Introduction
Ozone in the lower troposphere is a harmful air pollutant for both humans and the
ecosystem (Monks et al., 2015), and plays a central role in atmospheric
chemistry as the major source of hydroxyl radicals (OH) (Jacob, 2000). It is
also a short-lived greenhouse gas with a global mean radiative forcing of
0.40 (0.20–0.60) W m-2 since the preindustrial era (Myhre et al.,
2013; Stevenson et al., 2013). Tropospheric ozone is produced by
sunlight-driven photochemical oxidation of carbon monoxide (CO) and
hydrocarbons in the presence of nitrogen oxides (NOx ≡ NO + NO2). These ozone precursors are released not only from
anthropogenic sources such as industry and transportation, but also from a
number of climate-sensitive natural sources such as lightning, biomass
burning, and biogenic emissions. It is also transported from the stratosphere
(about 550 Tg yr-1 or 10 % of chemical production in the
troposphere) (Stohl et al., 2003; Stevenson et al., 2006). Tropospheric ozone burden
(present-day 337 ± 23 Tg) has enhanced 43 % since the
preindustrial era due to rapid industrialization (Young et al., 2013). Zhang
et al. (2016) recently revealed that increases in the tropospheric ozone
burden over the past 30 years were dominated by the equatorward redistribution
of anthropogenic emissions to developing regions such as East and South Asia,
increasing the interest in ozone pollution over those regions.
Unlike developed regions such as Europe and eastern US, where anthropogenic
emission reductions have led to surface ozone levels flatten or decrease
since 1990s (Parrish et al., 2012; Cooper et al., 2012; Oltmans et al., 2013;
Strode et al., 2015; Lin et al., 2017), developing countries such as China
and India have been experiencing anthropogenic emission rises and ozone
enhancements (Xu et al., 2008; Cooper et al., 2014; Sun et al., 2016; Wang et
al., 2017). Recent studies have shown that NOx emissions in China have
been decreasing since 2012 due to stringent air pollution controls (Krotkov
et al., 2016; Liu et al., 2017). However, air quality in India is
continuously deteriorating as indicated by increasing NO2 columns
observed by satellite (Krotkov et al., 2016; Geddes et al., 2016), and may
become worse in the near future considering projected trends in population
and the associated anthropogenic emissions (Ghude et al., 2016). Exposure to
ozone pollution in India is estimated to have caused 12 000 premature deaths
in 2011 due to chronic obstructive pulmonary disease (Ghude et al., 2016),
and up to a 36 % loss of wheat and other crop productions (Ramanathan et
al., 2014; Sinha et al., 2015). In addition, frequent deep convection in
tropical Asia allows the uplifted pollutants to influence global ozone
distribution (Lelieveld et al., 2001; Sahu et al., 2006; Beig and Brasseur,
2006; Park et al., 2007; Lawrence and Lelieveld, 2010; Srivastava et al.,
2012a; Lal et al., 2013). A better understanding of the processes controlling
lower tropospheric ozone over India thus becomes important to address its
local and global environmental effects.
Distinct seasonal transitions in prevailing wind and rainfall associated with
the monsoon circulation result in unique ozone variations in South and East
Asia. Winter monsoon prevails in October to March and brings dry and cool
weather conditions. With the onset of South Asian (East Asian) summer monsoon
in May, stronger westerlies (southerlies) bring marine air from Arabian Sea
(western Pacific) to the Indian subcontinent (East Asia), leading to
significant enhancements of cloud fractions and rainfall (Wang and LinHo,
2002; Ding and Chan, 2005). Decreases of tropospheric ozone with the summer
monsoon in South and East Asia have been reported from surface (Lal et al.,
2000; Naja and Lal, 2002; Naja et al., 2003; Beig et al., 2007; Reddy et al.,
2008; Wang et al., 2009; Kumar et al., 2010; Ding et al., 2013; Hou et al.,
2015), ozonesonde (Zhou et al., 2013; Lal et al., 2014; Ojha et al., 2014;
Sahu et al., 2014), aircraft measurements (Bhattacharjee et al., 2015; Ding
et al., 2008; Srivastava et al., 2015; Ojha et al., 2016), and satellite
observations (Liu et al., 2009; Dufour et al., 2010; Safieddine et al.,
2016). A number of modeling studies attribute the summertime ozone minimum
over India to transport of clean marine air (Lal et al., 2014; Sahu et al.,
2014) or reduced ozone photochemical production (Roy et al., 2008; Kumar et
al., 2012). This seasonality is in contrast to that at mid-latitudes where
surface ozone levels are usually higher in spring and summer due to stronger
stratosphere-to-troposphere transport and photochemistry (Parrish et al.,
2013; Cooper et al., 2014).
Most of the abovementioned studies used individual ground-based observations
or regional chemistry models to study seasonal or short-term interannual (up
to 5 years) variability of tropospheric ozone in India. Long-term
ground-based ozone observations are extremely scarce in South Asia (Cooper et
al., 2014). We also lack a comprehensive analysis of the spatiotemporal
distribution of lower tropospheric ozone at a domestic scale in India. In
particular, key processes that influence the tropospheric ozone budget over
India have not been analyzed and quantified. In this study, we present an
integrated analysis of the processes controlling lower tropospheric (surface
to 600 hPa) ozone concentrations over the terrestrial land of India and
their linkage to the South Asian monsoon. Satellite observations from the
Ozone Monitoring Instrument (OMI) over 2006–2014 and simulations with the
GEOS-Chem chemical transport model (CTM) for 1990–2010 are used to analyze
the spatial, seasonal, and interannual variability of lower tropospheric
ozone pollution over India, before and during the South Asian summer monsoon.
We will further examine the potential drivers of long-term trends in lower
tropospheric ozone over India.
