Simulation of tropical tropospheric ozone variation from 1982 to

PUBLICATIONS
Journal of Geophysical Research: Atmospheres
RESEARCH ARTICLE
10.1002/2016JD024945
Key Points:
• Correlation between tropical O3
anomalies and N34A shows a
dipole structure
• Direct transport anomalies contribute
to O3 anomalies in WP during
ENSO event
• Chemical process anomalies induced
by specific humidity lead to O3
anomalies in CP and EP
Supporting Information:
• Supporting Information S1
Correspondence to:
B. Zhu,
[email protected]
Citation:
Hou, X., B. Zhu, D. Fei, X. Zhu, H. Kang,
and D. Wang (2016), Simulation of
tropical tropospheric ozone variation
from 1982 to 2010: The meteorological
impact of two types of ENSO event,
J. Geophys. Res. Atmos., 121, 9220–9236,
doi:10.1002/2016JD024945.
Received 15 FEB 2016
Accepted 8 JUL 2016
Accepted article online 12 JUL 2016
Published online 13 AUG 2016
Simulation of tropical tropospheric ozone variation
from 1982 to 2010: The meteorological impact
of two types of ENSO event
Xuewei Hou1, Bin Zhu1, Dongdong Fei1, Xiaoxin Zhu1, Hanqing Kang1, and Dongdong Wang1
1
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Joint International Research Laboratory of
Climate and Environment Change (ILCEC), Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of
Information Science & Technology, Nanjing, China
Abstract The effects of two types of ENSO events on tropical ozone (O3) variations from 1982 to 2010,
and the mechanisms underlying these effects, were analyzed using observations and model simulations.
Tropospheric column O3 anomalies (TCOA) during canonical El Niño were different from El Niño Modoki.
Absolute TCOA values are larger during canonical El Niño than during El Niño Modoki in most regions. La Niña
events were not separated into the different types because of their similarity in terms of sea surface
temperature patterns. TCOA in La Niña showed a reversed dipole from canonical El Niño. During canonical
El Niño, anomalous downward motion together with suppressed convection weakened O3 outflow from the
troposphere, causing an increase in tropospheric O3 over western Pacific. Over central and eastern Pacific,
decreased O3 concentrations resulted primarily from a change in net chemical production of O3. The change
in net O3 chemical production relates to increased levels of HOx under wetter condition. During El Niño
Modoki, transport and chemical fluxes were similar but weaker than during canonical El Niño. During La Niña,
O3 anomalies and transport fluxes were the opposite of those during the El Niño Modoki. Stratospheric
O3 played a key role in the development of O3 anomaly above 250 hPa during ENSO events, contributing
>30% to the O3 anomalies. The change in free tropospheric O3 affected the O3 anomaly from 850 hPa to
200 hPa (60% of O3 anomaly). The contribution of O3 from planetary boundary layer was concentrated at the
surface, with a contribution of <15%.
1. Introduction
El Niño Southern Oscillation (ENSO) is the dominant mode of interannual variability in tropical climates.
Its impact on tropospheric circulation causes significant changes in the distribution of ozone (O3) [Oman
et al., 2013]. During warm ENSO events (El Niño), warm ocean temperatures in the western Pacific (to the west
of the dateline) shift gradually eastward into the eastern Pacific, eventually reaching as far as South America.
This movement is accompanied by enhanced convection and it coincides with dramatic changes in the
tropical tropospheric wind [Rasmusson and Carpenter, 1982]. During cold ENSO events (La Niña), a generally
opposite trend is observed, in which warm oceanic temperatures to the west of the dateline, induce colder
than normal sea surface temperatures (SSTs) and lower than normal tropical convection events.
©2016. American Geophysical Union.
All Rights Reserved.
HOU ET AL.
Coincident with wildfires over Indonesia during drought conditions induced by the 1997/1998 El Niño event,
unprecedented levels of tropospheric O3 were measured [Chandra et al., 1998; Fujiwara et al., 1999, 2000;
Doherty et al., 2006]. This event heightened interest in the impact of ENSO on tropospheric O3. Using Total
Ozone Mapping Spectrometer (TOMS) data acquired at the peak of the 1997/1998 El Niño, Chandra et al.
[1998] derived tropospheric column O3 (TCO) decreases of 4–8 DU over the eastern Pacific and increases
of 10–20 DU over the western Pacific and Indonesia. Ziemke and Chandra [1999, 2003] examined ENSOinduced tropospheric O3 changes over longer periods using a regression model on 20–30 years of TOMS
TCO data, and suggested that interannual changes in TCO from combined La Niña and El Niño events are
the dominant source of interannual variability of O3 in the tropics. La Niña leads to a nearly opposite spatial
distribution of TCO in the tropics compared with El Niño. Anomalous wildfires always occurred around
Indonesia during most El Niño events. Chandra et al. [2009] and Ziemke et al. [2009] quantified the effects
of 2006 Indonesia fire emission on TCO. They suggested that the impact of biomass burning on O3 is significant within and near the burning regions with increases of ~10–25% in the tropospheric column relative to
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average background concentrations. Chandra et al. [2007] analyzed the effects of a weak El Niño on tropospheric O3 based on Aura Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) measurements, and suggested tropospheric O3 increased by 10–20% over the western Pacific region and decreased
by about the same amount over the eastern Pacific region. Similar TCO changes have been simulated for
ENSO events by Sudo and Takahashi [2001]; Chandra et al. [2002]; Zeng and Pyle [2005]; Doherty et al.
