundary conditions (LBCs) for the nested models.
Compared to the global model alone, the two-way coupled simulation results
in enhanced CO concentrations in the nested domains. Sensitivity tests
suggest the enhancement to be a result of improved representation of the
spatial distributions of CO, nitrogen oxides, and non-methane volatile
organic compounds, the meteorological dependence of natural emissions, and
other resolution-dependent processes. The relatively long lifetime of CO
allows for the enhancement to be accumulated and carried across the globe.
We found that the two-way coupled simulation increased the global
tropospheric mean CO concentrations in 2009 by 10.4 %, with a greater
enhancement at 13.3 % in the Northern Hemisphere. Coincidently, the global
tropospheric mean hydroxyl radical (OH) was reduced by 4.2 %, resulting in
a 4.2 % enhancement in the methyl chloroform lifetime (MCF; via reaction
with tropospheric OH). The resulting CO and OH contents and MCF lifetime are
closer to observation-based estimates.
Both the global and the two-way coupled models capture the general
spatiotemporal patterns of HIPPO CO over the Pacific. The two-way coupled
simulation is much closer to HIPPO CO, with a mean bias of 1.1 ppb (1.4 %)
below 9 km compared to the bias at -7.2 ppb (-9.2 %) for the global model
alone. The improvement is most apparent over the North Pacific. Our test
simulations show that the global model alone could resemble the two-way
coupled simulation (especially below 4 km) by increasing its global CO
emissions by 15 % for HIPPO-1 and HIPPO-3, by 25 % for HIPPO-2 and
HIPPO-4, and by 35 % for HIPPO-5. This has important implications for
using the global model alone to constrain CO emissions. Thus, the two-way
coupled simulation is a significantly improved model tool for studying the
global impacts of air pollutants from major anthropogenic source regions.
Introduction
Global air pollution and transport are of interest worldwide, concerning the
impacts on atmospheric chemistry, environment, and climate (Akimoto, 2003;
Fiore et al., 2009; HTAP, 2010; Guan et al., 2014; Lin et al., 2014).
Atmospheric transport across the Pacific Ocean is studied extensively due to
concerns on air quality over North America (Wuebbles et al., 2007; Lin et
al., 2008a; Zhang et al., 2008; Cooper et al., 2010; Yu et al., 2012; Lin et
al., 2014). Carbon monoxide (CO) is often used as a transport tracer because
of its relatively long lifetime in the troposphere (1–3 months) (Liu et al.,
2003; Liang et al., 2004; Zhang et al., 2008; Liu et al., 2013). Transport
of CO and other pollutants to the Pacific Ocean is often measured by
aircraft campaigns and satellite remote sensing (Liu et al., 2003; Zhang et
al., 2008). The recent five High-performance Instrumented Airborne Platform for
Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO)
campaigns measured various atmospheric constituents over the Pacific from
2009 to 2011 (Wofsy, 2011; see Fig. 1 for flight tracks and times). They
provide detailed information on the seasonal and vertical structures of CO
over the Pacific (Kort et al., 2012), ideal for studying its spatiotemporal
variability and for source attribution.
Times and flight tracks of five HIPPO campaigns. Our analysis is
focused on CO over the Pacific Ocean.
Analyses of global air pollution and transport are facilitated often by
global chemical transport models (CTMs) that simulate various chemical,
physical, and transport processes affecting air pollutants in the troposphere
(Wild and Akimoto, 2001; Lin et al., 2008b; Zhang et al., 2008; Fiore et
al., 2009; Liu et al., 2013; Lin et al., 2014). Global CTMs normally have a
horizontal resolution of 200–500 km (Fiore et al., 2009; Lamarque et al.,
2013), which is not able to capture nonlinear processes at various fine scales over
the pollutant source regions. Over the past decades, nested CTMs with much
increased horizontal resolutions have been developed by the global CTM
community, e.g., GEOS-Chem (Chen et al., 2009) and the Transport Model, version 5 (TM5) (Krol et al.,
2005),
to better study air pollution characteristics over major pollutant source
regions. For similar purposes, regional air quality models, such as the air quality model (AQM), the community multi-scale air quality model
(CMAQ),
and the Weather Research and Forecasting Model with Chemistry (WRF-Chem) model, have also been established with high horizontal resolutions
(Huang et al., 2008; Lam and Fu, 2009; Pfister et al., 2011). These regional
or nested models often obtain lateral boundary conditions (LBCs) of
chemicals from global CTM simulations, and they capture many small-scale
processes under-represented by global CTMs. However, the high-resolution
simulation results are rarely used for feedback to improve global CTM
simulations. Such a one-way combination of global and regional models does
not allow for high-resolution regional models to help study the global air
pollutant transport.
In this paper, we use the GEOS-Chem CTM simulations to analyze the
tropospheric CO over the Pacific Ocean measured from the five HIPPO
campaigns over 2009–2011. For this purpose, we develop a two-way coupler,
the PeKing University CouPLer (PKUCPL), to integrate results of the global
GEOS-Chem model (at 2.5∘ long. × 2∘ lat.)
and its three nested models (at 0.667∘ long. × 0.5∘ lat.). These nested models cover Asia (Chen et al.,
2009), North America (Zhang et al., 2011), and Europe (Vinken et al., 2014).
These regions are main pollution source regions where
small-scale nonlinear chemical and physical processes may have significant
impacts on the global environment. The coupler acquires nested model results
to replace global model results within the respective nested domains, in
addition to letting the global CTM provide LBCs to the nested models. The
coupler minimizes the computational cost of two-way integration by allowing
the four models to run parallel to each other.
The rest of the paper is organized as follows. Section 2 describes the four
GEOS-Chem models together with the two-way coupling framework. Section 3
presents an overall analysis of tropospheric CO simulated by the global CTM
alone and by the two-way coupled model. Section 4 analyzes the tropospheric
CO over the Pacific during the HIPPO campaigns, evaluating simulation
results from the global model alone and the two-way coupled model. The
analysis is focused on the seasonal and vertical variability of CO, with
important implications found for using the coarse-resolution global model to
constrain CO emissions. Section 5 concludes the present study.
(a) Domains of three nested models covering Asia
(70–150∘ E, 11–55∘ N), North America (140–40∘ W, 10–70∘ N) and
Europe (30–50∘ E, 30–70∘ N). (b) Illustration of how global and
nested models exchange data in the northwestern corner of the Asian nested
domain. Blue denotes nested model grids and red denotes global model grids.
Thick yellow lines separate the buffer zone (three outer grid cells) and the
inner domain of the nested model. To obtain LBCs, the nested model grid
cells in the buffer zone adopt mixing ratios of chemicals from the global
model. Global model results in the grid cells fully covered by the inner
domain of the nested model (bounded by thickened red lines) are replaced
with the nested model results after a mass-conversed regridding process.
GEOS-Chem and the two-way coupling frameworkGEOS-Chem models
Both the global and three nested GEOS-Chem CTMs (version 08-03-02;
)
are driven by the GEOS-5 assimilated meteorological data from the National
Aeronautic and Space Administration Global Modeling and Assimilation Office.
The nested models are run at a horizontal resolution of
0.667∘ long. × 0.5∘ lat. which is native to the
GEOS-5 data (see Fig. 2a for nested domains). The global model is run at a
reduced resolution of 2.5∘ long. × 2∘ lat.
with meteorological data regridded from the high-resolution GEOS-5 data. All
models have 47 vertical layers extending from the surface up to 0.01 hPa in
a hybrid pressure–sigma coordinate, and the lowest 10 layers are
each ∼ 130 m thick (see Appendix 3 at http://acmg.seas.harvard.edu/geos/doc/man/). The chemistry time step is
1 h in all models, while the transport time step is 15 min in the
global model and 10 min in all nested models. Hereafter, we refer to the
global model as the stand-alone global model and the two-way coupled model as
the combined model system integrating the global model and all three nested
models.