Observations and model description
OMI satellite observations
The OMI instrument is onboard the NASA Earth Observing System (EOS) Aura
satellite launched in July 2004 with an ascending equator crossing time of
∼ 13:45 LT (local time) (Schoeberl et al., 2006). OMI is a
nadir-viewing instrument that measures backscattered solar radiation in the
0.27–0.5 µm wavelength range with a spectral resolution of
0.42–0.63 nm (Levelt et al., 2006). Its nadir footprint has a spatial
resolution of 13 × 24 km2 with near-daily global coverage
achieved by a wide view field of 114∘ and a 2600 km wide swath.
We use the OMI PROFOZ ozone profile retrievals developed by Liu et al. (2010)
based on the optimal estimation method (Rodgers, 2000). Details of the PROFOZ
product have been given in Liu et al. (2010) and Kim et al. (2013), and were recently
comprehensively validated by Huang et al. (2017, 2018). This OMI
ozone profile algorithm retrieves partial ozone columns for 24 layers with
about 2.5 km thickness for each layer. Here we grid the monthly mean OMI
data to the 2∘ × 2.5∘ horizontal resolution with
focus on the spatial and temporal distributions of Indian lower tropospheric
ozone concentrations for the period of 2006–2014. Comparisons of model
simulations with OMI retrievals need to consider OMI a priori profiles and
averaging kernel matrices as described in Zhang et al. (2010). OMI a priori
profiles are from the monthly ozone profile climatology of McPeters et
al. (2007). The degrees of freedom for signals (sum of the diagonal elements
of averaging kernel matrices) for OMI ozone retrievals are typically 0.3–0.5
in the lower troposphere over India. Previous evaluations of the OMI
retrievals with ozonesonde measurements have shown a clear improvement over
the a priori in the lower troposphere of the tropics
(30∘ S–30∘ N), and the mean retrieval biases in the
tropics are less than 6 % with little seasonality (Huang et al., 2017).
A summary of physical processes, anthropogenic and natural emissions
used in GEOS-Chem.
Descriptions
Sources and references
Physical processes
Wet deposition
Parameterization for scavenging in both convection and large-scale precipitation for soluble gases and aerosols
Mari et al. (2000), Liu et al. (2001) and Amos et al. (2012)
Dry deposition
Resistance-in-series algorithm
Wesely (1989), Zhang et al. (2001)
PBL mixing
Non-local mixing scheme
Lin and McElroy (2010)
Anthropogenic emissions
Global
Emissions Database for Global Atmospheric Research (EDGAR v4.2)
http://edgar.jrc.ec.europa.eu/
East Asia and South Asia
MIX emission inventory
Li et al. (2017)
United States
Environmental Protection Agency (EPA) National Emission Inventory (NEI)
https://www.epa.gov/air-emissions-inventories
Canada
Canadian Criteria Air Contaminant
http://www.ec.gc.ca/
Europe
European Monitoring and Evaluation Program (EMEP)
http://www.emep.int
Mexico
Big Bend Regional Aerosol and Visibility Observational study inventory (BRAVO)
Kuhns et al. (2005)
Natural sources
Biogenic emissions
Model of Emissions of Gases and Aerosols from Nature (MEGAN)
Guenther et al. (2006)
Lightning (NOx) emissions
Parameterization based on cloud top height, and spatially constrained by satellite observed lightning flashes
Price and Rind (1992), Sauvage et al. (2007) and Murray et al. (2012)
Soil NOx emissions
empirical parameterization of available nitrogen (N)
Hudman et al. (2012)
Biomass burning emissions
Atmospheric Chemistry and Climate Model Intercomparison Project (ACCIMP) for 1990–1996 and Global Fire Emission Database version 3 (GFED3) for 1997–2010
Lamarque et al. (2010) and van der Werf et al. (2010)
Methane
Prescribed over four latitudinal bands with year-specific mixing ratios constrained by measurements from the NOAA Global Monitoring Division (GMD). Concentration ranges over 1990–2010 are given below: 90–30∘ S (1663–1732 ppbv), 30∘ S–0∘ (1666–1741 ppbv), 0–30∘ N (1733–1801 ppbv), and 30–90∘ N (1792–1855 ppbv)
GEOS-Chem simulations
We use the GEOS-Chem global CTM (v10-01; http://www.geos-chem.org) in
this study. The model includes a detailed mechanism of
ozone–NOx–VOC–aerosol tropospheric chemistry (Bey et al., 2001; Park
et al., 2004; Mao et al., 2010, 2013) using the chemical kinetics recommended
by Jet Propulsion Laboratory (JPL) and International Union of Pure and
Applied Chemistry (IUPAC) (Sander et al., 2011; IUPAC, 2013), and photolysis
rates calculated by the Fast-JX scheme (Bian and Prather, 2002).
Stratospheric ozone chemistry is represented by the linearized ozone
parameterization (LINOZ) (McLinden et al., 2000), and other stratospheric
species are simulated using monthly averaged production and loss rates
archived from the Global Modeling Initiative (GMI) model (Murray et al.,
2013). Physical processes such as deposition and planetary boundary layer
(PBL) mixing schemes are summarized in Table 1. The model has been applied in
a number of studies on global and regional tropospheric ozone (Wang et al.,
2013; Fiore et al., 2014; Zhang et al., 2014; Lou et al., 2015; Yan et al.,
2016; Zhao et al., 2017). A recent model evaluation with global tropospheric
ozone datasets shows that GEOS-Chem (v10-01) provides an improved ozone
simulation relative to previous model versions (e.g., v8-01 in Zhang et al.,
2010) with no significant seasonal and latitudinal biases (Hu et al., 2017).