[2006], and Oman et al. [2011, 2013]. They attributed these changes in TCO to large-scale circulation processes associated with the shift in the tropical convection pattern and to large emissions of O3 precursors
from pronounced biomass burning around Indonesia. El Niño events produce enhanced convection over
the eastern Pacific and suppressed convection in the western Pacific. Generally, the opposite conditions prevail during La Niña events. Thus, this leads to an east–west dipole difference in TCO across the Pacific.
Chandra et al. [2002, 2009] and Doherty et al. [2006] revealed that, because of suppressed convection, the
effect of biomass burning over Indonesia was largely confined to a small spatial area and mainly to the lower
troposphere. Therefore, this study is focused on the effects of meteorological changes caused by the ENSO,
and not so much on the effects of the Indonesian biomass burning.
Several studies have investigated ENSO-related changes in atmospheric chemistry that influence tropospheric O3 production [Sudo and Takahashi, 2001; Sekiya and Sudo, 2012]. In situ O3 production occurs via
the oxidation of carbon monoxide and volatile organic compounds in the presence of nitrogen oxides
(NOx). The destruction of O3 occurs via photolysis and reactions with radicals. Sudo and Takahashi [2001]
investigated the changes in humidity, downward/upward motion, and suppressed/enhanced convection
associated with ENSO events in the tropics that affect the tropospheric O3 loss rate and lifetime of O3 and
its precursors. Stevenson et al. [2005] demonstrated that interannual variations in global isoprene and lightning NOx emissions were strongly affected by ENSO. Doherty et al. [2006] highlighted that during the peak
of El Niño events, tropospheric column NOx decreased over Indonesia, which modified O3 chemical production and loss. Moreover, several previous studies have revealed that ENSO also influences tropospheric O3
through changes in the stratosphere–troposphere exchange [Langford et al., 1998; James et al., 2003; Zeng
and Pyle, 2005].
Recently, based on the spatial distribution of the maximum SST anomaly, a number of studies have argued
that more than one type of ENSO exists [Larkin and Harrison, 2005a, 2005b; Ashok et al., 2007; Yu and Kao,
2007; Kao and Yu, 2009; Yu and Kim, 2010; Kug et al., 2009, 2010; Kug and Ham, 2011]. For eastern Pacific
ENSO events (also called canonical ENSO events), the maximum of the SST anomaly is generally located in
the cold tongue region of the eastern Pacific. Central Pacific ENSO events (also called ENSO Modoki events)
are characterized by the maximum of the SST anomaly being located in the central Pacific, farther west than
during canonical ENSO events. These two types of ENSO events lead to different effects on circulation and
climate variability [Weng et al., 2009; Ashok and Yamagata, 2009; Feng et al., 2010, 2011a; Feng and Li
[2011b]; Xie et al., 2012; Gouirand et al., 2013; Tedeschi et al., 2013; Yadav et al., 2013]. Ashok et al. [2007] noted
that during El Niño Modoki events, two anomalous Walker circulation cells exist (compared with the single
cell related to canonical El Niño events). The different climatic impacts and mechanisms of the two types
of ENSO event have different effects on O3. As mentioned above, the response of O3 to canonical ENSO events
has been widely investigated in previous studies. However, the relationship between ENSO Modoki events and
O3, and the effects of ENSO Modoki events on the interannual variability of O3 compared with canonical ENSO
events have not yet received sufficient attention. Based on ERA-Interim O3 data for 1980–2010, Xie et al. [2014a,
2014b] suggested that El Niño Modoki events modulate the tropical upwelling that significantly influences
mid–lower stratospheric O3. Furthermore, they highlighted that El Niño Modoki events have effects on global
O3 that are more significant than canonical El Niño events. The present study examined the concept proposed
by of Xie et al. [2014a, 2014b] from different perspectives. The objective was to explain the mechanisms
underlying these effects using satellite observational data and simulations with the Model of Ozone and
Related chemical Tracers, version 4 (MOZART-4).
2. Data and Model
2.1. Data Description
We evaluated the performance of MOZART-4 using satellite observations and ozonesonde data. The monthly
geographical distribution and interannual variation of the tropical TCO distribution were evaluated using
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Table 1. The Locations, Elevations and Data Periods of Ozonesonde Sites
satellite observations derived by
TOMS on Nimbus 7 and Earth
Probe, and OMI/MLS data [Ziemke
Hilo (19.43°N, -155.04°E)
11 m
1995-2011
et al., 2006]. The TCO from TOMS
Paramaribo (5.81°N, -55.21°E)
7m
2000-2010
Watukosek (-7.5°N, 112.6°E)
50 m
1998-2011
was calculated using the convecSamoa (-14.23°N, -170.56°E)
82 m
1995-2009
tive cloud differential (CCD)
Fiji (-18.13°N, 178.4°E)
6m
1997-2011
method [Ziemke et al., 1998]. We
used a data series covering the
24 year period of 1982–2009 (with missing data for 1994–1996) that combined TOMS/CDD data from
1982–2004 with OMI/MLS data from 2005–2009. An ozonesonde climatology based on O3 soundings from
five sites in the tropics for the years 1995–2011 was also used for the model evaluation [Tilmes et al., 2012;
Thompson et al., 2007]. The locations of the ozonesonde sites are listed in Table 1. Outgoing Longwave
Radiation (OLR) data from NOAA [Liebmann and Smith, 1996] were also used to analyze the impact of convection anomalies on O3 anomalies.
Site
Elevation
Period
The Niño3.4 index comprises SSTs averaged across the region (5°S–5°N, 170°W–120°W), and their monthly
anomalies relative to the 1982–2015 means constitute the indices commonly used in studies of ENSO events.
We obtained these data from http://www.cpc.ncep.noaa.gov/data/indices/.