All models are run with the full Ox–NOx–VOC–CO–HOx gaseous chemistry with
online aerosol calculations. Model convection follows a modified relaxed
Arakawa–Schubert scheme (Rienecker et al., 2008). Vertical mixing in the
planetary boundary layer is parameterized with a non-local scheme (Holtslag
and Boville, 1993; Lin and McElroy, 2010).
(a) Monthly anthropogenic (fossil + biofuel) emissions of CO
within the nested or global model domains; emissions are unchanged from one
year to another. (b) Monthly biomass burning emissions of CO over 2008–2011
within the nested or global model domains. (c) Spatial distributions of CO,
NOx, and non-methane volatile organic compounds (NMVOCs) emissions from all sources in 2009 over eastern China on the
global vs. nested grids.
Anthropogenic and biomass burning emission inventories used by
GEOS-Chem.
Source typeRegionData setResolution1References and notesAnthro.GlobalEDGAR 3.2-FT20001∘ long. × 1∘ lat.Olivier et al., 2005AsiaINTEX-B0.5∘ long. × 0.5∘ lat.Zhang et al., 2009USNEI054 km × 4 kmhttp://www.epa.gov/ttn/chief/net/2005inventory.html#inventorydata Gridded data were adopted from WRF-Chem (ftp://aftp.fsl.noaa.gov/divisions/taq/emissions_data_2005)CanadaCAC1∘ long. × 1∘ lat.http://www.ec.gc.ca/pdb/cac/cac_home_e.cfmMexicoBRAVO1∘long. × 1∘ lat.Kuhns et al., 2003EuropeEMEP0.5∘ long. × 0.5∘ lat.Auvray and Bey, 2005Biomass burningGlobalGFED2; GFED3.11∘ long. × 1∘ lat.van der Werf et al., 2006; 2010 GFED3.1 for CO and GFED2 for others
1 Before re-gridded to model resolutions.
Global anthropogenic emissions of nitrogen oxides (NOx), CO, and non-methane
volatile organic compounds (NMVOCs) are taken from the EDGAR 3.2-FT2000
(Emission Database for Global Atmospheric Research 3.2 Fast Track 2000)
data set (Olivier et al., 2005). Emissions over Asia, North America, and
Europe are further replaced by various regional inventories shown in Table 1. Emission data are available at 1∘ long. × 1∘ lat. or
finer resolutions, and are regridded to model resolutions prior to the
simulation of photochemistry. No interannual variability is imposed. Over
Asia, temperature dependence is imposed upon residential emissions (Streets,
2003; Lin, 2012). Figure 3a shows that global annual anthropogenic emissions
of CO amount to ∼590 Tg yr-1 with weak seasonality below 20 %.
Note that for areas not replaced by regional inventories (mostly in the
Southern Hemisphere), EDGAR 3.2-FT2000 CO emissions are 131 Tg yr-1,
slightly larger than the value at 123 Tg yr-1 in the updated EDGAR v4.2
(Janssens-Maenhout et al., 2010) for 2008 (the latest year in EDGAR v4.2).
Monthly biomass burning emissions are taken from the GFED2 data set (van der
Werf et al., 2006), with CO emissions updated to GFED3.1 (van der Werf et
al., 2010). Figure 3b shows that global monthly biomass burning emissions of
CO vary between 6.3 and 96 Tg month-1 over the course of 2008–2011,
depending on the climatic conditions. Over the nested domains (Fig. 3b),
biomass burning emissions of CO vary more drastically with time.
Other emissions are calculated online, which are dependent on model
meteorology and resolution. Lightning emissions of NOx are parameterized
based on cloud heights (Price et al., 1997), with a local adjustment based
on the optical transient detector and lightning imaging sensor (OTD–LIS) satellite measurements (Sauvage et al., 2007; Murray et al.,
2013) and a backward C-shape vertical profile (Ott et al., 2010). Soil
emissions of NOx follow Yienger and Levy (1995) and Wang et al. (1998).
Biogenic emissions of NMVOCs follow MEGAN (Model
of Emissions of Gases and Aerosols from Nature) v2.1 (Guenther et al., 2006).
For 2009, global emissions from all sources used in the coarse-resolution
global model are ∼45 Tg N yr-1 for NOx, and 587 TgCyr-1 for
NMVOCs. Due to the resolution-dependent online calculation of biogenic NMVOC
emissions and soil and lightning NOx emissions, the global all-source
emissions in the two-way coupled model are larger than those in the global
model by 5 % for NMVOCs and by 1 % for NOx.
Figure 3c presents illustrative horizontal distributions of emissions over
eastern China (part of the Asian nested domain). It shows that the spatial
variability of emissions is much better resolved on the nested grid than on
the global grid. As detailed in Sect. 3.2, better resolved emissions
contribute to a significantly improved simulation of CO.
Simulations of both the global model and the two-way coupled model are
conducted from July 2008 to 2011 to analyze the tropospheric CO during
the HIPPO campaigns. Initial conditions of chemicals are regridded from a
simulation at 5∘ long. × 4∘ lat. started from 2004. Simulations over July–December 2008 allow for a 6-month spin-up
for our focused analysis over 2009–2011. Ancillary test simulations are also
performed over shorter periods (from July 2008 to January or to December
2009) to elaborate the physical mechanisms affecting the CO simulation under
the two-way coupling framework.
Flowchart of the two-way coupling process. Green indicates the key
steps to achieve the two-way integration via the PKUCPL coupler.
Two-way coupling setup
Figure 4 shows the flowchart of the global and nested model coupling process. All
models are regulated by the PKUCPL coupler. The coupler determines when and
how to output global model results to update the LBCs of nested models, and
to output nested model results to adjust the global model. Information of
all chemical species is exchanged every 3 h between the global and
nested models. At the time step for information exchange, the propagation of
a particular model is paused until all relevant information has been
updated.
Figure 2b illustrates the global and nested model grid cells for information
exchange. For a nested model, a buffer zone consisting of three nested grid
cells is implemented on each edge of the nested domain (Fig. 2b). At the
time of updating LBCs, mixing ratios of all chemical species in the buffer
zone are taken directly from the respectively grid cells of the global
model. A more detailed description of the LBC setups can be found in Chen et
al. (2009). While providing the LBCs, global model results in the
troposphere are replaced by nested model results within the respective
nested domains. Specifically, mass concentrations of all chemical species
are output from nested models, regridded horizontally to match the global
model grid with mass conservation guaranteed, and then used to replace
global model results in the troposphere. Global model grid cells overlapping
with the buffer zone of a nested model are not adjusted (Fig. 2b). To
guarantee mass conservation in the horizontal regridding process, pollutant
mass in a nested grid cell is calculated and then allocated to global
grid cells based on the fraction of the area of the nested grid cell belonging
to a given global grid cell.
Under the two-way coupling framework, all models proceed in parallel. The
wall-clock time of the coupled system is greater than the slowest individual
model (the nested model for North America) by only 10 %; the additional
time is used for information exchange between models. With an eight-core (Intel(R)
Xeon(R) CPU X7550 at 2.00 GHz) OpenMP parallelization for each global or nested
model, the coupled system takes about 10 days to finish one simulation year.
Testing the accuracy of the two-way coupling
Several issues warrant considerations for the two-way coupling. First, the
coupled simulation may be affected by the frequency of inter-model data
exchange. We find that increasing the exchange frequency from every 3 h to every 1 h does not affect the CO simulation after the 6-month
spin-up period.
Daily mean tropospheric mean CO mixing ratio from July 2008 to
December 2009 averaged over the global or nested model domains simulated by
the global model alone (black), by the two-way coupled model (red), and by
the one-way nested model (blue; with no feedbacks to the global model; only
for nested domains). Also shown in (b–d) is the difference between the
one-way nested and the global model (green; right axis).