The model is driven by the Modern Era Retrospective-analysis for Research and
Application (MERRA) assimilated meteorological fields (Rienecker et al.,
2011). For input to GEOS-Chem, we downgrade the MERRA data to 2.5∘
longitude × 2∘ latitude and 47 vertical layers (extending
from surface to 0.01 hPa) from the raw resolution of 0.667∘
longitude × 0.5∘ latitude and 72 layers. Emissions in the
model are processed using the Harvard-NASA Emission Component (HEMCO) (Keller
et al., 2014). Year-specific anthropogenic emissions are from the Emissions
Database for Global Atmospheric Research (EDGAR v4.2 for emissions over
1990–2008, 2008 emissions are used for simulation afterwards), overwritten
with regional emission inventories as summarized in Table 1. Asian
anthropogenic emissions are from the MIX emission inventory (Li et al.,
2017).
Climate-sensitive natural ozone emissions such as biogenic non-methane
volatile organic compounds (NMVOCs) emissions, lightning NOx emissions, and
soil NOx emissions are implemented in GEOS-Chem as summarized in
Table 1. For the biomass burning emissions, we combine the Atmospheric
Chemistry and Climate Model Intercomparison Project (ACCIMP) (Lamarque et
al., 2010) for 1990–1996 and the Global Fire Emission Database version 3
(GFED3) (van der Werf et al., 2010) for 1997–2010. Comparison of GFED3 and
ACCMIP biomass burning CO emissions for their overlapping years (1997–2000)
suggests that ACCMIP is 30 % higher. Here we reduce the 1990–1996 ACCMIP
emissions by 30 % to reconcile the two inventories, although this may
lead to underestimates of biomass burning emission contributions for the
period. We find that biomass burning emissions of CO over India
(2.6 Tg a-1 (per annum) for 2006–2010) are relatively small compared
with anthropogenic emissions (61.9 Tg a-1). As atmospheric methane has
a relatively long lifetime (about 9 years), its concentrations are prescribed
in GEOS-Chem using year-specific measured concentrations from the NOAA Global
Monitoring Division (GMD) (see Table 1).
We conduct a standard simulation (BASE) with year-specific assimilated
meteorology and anthropogenic emissions from 1990 to 2010 with the initial
conditions generated by a 2-year spin-up simulation. We also conduct
sensitivity simulations by fixing one of the sources at the 1990 conditions,
including anthropogenic emissions (FEMIS), global methane concentrations
(FCH4), and biomass burning emissions (FBIOB) as summarized in Table 2.
Differences between the standard simulation and the sensitivity simulations
are then used to estimate influences of interannual changes in the specific
source on tropospheric ozone concentrations over India. All simulations are
conducted for 1990–2010 as constrained by the availability of MERRA
meteorology and emissions.
Spatial distributions of bimonthly mean (a) surface
temperature, (b) 850 hPa specific humidity, (c) cloud
cover, (d) anthropogenic NO emissions, and (e) biomass
burning CO emissions averaged for 2006–2010.
Ozone budgets diagnosed in GEOS-Chem
We analyze processes affecting lower tropospheric ozone budgets in each model
grid including ozone chemical production and loss, horizontal and vertical
transport, and dry deposition. These processes are diagnosed at every hour
and averaged to monthly mean. Net productions are calculated as the
differences between ozone chemical production and loss rates. Horizontal
transport for each grid is calculated by horizontal fluxes from or to
adjacent grids. Here we define transport from west to east or from south to
north as positive values. Vertical transport is estimated as the flux at the
top of the lower troposphere (600 hPa in this study) with positive values
representing downward transport. The GEOS-Chem model also includes cloud
chemistry (e.g., formation of sulfate aerosol via aqueous-phase reactions
with ozone and H2O2) and wet deposition of soluble gases. The two
processes have small effects on ozone directly due to its low solubility and
thus are not diagnosed here.
Seasonal variation of lower tropospheric ozone over India
Variations of meteorology and emissions
Variations in tropospheric ozone are subject to changes in precursor
emissions and meteorology conditions such as local temperature and transport
pattern. Displayed in Figs. 1 and 2 are the spatial and seasonal variations in MERRA
meteorological variables (surface temperature, 850 hPa specific humidity
(SPHU), and cloud cover), as well as anthropogenic NO emissions, and biomass
burning CO emissions over India averaged for the 5-year period (2006–2010).
Meteorological conditions in India have distinct seasonal variations
associated with the monsoon onset and retreat. Temperature increases from
winter (January) to late spring (May) with increasing solar radiation. The
onset of the summer monsoon in late May brings moist air from oceans and drives
strong air convergence and uplift over India, which lead to cloudy
conditions, large decreases in surface temperature (about 8 ∘C from
May to August), and enhancements in SPHU (5 g kg-1) (Figs. 1 and 2a).
Surface temperature and SPHU become relatively stable with the retreat of the
summer monsoon in September, and then both decrease in winter when the
winter monsoon brings cold and dry air.
Configuration of the GEOS-Chem simulations. “V” indicates that
specific inputs vary interannually in the simulation, and “1990” denotes
that the inputs are fixed to 1990 conditions.
Simulation
BASE
FEMIS
FBIOB
FCH4
Anthropogenic emissions
V
1990
V
V
Biomass burning emissions
V
V
1990
V
Global methane concentrations
V
V
V
1990
Monthly mean (averaged for 2006–2010) (a) surface
temperature (red) and 850 hPa averaged specific humidity (SPHU, blue);
(b) anthropogenic NO emissions (red) and biomass burning CO
emissions (blue); and (c) lower tropospheric ozone (averaged for
surface to 600 hPa, in unit of ppbv) from OMI satellite observations (black)
and GEOS-Chem model simulations with OMI averaging kernel matrices and a
priori profiles applied (dashed purple). The shading in (a, b) represents pre-summer, summer, and post-summer South Asian monsoon
periods. Colored bars in (c) show processes that affect lower
tropospheric ozone budget over the Indian land diagnosed in GEOS-Chem
simulations. Upward (U) and downward (D) vertical transport fluxes are
calculated at 600 hPa. Horizontal transport from west (W) to east (E) and
from south (S) to north (N), and downward vertical transport are defined as
positive values.