2.2. Model Description
Most climate models have difficulty in distinguishing the two types of El Niño and La Niña events; however,
they are better at simulating the two types of El Niño event than they are at simulating the two types of La
Niña event [Kug and Ham, 2011]. MOZART is an offline global tropospheric chemical transport model driven
by reanalysis data of meteorological fields, which is useful for investigating the effects of ENSO on chemical
tracers. The model has been evaluated extensively through comparison with observations from ozonesonde,
aircraft, and surface monitoring stations. A description and evaluation of MOZART has been conducted by
Brasseur et al. [1998]; Horowitz et al. [2003]; Emmons et al. [2010] and Hou et al. [2013, 2014, 2015].
MOZART’s output data have been shown to simulate concentrations of tropospheric O3 and its precursors
reasonably well [Horowitz et al., 2003; Pfister et al., 2008a, 2008b; Emmons et al., 2010; Hou et al., 2013]. We
used MOZART version 4 in this study, which has been described and evaluated in detail by Emmons
et al. [2010].
In MOZART-4, the lightning strength depends on the convective cloud top height, with a stronger dependence over land than over the ocean [Price et al., 1997]. The emission of NO from lightning was calculated
online [Emmons et al., 2010]. The vertical distribution of NO emissions was parameterized following the work
of Price et al. [1997], with a reduced proportion of emissions produced near the surface, similar to that used by
DeCaria et al. [2006]. Estimates of NO production by lightning (LNO) have been performed for many decades
into the future. Some recent studies have published estimates that vary between 0.9 Tg[N]/year [Nesbitt et al.,
2000] and 12.2 Tg[N]/year [Price et al., 1997]. Most studies estimate total LNO production to be close to 5 Tg
[N]/year [Huntrieser et al., 1998; Wang et al., 1998; Tie et al., 2002]. The fifth Intergovernmental Panel on
Climate Change (IPCC) assessment report suggested the total LNO production to be 3–5 Tg[N]/year
[Intergovernmental Panel on Climate Change, 2013]. In this study, LNO was calculated to be approximately
3.5 Tg[N]/year, which is consistent with the results of the previous studies.
MOZART-4 was run with the standard chemical mechanism [see Emmons et al., 2010 for details]. It was driven
by Modern-Era Retrospective analysis for Research and Applications (MERRA) meteorological parameters,
with horizontal resolution of approximately 1.9° × 2.5° and 56 vertical levels from the surface to approximate
2 hPa. MERRA was undertaken by NASA’s Global Modeling and Assimilation Office with two primary objectives: to place observations from NASA’s Earth Observing System satellites in a climate context and to
improve upon the hydrologic cycle represented in the earlier generations of reanalyses. From 1979 to the
present, MERRA has achieved significant improvements in precipitation and water vapor climatology
[Rienecker et al., 2011].
The initial conditions and emissions were based on the NCAR Community Data Portal. The majority of the
anthropogenic emissions used for this simulation came from the Precursors of Ozone and their Effects in
the Troposphere (POET) database for 2000, which includes anthropogenic emissions (from fossil fuel and
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Figure 1. Geographical distribution of TCO (DU) during January (a) and July (b), averaged for the period from 1982 to 2009
(contours: observations, colors: simulation). Observational data are derived from the combination of TOMS/CCD and OMI/
MLS data; data are missing for 1994–1996 for some locations.
biofuel combustion) based on the EDGAR-3 inventory. For Asia, these inventories were replaced by the
Regional Emission inventory for Asia (REAS), with the corresponding annual inventory for each year being
simulated. The monthly average emissions from biomass burning for each year were obtained from the
Global Fire Emissions Database, version 2 (GFED-v2), which is available for the period of 1997–2007. These
inventories, as used in MOZART-4, are available at http://cdp.ucar.edu. The information on the inventories
has been detailed by Emmons et al. [2010], especially in section 4.1 of their work.
2.3. Experiments
The model was run with a time step of 15 min for the period January 1, 1981 to December 31, 2010, with the first
year spin-up discarded (hereinafter, called the standard run). In the standard run, emissions of O3 precursors
did not vary annually, except that the emissions of isoprene and monoterpenes from vegetation, and NO from
soil and lightning were calculated online. The surface emissions were maintained at the 2006 levels, including
the effect of seasonal changes, which facilitated the evaluation of the impact of interannual meteorological variations on tropospheric O3, independently of the interannual variation in emissions. We checked the surface
emissions of all species; there were no interannual variations, except for isoprene, with slight changes.
A tagged experiment, introduced by Sudo and Akimoto [2007], was performed to quantify the source of O3.
The tagged method treats a chemical species emitted or chemically produced with in a certain region as a
separate tracer and it calculates its transport, chemical transformation, and surface deposition. We classified
O3 by three source regions in the vertical direction: planetary boundary layer, free troposphere, and stratosphere. The planetary boundary layer was defined as the layer from the ground surface to the top of boundary layer in the model; the free troposphere was defined as the layer extending to tropopause from the top of
the planetary boundary layer, and the stratosphere was defined as the layer extending from tropopause to
2 hPa. In this study, we used a mixed tropopause in MOZART-4. It was defined as a dynamical tropopause
poleward of 30° and as a thermal tropopause equatorward of 20°. Between these latitudes, the two definitions were linearly interpolated. This mixed tropopause was also used by James et al. [2003].