In addition, our treatment of LBCs is relatively simplified as the
horizontal fluxes of chemicals (Krol et al., 2005) are not accounted for
explicitly. This introduces certain random perturbations to the nested
models that might in turn affect the global simulation in the two-way
coupled system. We thus conduct a test two-way coupled simulation that
successively increases the LBCs by 5 % for all chemical species at an
exchange time step and then decreases the LBCs by 5 % at the next time
step. We find that, even with such a large perturbation, the test simulation
reproduces the two-way coupled simulation without the 5 % perturbation
after the 6-month spin-up period.
Furthermore, mass conservation is required in regridding the nested model
results to modify the global model. To address the mass conservation issue,
we conduct additional test simulations by turning off all source and sink
processes in both the global model and the two-way coupled model. Since the
two simulations use the same initial conditions and only simulate the
transport processes, mass conservation means that the total atmospheric
content of CO should be the same between the global model and the two-way
coupled model. Indeed, the test two-way coupled simulation reproduces the CO
content simulated by the test global model.
General comparisons of tropospheric CO and hydroxyl radical
simulated by the two-way coupled model vs. the global model
Figure 5 compares the day-to-day variations of tropospheric mean CO mixing
ratios from July 2008 to December 2009 simulated by the global model
(black lines) vs. the two-way coupled model (red lines). The spin-up
period of July–December 2008 is included to elaborate the propagation
differentiating the global model simulation from the two-way coupled
simulation. In the three nested domains (Fig. 5b–d), the tropospheric mean
CO simulated by the global model varies from 70 ppb to 115 ppb over the
1.5 yr period. CO reaches a maximum in the Northern Hemisphere winter–spring with a
minimum in summer, reflecting the seasonal variation in CO sources and
lifetime (Liang et al., 2004). The two-way coupled model produces more CO
than the global model; the difference in 2009 is about 12.9 ppb (13.9 %)
averaged over Asia, 8.9 ppb (11.2 %) over North America, and 9.4 ppb (11.7 %) over Europe.
Measured and modeled vertical distributions of tropospheric CO along
the flight tracks of HIPPO campaigns. Grey denotes times and locations with
no HIPPO data. Data over the North Pacific (NP) and South Pacific (SP) are
distinguished in (a).
Globally (Fig. 5a), CO mixing ratios in 2009 simulated by the two-way
coupled model are about 7.4 ppb (10.4 %) higher than those simulated by the
global model alone. The enhancement is more significant in the Northern
Hemisphere (13.3 %), which alleviates the negative bias over the Northern
Hemisphere typical for coarse-resolution global models (Naik et al., 2013).
The enhancement is smaller in the Southern Hemisphere (6.9 %).
Global budget of tropospheric CO for 2009.
Global modelTwo-way coupled modelLoss by OH reaction (Tgyr-1)23642400Transport to stratosphere (Tgyr-1)2.83.3Production from methane and NMVOC oxidation (Tgyr-1)14651497Emissions (Tgyr-1)913*917*Fossil + biofuel585589Biomass burning328328Burden (Tg)346383Tropospheric lifetime (month)1.751.91
* The slight difference of 4 Tgyr-1 (i.e., 913 vs.
917) is related to the treatment of various offline anthropogenic
emission inventories; the effect on CO concentrations is negligible
since such a emission difference is less than 0.2 % of total CO sources.
Table 2 further shows the CO budget in 2009. The tropospheric CO loss
simulated by the two-way coupled model is close to that simulated by the
global model. This is due to the decrease in hydroxyl radical (OH) content (see Sect. 3.3)
compensating for the enhancement in CO. However, the mean tropospheric
lifetime of CO (burden divided by loss) is enhanced by 9.0 %, from 1.75 months to 1.91 months.
Accumulation of small differences differentiates the two-way
coupled simulation from the global model
To elucidate how the difference between the two-way coupled model and the
global model accumulates, we also conduct simulations with one-way nested
models (i.e., no feedbacks to the global model; blue lines in Fig. 5b–d). CO
concentrations simulated by the one-way nested models differ slightly from
global model results on any given day, but the difference varies from one
day to another (as evident from the green lines). On average, the one-way
nested models produce daily mean CO higher than the global model by 1.24 ppb over
Asia and 0.68 ppb over North America and lower by 0.15 ppb over Europe.
Here, the nested models adopt LBCs from the global model every 3 h
with no influences on the global model; thus, the CO difference cannot be
accumulated effectively throughout time. With the two-way coupling (red
lines), however, these regional differences are used to modify the global
model every 3 h and, with the long lifetime of CO, are accumulated
and carried across the globe.
Figure 5d shows that over Europe, the one-way nested model (blue line)
results in lower CO than the global model (black line), while the two-way
coupled model (red line) produces higher CO than the global model. This is
due to the elevated transport of CO produced in Asia and North America via the
two-way coupling mechanism. Such interaction between high-resolution
simulations in multiple regions has been unexplored previously and warrants
further research.
Percentage contributions of individual factors to the difference in January 2009
tropospheric CO between the two-way coupled model and the global model, after
a 6-month spin-up from July 2008 with consistent initial conditions of chemicals.
Factors% contributionAll factors9.6 %A. Emission magnitude (mainly related to biogenic NMVOCs)3.5 %B. Nonlinear processes within the troposphere6.1 % B1. Small-scale variability in emissions of NOx, NMVOCs, CO, etc.4.6 % B2. Non-emission small-scale processes1.5 %
A. Obtained by contrasting simulations of the global model with vs. without adopting the nested model emissions at individual time steps;
emissions are regridded from the nested to coarse resolution.B. Residual of all factors subtracting A.B1. Residual of B subtracting B2, as driven by small-scale horizontal
distributions of emissions resolved on the nested grid but not on the coarse
global grid.B2. Obtained by contrasting simulations of the two-way coupled model with
versus without adopting the global model emissions at individual time steps;
emissions are regridded from the coarse to nested resolution, and are thus
resolved only at the scale of the coarse grid.
Factors differentiating the two-way coupled model from the global
model
Table 3 identifies various factors differentiating the two-way coupled model
from the global model, taking the simulated CO in January 2009 for analysis.
In January 2009, the global tropospheric mean CO simulated by the two-way
coupled model is larger than that simulated by the global model by 9.6 %.
Various test simulations are conducted from July 2008 to January 2009
to help delineate the differentiating factors. As shown below, the
contributions of these factors are derived in a linear manner as a
first-order estimate. The contributions of these factors may be different in
other months and years as a result of changes in emissions, meteorology, and
chemical reactivity; moreover, further research is needed for more systematic
evaluation.
Correlation and mean normalized bias of modeled CO with respect to
HIPPO measurements throughout the HIPPO campaigns, for individual vertical
layers (e.g., 0–1 km, 1–2 km).
First, the magnitude of emissions differs between the two simulations. As
shown in Sect. 2.1, due to changes in online emissions, global total
emissions are larger in the two-way coupled model than in the global model
by 5 % for NMVOCs and by 1 % for NOx. The additional NMVOC emissions
increase the tropospheric CO, whereas the extra NOx emissions have a
negative impact on CO (by increasing the OH content). A test simulation with
the global model adopts all emissions in the nested domains from the two-way
coupled model at every time step, via a mass-conserved grid-conversion
process. The test simulation increases the tropospheric CO in January 2009
by 3.5 % compared to the standard global model (Table 3), roughly
representing the effect of differences in emission magnitude between the
two-way coupled model and the global model. The residual difference of
6.1 % (i.e., 9.6 % minus 3.5 %) represents the combined effect of all
factors other than emission magnitude.