Figures 1 and 2b also show anthropogenic NO emissions of 5.44 Tg a-1
(per annum) in India, with emissions in winter (December, January, and
February) 3.7 % higher than summer (June, July, and August) due to more
active residential heating. Anthropogenic CO and NMVOC emissions over India
are 61.9 and 15.5 Tg a-1, respectively, with similar seasonal
variations as anthropogenic NO emissions (Fig. S1 in the Supplement).
Anthropogenic emissions are higher over northern India including the
Indo-Gangetic Plain (IGP, extending from the plain of the Indus River to the
plains of the Ganges River) and southern India, consistent with the distribution of
population density (Beig and Brasseur,
2006; Kumar et al., 2012). Biomass burning emissions in Southeast Asia are
active in March and April (Fig. 1e) and account for 62 % of the annual
biomass burning CO emissions in India (Fig. 2b). These emissions are likely
due to open burnings during post-harvesting seasons as agricultural field
clearance (Venkataraman et al., 2006; Sinha et al., 2014). Hot and dry air
conditions in March and April also likely enhance wildfire frequency and
strength (Westerling et al., 2006; Jaffe et al., 2008; Lu et al., 2016).
Model calculated biogenic isoprene emissions in India are 39.8 Tg a-1,
with a strong seasonality peaking in May and June (Fig. S1 in the
Supplement). Previous studies have shown that the ratio of NOx emissions
to CO and NMVOCs emissions over India is relatively small compared to other
regions at northern mid-latitudes (Lelieveld et al., 2001; Li et al., 2017).
Here we also examine the model simulated H2O2 / HNO3
concentration ratios, which have been used as an indicator of ozone
production chemical regime (Sillman et al., 1997; Zhang et al., 2016). We
find that the H2O2 / HNO3 ratios in the Indian lower
troposphere range from 1.0 to 5.0 for all four seasons, higher than those in
eastern China and the eastern US (Fig. S2 in the Supplement). This indicates
strong NOx-limited conditions for ozone chemical production over India,
consistent with previous studies (Kumar et al., 2012; Sharma et al., 2016).
Spatial distributions of bimonthly mean lower tropospheric ozone
from (a) OMI satellite observations and (b) GEOS-Chem model
results (with OMI averaging kernel matrices and a priori profiles applied).
Also shown are changes in lower tropospheric ozone burden contributed by
(c) net chemical production, (d) net horizontal transport,
and (e) vertical transport flux at 600 hPa. All values are averaged
for 2006–2010. Wind patterns are overlaid in (a). White dots
in (e) denote model grid cells where mean absolute vertical
velocities at 600 hPa exceed 5 mm s-1.
Variations in the pre-summer monsoon season
Figure 2c shows OMI observed and GEOS-Chem model simulated seasonal
variations of lower tropospheric ozone concentrations averaged over India and
over the 5-year (2006–2010) period. Figure 3 shows their spatial
distributions. Model results are applied with OMI averaging kernel matrices
and a priori profiles. OMI shows an annual mean lower tropospheric ozone
concentration of 45.9 ppbv over India with a maximum (54.1 ppbv) in the
pre-summer monsoon season and a minimum (40.5 ppbv) in July–August when the
summer monsoon reaches its strongest stage for the year. A similar seasonal cycle
of lower tropospheric ozone was found in the Tropospheric Emission
Spectrometer (TES) observations (Kumar et al., 2012). Spatially, observed
ozone concentrations are higher in northern India and IGP regions than
southern regions, consistent with reported surface measurements (Lal et al.,
2000; Naja and Lal, 2002; Beig et al., 2007; Reddy et al., 2008; Ojha et al.,
2012; Kumar et al., 2012). While model results are about 2 ppbv (4.4 %)
higher annually with the largest overestimate occurring in January–May, they
generally capture the seasonal and spatial variations of OMI observed lower
tropospheric ozone concentrations (r=0.81–0.97 for the spatial
variations of OMI observations vs. model results). Comparison of GEOS-Chem
surface ozone concentrations for the year 2010 with measurements at six Indian
surface sites reported by Sharma et al. (2016) also shows consistent seasonal
variations but with positive biases of about 8 ppbv (Fig. S3 in the
Supplement; 43.5 ± 7.4 ppbv in the model vs. 35.0 ± 8.4 ppbv in
measurements). Similar model overestimates over India are reported in Kumar
et al. (2012) using the WRF-Chem and MOZART models, and are likely due to
uncertainties in NOx emissions and the coarse model resolution.
Figures 2c and 3 also identify the processes affecting lower tropospheric
ozone burden over India. Here we separate our analysis into to four time
periods: pre-summer monsoon seasons (March–April), summer monsoon seasons
(May–August), post-summer monsoon seasons (August–October), and wintertime
(November–following March). In March and April, the mean lower tropospheric
ozone concentration over India increases by 9.8 ppbv from the wintertime
which can be explained by significant enhancements of ozone production (from
5.1 Tg in January to 10.9 Tg in April) and net production (from 2.9 to
5.0 Tg month-1). As anthropogenic emissions slightly decrease from
winter, ozone production enhancements are more likely associated with
ozone-favorable weather conditions such as stronger solar radiation and
increasing temperature. These changes not only enhance ozone photochemistry
efficiencies in the presence of NOx (Jacob and Winner, 2009; Doherty et
al., 2013; Pusede et al., 2015), but also increase natural emissions such as
biogenic NMVOCs and soil NOx emissions. Biogenic isoprene emissions over
India increase from 1.8 Tg month-1 in winter to 5.2 Tg month-1
in the pre-summer monsoon season. The soil NO emissions also increase from
0.08 to 0.21 Tg month-1 (Fig. S1 in the Supplement). Additional ozone
enhancements are due to intense biomass burning emissions. Strong ozone
production is seen in central eastern India in the pre-summer monsoon season
(Fig. 3c) associated with biomass burning regions (Fig. 1e).