3. Results and Discussion
3.1. Model Validation
First, we compared the multi-year mean of simulated TCO with that of the TCO derived from TOMS/CCD–OMI/
MLS for January and July (Figure 1). We focused on the tropical Pacific region between 10°S and 10°N. The
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Figure 2. Vertical profile of tropospheric O3 (ppbv) for Juanary (a), April (b), July (c), and October (d) averaged over the period
from 1995–2011 based on ozonesonde (color spots) and for 1982–2010 based on simulations (color lines) at five sites.
model captures the general features of the observed TCO, reproducing the seasonal pattern of tropical TCO
with a minimum of 20 DU at 180°E during January and to the west of 180°E during July. The tropical TCO in
the eastern Pacific is overestimated, especially during January. In the simulated results, TCO was calculated by
integrating the O3 from the surface to the tropopause. Compared with the tropopause in NCEP data, the
simulated tropopause is overestimated (not shown in the figures). Thus, the overestimation of TCO has been
due to the overestimated tropopause height in MOZART-4. Deviations in the satellite data and missing of
inter-annual surface emission in the simulation might also have contributed to the differences between
the satellite data and simulated results. In addition, comparison of the seasonal vertical distributions of O3
between the simulation and ozonesonde data from the five sites over the tropics and sub-tropics, indicates
that most of the simulated results reproduce the vertical distribution and seasonal patterns of tropical tropospheric O3 (Figure 2). For example, at Hilo, the simulated O3 matches the observations except for an underestimation in October. At other sites, the simulated O3 is overestimated at about 200 hPa in all seasons. Tilmes
et al. [2012] highlighted that O3 over most of the tropics is strongly influenced by deep convection, which
results in a distinct upper tropospheric minimum at about 200 hPa in all seasons. In MOZART-4, convective
mass fluxes are diagnosed using the shallow and mid-level convective transport formulation of Hack
[1994] and deep convection scheme of Zhang and MacFarlane [1995]. Deep convection is difficult to reproduce accurately in most global chemical models. Therefore, the overestimation of O3 at about 200 hPa
may be the result of the errors in the simulation of deep convection.
Figures 3a and 3b) shows the geographical distributions of the temporal correlation coefficient between
Niño3.4 anomalies (N34A) and gridded TCO anomalies (TCOA) from the observations and simulation, respectively. The abbreviation WP represents the tropical western Pacific region including Indonesia (90°E–130°E),
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Figure 3. Geographical distribution of the temporal correlation between Niño3.4 anomalies and gridded TCOA from observations (a) and simulation (b). Point shaded areas are statistically significant at 95% confidence level (i.e., p < 0.05) between
time series from 1982 to 2010. The three black boxes represent the three representative Pacific regions in our study.
CP represents the tropical central Pacific region (170°E–140°W), and EP represents the tropical eastern Pacific
region (130°W–80°W). The latitudinal extent of all three regions is 10°S–10°N. The correlation coefficients
between TCOA and N34A show an asymmetric dipole structure with a present positive correlation (R = 0.6)
for WP and a negative correlation (R = -0.5) for CP and EP (also shown in Figure S1 in the Supporting
Information). The model captures the general features of the correlation between TCOA and N34A with slight
differences. The simulated correlation is weaker than that from the satellite observations of the WP, but stronger than that over the CP and EP. Comparisons between the simulated and observed results confirm the suitability of the model for investigations of the influence of ENSO on O3.
3.2. Influence of ENSO Events on TCO
El Niño and La Niña events are identified by the Niño3.4 index, which is based on the definition established by
the Climate Prediction Center. As mentioned previously, this study classified ENSO events into two types:
canonical and Modoki events, using the indices proposed by Qin and Wang [2014] (Table 2). ENSO events
have also been classified in previous research [Kug et al., 2009; Ren and Jin, 2011; Ren et al., 2013; Wang
et al., 2013; Zhang and Guan, 2014]. Among the El Niño events, those that occurred in 1986.09–1988.02
and 1991.10–1992.06 were considered a mixture of the two types defined in this study, which is in accord
with Kug et al. [2009]. Therefore, these two events were excluded from further involvement in this study.
Table 2. ENSO Events and Types for1982–2010
Canonical
El Niño
La Niña
Modoki
1982.05-1983.06 (1983.01)
1997.05-1998.04 (1997.12)
1994.09-1995.02 (1994.12)
2002.07-2003.02 (2002.11)
2004.08-2005.01 (2004.11)
2006.09-2007.01 (2006.11)
2009.07-2010.03 (2009.12)
1983.10-1984.02 (1983.11), 1984.10-1985.07 (1984.12)
1988.05-1989.06 (1988.11), 1995.09-1996.02 (1995.11)
1998.07-2000.07 (2000.01), 2000.10-2001.02 (2000.12)
2007.08-2008.05 (2008.01)
Note: Dates in parentheses are the peak months for each
ENSO event.
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THE IMPACT OF ENSO EVENTS ON O3
Previous research has demonstrated
the existence of La Niña Modoki
events [Ashok et al., 2007; Kao and
Yu, 2009]. However, it is difficult to
distinguish La Niña Modoki and canonical La Niña events because of the
similarity in their SST patterns and
their weak separation between warm
events [Kug et al., 2009; Kug and Ham,
2011]. Therefore, we did not study
these two types of La Niña event
separately. Table 2 shows two canonical El Niño years (1982 and 1997)
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Figure 4. Time–longitude variation of monthly TCOA (unist: DU) during ENSO events from satellite observation (a, b, c) and
2
simulation (d, e, f) and monthly OLR anomalies (unit: W/m ) from NOAA (g, h, i). Data are multi-year means and areaaveraged between latitudes10°S and 10°N. The time period for each ENSO event is listed in Table 2.
used as typical examples of such an event, which have been identified as strong canonical El Niño years in
numerous earlier studies [Zhang and Guan, 2014; Xie et al., 2014b].