Furthermore, the two-way coupled model better captures the small-scale
spatial variability of NOx, NMVOCs, and CO concentrations in the nested
domains with a consequence on the photochemical efficiency. For example, the
efficiency of NOx in producing ozone (and thus affecting OH and CO) highly
depends on its abundance relative to NMVOCs and CO. Decreasing the horizontal
resolution leads to more significant artificial mixing of NOx, NMVOCs, and CO
with a resulting effect on its photochemical efficiency. In our study, the
spatial variability in concentration is driven by the variability in
emissions (see Fig. 3c for example), since the two-way coupled model uses the
same initial conditions as the global model. To derive the effect of
small-scale emission variability, we conduct a test simulation of the
two-way coupled model by adopting all emissions from the global model at
every time step. Here, emissions are regridded from 2.5∘ long. × 2∘ lat. to 0.667∘ long. × 0.5∘ lat.
in the nested domains such that horizontal
variability at scales smaller than 2.5∘ long. × 2∘ lat. is not resolved. As a result, the test two-way
coupled simulation produces higher CO by 1.5 % than the standard global
model in January 2009. The 1.5 % difference represents the combined effect
of non-emission small-scale variability (related to vertical transport,
radiation, and other resolution-dependent processes) that is resolved on the
0.667∘ long. × 0.5∘ lat. grid but not on the
2.5∘ long. × 2∘ lat. grid. Therefore, the
effect of small-scale variability in NOx, NMVOCs, and CO emissions
(which
greatly determine their concentration variability) is estimated at 4.6 % (i.e., 6.1 % minus 1.5 %), contributing about half of the difference
between the two-way coupled model and the global model.
Measured and simulated vertical profiles of CO at 0.1 km intervals
averaged over (a–e) individual and (f) all HIPPO campaigns. Model results
are sampled at times and locations coincident to HIPPO measurements. Green
lines represent global model simulations with increased global CO emissions
that roughly resemble the two-way coupled simulation (especially below 4
km); increases in CO emissions are 15 % for HIPPO-1, 25 % for HIPPO-2,
15 % for HIPPO-3, 25 % for HIPPO-4, and 35 % for HIPPO-5. Also shown
are the number of profiles and mean model biases below 9 km (with abundant
measurements).
Global budget of tropospheric OH for 2009.
Global model1Two-way coupled model1Total loss (Tgyr-1)37803756 OH + CO1440 (38 %)1452 (38 %) OH +CH42540 (14 %)516 (14 %) OH + NMVOCs840 (22 %)852 (23 %) OH +O3204 (5 %)204 (5 %) OH +HOy396 (10 %)384 (10 %) OH +NOy72 (2 %)60 (2 %) OH +H2, SO2, etc.132 (9 %)132 (8 %)Total production (Tgyr-1)37803756 Photolysis of O31608 (43 %)1584 (42 %) Photolysis of other species480 (12 %)504 (14 %) Reactions1692 (45 %)1668 (44 %)Air mass weighted mean concentration (105cm-3)12.411.9MCF loss rate weighted mean concentration (105cm-3)12.512.1Methyl chloroform lifetime (yr) 35.35.5
1 In the parentheses is the percentage contribution to total loss or production.2 In the simulations, the tropospheric mixing ratio of methane (CH4) is fixed
at the 2007 level (1732.5 ppb south of 30∘ S, 1741.7 ppb between
30∘ S and Equator, 1801.4 ppb between Equator and 30∘ N, and
1855.6 ppb north of 30∘ N).3 Via the reaction with tropospheric OH, defined as 0.92⋅∑i=1T+SΔPi⋅A/∑i=1Tκi⋅ΔPi⋅Ci⋅A, where i denotes a layer, T the troposphere, S the stratosphere, ΔPi
the delta air pressure, κi the reaction constant, Ci the OH concentration,
and A the area occupied by a grid cell. The coefficient of 0.92 accounts for the vertical
gradient of methyl chloroform mixing ratio (Prather et al., 2012). The horizontal and time
dimensions are omitted from the equation for simplicity.
Impacts on tropospheric OH abundance and methyl chloroform lifetime (MCF)
Table 4 shows the impacts of two-way coupling on the tropospheric OH budget.
Consistent with the increased CO content, the air mass weighted global mean
tropospheric OH in 2009 simulated by the two-way coupled model is lower than
that simulated by the global model by 4.2 % (11.9 × 105 vs. 12.4 × 105moleccm-3). The 4.2 % difference exceeds the standard
deviation of OH interannual variation estimated at 2.3 % (Montzka et al.,
2011). The OH reduction is more significant in the Northern Hemisphere
(5.7 %) than in the Southern Hemisphere (2.1 %), reducing the
northern–southern hemispheric OH ratio from 1.27 to 1.24. This change helps
alleviate the overestimation in hemispheric OH contrast typical for
coarse-resolution global CTMs (Naik et al., 2013); however, the production and loss
rates of OH are affected insignificantly (Table 4). For example,
the loss via OH + CO reaction changes marginally due to the increased CO
concentration compensating for the decreased OH.
The reduced OH abundance leads to an enhanced lifetime of methyl chloroform
via tropospheric OH from 5.3 yr to 5.5 yr, a 4.2 % increase. The enhanced
lifetime is closer to the observation-based estimate at 6.0–6.3 yr (Prinn et
al., 2005; Prather et al., 2012).
Measured and simulated vertical profiles of CO at 0.1 km intervals
averaged across all HIPPO campaigns at the six latitude bands of
30∘ width. Model results are sampled at times and locations
coincident to HIPPO measurements. Also shown are the number of profiles and
mean model biases below 9 km (with abundant measurements) at each band.
Evaluation of simulated CO over the Pacific during the five HIPPO
campaignsSelection of HIPPO CO data and coincident model results
Figure 1 shows the flight tracks and dates of five HIPPO aircraft campaigns
conducted in various seasons between 2009 and 2011. These campaigns were
designed to measure atmospheric trace constituents in the remote troposphere
over the Pacific, Arctic, and near-Antarctic regions (Wofsy, 2011). In these
campaigns, aircrafts took off in central North America, flew northward to
almost 85∘ N, turned southward until 75∘ S,
and finally went back to North America. The measurements provide a large
quantity of global-scale high-quality data for analysis of atmospheric
chemistry in these remote areas.
During HIPPO, CO was measured by direct absorption spectroscopy using the
Harvard University/Aerodyne Research Quantum Cascade Laser Spectrometer
(Jimenez et al., 2005; Kort et al., 2012). To evaluate GEOS-Chem
simulations, this study uses the merged data set providing the tropospheric
CO mixing ratios at a vertical resolution of 0.1 km (see http://hippo.ornl.gov/node/16). A total of 620 vertical profiles over the
Pacific Ocean are employed, with 124 profiles from HIPPO-1, 98 from HIPPO-2,
103 from HIPPO-3, 143 from HIPPO-4, and 152 from HIPPO-5. Model results for CO are
sampled at the times and locations of individual measurements to ensure
spatiotemporal consistency with the HIPPO data. In particular, model results
in the grid box encompassing the location (longitude, latitude, and altitude)
of a given measurement are used for comparison with the observation.
General spatiotemporal pattern of the Pacific CO during HIPPO
Figure 6a shows the time–height distribution of CO mixing ratios over the
Pacific measured from the five campaigns. Most measurements are concentrated
below 10 km, especially below 9 km. Below 10 km, CO normally exceeds 80 ppb at
the beginning and end of each campaign measured over the North Pacific,
with values normally lower than 70 ppb measured over the South
Pacific (Wofsy, 2011). The hemispheric contrast is due mainly to the larger
sources of CO in the Northern Hemisphere.
Figure 6a shows that the measured North Pacific CO mixing ratios reach a
maximum during HIPPO-3 in March–April 2010. This reflects in part the strong
Asian biomass burning emissions in the period (Fig. 3b). Asian influences
are also enhanced in spring because of increased midlatitude cyclonic
activity supplemented by a relatively long lifetime of CO (Liu et al.,
2003; Liang et al., 2004). By comparison, the North Pacific CO mixing ratios
are the lowest during HIPPO-4 and HIPPO-5 over June–September 2011 when the
lifetime of CO reaches a minimum (Liang et al., 2004).