Schematic diagram of the lower-tropospheric ozone budget
integrated over the Indian terrestrial land in the pre-summer monsoon season
(March and April) and in the summer monsoon season (June, July, and August).
Values are in unit of Tg month-1.
Horizontal transport (both west–east and north–south transport) decreases
Indian lower tropospheric ozone in March–April (Fig. 2c). As shown in
Fig. 3a, an anticyclonic wind pattern that dominates the Indian subcontinent
during January–April. Prevailing northeastern winds in northern India
efficiently transport the ozone-rich air downwind, and circulate into
southern India, resulting in a deficient budget in northern India but a
positive one in southern India
(Figs. 3d and S4 in the Supplement). This low
tropospheric transport pattern implies that southern India would likely
suffer ozone pollution transported from the ozone-rich northern India in
winter and pre-monsoon seasons. Vertical transport at 600 hPa has a positive
contribution (2.5 Tg month-1) to the lower tropospheric ozone budget
over India in March–April, partly due to the offset from downward import
over northern India and upward export over southern India (Fig. 3e). Northern India
with higher elevation is likely subject to more stratospheric ozone
influences in the period as evidenced by ozonesonde observations and modeling
studies (Kumar et al., 2010; Ojha et al., 2014, 2017), while southern India
is usually characterized by strong air uplift through convection.
Variations in the summer monsoon season
The monthly mean lower tropospheric ozone concentration over India decreases
from 54 ppbv in May to the seasonal minimum of 40.5 ppbv in August. We find
that ozone decrease starts earlier in southern India than in northern India.
It can be seen from Fig. 3a that ozone concentrations in the south are higher
in March–April than May–June, while ozone in the north reaches its annual
maximum in May–June. These patterns can be explained by the temporal
differences in the arrival of the summer monsoon (Kumar et al., 2012).
The onset of the South Asian summer monsoon in late May is usually signaled
by the prevailing westerly winds from the Arabian Sea
to the Bay of Bengal (Wang and Ho, 2002; Gadgil, 2003), leading to cloudy and
rainy weather conditions over the Indian subcontinent. Rainfalls efficiently
remove ozone precursors as shown from satellite observation of NO2
column (Kumar et al., 2012). Weak solar radiation and low temperature are not
favorable for ozone photochemical formation. In addition, water vapor from
marine air serves as a chemical loss of ozone at low NOx conditions
(Jacob et al., 2000). The onset of the summer monsoon also brings strong air
convergence and uplift as indicated by the large-scale air upward velocity in
May–August (Fig. 3e). Biomass burning emissions are negligible in the summer
monsoon season, and anthropogenic emissions also reach their annual minimum.
These changes combined lead to declines in monthly ozone chemical production
by 4.2 Tg (from 11.8 Tg in May to 7.6 Tg in August) as integrated over the
Indian terrestrial land.
Comparable to chemical production, changes in vertical transport also show a
large contribution to the decline of lower tropospheric ozone over India in
the summer monsoon season. The monthly vertical transport flux at 600 hPa
integrated over India is near zero in May, and reaches -3.3 Tg in August,
offsetting the net ozone production in this month (2.9 Tg). Strong vertical
convection with the 600 hPa uplift velocity greater than 5 mm s-1 in
July–August effectively uplifts ozone pollution from the lower troposphere
to the upper troposphere which means it can then be carried by the easterly jet to
other parts of the world, such as the Mediterranean (Park et al., 2007; Lawrence
and Lelieveld, 2010; Lal et al., 2013), affecting the global tropospheric
ozone distribution. We find that horizontal transport from the ocean can
lower ozone over northwestern India, especially in May and June when the
summer monsoon arrives, consistent with previous observations (Srivastava et
al., 2012b; Lal et al., 2014). Horizontal transport also enhances lower
tropospheric ozone concentrations in eastern India and the Bay of Bengal
(Fig. 3d). The overall contribution of horizontal transport to the Indian
lower tropospheric ozone budget is thus relatively small in July–August
(0.91 Tg month-1) relative to vertical export (Fig. 2c). Figure 4
summarizes changes in the lower tropospheric ozone budgets over India in the
pre-summer monsoon season (March–April) and the summer monsoon season
(June–July–August). One can see that decreases in the Indian lower
tropospheric ozone in the summer monsoon season are mainly associated with
the reduction in ozone net chemical production and strengthening upward
transport. Dry deposition of ozone to India shows a weak seasonal variation
(1.5 ± 0.15 Tg month-1; Figs. 2c and S1d in the Supplement).
Variations in the post-monsoon season and wintertime
Lower tropospheric ozone concentrations over India increase slightly in
September and reach a second peak in October, associated with increases in
precursor emissions and decreases in upward transport. With the southward
movement of solar radiation and the summer monsoon retreat, both surface
temperature and lower tropospheric specific humidity show decreasing patterns
(Fig. 1), leading to reductions in ozone chemical loss. In addition, the
retreat of the summer monsoon reduces air uplift over the Indian subcontinent
(Fig. 3e), which allows 2.8 Tg more ozone to remain in the Indian lower troposphere
in October compared to September (Fig. 2c).