Figures 4a–4f shows time–longitude variations of monthly TCOA during ENSO events based on satellite
observations and model simulation. The results indicate that TCOA variations differ during canonical El
Niño and El Niño Modoki events. During canonical El Niño (Figure 4a), the absolute values of TCOA are larger
than during El Niño Modoki events in most regions (Figure 4b). In addition, TCOA shows a significant east–west
dipole difference (except during June), i.e., the amount of TCO is increased in the WP, and decreased in the CP
and EP. In October and November, enhanced levels of TCO reach a maximum value (>8 DU), and the area of
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positive TCOA covers a wider region from Indonesia to 180°E. During January, decreased levels of TCO reach a
minimum value (<-6 DU). The duration of an El Niño Modoki event is shorter than that of a canonical El Niño
event, as shown in Figure 4b; it usually starts in July–September and ends in December–February. Before the
peak month (July to October), TCOA is positive over 90°E–150°W. After the peak months (November to
February), negative TCOA covers 160°E–90°W, which is wider than that during the canonical El Niño.
Over the WP, levels of TCO are increased and the TCOA reaches its maximum value (4 DU) in October–
November. At the same time, levels of TCO in the CP and EP are decreased to approximately 2 DU. La
Niña events show a dipole in TCOA, which is the reverse of that of the El Niño events [Ziemke and
Chandra, 2003], especially the canonical El Niño, as shown in Figure 4c. The simulated results properly
reflect the TCOA patterns during canonical El Niño (Figure 4d). During the El Niño Modoki and La Niña, their
performances are not as good as during the canonical El Niño, especially before the peak months (Figures 4e
and 4f); the TCOA is underestimated. The underestimation in the WP may be attributed to the lack of consideration of anomalous Indonesian forest fires in the model [Fujiwara et al., 1999; Sudo and Takahashi, 2001]. From
October to January, the simulations are similar to the observations during the ENSO events, which demonstrates the validity of our simulated results in terms of the variation of O3 in the peak months. The significant
differences in the distributions of TCOA during the different types of ENSO event illustrate the necessity for distinguishing the different types of ENSO event and their influence on O3.
The value of OLR quantifies the amount of radiative flux (W/m2) re-emitted back to space in the 3.55–3.91 μm
wavelength band and it represents a measurement of convective activity. Low/high values for OLR indicate
the presence of strong/weak convection [Chandra et al., 1998]. As shown in (Figures 4g–4i), the similarities
between OLR anomalies and TCOA in ENSO events suggest that TCOA over the tropical Pacific region is partly
attributed to the changes of convection, which is in accord with pervious conclusions [Chandra et al., 1998,
2002; Sudo and Takahashi, 2001; Zeng and Pyle, 2005].
3.3. Influence of ENSO Events on the Vertical Distribution of O3
The peak months of ENSO events are between November and January, as listed in Table 2. The analysis of
TCOA in Figure 4 shows that the anomalies during ENSO events are relatively larger in October–January,
and the simulated results perform well. Therefore, we used the October–January means for 1982–2010 to
discuss the vertical distribution of O3 during the different types of ENSO events (Figure 5).
Figure 5a shows the simulated vertical distribution of tropospheric O3 mixing ratios and vectors of zonal
and vertical velocity, averaged over 10°S–10°N for October–January from 1982 to 2010. The dominant zonal
structure of the vertical wind appears as a classic Walker circulation [Webster, 1983] with rising motion over
90°E–180°E and sinking motion to the east of this region. The O3 concentration in the region of 120°E–180°E
is lower than in other regions at the same height, which is due to the transport of a low-O3 air mass from
the surface to the mid and upper troposphere under the influence of the updraft of the Walker circulation.
Near 120°W, air masses with high O3 concentrations in the upper troposphere are transported to the lower
troposphere because of downdrafts that lead to relatively high concentrations of tropospheric O3 at
around 120°W.
Figure 5b shows simulated anomalies in the vertical distribution of O3 and vertical wind during canonical El
Niño events, averaged over 10°S–10°N and October–January. As expected from Figures 3 and 4, an asymmetric dipole structure of O3 anomalies from the surface to the tropopause is seen from 80°E to 80°W, which
is strongly anti-correlated with changes in the vertical wind velocity. Relative to the average, motion during
canonical El Niño events (Figure 5a) is predominantly downward in the WP and upward in the CP and EP
(encompassing most of the area from the dateline eastward to the coast of Peru in South America). As shown in
Figures 5b and 5c, canonical El Niño and El Niño Modoki events have different effects on tropospheric O3 concentration and circulation. The locations of anomalous upward motions during El Niño Modoki events are
further westward than in canonical El Niño events and their strengths are weaker. The dipole structure of the
O3 anomalies appears more asymmetric. Negative O3 anomalies in the mid and upper troposphere cover
120°E to 80°W, wider than in canonical El Niño events. Below 700 hPa, the O3 anomalies are positive in the EP,
which is different to the canonical El Niño events. Positive anomalies in 700–150 hPa cover only a small region
from 80°E to 140°E. Therefore, canonical El Niño events have strong influence on the variation of the amplitude
of tropospheric O3 anomalies, while El Niño Modoki events decrease the mid and upper tropospheric O3
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Figure 5. Simulated vertical distribution of O3 mixing ratio (ppbv, colors) and vectors of zonal and vertical velocity (vectors)
by longitude, averaged for10°S–10°N and October–January from 1982 to 2010 (a); anomalies of O3 mixing ratios (ppbv,
colors) and wind vectors of zonal and vertical velocity (vectors), averaged for 10°S–10°N October–January during Canonical
El Niño (b), El Niño Modoki (c), and La Niña (d). Grey lines represent the heights of tropopause (hPa) during different events.