Figure 6b and c show that both the global model and the two-way coupled model
reproduce the general spatiotemporal structure of HIPPO CO. However, the
two-way coupled simulation is closer to HIPPO CO than the global model,
particularly for the high values over the North Pacific. The mean bias in
the two-way coupled model is only 1.1 ppb (1.4 %) below 9 km, with a bias
of -1.8 ppb (-2.3 %) for the North Pacific and 2.6 ppb (3.3 %) for the
South Pacific. The global model generally underestimates HIPPO CO with a
mean bias by -7.2 ppb (-9.2 %) below 9 km; the bias is much larger over
the North Pacific (-10.2 ppb, -13.1 %) than over the South Pacific
(-1.6 ppb, -2.1 %). Such a negative bias in the Northern Hemisphere is typical
for coarse-resolution global CTMs (Naik et al., 2013).
Figure 7 further evaluates the simulated CO mixing ratios in each vertical
layer with a thickness of 1 km (layer 1 for 0–1 km, layer 2 for 1–2 km, etc.) during
the five HIPPO campaigns. Compared to the global model, the two-way coupled
simulation reduces the mean normalized bias relative to HIPPO CO in all but
the 10th, 11th, and 12th layers (between 9 km and 12 km). Note
that comparisons at higher altitudes, especially above 10 km, are subject to
scarcity in measurements (Fig. 6a). In all layers, the two-way coupled model
also slightly improves the correlation with HIPPO CO.
Vertical profile of Pacific CO during HIPPO
The thick yellow lines in Fig. 8a–e show the mean vertical distributions of
CO measured from individual HIPPO campaigns. In general, the measured CO
mixing ratios are larger in the lower and mid-troposphere than in the
upper troposphere. The largest vertical contrast occurs during HIPPO-3 in
March–April 2010, with CO reaching 105 ppb near the surface and at around
5–6 km in contrast to a value of 60 ppb at 12 km. The vertical contrast
reflects the strong springtime Asian outflow in the lower and mid-troposphere (Liu et al., 2003; Liang et al., 2004). By comparison, the
vertical contrast is only about 10 ppb during HIPPO-4 in June–July 2011, due
to strong convective activities in the northern hemispheric summer that mix
CO more evenly. During HIPPO-5 in August–September 2011, CO mixing ratios
change by 20 ppb at around 9 km due to the stratospheric influences. A
similar change is also shown in model simulations, albeit with a smaller
magnitude due possibly to model overestimations in the stratosphere.
The red lines in Fig. 8a–e show that the two-way coupled simulation captures
the vertical distribution of HIPPO CO. The bias is the smallest during HIPPO-1
(winter) where the model reproduces various fine structures of HIPPO CO
throughout the troposphere (Fig. 8a). The two-way coupled simulations
compare fairly well with HIPPO-2 (late fall) and HIPPO-5 (summer; except for
the positive biases above 9 km as influenced by the stratosphere). For
HIPPO-3 (spring), the coupled model reproduces the mid-tropospheric peak in
the HIPPO data, but with a much smaller magnitude. This is likely due to the
use of monthly biomass burning emissions that do not capture the episodic
emissions occurring during the HIPPO-3 times. As shown in Fig. 3b, biomass
burning is very active in this spring period. Likely for similar reasons,
the coupled model also does not well capture the observed peaks in the lower
and mid-troposphere in HIPPO-4 (summer). Averaged across the five
campaigns (Fig. 8f), the two-way coupled simulation is within 3 ppb of HIPPO
CO below 9 km with an overestimation of 1–5 ppb above 9 km.
The black lines in Fig. 8a–e show that the global model also captures the
general vertical structure of HIPPO CO, but with negative biases during all
campaigns. Averaged across the five campaigns (Fig. 8f), the global model
underestimates the HIPPO CO by 1–10 ppb throughout the troposphere.
Figure 9 further evaluates the model simulations at six latitude bands of
30∘ width (e.g., 60–90∘ N, 30–60∘ N). Consistent with the above analyses, the two-way
coupled model captures the vertical profiles of HIPPO CO measurements much
better than the global model in the Northern Hemisphere (Fig. 9a, c, e). At
60–90∘ N, both models do not capture the peak around
5 km, while the two-way coupled model still has a smaller mean bias below 9
km. Overall, the mean biases below 9 km are within 3 ppb (3 %) for the
two-way coupled model at the three northern hemispheric bands, compared to
the negative biases exceeding 11 ppb (> 10 %) for the global
model. At the three southern hemispheric bands (Fig. 9b, d, f), although the
global model is slightly closer to HIPPO CO than the two-way coupled model
at 30–60∘ S, both models have small mean biases below
9 km (within 3 ppb) at all these bands.
Implications of model resolution dependence for CO emission
constraint
Global models are often used to constrain CO emissions, where the mean model
bias relative to measurements is attributed to emission errors (e.g.,
Stavrakou and Müller, 2006; Kopacz et al., 2010; Hooghiemstra et al.,
2011). These studies tend to suggest CO emissions, as constrained by
modeling and measurements, to be higher than those in emission inventories.
For example, Kopacz et al. (2010) used satellite CO measurements and a
5∘ long. × 4∘ lat. global GEOS-Chem model to constrain
CO emissions for 2004–2005. Their results suggested a total of global CO
emissions from combustion (fossil fuel, biofuel and biomass burning
combined) at 1350 Tg yr-1, much larger than current bottom-up emission
inventories (48 % larger than our value at 913 Tg yr-1 shown in
Table 2). Previous studies have discussed uncertainties in model meteorology (Lin et al., 2012),
transport (Liu et al., 2010; Jiang et al., 2013), chemistry (Lin et al., 2012) and the OH field
(Kopacz et al., 2010; Hooghiemstra et al., 2011) affecting emission constraints. Here we show that
the tropospheric CO simulated by the two-way coupled model is much higher
than that simulated by the global model and is much closer to HIPPO measurements, as a
consequence of improved representation of resolution-dependent emissions,
chemistry, physics, and transport. This resolution effect is also consistent
with our previous global model simulations, which show that a
5∘ long. × 4∘ lat. model leads to a 3 % reduction in global tropospheric mean CO compared to a model at
2.5∘ long. × 2∘ lat. These results have
important implications for using the global model to constrain CO emissions.
To elaborate on this point, we adjust CO emissions in the global model in an
attempt to reproduce the two-way coupled simulation. We find that the global
model simulation can resemble the two-way coupled simulation during HIPPO-1,
especially below 4 km, by increasing global CO emissions from all sources by
15 % (Fig. 8a, green line). Emission increases with respect to other
campaigns are 25 % for HIPPO-2, 15 % for HIPPO-3, 25 % for HIPPO-4,
and 35 % for HIPPO-5 (Fig. 8b–e; green lines). Here all simulations start
from July 2008 with adjusted CO emissions. The extent of required emission
increases is larger than the magnitude of CO difference (about 11 % based
on Sects. 4.2 and 4.3), because emissions contribute less than half of
tropospheric CO sources. As shown in Table 2, emissions contributed about
38 % of tropospheric CO in 2009, with the residual 62 % attributed to
oxidation of methane and NMVOCs. The simulation results here imply that, when
used for emission constraints, the two-way coupled simulation would suggest
much lower CO emissions to match measurements than the coarse-resolution
global model.
Conclusions
We develop a two-way coupler (PKUCPL) to integrate the global GEOS-Chem CTM
(at 2.5∘ long. × 2∘ lat.) and its three
high-resolution nested models (0.667∘ long. × 0.5∘ lat.) covering Asia, North America, and
Europe.
Under the coupling framework, the global model provides LBCs
of chemicals for the nested models, while the nested models produce
high-resolution results to improve the global model within the respective
nested domains. The nested models encompass major anthropogenic pollutant
source regions and better capture many small-scale nonlinear processes
under-represented by the global model; moreover, the two-way coupling allows for
such improvements to have a global impact.