Time series of mean (a) surface temperature and (b) lower
tropospheric ozone anomaly averaged for pre-summer monsoon seasons over the
Indian terrestrial land. OMI observed ozone anomalies are shown by black
circles, and model results from the BASE and FEMIS simulations are shown by
black and red lines, respectively. The green line shows the ozone anomaly
contributed by biomass burning emissions. Interannual correlation
coefficients (r) between surface temperature and ozone (number of regional
averages n=9 for observations and 21 for model results) are shown in the inset.
From November to the following March, ozone production in the Indian lower
troposphere reaches its annual minimum (4.7 Tg month-1 in December)
due to low temperature conditions. Horizontal wind patterns are similar to
those in the pre-summer monsoon season with a large negative contribution
(-5.2 Tg month-1 in November–February) to the Indian lower
tropospheric ozone budget. The emergence of the winter monsoon leads to
large-scale air subsidence over northern India, with a total import flux of
2.9 Tg month-1 in November–February. The low ozone net production and
strong horizontal export result in relatively low ozone levels in wintertime
(43.6 ppbv in the lower troposphere). It should be noted that some
observational studies reported the highest surface ozone concentrations at
several urban or semi-urban sites in southern India (e.g., Ahmedabad,
23∘ N, 73∘ E; Pune, 18∘ N, 74∘ E;
Trivandrum, 8∘ N, 77∘ E) in wintertime instead of the
pre-summer monsoon season likely due to high local precursor emissions (Beig
et al., 2007; David and Nair, 2011; Kumar et al., 2012; Lal et al., 2014;
Sahu et al., 2014). Ozonesonde observations at the Ahmedabad and Hyderabad
airports (Lal et al., 2014; Sahu et al., 2014) indicate that ozone
concentrations in the free troposphere (near 5 km) are higher in the
pre-summer monsoon season than winter, consistent with OMI observations in
our study.
Interannual variability of lower tropospheric ozone over India
Correlation with surface temperature in pre-summer monsoon
seasons
We now analyze interannual variability of lower tropospheric ozone in India
with focus on pre-summer monsoon seasons when concentrations are highest and
summer monsoon seasons when concentrations are subject to monsoon
variability. As discussed in Sect. 3.2, tropospheric ozone
concentrations in the pre-summer monsoon season are largely controlled by
ozone production enhancements due to ozone-favorable weather conditions such
as high temperature, and are likely amplified by biomass burning emissions.
We also find strong interannual correlations between surface temperature
and lower tropospheric ozone concentrations over India. Figure 5 shows that
9-year (2006–2014) time series of OMI observations have a positive
interannual correlation (r=0.55) with MERRA surface temperature averaged
over India in pre-summer monsoon seasons. GEOS-Chem model results (the BASE
simulation) for the period of 1990–2010 captured this positive correlation
(r=0.58), and the correlation persists when both variables are detrended
(r=0.50).
Figure 6 shows the spatial distribution of correlation coefficients between
the lower tropospheric ozone concentration (OMI vs. GEOS-Chem) and surface
temperature in pre-summer monsoon seasons. Stronger correlations (r≈0.8) are found in northern India (e.g., the IGP regions) and southern India
where NOx emissions are high. The dependence of the ozone–temperature
relationship on NOx emission levels is consistent with previous studies
reflecting higher ozone formation potential over high NOx regions (Jacob
and Winner, 2009; Doherty et al., 2013; Pusede et al., 2015). We examine the
overall sensitivity of the ozone–temperature correlation in India to
emissions. As shown in Fig. 5, the sensitivity simulation with fixed
anthropogenic emissions (FEMIS) shows a slightly lower correlation (r=0.54). It indicates that despite the dependence of ozone–temperature
correlations on NOx emission levels regionally, as shown above, the mean
correlation over India shows a positive effect of temperature on ozone
chemical production, which can be driven by solar radiation affecting both
temperature and ozone production rates, as well as the sensitivities of
natural sources to temperature as discuss above.
Spatial distribution of correlation coefficients between seasonal
mean surface temperature and lower tropospheric ozone in pre-summer monsoon
seasons calculated for each model grid from (a) OMI satellite
observations for 2006–2014 and (b) the GEOS-Chem BASE simulation
for 1990–2010. Black dots denote statistical significance
(p value < 0.05).
We also calculate the interannual variability contributed by biomass burning
emissions as ozone differences between the BASE simulation and the FBIOB
simulation. Biomass burning emissions have a large interannual variability
with CO emissions ranging from 0.97 to 4.7 Tg over 1990–2010 (Fig. S5 in
the Supplement). As can be seen from Fig. 5, the ozone interannual
variability contributed by biomass burning emissions is weakly correlated
with the BASE lower tropospheric ozone (r=0.29). However, they are
important in high ozone and high temperature years. In years such as 1999 and
2010, biomass burning caused 1.5–2.2 ppbv higher ozone, enhancing the
variability of lower tropospheric ozone. This eventuality has also been noted
in the western US (Jaffe et al., 2008; Lu et al., 2016) as high temperature
conditions favored both biomass burning emissions and ozone production, and
thus amplified lower tropospheric ozone concentrations.
Impact of monsoon strength in summer monsoon seasons
We have also shown above that lower tropospheric ozone concentrations over
India vary relative to the onset and retreat of the South Asian summer
monsoon. The interannual variability of lower tropospheric ozone over India
in the summer monsoon seasons (May–August) can then be affected by the
strength of the South Asian summer monsoon. To quantify this relationship, we
calculate the monsoon strength using the monsoon index proposed by Li
and Zeng (2002) which has been applied to quantify impacts of the East Asian
monsoon on air pollution over China (Zhu et al., 2012; Yang et al., 2014).