Vertical velocity is scaled up by a factor of 1000. The time period of each ENSO event is listed in Table 2.
concentrations over a much wider region but to a lesser extent. During La Niña events (Figure 5d), anomalies of
tropospheric O3 and wind are opposite to those during El Niño events, especially El Niño Modoki events.
In summary, during canonical El Niño events, anomalous downward motion and suppressed convection
cause tropospheric O3 to increase over the WP, while anomalous upward motion and enhanced convection
decrease tropospheric O3 over the CP and EP (Figures 4g and 5b). During El Niño Modoki events, the westerly
anomalous upward motions and enhanced convection lead to a wide-scale but weak reduction in O3 over the
CP and EP (Figures 4 h and 5c). During La Niña events, anomalous upward motion and enhanced convection
cause tropospheric O3 to decrease over the WP, while anomalous downward motion and suppressed convection reduce tropospheric O3 over the CP and EP (Figures 4i and 5d), which is the opposite of that during El
Niño Modoki events.
4. Source of O3 Anomalies During ENSO Events
As discussed in sections 3.2 and 3.3, we conclude that O3 anomalies are related to anomalies of convection
and wind during ENSO events. Changes in convection and circulation, which accompany changes in the
direct transport of O3, affect the occurrence of lightning and transport of O3 precursors and lead to changes
in the chemical production of O3. Therefore, we analyze anomalies in O3 process variables for the different
types of ENSO event and their impact on O3 chemistry.
In this study, the rate of change of O3 concentration (units: ppbv/d, parts per billion by volume per day) in the
troposphere was determined from the net O3 transport fluxes including advection, convection, and diffusion
(Figure 6), and the net chemical production of O3 (Figure 7).
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Figure 6. Averaged (a) and anomalous (b, c, d) vertical distributions of net O3 transport flux (colors, ppbv/d) and CO transport flux vector (vectors, ppmv*m/s), averaged for10°S–10°N and October–January; (a) is the mean for1982–2010; (b) is the
anomaly during canonical El Niño events;(c) is the anomaly during El Niño Modoki events; (d) is the anomaly during La Niña
events. The time period of each ENSO event is listed in Table 2. Grey line represents the height of tropopause (hPa). The
physical processes of transport flux include advection, convection, and diffusion. Vertical CO transport flux is scaled up by a
factor of 1000.
4.1. Transport
Figure 6a shows the vertical distribution of net O3 transport flux (color) and CO transport flux vector (vector),
averaged over 10°S–10°N during October–January from 1982 to 2010. Above 400 hPa, net O3 transport flux
shows an outflow of O3. At 90°E–120°E and 300–150 hPa, the outflow reaches its maximum. The CO transport
flux vectors (vector in Figure 6a) at this place also are relatively large, and originate from low troposphere.
This reveals that the maximum outflow is mainly due to the updraft of a low O3 air mass at the surface. At
180°W–120°W, above 400 hPa, the vectors are weak, and the net outflow of O3 is also weak. From the surface
to 400 hPa, net inflow plays a dominant role and it reaches a maximum in the region of 140°E–90°W from the
surface to 700 hPa.
During canonical El Niño event (Figure 6b), net O3 transport flux anomalies are positive in most regions of the
troposphere (warm color in Figure 6b). Positive anomalies of net O3 transport flux suggest decreased net O3
outflow from 400 hPa to the tropopause. In the region of 130°E–180°E, from the surface to 700 hPa, positive
anomalies of net O3 transport flux indicate increased net O3 inflow. Decreased outflow above 400 hPa and
increased inflow from the surface to 700 hPa account for the positive tropospheric O3 anomalies in the WP
(warm color in Figure 5b). In the CP and EP, net O3 outflow is also weakened above 400 hPa, and the net
O3 inflow is enhanced from 700 hPa to 400 h Pa, which cannot account for the reduction in O3 concentrations
within the entire troposphere. From surface to 700 hPa, O3 inflow is weakened in the CP and EP, but O3 is low
at the surface, contributing little to TCO.
The strong net O3 outflow above 400 hPa shown in Figure 6a is weak during El Niño Modoki events (Figure 6c).
Anomalous net O3 transport flux in El Niño Modoki events shows a similar distribution to that in the canonical
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Figure 7. Averaged and anomalous vertical distributions of net O3 chemistry (colors, ppbv/d), NOx (contours, pptv) (a, c, e, g); specific humidity (colors, g/kg) and
HOx (contours, pptv) (b, d, f, h), averaged for 10°S–10°N and October–January; (a) and (b) are the means for 1982–2010; (c) and (d) are anomalies during canonical
El Niño events; (e) and (f) are anomalies during El Niño Modoki events; (g) and (h) are anomalies during La Niña events. Grey line represents the height of tropopause
(hPa). The time period of each ENSO event is listed in Table 2.
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El Niño events but weaker over most regions. In CP and EP, the strong anomalies at 180°E are located more
toward west than in the canonical El Niño events. During La Niña events (Figure 6d), the O3 outflow above
400 hPa is enhanced in the WP. The anomalies of net O3 transport flux are the opposite of those during El
Niño Modoki events.
4.2. O3 Chemistry
Previous simulations have indicated that the photochemical progress of O3 production is affected by ENSO
events [Sudo and Takahashi, 2001; Sekiya and Sudo, 2012]. Moreover, we found that direct O3 transport did
not account for the anomalies of O3 concentrations in the CP and EP (section 4.1). In this section, we focus
our attention on the vertical distribution of net O3 chemistry and the changes in NOx, NO from lightning, specific humidity and HOx (OH + HO2 radical) during different types of ENSO events. The impact of the chemical
processes on the anomalies of tropospheric O3 is evaluated.