Analysis for 2009 shows that the tropospheric CO concentrations simulated by
the two-way coupled model are much higher than those simulated by the global
model, with a difference of 10.4 % averaged across the globe. The
enhancement reaches 13.3 % in the Northern Hemisphere, alleviating the
northern hemispheric underestimation typical for global models (Naik et al.,
2013). The increase in CO is accompanied by a 4.2 % reduction in global
mean tropospheric mean OH, the magnitude of which is larger than the OH
interannual variability estimated at 2.3 % (Montzka et al., 2011). The
reduction in OH content results in a 4.2 % enhancement (from 5.3 yr to
5.5 yr) in the MCF lifetime with respect to reaction with tropospheric
OH, bringing it closer to observation-based estimates at 6.0–6.3 yr (Prinn
et al., 2005; Prather et al., 2012).
We delineate the factors differentiating the two-way coupled model from the
global model in a simplified linear manner, taking for illustration the
simulated CO concentration in January 2009. The two-way coupled simulation
results in higher CO by 9.6 % in January 2009. We find that a 4.6 % enhancement is due to improved representation of small-scale spatial
variability in NOx, NMVOCs, and CO emissions (which greatly determine their
concentration variability) resolved on the fine grid but not on the coarse
grid. Another 3.5 % enhancement is due to increased soil and lightning
emissions of NOx and especially biogenic emissions of NMVOCs that are
dependent of model meteorology and resolution. Furthermore, an additional 1.5 % enhancement is due to improved simulation of vertical transport, radiation,
and other resolution-dependent processes.
We use the two-way coupled model and the global model to simulate the
tropospheric CO mixing ratios over the Pacific during the five HIPPO
campaigns in various seasons between 2009 and 2011. Both models capture the
general seasonal, horizontal, and vertical distributions of HIPPO CO.
Compared to the global model, the two-way coupled model correlates better
with HIPPO CO spatiotemporally. Averaged across the five campaigns, CO
simulated by the two-way coupled model is within 3 ppb of HIPPO CO below 9
km with a positive bias of 1–5 ppb above 9 km; the mean bias is 1.1 ppb (1.4 %)
between 0 and 9 km. The global model underestimates HIPPO CO by 1–10 ppb throughout the troposphere with a mean bias of -7.2 ppb (-9.2 %) below
9 km; the bias is most apparent over the North Pacific, consistent with the
northern hemispheric underestimation typical for global models (Naik et al.,
2013).
Our test simulations with the global model suggest that increasing the
global CO emissions from all sources by about 15 % would lead to CO mixing
ratios comparable to those simulated by the two-way coupled model during
HIPPO-1, especially below 4 km; the respective emission increases are 25 % for HIPPO-2, 15 % for HIPPO-3, 25 % for HIPPO-4, and 35 % for HIPPO-5.
These results imply an important model dependence on horizontal resolution
that is largely unaccounted for in the literature on CO emission
constraints.
Our two-way coupling framework minimizes the computational time and model
complexity commonly concerned for multi-model integration. This is achieved
by running all models in parallel under the regulation of the master coupler
PKUCPL. With this coupler, it is straightforward to incorporate additional
nested models with the same or different horizontal resolutions. In
particular, a much finer nested GEOS-Chem model (at 0.3125∘ long. × 0.25∘ lat.) is currently available for North America
and under development for other regions. As such, it is feasible to develop
a low-computational-cost multi-regional multi-layer (e.g., from global
∼ 2∘, regional ∼ 0.5∘, and local ∼ 0.25∘)
two-way coupling system to facilitate research on the interactions between
global, regional, and local scales. The coupled system will help address
questions such as the impacts of megacities and urbanization on pollutant
transport, global environment, and climate change (Parrish and Zhu, 2009).
Acknowledgements
This research is supported by the National Natural Science Foundation of
China, grant 41175127, and the 973 program, grant 2014CB441303. We acknowledge the free use of HIPPO CO data from
http://hippo.ornl.gov/dataaccess. HIPPO is funded by NSF and NOAA. We thank
Yuanhong Zhao and Yan Xia for discussions.
Edited by: P. Jöckel
References
Akimoto, H.: Global air quality and pollution, Science, 302, 1716–1719,
2003.Auvray, M. and Bey, I.:
Long-range transport to Europe: seasonal variations and implications for the European ozone budget,
J. Geophys. Res.-Atmos.,
110, D11303, 10.1029/2004jd005503, 2005.Chen, D., Wang, Y., McElroy, M. B., He, K., Yantosca, R. M., and Le Sager, P.: Regional CO
pollution and export in China simulated by the high-resolution nested-grid GEOS-Chem model,
Atmos. Chem. Phys., 9, 3825–3839, 10.5194/acp-9-3825-2009, 2009.Cooper, O. R., Parrish, D. D., Stohl, A., Trainer, M., Nedelec, P., Thouret, V., Cammas, J. P.,
Oltmans, S. J., Johnson, B. J., Tarasick, D., Leblanc, T., McDermid, I. S., Jaffe, D., Gao, R.,
Stith, J., Ryerson, T., Aikin, K., Campos, T., Weinheimer, A., and Avery, M. A.:
Increasing springtime ozone mixing ratios in the free troposphere over western North America,
Nature,
463, 344–348, 10.1038/nature08708, 2010.Fiore, A. M., Dentener, F. J., Wild, O., Cuvelier, C., Schultz, M. G., Hess, P., Textor, C.,
Schulz, M., Doherty, R. M., Horowitz, L. W., MacKenzie, I. A., Sanderson, M. G., Shindell, D. T.,
Stevenson, D. S., Szopa, S., Van Dingenen, R., Zeng, G., Atherton, C., Bergmann, D., Bey, I.,
Carmichael, G., Collins, W. J., Duncan, B. N., Faluvegi, G., Folberth, G., Gauss, M., Gong, S.,
Hauglustaine, D., Holloway, T., Isaksen, I. S. A., Jacob, D. J., Jonson, J. E., Kaminski, J. W.,
Keating, T. J., Lupu, A., Marmer, E., Montanaro, V., Park, R. J., Pitari, G., Pringle, K. J.,
Pyle, J. A., Schroeder, S., Vivanco, M. G., Wind, P., Wojcik, G., Wu, S., and Zuber, A.:
Multimodel estimates of intercontinental source-receptor relationships for ozone pollution,
J. Geophys. Res.-Atmos.,
114, D04301, 10.1029/2008jd010816, 2009.
Guan, D.-B., Lin, J.-T., Davis, S. J., Pan, D., He, K.-B., Wang, C., Wuebbles, D. J., Streets, D. G., and Zhang, Q.:
Response to Lopez et al.: Consumption-based accounting helps mitigate global air pollution,
P. Natl. Acad. Sci. USA,
doi:10.1073/pnas.1407383111, 2014.Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron, C.: Estimates of global
terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature),
Atmos. Chem. Phys., 6, 3181–3210, 10.5194/acp-6-3181-2006, 2006.Holtslag, A. A. M. and Boville, B. A.:
Local versus nonlocal boundary-layer diffusion in a global climate model,
J. Climate,
6, 1825–1842, 10.1175/1520-0442(1993)006<1825:lvnbld>2.0.co;2, 1993.Hooghiemstra, P. B., Krol, M. C., Meirink, J. F., Bergamaschi, P., van der Werf, G. R.,
Novelli, P. C., Aben, I., and Röckmann, T.: Optimizing global CO emission estimates
using a four-dimensional variational data assimilation system and surface network
observations, Atmos. Chem. Phys., 11, 4705–4723, 10.5194/acp-11-4705-2011, 2011.
HTAP:
Hemispheric Transport of Air Pollution 2010 Executive SummaryECE/EB.AIR/2010/10 Corrected,
United Nations, 2010.Huang, H. C., Lin, J. T., Tao, Z. N., Choi, H., Patten, K., Kunkel, K., Xu, M., Zhu, J. H., Liang, X. Z., Williams, A., Caughey, M., Wuebbles, D. J., and Wang, J. L.:
Impacts of long-range transport of global pollutants and precursor gases on US air quality under future climatic conditions,
J. Geophys. Res.-Atmos.,
113, D19307, 10.1029/2007jd009469, 2008.