The monsoon index is first calculated for each model grid (δ(i,j))
in the Northern Hemisphere in the month m and year y as
δy,mi,j=V‾1(i,j)-V‾y,m(i,j)(V‾1(i,j)+V‾7(i,j))/2-2,
where V‾ represents monthly mean wind speed from the MERRA
dataset, and V‾1 and V‾7 are climatological
(1990–2010 in our study) monthly wind speed in January and July,
respectively. The norm of a given variable A is defined as
A=∫∫A2dS1/2,
where S represents the spatial area of a model grid cell. Details for the
calculation of A are given in Li and Zeng (2002) and Zhu
et al. (2012). In this study, we then average δ(i,j) over the region
of 35–90∘ E, 5–35∘ N (Fig. S2 in the Supplement) at
850 hPa and over May–August to represent the South Asian summer monsoon
index (SASMI).
Figure 7 shows the time series of SASMI anomalies relative to the 1990–2014
climatology, and their correlations with OMI observed and model simulated
lower tropospheric ozone concentration anomalies over India for the summer
monsoon seasons. Positive and negative SASMI values represent strong and weak
summer monsoons, respectively. We find no significant trend in the South
Asian summer monsoon strength over 1990–2014. Interannual variations of
Indian regional mean lower tropospheric ozone concentrations are
significantly negative correlated with the SASMI, as can be seen for both OMI
observations (r=-0.46, 2006–2014, n=9) and GEOS-Chem BASE results
(r=-0.52, 1990–2010, n=21). Removing interannual changes in
anthropogenic emissions (FEMIS) results in a stronger correlation of -0.72,
reflecting the dominant role of monsoon strength on the interannual
variability of Indian lower tropospheric ozone in summer monsoon seasons. We
find that the correlations between SASMI and simulated ozone are even
stronger at the surface level (r=-0.72 for BASE and r=-0.83 for
FEMIS; figure not shown).
Time series of (a) South Asian summer monsoon index and (b) lower
tropospheric ozone averaged over India in summer monsoon seasons. Values are
anomalies over 1990–2010. OMI observed ozone anomalies are shown by black
circles, and model results from the BASE and the FEMIS simulation are shown
by black and red lines, respectively. Interannual correlation coefficients
with the monsoon index are shown inset.
Differences in May–August monthly mean (a) lower tropospheric
ozone concentration, (b) surface temperature, (c) 850 hPa specific humidity,
(d) lower tropospheric net ozone production, (e) lower tropospheric net
horizontal transport with wind vectors overlaid, and (f) vertical
transport at 600 hPa between the lowest and highest SASMI conditions. Values
are calculated using averages of the five lowest SASMI years minus averages of
the five highest SASMI years. Values inset are averages (a–c) or totals
(d–f) over the Indian terrestrial land. Red dots in (f) denote regions with
stronger air uplift in weak compared to strong summer monsoon years.
Yang et al. (2014) previously found positive correlations between the East
Asian summer monsoon strengths and surface ozone concentrations over mainland
China. They attributed higher surface ozone in stronger summer monsoon years
to a smaller outflow of ozone to the East China Sea. Our results show the
opposite response from lower tropospheric ozone to summer monsoon strengths
over India. To understand the negative correlations, we illustrate in Fig. 8
the differences in meteorological variables, lower tropospheric ozone
concentrations, and relevant processes between the weakest and strongest
monsoon years (represented by averages over 5 years with the lowest and the
highest SASMI over 1990–2010, respectively). We focus on model results from
the FEMIS simulation to exclude the influence of interannual changes in
anthropogenic emissions. We find that lower tropospheric ozone concentrations
averaged over India are 3.4 ppbv higher in weak summer monsoon years than
those in strong monsoon years. Weak summer monsoon conditions show higher
surface temperature (1.1 ∘C), drier air (-0.5 g kg-1), and
lower cloud cover over India, together accounting for a higher ozone net
production of 0.4 Tg month-1 than the strong summer monsoon conditions
(Fig. 8). In addition, weaker convergence and convection in weak summer
monsoon years can cause the total upward ozone flux to be 0.2 Tg month-1
smaller, but this is closely offset
by stronger horizontal outflows. Together, we find that the difference in ozone
net production is the key factor explaining the difference in the lower
tropospheric ozone burden over India between strong and weak summer monsoon years.
Long-term trend and contributing drivers
As for the long-term trend in the Indian lower tropospheric ozone, the 9-year
OMI observations appear to be too short to provide a long-term trend
estimate. As can be seen from Figs. 5 and 7, OMI observed mean Indian lower
tropospheric ozone concentrations over 2006–2014 show large positive trends
of 0.42 ± 0.38 ppbv yr-1 (mean ± 95 % confidence
level, p value = 0.03) for the pre-summer monsoon seasons and 0.58 ± 0.71 ppbv yr-1 (p value = 0.09) for the summer monsoon
seasons. However, these 9-year trends are mainly driven by the low values from the years 2006 to 2008. It should be acknowledged that the OMI dataset are likely
influenced by the OMI row anomaly that potentially results in overestimates
in ozone trends over the tropics (Huang et al., 2017).
Here we analyze the long-term trends over the period 1990–2010 simulated by the
GEOS-Chem model. Figure 9 shows the spatial distribution of simulated lower
tropospheric ozone trends for the annual average, as well as for averages in
pre-summer monsoon seasons and summer monsoon seasons. Annually, the Indian
lower tropospheric ozone is increasing at a statistically significant rate of
0.19 ± 0.07 (p value < 0.01) ppbv yr-1. Larger ozone
trends (0.27 ± 0.12 ppbv yr-1, p value < 0.01) are shown
in pre-summer monsoon seasons than those in summer monsoon seasons
(0.16 ± 0.14 ppbv yr-1).