Figure 7a shows an averaged longitude–height cross section of the net chemical production of O3 (ppbv/day,
color in Figure 7a) and NOx (pptv, contours in Figure 7a) simulated for 1982–2010. Above 400 hPa, the net
chemistry is O3 production (P-L > 0). The area of net O3 chemical production is enlarged downward to below
500 hPa over Indonesia (around 120°E). At 250 hPa, net O3 production over Indonesia increases to 2–3
ppbv/day, which mostly results from the influence of lightning in producing high values of NO (LNO,
Figure S4a in supporting information). Over the CP and EP (180°W–90°W), LNO is lower than over the WP
(Figure S4a in supporting information), leading to a relatively low concentration of tropospheric NOx, which
reduces net chemical production of O3. Murray et al. [2013] also showed that the impact of interannual variation of lightning on TCO is significantly different over the WP and the EP. Above 150 hPa, net chemical production of O3 might be partly due to subsidence of stratospheric NOx. From the surface to 700 hPa, the net
chemistry is O3 loss (P-L < 0) of approximately -1 ppbv/day in the CP and EP, resulting from the low level of
NOx (<10 pptv). Over this region, the specific humidity and HOx are high (Figure 7b), and they also contribute
to O3 loss through the reactions: O3 + hv- > O2 + O(D 1) and O(D 1) + H2O- > 2OH.
Figure 7c shows anomalies of net O3 chemistry and NOx during canonical El Niño events. A significant NOx
decrease at 500–150 hPa over ~120°E is attributed to a decrease in LNO over Indonesia (Figure S1b). The
anomalies of net O3 production are consistent with decreases of NOx over the region. However, the negative
anomalies of net O3 chemistry cannot account for the positive O3 anomalies in Figure 5b. From 750 hPa to
400 hPa in 90°E–120°E, there is a positive anomaly in net O3 chemical production. It is more likely caused
by the suppression and slight changes in the surface emission of isoprene over the Indonesia region. MLS
CO suggested a westward transport of emissions from biomass burning in Indonesia [Chandra et al., 2009;
Ziemke et al., 2009], which seems consistent with the positive anomaly. Therefore, based on section 4.1,
the positive O3 anomalies in the WP must be mainly due to direct O3 transport. Over 180°W–120°W, the negative anomaly of net O3 chemical production above 700 hPa is attributed to enhanced specific humidity (warm
color in Figure 7d). Above 250 hPa, it is partly attributed to the reduction of NOx (dashed contour in Figure 7c).
Finally, the negative anomaly of net O3 chemical production leads to a decrease in O3 concentration over the CP
and EP in Figure 5b which is in accordance with the study of Sekiya and Sudo [2014]. During the El Niño Modoki
and La Niña events, the anomalies of net O3 chemistry (color in Figures 7e and 7g), respectively) contrary to the
anomalies of specific humidity and HOx (Figures 7f and 7h), respectively) in the troposphere in the CP and EP.
This reveals that the change in O3 concentration in the CP and EP are mainly due to the change in specific
humidity. The anomalies of net O3 chemistry during the El Niño Modoki events show a pattern similar to
canonical El Niño events. However, the negative anomalies are further west and weaker in intensity which
due to a westward shift of regions with anomalous updraft (vector in Figure 5c). The anomalies of net O3
chemistry during La Niña events are the opposite of those during El Niño events.
4.3. Contributions From the Stratosphere, Free Troposphere, and Planetary Boundary Layer
Several previous studies have revealed that ENSO also influences tropospheric O3 concentration through
changes in the stratosphere–troposphere exchange [Langford et al., 1998; James et al., 2003; Zeng and Pyle,
2005]. In this section, the contributions of O3 from stratosphere, free troposphere, and planetary boundary
layer are evaluated for the different types of ENSO event using the tagged experiment (tag–O3 method).
Figures 8a–8c) shows the mean contributions of stratosphere, free troposphere and planetary boundary layer
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Figure 8. Averaged and anomalous contributions of O3 (ppbv) from the stratosphere (Strato in a, d, g, j), free troposphere (FT in b, e, h, k), and planetary boundary
layer (PBL in c, f, i, l), averaged for 10°S–10°N and October–January; (a), (b), and (c) are the means for 1982–2010; (d), (e), and (f) are anomalies during canonical El Niño
events; (g), (h), and (i) are anomalies during El Niño Modoki events; (j), (k), and (l) are anomalies during La Niña events. Grey line represents the height of tropopause
(hPa). The time period of each ENSO event is listed in Table 2.
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(color shading) and their mean contribution rates (contours) for 1982–2010, respectively. The contribution of
stratosphere increases with increasing height and reaches a maximum of >30 ppbv (60% of the total O3)
above the tropopause (contour, Figure 8a). At the surface, its contribution decreases to < 3 ppbv (or 10%
of the total O3). The contribution of stratosphere above the WP is lower than over the EP because of the transport paths of Walker circulation. This is consistent with the discussion in section 3.3. The contribution of free
troposphere is high throughout the entire troposphere, and it accounts for >70% of the total tropospheric O3
(contour, Figure 8b). Over Indonesia, it reaches 70% above 350 hPa. The contribution of planetary boundary
layer is highest at the surface, with a maximum over Indonesia, accounting for approximately 30% of the total
O3 (contour, Figure 8c).