Janssens-Maenhout, G., Petrescu, A. M. R., Muntean, M., and Blujdea, V.:
Verifying Greenhouse Gas Emissions: Methods to Support International Climate
Agreements, The National Academies Press, 124 pp., 2010.
Jiang, Z., Jones, D., Worden, H. M., Deeter, M. N., Henze, D. K., Worden, J., Bowman, K. W., Brenninkmeijer, C., and Schuck, T.:
Impact of model errors in convective transport on CO source estimates inferred from MOPITT CO retrievals,
J. Geophys. Res.-Atmos.,
118, 2073–2083, 2013.Jimenez, R., Herndon, S., Shorter, J. H., Nelson, D. D., McManus, J. B., and Zahniser, M. S.:
Atmospheric trace gas measurements using a dual quantum-cascade laser mid-infrared absorption spectrometer,
Proc.SPIE5738, Novel In-Plane Semiconductor Lasers IV, 318–331, 10.1117/12.597130, 2005.Junhua Liu, Logan, J. A., Jones, D. B. A., Livesey, N. J., Megretskaia, I., Carouge, C.,
and Nedelec, P.: Analysis of CO in the tropical troposphere using Aura satellite data and the
GEOS-Chem model: insights into transport characteristics of the GEOS meteorological products,
Atmos. Chem. Phys., 10, 12207–12232, 10.5194/acp-10-12207-2010, 2010.Kopacz, M., Jacob, D. J., Fisher, J. A., Logan, J. A., Zhang, L., Megretskaia, I. A.,
Yantosca, R. M., Singh, K., Henze, D. K., Burrows, J. P., Buchwitz, M., Khlystova, I.,
McMillan, W. W., Gille, J. C., Edwards, D. P., Eldering, A., Thouret, V., and Nedelec, P.:
Global estimates of CO sources with high resolution by adjoint inversion of multiple
satellite datasets (MOPITT, AIRS, SCIAMACHY, TES), Atmos. Chem. Phys., 10, 855–876, 10.5194/acp-10-855-2010, 2010.Kort, E. A., Wofsy, S. C., Daube, B. C., Diao, M., Elkins, J. W., Gao, R. S., Hintsa, E. J.,
Hurst, D. F., Jimenez, R., Moore, F. L., Spackman, J. R., and Zondlo, M. A.:
Atmospheric observations of Arctic Ocean methane emissions up to 82 degrees north,
Nat. Geosci.,
5, 318–321, 10.1038/ngeo1452, 2012.Krol, M., Houweling, S., Bregman, B., van den Broek, M., Segers, A., van Velthoven, P.,
Peters, W., Dentener, F., and Bergamaschi, P.: The two-way nested global chemistry-transport
zoom model TM5: algorithm and applications, Atmos. Chem. Phys., 5, 417–432, 10.5194/acp-5-417-2005, 2005.Kuhns, H., Etyemezian, V., Green, M., Hendrickson, K., McGown, M., Barton, K., and Pitchford, M.:
Vehicle-based road dust emission measurement – Part II: Effect of precipitation, wintertime
road sanding, and street sweepers on inferred PM10 emission potentials from paved and unpaved roads,
Atmos. Environ.,
37, 4573–4582, 10.1016/s1352-2310(03)00529-6, 2003.Lam, Y. F. and Fu, J. S.: A novel downscaling technique for the linkage of global and regional air
quality modeling, Atmos. Chem. Phys., 9, 9169–9185, 10.5194/acp-9-9169-2009, 2009.Lamarque, J.-F., Shindell, D. T., Josse, B., Young, P. J., Cionni, I., Eyring, V., Bergmann, D.,
Cameron-Smith, P., Collins, W. J., Doherty, R., Dalsoren, S., Faluvegi, G., Folberth, G., Ghan, S. J.,
Horowitz, L. W., Lee, Y. H., MacKenzie, I. A., Nagashima, T., Naik, V., Plummer, D., Righi, M.,
Rumbold, S. T., Schulz, M., Skeie, R. B., Stevenson, D. S., Strode, S., Sudo, K., Szopa, S.,
Voulgarakis, A., and Zeng, G.: The Atmospheric Chemistry and Climate Model Intercomparison Project
(ACCMIP): overview and description of models, simulations and climate diagnostics, Geosci. Model Dev.,
6, 179–206, 10.5194/gmd-6-179-2013, 2013.Liang, Q., Jaegle, L., Jaffe, D. A., Weiss-Penzias, P., Heckman, A., and Snow, J. A.:
Long-range transport of Asian pollution to the northeast Pacific: seasonal variations and transport pathways of carbon monoxide,
J. Geophys. Res.-Atmos.,
109, D23s07, 10.1029/2003jd004402, 2004.Lin, J.-T.: Satellite constraint for emissions of nitrogen oxides from anthropogenic,
lightning and soil sources over East China on a high-resolution grid, Atmos. Chem. Phys.,
12, 2881–2898, 10.5194/acp-12-2881-2012, 2012.Lin, J.-T. and McElroy, M. B.:
Impacts of boundary layer mixing on pollutant vertical profiles in the lower troposphere:
implications to satellite remote sensing,
Atmos. Environ.,
44, 1726–1739, 10.1016/j.atmosenv.2010.02.009, 2010.Lin, J.-T., Wuebbles, D. J., and Liang, X. Z.:
Effects of intercontinental transport on surface ozone over the United States: present and future assessment with a global model,
Geophys. Res. Lett.,
35, L02805, 10.1029/2007gl031415, 2008a.Lin, J.-T., Youn, D., Liang, X., and Wuebbles, D.:
Global model simulation of summertime U.S. ozone diurnal cycle and its sensitivity to PBL mixing, spatial resolution, and emissions,
Atmos. Environ.,
42, 8470–8483, 10.1016/j.atmosenv.2008.08.012, 2008b.
Lin, J.-T., Liu, Z., Zhang, Q., Liu, H., Mao, J., and Zhuang, G.: Modeling uncertainties for tropospheric
nitrogen dioxide columns affecting satellite-based inverse modeling of nitrogen oxides
emissions, Atmos. Chem. Phys., 12, 12255–12275, doi:10.5194/acp-12-12255-2012, 2012.Lin, J.-T., Pan, D., Davis, S. J., Zhang, Q., He, K., Wang, C., Streets, D. G., Wuebbles, D. J., and Guan, D.:
China's international trade and air pollution in the United States,
P. Natl. Acad. Sci. USA, 111, 1736–1741, 10.1073/pnas.1312860111, 2014.Liu, H. Y., Jacob, D. J., Bey, I., Yantosca, R. M., Duncan, B. N., and Sachse, G. W.:
Transport pathways for Asian pollution outflow over the Pacific: interannual and seasonal variations,
J. Geophys. Res.-Atmos.,
108, 8786–8800, 10.1029/2002jd003102, 2003.Liu, J., Logan, J. A., Murray, L. T., Pumphrey, H. C., Schwartz, M. J., and Megretskaia, I. A.:
Transport analysis and source attribution of seasonal and interannual variability of CO in the
tropical upper troposphere and lower stratosphere, Atmos. Chem. Phys., 13, 129–146,
10.5194/acp-13-129-2013, 2013.Montzka, S. A., Krol, M., Dlugokencky, E., Hall, B., Jockel, P., and Lelieveld, J.:
Small interannual variability of global atmospheric hydroxyl,
Science,
331, 67–69, 10.1126/science.1197640, 2011.