Simulated (1990–2010) trends in lower tropospheric ozone and
factors contributing to simulated trends. Trends are calculated for annual
averages (left column), pre-summer monsoon seasons (central column), and
summer monsoon seasons (right column). Values inset are mean trend in
ppbv yr-1 averaged over the Indian land. Black dots in the first rows
denote significant trends in BASE simulations. Trends contributed by
interannual changes in anthropogenic emissions (EMIS), global methane
concentrations (CH4), and biomass burning emissions (BIOB) are
estimated.
The sensitivity simulations allow us to quantify potential ozone trend
drivers, including changes in anthropogenic emissions, biomass burning
emissions (Fig. S5 in the Supplement), and global methane concentrations.
Figure 9 also shows the contributions from each factor calculated as differences
in trends between the BASE simulation and the sensitivity simulations.
Changes in anthropogenic emissions largely explain the increasing trends in lower tropospheric ozone over India, which account for 0.18, 0.21, and
0.19 ppbv yr-1 of the annual, pre-summer monsoon seasonal, and summer
monsoon seasonal means, respectively. Increasing anthropogenic NO emissions
(about 3 % yr-1, Fig. S5 in the Supplement) likely dominate
ozone increases due to the NOx-limited ozone production condition over
this region as discussed above. Global methane concentration increases
also contribute to small increases of 0.02 ppbv yr-1 in the lower
tropospheric ozone, and the contributions are larger in the middle and upper
troposphere (figure not shown). Biomass burning emissions in East and South
Asia showed a decreasing trend over 1990–2010 (Fig. S5 in the Supplement) that
resulted in small negative trend contributions in the lower tropospheric
ozone. As anthropogenic emissions in India are projected to rise in the
future (Ghude et al., 2016), one may expect further increases in Indian
tropospheric ozone. Continuous ozone monitoring measurements are required to
better quantify long-term changes in tropospheric ozone over India.
Conclusions
In summary, we investigated the processes controlling seasonal and
interannual variations in the lower tropospheric ozone concentrations over India
and their linkages to the South Asian summer monsoon. We use OMI satellite
observations of lower tropospheric ozone made over 2006–2014 and GEOS-Chem
global model simulations over 1990–2010 driven by assimilated
meteorological fields and best-known emissions to better quantify the
controlling processes.
Both OMI satellite observations and GEOS-Chem simulations show that ozone in
the Indian lower troposphere (surface to 600 hPa) peaks in the pre-summer
monsoon season (March–April, 54.1 ppbv), and decreases dramatically to the
annual minimum (40.5 ppbv) during the summer monsoon season (May–August).
It then increases again in the post-summer monsoon season (September–October), and
levels out in winter. GEOS-Chem process analyses on the Indian lower
tropospheric ozone budget indicate that the pre-summer monsoon seasonal ozone
maximum is mainly driven by enhanced ozone chemical production due to
favorable meteorological conditions (strong solar radiation with low cloud
cover, high temperature, and relatively dry air), as well as active biomass
burning emissions in spring. We find that overall horizontal transport is
important for ventilating lower tropospheric ozone, while vertical transport
has a positive contribution on the lower tropospheric ozone budget over India
in the pre-summer monsoon season.
The onset and evolution of the summer monsoon in May–August brings low
temperatures, weak solar radiation conditions and moist air from the Arabian
Sea, leading to a significant reduction in ozone production
(-4.2 Tg month-1 from May to August) over India. We also highlight
the contribution of upward transport on the Indian lower tropospheric ozone
budget in June–August (-2.9 Tg month-1), which is comparable to the
change in ozone production, and potentially transports Indian ozone to other
parts of the world. In the post-summer monsoon season (September–November),
lower tropospheric ozone over India increases again due to weakening ozone
upward transport associated with the summer monsoon retreat. In winter, low
temperature conditions limit ozone production, and strong horizontal outflows
largely lower the ozone burden over India.
We show that interannual variability of lower tropospheric ozone over India
in pre-summer monsoon and summer monsoon seasons are strongly linked to
climate variability. Both OMI observed and model simulated lower tropospheric
ozone in pre-summer monsoon seasons are significantly correlated with surface
temperature (r=0.55–0.58). Higher ozone–temperature correlations (r>0.7) are found over high NOx emission regions. Lower
tropospheric ozone in summer monsoon seasons is strongly influenced by the
South Asian monsoon strength. Comparing the five weakest South Asian summer
monsoon years with the five strongest monsoon years, we find that lower
tropospheric ozone levels over India are 3.4 ppbv higher in the weakest
monsoon years. This is mainly due to higher temperature, drier air, and lower
cloud cover which enhances ozone production, as well as less ozone vertical
export. These interannual variations indicate that lower tropospheric ozone
concentrations in India are potentially affected by decadal climate
variability such as the El Niño–Southern Oscillation (Kumar et al.,
1999) and Atlantic Multidecadal Oscillation (Lu et al., 2006).
We also analyzed the long-term trends in lower tropospheric ozone over
India and their drivers as suggested by the GEOS-Chem model. Model results
over 1990–2010 show an annual mean trend of
0.19 ± 0.07 ppbv yr-1, which is mainly driven by
rising anthropogenic emissions with small contributions
(0.02 ppbv yr-1) from global methane concentration increases. Our
study emphasizes the importance of understanding tropospheric ozone changes and
drivers at multiple time scales in India. Ozone pollution in India may become
more severe due to increasing anthropogenic emissions and population, and could
potentially exert large impacts on the global tropospheric ozone distribution
due to frequent deep convection over South Asia. Analyses of long-term ozone
measurements in India are needed to better understand ozone variations and the
associated environmental effects.