During canonical El Niño events (shown in Figure 8d), the contribution of stratosphere is enhanced over
90°E–180°E above 400 hPa, but it is weakened over 180°W–90°W, similar to the asymmetric dipole structure
of the O3 anomaly shown in Figure 5b. The similarity of the patterns between the stratospheric anomalies and
O3 anomalies further indicates that transport of stratospheric O3 partly accounts for the development of O3
anomalies during canonical El Niño events. In the WP, the contribution rate of stratospheric anomalies to the
O3 anomaly is >20% above 400 hPa, and it decreases with reduced height. In the EP, it is >10% above
400 hPa. The contribution of free tropospheric anomalies is concentrated in the region from 850 hPa to
120 hPa (Figure 8e). From the surface to 300 hPa, the contribution rate is 70% over 90°E–160°E. Over
160°E–90°E, the 70% contribution rate reaches the level of 200 hPa. The contribution of planetary boundary
layer’s anomalies is relatively low, at -2–2 ppbv (Figure 8f). The contribution rate is 5–15%, with relatively high
values at the surface over Indonesia.
During the El Niño Modoki events (Figure 8g), the contribution of stratospheric anomalies is large above
150 hPa, with positive values over 90°E–150°E and negative values over 150°E–90°W. The region covered
by the negative anomaly is wester than during canonical El Niño events. From the surface till the region with
200 hPa, the contribution of stratospheric anomaly is very weak (<1 ppbv). The contribution of free troposphere is enhanced over 90°E–120°E in troposphere (Figure 8h). Over 120°E–90°W, the contribution of free
tropospheric anomaly is weakened from 700 hPa to tropopause. The maximum anomalous contribution of
free troposphere is located in 150–100 hPa above ~180°E, which contributes approximately 70% of the O3
anomaly. In the tropopause, the anomalous contribution rate is approximately 30%. The contribution of
planetary boundary layer’s anomaly is the largest above Indonesia, approximately 2 ppbv (Figure 8i). For
the remaining region, the contribution is <1 ppbv (5–10%). During La Niña events (Figure 8j–8l), the vertical
distributions of the anomalous contributions of the stratosphere, free troposphere, and planetary boundary
layer are almost completely the opposite of those during El Niño Modoki events.
5. Conclusions
This study focused on the influence of ENSO events on O3 variations in the tropical troposphere using satellite
observations, ozonesonde data, and simulations by MOZART-4. The results indicated that the model adequately described the observed spatial and temporal features of O3 distribution. Temporal correlation coefficients between Niño3.4 anomalies and gridded O3 anomalies from the observations and simulations both
showed an asymmetric dipole structure with a present positive correlation (R = 0.6) in the WP region (including Indonesia) and a negative correlation (R = -0.5) in the CP and EP region.
ENSO events were separated into two types: canonical and Modoki events, using the Niño3.4 index and a new
index proposed by Qin and Wang [2014]. Variations of TCOA were different during canonical El Niño and El
Niño Modoki events. The absolute values of TCOA during canonical El Niño events were larger than during
El Niño Modoki events in most regions, while the area covered by the negative TCOA during El Niño
Modoki events after the peak months was wester than during canonical El Niño events. La Niña events indicated a dipole in TCOA that was the opposite of that established for the El Niño events.
During the peak period of canonical El Niño events, anomalous downward motion, together with suppressed
convection, was found to weaken the outflow of O3 above 400 hPa, causing tropospheric O3 to increase over
the WP. Suppressed convection weakens LNO and decreases net chemical production of O3 above 500 hPa
over ~120°E, reducing net O3 chemical loss. Over the CP and EP, the decrease in O3 primarily resulted from
the change of net O3 chemistry induced by an increased specific humidity and the suppressed transport of
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stratospheric NOx. During El Niño Modoki events, the fluxes of transport and chemistry were found to have a
similar effect on O3 to that during canonical El Niño events, but with less intensity. The enhanced net O3 outflow partly explains the decrease in O3 over a relatively large region in the mid and upper troposphere.
During La Niña events, not only the O3 anomalies but also the transport fluxes were the complete opposites
of those during El Niño Modoki events.
The contribution of the stratosphere to the total O3 was found to be largest above the tropopause at more
than 30ppbv (30% of the total O3), but it decreases to 10% with decreased height. This contribution partly
accounts for the O3 anomaly above 400 hPa. The contribution of the free troposphere was established as
large throughout the entire troposphere at >60% of the total O3. It greatly affected the O3 anomaly in
850–150 hPa during the ENSO events. The anomalous contribution of planetary boundary layer was found
to be relatively low at -2 to 2 ppbv (5–15% of the O3 anomaly).
Acknowledgments
This work is supported by grants from
the National Key Research and
Development Program
(2016YFA0602003), the National Natural
Science Foundation of China
(91544229), the Six Talent Peaks of
Jiangsu Province, China, and the Startup
Foundation for Introducing Talent of
NUIST (2015r036). Special thanks go to
Stacy Walters and Louisa Emmons at
NCAR who provided MOZART-4 source
codes and helped us with the model
simulations. The TOMS/CCD and OMI/
MLS data are available at http://acd-ext.
gsfc.nasa.gov/Data_services/cloud_slice. The ozone sounding data are
available at http://acd.ucar.edu/~tilmes/
ozone.html and http://croc.gsfc.nasa.
gov/shadoz/. The TRMM LIS-OTD data
are available at http://thunder.nsstc.
nasa.gov. The Niño indices data are
available at http://www.cpc.ncep.noaa.
gov/data/indices/. The outgoing long
radiation data from NOAA are available
at http://www.esrl.noaa.gov/psd/data/
gridded/data.interp_OLR.html. The
MOZART-4 output data are available
upon request from the authors via
[email protected] or houxw@nuist.
edu.cn.
HOU ET AL.
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