Murray, L. T., Logan, J. A., and Jacob, D. J.:
Interannual variability in tropical tropospheric ozone and OH: the role of lightning,
J. Geophys. Res.-Atmos.,
118, 11468–11480, 2013.Naik, V., Voulgarakis, A., Fiore, A. M., Horowitz, L. W., Lamarque, J.-F.,
Lin, M., Prather, M. J., Young, P. J., Bergmann, D., Cameron-Smith, P. J., Cionni, I.,
Collins, W. J., Dalsøren, S. B., Doherty, R., Eyring, V., Faluvegi, G., Folberth, G. A.,
Josse, B., Lee, Y. H., MacKenzie, I. A., Nagashima, T., van Noije, T. P. C., Plummer, D. A.,
Righi, M., Rumbold, S. T., Skeie, R., Shindell, D. T., Stevenson, D. S., Strode, S., Sudo, K.,
Szopa, S., and Zeng, G.: Preindustrial to present-day changes in tropospheric hydroxyl radical
and methane lifetime from the Atmospheric Chemistry and Climate Model Intercomparison Project
(ACCMIP), Atmos. Chem. Phys., 13, 5277–5298, 10.5194/acp-13-5277-2013, 2013.
Olivier, J. G., Van Aardenne, J. A., Dentener, F. J., Pagliari, V., Ganzeveld, L. N., and Peters, J. A.:
Recent trends in global greenhouse gas emissions: regional trends 1970–2000 and spatial
distributionof key sources in 2000,
Environm. Sci.,
2, 81–99, 2005.Ott, L. E., Pickering, K. E., Stenchikov, G. L., Allen, D. J., DeCaria, A. J., Ridley, B., Lin, R.-F., Lang, S., and Tao, W.-K.:
Production of lightning NO(x) and its vertical distribution calculated from three-dimensional cloud-scale chemical transport model simulations,
J. Geophys. Res.-Atmos.,
115, D04301, 10.1029/2009jd011880, 2010.
Parrish, D. D. and Zhu, T.:
Clean air for megacities,
Science,
326, 674–675, 2009.Pfister, G. G., Parrish, D. D., Worden, H., Emmons, L. K., Edwards, D. P.,
Wiedinmyer, C., Diskin, G. S., Huey, G., Oltmans, S. J., Thouret, V., Weinheimer, A.,
and Wisthaler, A.: Characterizing summertime chemical boundary conditions for airmasses
entering the US West Coast, Atmos. Chem. Phys., 11, 1769–1790, 10.5194/acp-11-1769-2011, 2011.Prather, M. J., Holmes, C. D., and Hsu, J.:
Reactive greenhouse gas scenarios: systematic exploration of uncertainties and the role of atmospheric chemistry,
Geophys. Res. Lett.,
39, L09803, 10.1029/2012GL051440, 2012.Price, C., Penner, J., and Prather, M.:
NOx from lightning. 1. Global distribution based on lightning physics,
J. Geophys. Res.-Atmos.,
102, 5929–5941, 10.1029/96jd03504, 1997.Prinn, R., Huang, J., Weiss, R., Cunnold, D., Fraser, P., Simmonds, P., McCulloch, A., Harth, C., Reimann, S., and Salameh, P.:
Evidence for variability of atmospheric hydroxyl radicals over the past quarter century,
Geophys. Res. Lett.,
32, L07809, 10.1029/2004GL022228, 2005.
Rienecker, E., Ryan, J., Blum, M., Dietz, C., Coletti, L., Marin III, R., and Bissett, W. P.:
Mapping phytoplankton in situ using a laser-scattering sensor,
Limnol. Oceanogr.-Meth.,
6, 153–161, 2008.Sauvage, B., Martin, R. V., van Donkelaar, A., Liu, X., Chance, K., Jaeglé, L.,
Palmer, P. I., Wu, S., and Fu, T.-M.: Remote sensed and in situ constraints on processes
affecting tropical tropospheric ozone, Atmos. Chem. Phys., 7, 815–838, 10.5194/acp-7-815-2007, 2007.Stavrakou, T. and Müller, J. F.:
Grid-based versus big region approach for inverting CO emissions using Measurement of Pollution in the Troposphere (MOPITT) data,
J. Geophys. Res.-Atmos. (1984–2012),
111, D15304, 10.1029/2005JD006896, 2006.Streets, D. G.:
An inventory of gaseous and primary aerosol emissions in Asia in the year 2000,
J. Geophys. Res.,
108, 8809, 10.1029/2002jd003093, 2003.van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Kasibhatla, P. S.,
and Arellano Jr., A. F.: Interannual variability in global biomass burning emissions from
1997 to 2004, Atmos. Chem. Phys., 6, 3423–3441, 10.5194/acp-6-3423-2006, 2006.van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M., Kasibhatla, P. S.,
Morton, D. C., DeFries, R. S., Jin, Y., and van Leeuwen, T. T.: Global fire emissions and the
contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009),
Atmos. Chem. Phys., 10, 11707–11735, 10.5194/acp-10-11707-2010, 2010.Vinken, G. C. M., Boersma, K. F., van Donkelaar, A., and Zhang, L.: Constraints on ship
NOx emissions in Europe using GEOS-Chem and OMI satellite NO2 observations,
Atmos. Chem. Phys., 14, 1353–1369, 10.5194/acp-14-1353-2014, 2014.Wang, Y., Jacob, D. J., and Logan, J. A.:
Global simulation of tropospheric O3-NOx-hydrocarbon chemistry, 1. Model formulation,
J. Geophys. Res.,
103, 10713–10725, 10.1029/98JD00158, 1998.Wild, O. and Akimoto, H.:
Intercontinental transport of ozone and its precursors in a three-dimensional global CTM,
J. Geophys. Res.-Atmos.,
106, 27729–27744, 10.1029/2000jd000123, 2001.Wofsy, S. C.:
HIAPER Pole-to-Pole Observations (HIPPO): fine-grained, global-scale measurements of climatically important atmospheric gases and aerosols,
Philos. T. R. Soc. A,
369, 2073–2086, 10.1098/rsta.2010.0313, 2011.Wuebbles, D. J., Lei, H., and Lin, J. T.:
Intercontinental transport of aerosols and photochemical oxidants from Asia and its consequences,
Environ. Pollut.,
150, 65–84, 10.1016/j.envpol.2007.06.066, 2007.Yienger, J. J. and Levy, H.:
Empirical-model of global soil-biogenic NOx emissions,
J. Geophys. Res.-Atmos.,
100, 11447–11464, 10.1029/95jd00370, 1995.Yu, H., Remer, L. A., Chin, M., Bian, H., Tan, Q., Yuan, T., and Zhang, Y.:
Aerosols from overseas rival domestic emissions over North America,
Science,
337, 566–569, 10.1126/science.1217576, 2012.Zhang, L., Jacob, D. J., Boersma, K. F., Jaffe, D. A., Olson, J. R., Bowman, K. W.,
Worden, J. R., Thompson, A. M., Avery, M. A., Cohen, R. C., Dibb, J. E., Flock, F. M.,
Fuelberg, H. E., Huey, L. G., McMillan, W. W., Singh, H. B., and Weinheimer, A. J.:
Transpacific transport of ozone pollution and the effect of recent Asian emission
increases on air quality in North America: an integrated analysis using satellite,
aircraft, ozonesonde, and surface observations, Atmos. Chem. Phys., 8, 6117–6136,
10.5194/acp-8-6117-2008, 2008.
Zhang, L., Jacob, D. J., Downey, N. V., Wood, D. A., Blewitt, D., Carouge, C. C.,
van Donkelaar, A., Jones, D. B. A., Murray, L. T., and Wang, Y.:
Improved estimate of the policy-relevant background ozone in the United States using
the GEOS-Chem global model with 1/2∘×2/3∘ horizontal
resolution over North America,
Atmos. Environ.,
45, 6769–6776, 10.1016/j.atmosenv.2011.07.054, 2011.Zhang, Q., Streets, D. G., Carmichael, G. R., He, K. B., Huo, H., Kannari, A., Klimont, Z.,
Park, I. S., Reddy, S., Fu, J. S., Chen, D., Duan, L., Lei, Y., Wang, L. T., and Yao, Z. L.:
Asian emissions in 2006 for the NASA INTEX-B mission, Atmos. Chem. Phys., 9, 5131–5153,
10.5194/acp-9-5131-2009, 2009.